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Explicación del final de la segunda temporada, episodio nueve de la serie Evolution

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¡advertencia! Este artículo contiene spoilers de la temporada 2 de Criminal Minds: Evolution. Episodio 9.

resumen

  • El penúltimo episodio de la temporada 2 de Criminal Minds: Evolution marca un final serio de temporada con giros impactantes de eventos y revelaciones.
  • El plan de Jade para acabar con Frank Church conduce a un final sorprendente con posibles consecuencias para personajes clave como Rossi y Emily.
  • La participación de Aida Ltd. y Frank Church en el caso “Gold Star” revela una red de manipulación y conspiración que amenaza al equipo BAU.

Mentes criminales: evolución El penúltimo episodio de la temporada 2 terminó con un final impactante e impactante luego de revelar importantes giros en el caso “Gold Star”. Mentes criminales: evolución El episodio 8 de la temporada 2 se centró en Damian y Jade, pero el episodio 9 introdujo nuevos personajes, y uno de ellos parece ser el villano más grande de la temporada. Mentes criminales: evolución La segunda temporada de la serie preparaba un enfrentamiento entre el equipo BAU y los miembros del programa “Gold Star”, pero el noveno episodio sugirió grandes cambios al final de la temporada. No está claro hasta qué punto se resolverá este problema, pero Mentes criminales La tercera temporada ya ha sido confirmada.

a lo largo de Mentes criminales: evolución En la temporada 2, la BAU ha logrado avances lentos pero constantes en el caso “Gold Star”. Después de los episodios de persecución de Damian y Jade, el equipo tuvo que centrarse en un nuevo personaje. con la muerte de damián Mentes criminales: evolución Temporada 2, Episodio 8Parecía que Gad sería su próximo gran objetivo. Sin embargo, Mentes criminales Hubo un giro importante en la trama que reveló que Damián no era el titiritero que asumían. Damian y Jade matan a los médicos detrás de Stuart House, pero su mayor atacante sigue vivo.

¿Jade hizo estallar el centro de entrenamiento al final de Criminal Minds: Evolution Temporada 2, Episodio 9?

Criminal Minds: Evolution Episodio 9 Temporada 2 tuvo un final emocionante

Mentes criminales No sorprende que los eventos puedan terminar sorprendentemente bien, especialmente cuando se trata de configurar episodios importantes como finales de temporada. Mentes criminales: evolución El noveno episodio de la temporada 2 estuvo lleno de giros y eventos importantes, y los eventos se desarrollaron a un ritmo más rápido que los episodios anteriores de esta temporada. Los acontecimientos han dejado claro que el episodio final de la temporada no sólo será impactante, sino también peligroso para todos los involucrados. Si bien Jade creía que su mayor enemigo era el FBI, creía que fue un hombre llamado Frank Church quien la salvó.

Sin embargo, cuando habla con una joven llamada Mila, se da cuenta de que Frank la ha estado manipulando y fue su principal abusador mientras estaba en Stuart House. Después de asegurarse de que Mila salga con vida, Jade se prepara para volar el campo de entrenamiento de Frank. Estaba planeando eliminar a Frank y rescatar a los niños que estaba secuestrando, pero la explosión podría matar o herir a dos personas mayores. Mentes criminales: evolución Caracteres.

Relacionado

Mentes criminales: cada vez que Spencer Reid casi lo matan

Reed es uno de los personajes más queridos de Criminal Minds, pero ha habido varias situaciones que ponen en peligro su vida en el transcurso de sus 15 temporadas.

Mientras Jade preparaba la explosión, Rossi y Emily fueron en contra de las órdenes y entraron a las instalaciones. Mentes criminales: evolución Temporada 2, Episodio 9 Antes de que el edificio explote, por lo que hay pocas posibilidades de que Jad no lleve a cabo el plan o fracase. Sin embargo, si lo logra, pondrá a Rossi y Emily en grave peligro. es improbable Mentes criminales el sera asesinado Rossi y/o EmilyNo sería de extrañar que resultaran gravemente heridos. Mentes criminales: evolución Fin de la segunda temporada.

Explique el papel de Aida Limited en el caso Gold Star

Frank Church dirige un campo de entrenamiento para asesinos

Mientras profundizaba en el caso Gold Star, el equipo de la Unidad de Análisis de Comportamiento encontró información sobre una empresa llamada Aida Limited, dirigida por Frank Church. Descubrieron que todos los médicos y agentes de la ley de Stuart House asesinados por Gad estaban conectados porque les pagaban desde una cuenta de depósito en garantía financiada por Aida Limited. La empresa de seguridad privada tenía un contrato con el gobierno, pero su contrato fue cancelado después de que fue acusada de explotación. Se presentaron cargos contra Frank, pero luego se retiraron gracias a sus conexiones.

Frank ha contratado un equipo de sicarios para acabar con Gold Star, y cuando eso falla, comienza de nuevo con una joven llamada Mila.

La unidad de análisis de datos llevó a Frank para interrogarlo. Mentes criminales: evolución Temporada 2, Episodio 9, pero la directora Madison detuvo el interrogatorio porque los reporteros estaban descubriendo el “papel blanco”. Al principio del episodio, Jade le sacó los ojos a un oficial y le pidió que enviara un mensaje a la Unidad de Análisis de Comportamiento diciendo que solo quería volver a casa y encontrar la paz. Resulta que esta casa es un campo de entrenamiento para asesinos dirigido por Frank Church.

Frank había engañado a Gadd y a otros haciéndoles creer que estaba derribando lugares como Stuart House, salvándolos del abuso y dándoles las herramientas para luchar por una causa mejor. Fue Frank a quien se le ocurrió la teoría de la conspiración sobre salvar niños y hacer lo contrario. Frank había contratado un equipo de sicarios para acabar con Gold Star, y cuando eso falló, empezó de nuevo con una joven llamada Mila que le recordaba a Jade. Sin embargo, Mila ayudó a Jade a darse cuenta de que Frank estaba abusando de ellos, no ayudándolos.

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5 cosas que Criminal Minds se equivocó al identificar a los criminales (y 5 cosas que acertó)

Muchos fanáticos de Criminal Minds se preguntan: “¿Qué significa sudes?” y “¿Se utilizó correctamente en la presentación?” Así de precisa es la serie a la hora de identificar personas.

¿Por qué Frank Church mató a Dana en Criminal Minds: Evolution, temporada 2, episodio 9?

Frank Church no quería que Dana hiciera preguntas.

Nico Austin Smith como Dana Howe en Mentes criminales: Evolución.jpg

Mentes criminales: evolución El episodio 9 de la temporada 2 presentó a Dana como una criminal y a Frank Church. Dana fue uno de los cinco miembros del Programa Gold Star y uno de los tres que quedaron, ya que Damian y Aiden ahora están muertos. Después de que Jade mató a Damien, regresó con Dana, quien la ayudó a desactivar al oficial que usaron para enviar un mensaje a la Unidad de Análisis de Comportamiento. Al igual que Jade, Frank manipuló a Dana haciéndole creer que él era su salvador. Frank le ordenó a Dana que trajera a Jade “a casa”, lo cual ella logró.

Sin embargo, cuando Dana le dijo a Frank que todavía tenía preguntas, aunque no dijo qué, él sorprendentemente le disparó en la nuca. Jill había asumido que Frank planeó la muerte de Damien como una forma de poner a prueba su compromiso con el complot. Al decir que tenía preguntas, Dana también sugirió que tenía sospechas sobre el complot. Frank no puede lograr que ella abandone su plan y difunda sus sospechas, por lo que la mata. Hasta el final Mentes criminales: evolución En el episodio 9, Jade era el miembro más comprometido de Frank en el equipo “Gold Star”.

Relacionado

El tributo de Criminal Minds a un episodio icónico de hace 19 años le da un nuevo significado a la primera línea de Jason Gideon

El octavo episodio de la temporada 2 de Criminal Minds: Evolution rindió homenaje a uno de los episodios más icónicos de la serie, ofreciendo un nuevo giro a una serie que tuvo lugar 19 años después.

¿Por qué Jill Gideon besó a David Rossi en Criminal Minds: Evolution Temporada 2 Episodio 9?

Jill Gideon besó a David Rossi antes de entrar a la cancha

Jill Gideon y David Rossi tienen una relación complicada en… Mentes criminales: evolución la segunda temporada. Cuando Jill fue presentada en Mentes criminales: evolución En la temporada 2, episodio 7, afirma que Rossi dejó la Unidad de Análisis de Comportamiento en 1997 porque ella le rompió el corazón. En el siguiente episodio, Rossi negó sus afirmaciones y dijo que nunca pasó nada entre ellos, pero que se unieron por el mal trato que les dio Gideon. Sin embargo, también admitió que sentía algo por Jill y pensó en lo que podría haber pasado entre ellos si no fuera por el mal momento.

en Mentes criminales: evolución Temporada 2, Episodio 8 Jill besa torpemente a Rossi, pero luego se revela que lo hizo para incomodarlo y que no la siguiera cuando conoció a Demian. Jill le devolvió el beso. Mentes criminales: evolución Temporada 2, Episodio 9 Antes de ir al centro de entrenamiento de Frank, pero este beso se siente diferente. Ella le pide que esté a salvo, lo que probablemente quiere decir Jill besa a Rossi para hacerle saber lo importante que es para ella..

Relacionado

Criminal Minds finalmente revela un personaje original invisible después de 19 años

El séptimo episodio de la segunda temporada de Criminal Minds: Evolution marcó la primera aparición de un personaje con una larga historia con la serie y sus personajes.

Peter puede aparecer en el final de la temporada 2 de Criminal Minds: Evolution

Peter es el único miembro de Gold Star que no aparece en la temporada 2 de Criminal Minds: Evolution

Placa del Programa Gold Star con caras de Strike Team e identidades de Gold Star en Criminal Minds: Evolution.jpg

aunque Mentes criminales: evolución En la temporada 2, episodio 9, se menciona a Peter varias veces, y Jade y Dana quieren encontrarlo, pero él nunca aparece. Es el único integrante del programa “Gold Star” que no apareció. Eden fue el primero de los cinco miembros en ser presentado. Mentes criminales: evolución Temporada 2, con Damien, Jade y Dana. Si el plan de Jade tiene éxito… Mentes criminales Al final, probablemente se convertirá en el único miembro superviviente del Programa Gold Star.

No se sabe mucho sobre Peter, pero su falta de apariciones sugiere que no tiene una relación sólida con Frank. Esto lo convierte en una amenaza para Frank, ya que puede que no crea tanto en la teoría de la conspiración como los demás. Sin embargo, el hecho de que haya permanecido bajo el radar de la Unidad de Análisis de Comportamiento durante tanto tiempo también lo convierte en una amenaza. Si Peter no aparece Mentes criminales: evolución Al final de la temporada 2, podría desempeñar un papel en la temporada 3.

el Mentes criminales: evolución El final de la temporada 2 se lanzará en Paramount+ el jueves 1 de agosto.

Evolución de las mentes criminales
Mentes criminales: evolución

en Mentes criminales: evoluciónEl equipo de élite de expertos en análisis de conducta criminal del FBI se enfrenta a su mayor amenaza hasta el momento: un criminal que ha aprovechado la pandemia para construir una red de otros asesinos en serie. A medida que el mundo vuelve al trabajo y la red comienza a funcionar, el equipo debe localizarlos, un asesinato a la vez. Los miembros del elenco original que continúan con sus papeles incluyen a Joe Mantegna, AJ Cook, Kirsten Vangsness, Isha Tyler, Adam Rodriguez y Paget Brewster. Zach Gilford se une al dinámico elenco como estrella invitada recurrente en un arco que durará toda la temporada.

el calumnia
Joe Mantegna, AJ Cook, Kirsten Vangsness, Aisha Tyler, Adam Rodríguez, Paget Brewster, Zach Gilford
fecha de lanzamiento
24 de noviembre de 2022
Estaciones
2
Gente creativa
Erica Messer

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Politics

La temporada 2 de Evolution expone un problema importante que la Universidad Aplicada Balqa ha ignorado durante años

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Este artículo contiene una discusión sobre el trastorno de estrés postraumático.

¡advertencia! Este artículo contiene spoilers de Mentes criminales: Evolución de la temporada 2.

resumen

  • El equipo de BAU ha ignorado los avisos exigidos por el gobierno desde la temporada 1 de Crime Minds. A medida que aumentan las apuestas en la serie de avivamiento, se convierte en un gran problema en Crime Minds: Evolution Season 2.
  • Las luchas del equipo de BAU contra el trastorno de estrés postraumático continúan en Criminal Minds: Evolution, destacando la importancia de la comunicación.
  • La psicóloga forense Tara Lewis podría ser la clave para mantener unido al equipo de BAU en Crime Minds: Evolution Season 2.

Mentes criminales: evolución Temporada 2, también llamada Mentes criminales La temporada 17 revela los problemas de larga data de BAU que se remontan a 2005. Mentes criminales: evolución La primera temporada se estrenó en Paramount+ el 24 de noviembre de 2022, apenas dos años después. Mentes criminales Terminé. La rápida recuperación demostró que todavía había una fuerte base de fans que querían ver cómo continuaría la historia de BAU. Mentes criminales: evolución La temporada 3 se confirmó el día antes del estreno de la temporada 2.

Mentes criminales: evolución La tercera temporada probablemente se estrenará en el vigésimo aniversario de la franquicia. algunos son mejores Mentes criminales Los episodios son anillos distintos, como “100”, “200”, “300” y… con Mentes criminales: evolución Dado que hay 10 episodios en las temporadas, se podría hacer algo especial para el episodio 325.Cual sera la tercera temporada, episodio seis Mentes criminales El regreso de Reid y Morgan es un alivio, especialmente porque… desarrollo Continúa poniendo a sus personajes en situaciones traumáticas que revelan un importante problema del pasado.

Relacionado

¿Por qué Criminal Minds terminó después de la temporada 15 (¿fue cancelada?)

Después de 15 exitosas temporadas, Criminal Minds de CBS ha llegado a su fin. A pesar de su largo tiempo al aire, el motivo de su cancelación sigue siendo interesante.

Mentes criminales: los personajes de Evolution no se tomaron en serio el tratamiento ordenado por el gobierno

Los personajes de Criminal Minds: Evolution han pasado por muchas situaciones traumáticas

todo Mentes criminales La temporada agregó más traumas que cambiaron la vida de los personajes y ayudaron a desenterrar viejos traumas. uno de La mente criminal: evolución Los temas más importantes son cómo el trauma afecta al cerebro., y los miembros del equipo de Balqa Applied University no son una excepción a los cambios de personalidad, aunque sea temporalmente, debido a un trauma. Con el peligro y la presión que conlleva el trabajo. Mentes criminales Los personajes a menudo se ven obligados a asistir a sesiones de terapia exigidas por el gobierno.

Desafortunadamente, tienden a rechazar estas sesiones de terapia, asumiendo que saben lo que es mejor para ellos o no queriendo abrirse a alguien fuera del equipo. Esto ha estado sucediendo desde el comienzo de la serie. Por ejemplo, antes de que Elle dejara el equipo. Mentes criminales En la temporada 2, se saltaba las sesiones de terapia obligatorias después de que le diagnosticaran trastorno de estrés postraumático. en Mentes criminales En la temporada 7, Emily le mintió a su terapeuta para conseguir su tarea. A pesar de su aversión a la terapia, o quizás debido a ella, cada personaje ha sido diagnosticado con trastorno de estrés postraumático. En un momento u otro.

Con su edad y décadas de trauma compartido, comparten poco sobre cómo se sienten.

Mentes criminales: evolución

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Mentes criminales: los personajes de Evolution no comunican sobre sus problemas de salud mental en la segunda temporada

La falta de comunicación causa estrés y puede poner en riesgo a las personalidades de Mentes Criminales: Evolución

el Mentes criminales: evolución Que los personajes oculten sus sentimientos no es nada nuevo, pero los miembros del equipo generalmente pueden hablar entre ellos al respecto. Hotch fue comprensivo con Emily cuando descubrió que le mintió a su terapeuta. Mentes criminales Temporada 7. Sin embargo, en su ansiedad, él se convirtió en una importante caja de resonancia para ella. Cuando JJ comienza a sufrir flashbacks de trastorno de estrés postraumático después de ser torturado por un militar Mentes criminales En la temporada 10, Spencer y Emily estuvieron ahí para ella. La escena de la alucinación con el soldado arroja luz importante sobre el trastorno de estrés postraumático y cómo afecta a menudo a las personas.

pero, Mentes criminales: evolución El episodio 4 de la temporada 2 mostró al equipo dirigiéndose en diferentes direcciones física, mental y emocional. Elias Voigt y el Programa Gold Star revelan el trauma que siempre les ha resultado difícil afrontar. entre Mentes criminales Terminar y Mentes criminales: evolución Inicialmente, hubo un salto en el tiempo de 3 años en el que el equipo se dividió. Aunque siguen siendo cercanos, con la edad y décadas de trauma compartido, comparten menos cómo se sienten. Mentes criminales: evolución.

Como JJ con Ascari, Rossi alucinaba y hablaba con Voight Mentes criminales: evolución la segunda temporada. Sin embargo, cuando alguien le pregunta de quién está hablando, lo ignora. Cuando JJ intenta hablar con Emily sobre “BAU-Gate”, ella le dice a Emily que no es gran cosa. Aunque ella intenta ayudar como jefa de la BAU. García todavía está aprendiendo a sentirse cómoda nuevamente en el FBI, donde todos los que la rodean la manipulan para que los ayude. Los problemas de manejo de la ira de Luke, la ruptura de Tara con Rebecca y la determinación de Emily de vengarse de Doug Bailey causan más problemas de salud mental.

2:30

Relacionado

La historia sin resolver de Hotch finalmente persigue a Prentiss en Criminal Minds: Evolution, Temporada 2

Emily Prentiss es genial como jefa de la UAC, pero el problema de la repentina partida de Aaron Hotchner finalmente la alcanza en Crime Minds: Evolution Season 2.

Tara podría jugar un papel importante en Criminal Minds: Evolution Season 2

El equipo de Balqa Applied University necesitará a alguien que ayude a mantener a todos juntos

Irónicamente, el mismo equipo que se mostró reacio a recibir el tratamiento psiquiátrico adecuado tenía un psiquiatra forense trabajando con ellos. La Dra. Tara Lewis se unió a la Universidad Aplicada Al-Balqa en Mentes criminales Temporada 11 con Amplia experiencia entrevistando a criminales psicópatas para determinar si son aptos para ser juzgados.. Aunque no es una terapeuta tradicional, Tara fue reasignada como psiquiatra para agentes del FBI cuando Linda Barnes dividió el equipo de BAU en Mentes criminales Temporada 13. El trabajo fue tratado como una broma, pero ella usó sus habilidades curativas para ayudar al equipo con algo más que casos.

Tara no es inmune a la opresión, pero puede dar un paso adelante en este momento vital. Mentes criminales: evolución la segunda temporada. Verse obligado a trabajar con Voit provoca trastorno de estrés postraumático en todo el equipo; Alguien como Tara puede mantener a un equipo comprometido sin avergonzarse de su debilidad. La creencia de Voight en “Contagio social“Y cómo ha afectado realmente a BAU. Un viejo miedo para el equipo: verse obligado a convertirse Mentes criminales sudes. Con suerte, Tara les recordará que trabajar juntos les ayudó a sobrevivir traumas pasados. Mentes criminales: evolución.

Mentes criminales: evolución
La temporada 2 estrena nuevos episodios todos los jueves en Paramount+ hasta el 1 de agosto de 2024.

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Business Industry

Marvel trae ¿Y si…? MCU Multiverse a la realidad virtual con Infinity Stones Caper Evolution

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La parte más interesante de What If…? – An Immersive Story (al menos como se ve en este tráiler) es estar dotado de poderes mágicos como Doctor Strange. Usando tus manos, las agitarás y las girarás frente a ti para lanzar hechizos y defender las Gemas del Infinito, además de interactuar con el entorno y otros personajes. El juego también muestra un posible camino oscuro en el que podrías terminar usando las Infinity Stones tú mismo. Si eso sucede, intenta que todo ese poder no se te suba a la cabeza, ¿vale?

Seguramente habrá otras sorpresas multiversales en esta historia, por lo que podría valer la pena comprobar si tienes un Apple Vision Pro. Es de suponer que esto eventualmente llegará a otros dispositivos de realidad virtual, aunque no lo sabemos con certeza. Pero Disney+ comenzó con Apple Vision Pro.

“What If…? An Immersive Story” está dirigida por Dave Boushor (también productor ejecutivo) en Marvel Studios, escrita por David Dong y Phil McCarty (The Learning Curve), y cuenta con música de Laura Karpman, compositora de “Las Maravillas”. Brad Winderbaum y Bryan Andrew de Marvel también son productores ejecutivos junto a Sherif Fattouh.

El juego de realidad virtual estará disponible por tiempo limitado en Apple Vision Pro a partir del 30 de mayo de 2024. Aquí está la sinopsis oficial de la historia del juego:

En el tráiler recién lanzado de “¿Y si…?” – Una historia inmersiva, los fanáticos aprenden que The Watcher necesita ayuda para enfrentar variantes peligrosas de todo el multiverso y que han sido elegidos para intervenir. Bajo la guía de Wong, aprenderán a lanzar hechizos arcanos, aprovechar el poder de las Piedras Infinitas, únete a aliados en batallas épicas y también encontrarás… Versiones de los personajes favoritos de los fanáticos como Thanos, Hela, Collector, Guardian Red y más a medida que asumen el papel de héroe”.

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Life Style

African wild dogs with pleading eyes sparks rethink of dog evolution

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“Puppy-dog eyes didn’t just evolve for us, in domestic dogs,” says comparative anatomist Heather Smith. Her team’s work has thrown a 2019 finding1 that the muscles in dogs’ eyebrows evolved to communicate with humans in the doghouse by showing that African wild dogs also have the muscles to make the infamous pleading expression. The study was published on 10 April in The Anatomical Record2.

Now, one of the researchers who described the evolution of puppy-dog eyebrow muscles is considering what the African dog discovery means for canine evolution. “It opens a door to thinking about where dogs come from, and what they are,” says Anne Burrows, a biological anthropologist at the Duquesne University in Pittsburgh, Pennsylvania, and author of the earlier paper.

Evolution of canine eyebrows

The 2019 study garnered headlines around the world when it found that the two muscles responsible for creating the sad–sweet puppy-dog stare are pronounced in several domestic breeds (Canis familiaris), but almost absent in wolves (Canis lupus).

If the social dynamic between humans and dogs drove eyebrow evolution, Smith wondered whether the highly social African wild dog might also have expressive brows.

African wild dogs (Lycaon pictus) are native to sub-Saharan Africa. Between 1997 and 2012, their numbers dropped by half in some areas. With only 8,000 or so remaining in the wild, studying them is difficult but crucial for conservation efforts.

Smith, who is based at Midwestern University in Glendale, Arizona, and her colleagues dissected a recently deceased African wild dog from Phoenix Zoo. They found that both the levator anguli oculi medalis (LAOM) and the retractor anguli oculi lateralis (RAOL) muscles, credited with creating the puppy-dog expression, were similar in size to those of domestic dog breeds.

“We could see distinct fibres that are very prominent, very robust,” says Smith. Although the researchers only looked at one African wild dog, Smith says it’s unlikely that such a large and well-developed muscle would be present in one animal and not others.

A communication strategy

The team proposes that the gregarious African wild dogs evolved these muscles to communicate with each other. They use a range of vocal cues to organize hunts and share resources, but until now, non-vocal strategies haven’t been studied.

Burrows speculates that more dog species might have muscles for facial expression than the researchers realized when they compared wolves and domestic dogs. “I wonder if these muscles have been around for a really long time and wolves are the ones that lost them.”

Muhammad Spocter, an anatomist at Des Moines University in West Des Moines, Iowa, says the study is exciting, but cautions against making assumptions about wild dog behaviour based on their physical structure. “Just because the anatomy is there, is it being used?” says Spocter. “And how is it being used?”

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Featured

Gemini’s next evolution could let you use the AI while you browse the internet

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Gemini may receive a big update on mobile in the near future where it’ll gain several new features including a text box overlay. Details of the upgrade come from industry insider AssembleDebug who shared his findings with a couple of publications.

PiunikaWeb gained insight into the overlay and it’s quite fascinating seeing it in action. It converts the AI’s input box into a small floating window located at the bottom of a smartphone display, staying there even if you close the app. You could, for example, talk to Gemini while browsing the internet or checking your email. 



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Life Style

have we got evolution the wrong way round?

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Selfish Genes to Social Beings: A Cooperative History of Life Jonathan Silvertown Oxford Univ. Press (2024)

The fact that all life evolved thanks to natural selection can have depressing connotations. If ‘survival of the fittest’ is the key to evolution, are humans hardwired for conflict with one another? Not at all, says evolutionary biologist Jonathan Silvertown in his latest book, Selfish Genes to Social Beings. On the contrary, he argues, many phenomena in the natural world, from certain types of predation to parasitism, rely on cooperation. Thus “we need no longer fret that human nature is sinful or fear that the milk of human kindness will run dry”.

Silvertown uses examples from genes, bacteria, fungi, plants and animals to emphasize that cooperation is ubiquitous in nature. For instance, bacteria called rhizobia thrive in the root nodules of legumes — and turn nitrogen from the air into a soluble form that the plants can use. Some beetles cooperate to bury animal corpses that would be too large for any single insect to manage alone, both reducing the risk of other animals stealing food and providing a nest for beetle families to live in.

And many bacteria indicate their presence to each other using a chemical-signalling system called quorum sensing, which is active only when members of the same species are tightly packed together. This allows each cell to adjust its gene expression in a way that benefits the individuals in the group — to release a poison to kill other species, for instance, when enough bacteria are clustered together to mount a decent attack.

Even eighteenth-century piracy, says Silvertown, is a good example of effective cooperation. Pirates worked together on their ships, and used violence more often against outsiders than as an internal mechanism for law enforcement.

The author argues against the idea that cooperation is fundamentally at odds with competition — a view that emerged as a consequence of the sociobiology movement of the 1970s, in which some biologists argued that all human behaviour is reducible to a Darwinian need to be the ‘fittest’. The reality, as Silvertown shows, is not black and white.

Lichen on a wall in Ambleside, Lake District, UK.

Lichen is a composite organism, in which an alga lives within a fungus.Credit: Ashley Cooper/SPL

A matter of perspective

Take lichens, for instance — ‘composite organisms’ in which an alga or cyanobacterium lives within a fungus. The Swiss botanist Simon Schwendener, who discovered this relationship in the 1860s, argued that a lichen is a parasite: “Its slaves are green algals, which it has sought out or indeed caught hold of, and forced into its service.” Another way to view the relationship is that these algae and fungi are co-dependent — when they co-exist as a lichen, each grows better than it would alone. The line between parasitism and mutualism, competition and cooperation is not clear cut. It’s a matter of perspective.

Similarly hazy boundaries are found in the biology of our own cells. More than a billion years ago, cells absorbed bacteria, which eventually evolved into structures called mitochondria that generate energy. Mitochondria are an essential part of the cells of all plants, animals and fungi alive today. They could be considered slaves, with cells the parasites. Or perhaps they are more like adopted family members.

Fundamentally, Silvertown proposes, cooperation in each of these situations stems from selfishness. Animals did not evolve to act for the benefit of their species, but to spread their own genes. Cooperation happens because mutual benefits are better, biologically speaking, than working alone, as the case of lichens effectively demonstrates.

If this seems heartless, it’s a reflection of the human tendency to apply human moral frameworks to biological phenomena. The use of emotionally charged words such as ‘slave’ and ‘adopted’ takes us away from rigorous science and leads us to see biological interactions as ‘good’ or ‘bad’, rather than as the morally agnostic, transactional processes that they truly are.

The anthropomorphizing of biological processes is a deep and current problem. The tendency to falsely imply agency in the natural world is an easy trap to fall into — consider how often people might say that a virus such as SARS-CoV-2 ‘wants’ to be transmitted, for instance, or that ants act ‘for the good of their colony’. I would have liked to hear more about Silvertown’s views on this category error. But in places, I felt that he could have made his implied understanding more explicit. Instead, he sometimes sacrifices that carefulness for unnecessary jokes, noting, for instance, that bacteria “are essentially singletons who like to party”.

The author could also have talked more about how the amorality inherent in most of the natural world does not apply to humans. Similarly to other organisms, our evolutionary heritage makes us social, but whether that sociality is ‘good’ or ‘bad’ is a moral, not a scientific, question. This distinction from the other cooperative processes that Silvertown outlines could have been explained better.

Selfish Genes to Social Beings is at its best in the long, fascinating discussions of the complexity of cooperative behaviours across the natural world. For instance, although I’ve read a lot about biology, before reading this book I could never understand how RNA chains might have joined together and started the process of self-replication through which all life evolved. Silvertown can talk as easily about the compounds making up your genes as most people can about yesterday’s football match.

Competing Interests

The author declares no competing interests.

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Compensatory evolution in NusG improves fitness of drug-resistant M. tuberculosis

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Bacterial strains

Mtb strains are derivatives of H37Rv unless otherwise noted. ΔbioA Mtb was obtained from the Schnappinger laboratory64. E. coli strains are derivatives of DH5α (NEB), Rosetta2, or BL21(DE3) (Novagen).

Mycobacterial cultures

Mtb was grown at 37 °C in Difco Middlebrook 7H9 broth or on 7H10 agar supplemented with 0.2% glycerol (7H9) or 0.5% glycerol (7H10), 0.05% Tween-80, 1× oleic acid-albumin-dextrose-catalase (OADC) and the appropriate antibiotics (kanamycin 10–20 μg ml−1 and/or hygromycin 25–50 μg ml−1). ATc was used at 100 ng ml−1. Mtb cultures were grown standing in tissue culture flasks (unless otherwise indicated) with 5% CO2. Note that both 7H9 and 7H10 medium are normally supplemented with biotin (0.5 mg l−1; ~2 μM), thereby allowing growth of the ΔbioA Mtb auxotroph.

Selection of Rif-resistant Mtb isolates

For the selection of RifR H37Rv and ΔbioA Mtb, 5 independent 5-ml cultures were started at a density of ~2,000 cells per ml (to minimize the number of preexisting RifR bacteria) and grown to stationary phase (OD600 > 1.5). Cultures were pelleted at 4,000 rpm for 10 min, resuspended in 30 μl remaining medium per pellet and plated on 7H10 agar supplemented with Rif at 0.5 μg ml−1. After outgrowth, colonies were picked into 7H9 medium. After 1 week of outgrowth, an aliquot was heat-inactivated and the Rif resistance determining region of rpoB, rpoA and rpoC were amplified by PCR and Sanger sequenced. See Supplementary Table 4 for primer sequences.

Generation of structural models

The structural model of Mtb RNAP transcription initiation complex bound to Rif in Fig. 1a was generated by modelling Mycobacterium smegmatis RNAP bound to Rif (PDB: 6CCV)65 on to the transcription initiation complex structure (PDB: 6EDT)66.

The cryo-EM structures of a NusG-bound paused elongation complex from Mtb (PDB: 8E74) in Fig. 2d, and the location of clinical isolate mutations in Fig. 4a are derived from Delbeau et al.13.

Generation of individual CRISPRi strains

Individual CRISPRi plasmids were cloned as described67 using Addgene plasmid 166886. In brief, the CRISPRi plasmid backbone was digested with BsmBI-v2 (NEB R0739L) and gel-purified. sgRNAs were designed to target the non-template strand of the target gene open reading frame (ORF). For each individual sgRNA, two complementary oligonucleotides with appropriate sticky end overhangs were annealed and ligated (T4 ligase NEB M0202 M) into the BsmBI-digested plasmid backbone. Successful cloning was confirmed by Sanger sequencing.

Individual CRISPRi plasmids were then electroporated into Mtb. Electrocompetent cells were obtained as described68. In brief, an Mtb culture was expanded to an OD600 = 0.4–0.6 and treated with glycine (final concentration 0.2M) for 24 h before pelleting (4,000g for 10 min). The cell pellet was washed three times in sterile 10% glycerol. The washed bacilli were then resuspended in 10% glycerol in a final volume of 5% of the original culture volume. For each transformation, 100 ng plasmid DNA and 100 μl electrocompetent mycobacteria were mixed and transferred to a 2 mm electroporation cuvette (Bio-Rad 1652082). Where necessary, 100 ng plasmid plRL19 (Addgene plasmid 163634) was also added. Electroporation was performed using the Gene Pulser X cell electroporation system (Bio-Rad 1652660) set at 2,500 V, 700 Ω and 25 μF. Bacteria were recovered in 7H9 for 24 h. After the recovery incubation, cells were plated on 7H10 agar supplemented with the appropriate antibiotic to select for transformants.

CRISPRi library transformation

CRISPRi libraries were generated as described previously28. In brief, fifty transformations were performed to generate RifS and βS450L ΔbioA libraries. For each transformation, 1 μg of RLC12 plasmid DNA was added to 100 μl electrocompetent cells. The cells:DNA mix was transferred to a 2 mm electroporation cuvette (Bio-Rad 1652082) and electroporated at 2,500 kV, 700 Ω, and 25 μF. Each transformation was recovered in 2 ml 7H9 medium supplemented with OADC, glycerol and Tween-80 (100 ml total) for 16–24 h. The recovered cells were collected at 4,000 rpm for 10 min, resuspended in 400 μl remaining medium per transformation and plated on 7H10 agar supplemented with kanamycin (see ‘Mycobacterial cultures’) in Corning Bioassay dishes (Sigma CLS431111-16EA).

After 21 days of outgrowth on plates, transformants were scraped and pooled. Scraped cells were homogenized by two dissociation cycles on a gentleMACS Octo Dissociator (Miltenyi Biotec 130095937) using the RNA_01 program and 30 gentleMACS M tubes (Miltenyi Biotec 130093236). The library was further declumped by passaging 1 ml of homogenized library into 100 ml of 7H9 supplemented with kanamycin (see Mycobacterial cultures) for between 5 and 10 generations. Final RifS and βS450L ΔbioA Mtb library stocks were obtained after passing the cultures through a 10-μm cell strainer (Pluriselect SKU 43-50010-03). Genomic DNA was extracted from the final stocks and library quality was validated by deep sequencing (see ‘Genomic DNA extraction and library preparation for Illumina sequencing’).

Pooled CRISPRi screen

Pooled CRISPRi screens were performed as described28. In brief, 20-ml cultures were grown in vented tissue culture flasks (T-75; Falcon 353136) and 7H9 medium supplemented with kanamycin (see ‘Mycobacterial cultures’) and maintained at 37 °C, 5% CO2 in a humidified incubator.

The screen was initiated by thawing 4× 1-ml aliquots of the Mtb ΔbioA (RifS or βS450L) CRISPRi library (RLC12) and inoculating each aliquot into 24 ml 7H9 medium supplemented with kanamycin in a T-75 flask (starting OD6000.06). The cultures were expanded to approximately OD600 = 1.0, pooled and passed through a 10-μm cell strainer (pluriSelect 43-50010-03) to obtain a single cell suspension. The single cell suspension (flow-though) was used to set up six ‘generation 0’ cultures: three replicate cultures with ATc (+ATc) and three replicate control cultures without ATc (–ATc). From each generation 0 culture, we collected 10 OD600 units of bacteria (3 × 109 bacteria; 30,000X coverage of the CRISPRi library) for genomic DNA extraction. The remaining culture volume was used to initiate the pooled CRISPRi fitness screen. Cultures were periodically passaged in pre-warmed medium in order to maintain log phase growth. At generation 2.5, 5, and 7.5, cultures were back-diluted 1:6 (to a starting OD600 = 0.2) and cultivated for approximately 2.5 doublings. At generation 10, 15, 20, and 25, cultures were back-diluted 1:24 (to a starting OD600 = 0.05) and expanded for 5 generations before reaching late-log phase. ATc was replenished at every passage. By keeping the OD600 of the 20 ml cultures ≥ 0.05, we guaranteed sufficient coverage of the library (3,000X) at all times. At set time points (approximately 2.5; 5; 7.5; 10; 15; 20; 25 and 30 generations), we collected bacterial pellets (10 OD600 units) to extract genomic DNA.

Genomic DNA extraction and library preparation for Illumina sequencing of CRISPRi libraries

Genomic DNA was isolated from bacterial pellets using the CTAB-lysozyme method described previously69. Genomic DNA concentration was quantified using the DeNovix dsDNA high sensitivity assay (KIT-DSDNA-HIGH-2; DS-11 Series Spectrophotometer/Fluorometer).

Illumina libraries were constructed as described28. In brief, the sgRNA-encoding region was amplified from 500 ng genomic DNA using NEBNext Ultra II Q5 master Mix (NEB M0544L). PCR cycling conditions were: 98 °C for 45 s; 17 cycles of 98 °C for 10 s, 64 °C for 30 s, 65 °C for 20 s; 65 °C for 5 min. Each PCR reaction a unique indexed forward primer (0.5 μM final concentration) and a unique indexed reverse primer (0.5 μM) (Supplementary Table 4). Forward primers contain a P5 flow cell attachment sequence, a standard Read1 Illumina sequencing primer binding site, custom stagger sequences to ensure base diversity during Illumina sequencing, and unique barcodes to allow for sample pooling during deep sequencing. Reverse primers contain a P7 flow cell attachment sequence, a standard Read2 Illumina sequencing primer binding site, and unique barcodes.

Following PCR amplification, each 230 bp amplicon was purified using AMPure XP beads (Beckman–Coulter A63882) using two-sided selection (0.75X and 0.12X). Eluted amplicons were quantified with a Qubit 2.0 Fluorometer (Invitrogen), and amplicon size and purity were quality controlled by visualization on an Agilent 4200 TapeStation (Instrument- Agilent Technologies G2991AA; reagents- Agilent Technologies 5067-5583; tape- Agilent Technologies 5067-5582). Next, individual PCR amplicons were multiplexed into 20 nM pools and sequenced on an Illumina sequencer according to the manufacturer’s instructions. To increase sequencing diversity, a PhiX spike-in of 2.5–5% was added to the pools (PhiX sequencing control v3; Illumina FC-110-3001). Samples were run on the Illumina NextSeq 500 or NovaSeq 6000 platform (single-read 1 ×85 cycles, 8 × i5 index cycles, and 8 × i7 index cycles).

Differential vulnerability analysis of Rif-resistant versus Rif-sensitive strains

Gene vulnerability in the RifS and βS450L Mtb strains was determined using an updated vulnerability model based on the one previously described28. In the updated model, read counts for a given sgRNA in the minus ATc conditions were modelled using a negative binomial distribution with a mean proportional to the counts in the plus ATc condition, plus a factor representing the log2 fold change:

$${y}_{i}^{-{\rm{ATc}}} \sim {\rm{NegBinom}}\left({\eta }_{i},\phi \right)$$

$${\eta }_{i}=\log (\,{y}_{i}^{+{\rm{ATc}}}+{\lambda }_{i})+{\rm{TwoLine}}({x}_{i},{\alpha }_{l},{\beta }_{l},\gamma ,{\beta }_{e})$$

where λi is an sgRNA-level correction factor estimated by the model, xi represents the generations analysed for the ith guide, and the TwoLine function represents the piecewise linear function previously described, which models sgRNA behaviour over the logistic function describing gene-level vulnerabilities was simplified by setting the top asymptote of the curve (previously K) equal to 0, representing the fact that weakest possible sgRNAs are expected to impose no effect on bacterial fitness, that is:

$${\rm{Logistic}}\left(s\right)=\frac{{\beta }_{\max }}{\left(1+{{\rm{e}}}^{\left(-H\cdot \left(s-M\right)\right)}\right)}$$

The Bayesian vulnerability model was run for each condition independently, and samples for all the parameters were obtained using Stan running 4 independent chains with 1,000 warmup iterations and 3,000 samples each (for a total of 12,000 posterior samples for each parameter in the model after discarding warmup iterations).

Differential vulnerabilities were estimated by two approaches. First, for each gene, the difference in pairwise (guide-level) vulnerability estimates was obtained, resulting in posterior samples of the differential vulnerability (delta-vulnerability). This effectively estimated the difference in the integrals of the vulnerability functions. If the 95% credible region did not overlap 0.0 those were taken as significant differential vulnerabilities between the strains.

Next, to identify differences between genes which may not exhibit the expected dose–response curve, we estimated the fitness cost (log2FC) predicted by our model for a (theoretical) sgRNA of strength 0.0 (that is, Logistic(s = 0)). This represented the weakest phenotype theoretically possible with our CRISPRi system, which we call Fmin. The difference between this value was estimated for each gene (∆Fmin) and those where the 95% credible region did not overlap 0.0 were identified as significant differential vulnerabilities by this approach.

Pathway analysis

First, all annotated Mtb genes were associated with a pathway as defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database70,71,72. If necessary, annotations were manually curated to update or correct pathway assignments. To quantify pathway enrichment, the query set was defined as the union of the upper quartile of differential vulnerabilities defined by both the original gene vulnerability calling method (ΔV) and the Fmin approach. The background set was defined as all annotated Mtb genes. Enrichment of the pathways identified as differentially vulnerable was calculated by an odds ratio and significance was determined with a Fisher’s exact test.

phyOverlap

To detect associations between gene variants and Rif resistance, we employed a phylogenetic convergence test using the phyOverlap algorithm73 (https://github.com/Nathan-d-hicks/phyOverlap). In brief, FASTQ files were aligned to H37Rv genome (NC_018143.2) using bwa (version 0.7.17-r1188). FASTQ accession numbers are provided in Supplementary Table 3. Single-nucleotide polymorphisms (SNPs) were called and annotated using the HaplotypeCaller tool Genome Analysis Toolkit (version 3.5) using inputs from samtools (version 1.7). SNP sites with less than 10x coverage or missing data in >10% strains were removed from the analysis. Repetitive regions of the genome (PE/PPE genes, transposases, and prophage genes) are excluded from the analysis. Known drug-resistance regions were further excluded so as not to bias phylogenetic tree construction. M. canetti was provided as an outgroup (NC_015848). We performed Maximum Likelihood Inference using RAxML (v8.2.11) to construct the ancestral sequence and determine the derived state of each allele. Overlap with Rif resistance was scored by dividing the number of genotypically predicted (Mykrobe v0.9.012) RifR isolates containing a derived allele by the total number of isolates with a derived allele at a given genomic position. To generate a gene-wide score, we excluded synonymous SNPs and averaged the individual nonsynonymous SNP scores, weighting the scores by the number of times derived alleles evolved across the phylogenetic tree. The significance of the overlap is then tested by redistributing mutation events for each SNP randomly across the tree and recalculating the score. This permutation is done 50,000 times to derive the P value. This analysis additionally used FastTree (version 2.1.11) and figTree (v1.4.4).

dN/dS calculations

The ratio of nonsynonymous (dN) to synonymous (dS) nucleotide substitutions was used to quantify selective pressure acting on nusG and rpoC. A dN/dS value less than one suggests negative or purifying selection whereas a dN/dS value greater than one suggests positive or diversifying selection. For this analysis, we used a collection of ~50,000 Mtb clinical isolate whole-genome sequences, as described41. Isolates were grouped based on the presence of genotypically predicted Rif resistance (Mykrobe v0.9.012), as well as the identity of the rpoB mutation (S450X or H445X; where X indicates any amino acid other than Ser or His, respectively) conferring RifR. The number of samples used in the nusG dN/dS analysis shown in Fig. 3 are as follows: 1,365 RifS, 350 RifR, 270 S450X, and 26 H445X. The number of samples used in the rpoC dN/dS analysis shown in Fig. 3 are as follows: 23,024 RifS, 13,993 RifR, 11,067 S450X, and 1,215 H445X. Insertions and deletions were necessarily excluded from this analysis. A bootstrap-analysis was performed to calculate the dN/dS ratios to reduce any potential effects of recent clonal expansion events or convergent evolution of a specific site, like acquired drug-resistance mutations, as performed previously44. The analysis was performed by sub-sampling 80% of total variants in each group. The sub-sampling was repeated 100 times. dN/dS values were calculated for each subset of samples using a python script obtained from the github repository: https://github.com/MtbEvolution/resR_Project/tree/main/dNdS.

SNP calling and upset plot

SNP information for all Mtb clinical isolate whole-genome sequences were called as follows. FASTQ reads were aligned to the H37Rv genome (NC_018143.2) and SNPs were called and annotated using Snippy9 (version 3.2-dev) using default parameters (minimum mapping quality of 60 in BWA, samtools base quality threshold of 20, minimum coverage of 10, minimum proportion of reads that differ from reference of 0.9). Mapping quality and coverage was further assessed using QualiMap with the default parameters (version 2.2.2-dev). Samples with a mean coverage < 30, mean mapping quality ≤ 45, or GC content ≤ 50% or ≥ 70% were excluded. Drug resistance-conferring SNPs were annotated using Mykrobe (v0.9.012). The resulting SNP and drug-resistance calls were used to generate the values depicted in the upset plot.

Phylogenetic trees

Phylogenetic trees based on SNP calls described above were built using FastTree (version 2.1.11 SSE3). A list of SNPs in essential genes was concatenated to build phylogenetic trees. Indels, drug resistance-conferring SNPs, and SNPs in repetitive regions of the genome (PE/PPE genes, transposases and prophage genes) were excluded. Tree visualization was performed in iTol (https://itol.embl.de/).

Barcode library production

The barcode library was designed to include over 100,000 random 18-mer sequences cloned into an Giles-integrating backbone (attP only, no Integrase) containing a hygromycin resistance cassette with a premature stop codon (plNP472). Oligonucleotides were synthesized as a gBlocks Library by IDT, containing 104,976 fragments.

plNP472 (1.6 μg) was digested with PciI (NEB R0655) and gel-purified (QIAGEN 28706). The library was PCR amplified using NEBNext High-Fidelity 2X PCR Master Mix (NEB M0541L). One 50-μl reaction was prepared, containing 25 μl of PCR master mix, 0.0125 pmol of the gBlock library, and a final concentration of 0.5 μM of the appropriate forward and reverse primers (Fwd: 5′-TTACGCGTTTCACTGGCCGATTG-3′ + Rev: 5′-TTTTGCTGGCCTTTTGCTCAAC-3′). PCR cycling conditions were: 98 °C for 30 s; 15 cycles of 98 °C for 10 s, 68 °C for 10 s, 72 °C for 15 s; 72 °C for 120 s. The PCR amplicon were purified using the QIAGEN MinElute PCR purification kit (QIAGEN 28004). One Gibson assembly reaction (NEB E2621) was prepared with 0.01 pmol μl−1 digested plNP472 backbone, 0.009 pmol μl−1 cleaned PCR amplicon, and master mix, representing a 1:2 molar ratio of vector:insert.

Following incubation at 50 °C for 1 h, 7 μl the Gibson product was dialysed to remove salts and transformed into 100 μl MegaX DH10B T1R Electrocomp Cells (Invitrogen C640003) diluted with 107 μl 10% glyerol. For each of three total transformations, 75 μl of the cells:DNA mix was transferred to a 0.1 cm electroporation cuvette (Bio-Rad 1652089) and electroporated at 2,000 V, 200 ohms, 25 μF. Transformations were washed twice with 300 μl provided recovery medium and recovered in a total of 3 ml medium. Cells were allowed to recover at 37 °C with gentle rotation. Recovered cells were plated across three plates of LB agar supplemented with zeocin. After 1 d incubation at 37 °C, transformants were scraped and pooled. One fourth of the pellet (3.2 g dry mass) was used to perform 24 minipreps using a QIA prep Spin Miniprep Kit (Qiagen 27104).

Transformation of barcode library into Mtb

The barcode library was transformed into RifS and βS450L Mtb expressing RecT (mycobacteriophage recombinase) similarly to the CRISPRi library (see CRISPRi library transformation), with minor modifications. In brief, cultures for competent cells were grown in 7H9 supplemented with kanamycin to retain the episomal recT encoding plasmid (plRL4). Twenty-millilitre cultures were concentrated ten times and transformed with 250 ng of library and 100 ng of non-replicating, Giles integrase containing plasmid (plRL40). Additionally, after recovery cells were plated on 7H10 agar supplemented with kanamycin and zeocin. Transformants were scrapped after 29 days of outgrowth.

ssDNA recombineering and validation of strains

Clinical nusG, rpoB and rpoC mutants were introduced into RifS and βS450L Mtb using oligonucleotide-mediated (ssDNA) recombineering, as described previously68. In brief, 70-mer oligonucleotides were designed to correspond to the lagging strand of the replication fork, with the desired mutation in the middle of the sequence. Alterations were chosen to avoid recognition by the mismatch-repair machinery of RecT expression was induced ~16 h before transformation by addition of ATc to a final concentration of 0.5 μg ml−1. 400 μl of competent cells were transformed with 5 μg of mutation containing oligonucleotide and 0.1 μg of hygromycin resistance cassette repair oligonucleotide (1:50 ratio of mutant oligonucleotide to repair oligonucleotide) and recovered in 5 ml 7H9 medium.

After 24 h of recovery, 200 μl of cells were plated on 7H10 plates supplemented with hygromycin. After 21 days of outgrowth, 12 colonies per construct were picked into 100 μl 7H9 medium supplemented with hygromycin in a 96 well plate (Fischer Scientific 877217). 50 μl of culture were heat-inactivated at 80 °C for 2 h in a sealed microamp 96 well plate (Fischer Scientific 07200684; Applied Biosystems N8010560). Fifty microlitres of heat-inactivated culture was mixed with 50 μl of 25% DMSO and lysed at 98 °C 10 min.

Mutations of interest and unique barcodes were confirmed with PCR amplification and Sanger sequencing. The region of interest was PCR amplified with NEBNext High-Fidelity 2X PCR Master Mix (NEB M0541L) using 0.5 μl of heat-lysed product with the appropriate primers, annealing temperatures and extension times (see Supplementary Table 4). Residual PCR primers were removed with NEB Shrimp Alkaline Phosphatase (rSAP) and exonuclease I (exo) (rSAP- NEB M0371; exo- NEB M0293) per manufacturer’s instructions. Amplicons were then submitted for Sanger sequencing. One to three unique independent isolates were generated for all tested mutations.

Pooled barcode competitive growth assay

Validated mutants were first grown in 1 ml 7H9 with hygromycin and after 3 days, expanded to 5 ml 7H9 with hygromycin. Strains were pooled to contain approximately 1.2 × 107 cells for each mutant. The pool was then diluted to a starting OD600 of 0.01 in 7H9 supplemented with hygromycin. At this point, three 20 ml cultures in vented tissue culture flasks (T-75; Falcon 353136) were expanded to late-log phase and used as input for the competitive growth experiment. Sixteen OD600 units of cells were collected from flask as the input culture (generation 0). Triplicate cultures were then diluted back to OD600 = 0.05 and grown for ~4.5 generations, back-diluted again to OD600 = 0.05 and grown for an additional 4 generations. After this, cultures were collected for a cumulative 8.5 generations of competitive growth.

Genomic DNA extraction and library preparation for next-generation sequencing followed the same protocol as that of the CRISPRi libraries (see above), with minor modifications. In brief, the barcode region was amplified from 100 ng genomic DNA using NEBNext Ultra II Q5 master Mix (NEB M0544L). PCR cycling conditions were: 98 °C for 45 s; 16 cycles of 98 °C for 10 s, 64 °C for 30 s, 65 °C for 20 s; 65 °C for 5 min. Each PCR reaction contained a unique indexed forward primer (0.5 μM final concentration) and a unique indexed reverse primer (0.5 μM) (see Supplementary Table 4). Additionally, individual PCR amplicons were multiplexed into a 1 nM pool and sequenced on an Illumina sequencer according to the manufacturer’s instructions. To increase sequencing diversity, a PhiX spike-in of 20% was added to the pool (PhiX sequencing control v3; Illumina FC-110-3001). Samples were run on the Illumina MiSeq Nano platform (paired-read 2 ×150 cycles, 8 × i5 index cycles, and 8 × i7 index cycles).

WGS and SNP calling for passaging timepoints and ssDNA recombinants

Genomic DNA (gDNA) was extracted as described above. gDNA was diluted and subjected to Illumina whole-genome sequencing by SeqCenter. In brief, Illumina libraries were generated through tagmentation-based and PCR-based Illumina DNA Prep kit and custom IDT 10 bp unique dial indices, generating 320 bp amplicons. Resulting libraries were sequenced on the Illumina NovaSeq 6000 platform (2 × 150 cycles). Demultiplexing quality control, and adapter trimming was performed with bcl-convert (v4.1.5).

Reads were aligned to the Mtb (H37Rv; CP003248.2) reference genome using bwa (v1.3.1) with default parameters. Variant detection was performed by Snippy (v4.6.0)/freebayes (v1.3.1). Resulting vcf files were inspected for compensatory mutations (Supplementary Table 2) in rpoABC and/or the presence of the desired mutation.

Definition of putative compensatory nusG, rpoA, rpoB, rpoC variants

Compensatory mutations in rpoA, rpoB and rpoC were taken from published sources and are described in Supplementary Table 2. Inclusion as a putative compensatory mutation in our list required that each reported variant in rpoA, rpoB, or rpoC was found specifically in Rif-resistant strains, defined here as meaning that ≥90% of all strains harbouring the putative compensatory mutation were genotypically predicted (gDST) RifR. The use of the ≥90% gDST RifR cut-off allows for presumptive instances of incorrect gDST calls for strains harbouring rare compensatory variants. The strains used for this analysis are the approximately 50,000 Mtb WGS strain collection described previously41.

The rules to define putative compensatory nusG mutations are as follows. Each nusG variant observed was assessed according to the following three rules and, if it met one of them, was deemed a putative compensatory variant.

  1. (1)

    The nusG variant was found in ≥80% genotypically predicted (gDST) RifR strains and was present in at least two distinct Mtb (sub)lineages. The use of the ≥80% gDST RifR cut-off allows for presumptive instances of incorrect gDST calls for strains harbouring rare nusG variants.

  2. (2)

    The nusG variant was found in 100% gDST RifR strains but only present in a single Mtb sublineage, but the same or nearby NusG site (±5 amino acids) was also mutated to an alternative amino acid that met the criteria stated in rule 1.

  3. (3)

    Residues based on the Mtb NusG–RNAP structure13 that were predicted to be important for the NusG pro-pausing activity (for example, NusG Trp120).

The rules to define a putative compensatory mutation in the rpoB β-protrusion were similar to those described for nusG, except that only rpoB β-protrusion residues at or near the NusG interface (RpoB Arg392–Thr410) were included in the analysis. Note that two such β-protrusion mutations (Thr400Ala and Gln409Arg) were previously identified as putative compensatory mutation17,74,75 (Supplementary Table 2).

RifR rpoB allele frequency distribution calculations

To check whether the observed distribution of RifR rpoB mutations was different for each of the three groups (all RifR strains in our clinical strain genome database, those harbouring known compensatory mutations in rpoA or rpoC, or those harbouring compensatory mutations in nusG or the β-protrusion), we performed a chi-squared test on the observed RifR rpoB mutant frequencies. Specifically, we take the RifR rpoB mutant frequencies observed in all RifR samples as representing an estimate of the base probabilities under the null hypothesis. We then use these base probabilities to calculate the frequency of mutations that would be expected in the other groups, based on the null hypothesis. That is:

For each mutation (m):

$$p(m)=\frac{{\rm{Number}}\,{\rm{of}}\,{\rm{times}}\,m\,{\rm{occurs}}\,{\rm{in}}\,{\rm{RifR}}\,{\rm{samples}}}{{\rm{Total}}\,{\rm{number}}\,{\rm{of}}\,{\rm{RifR}}\,{\rm{samples}}}$$

For each group (G) and mutation (m),

$$E\left[m| G\right]=p\left(m\right)\times {\rm{total}}\,{\rm{number}}\,{\rm{of}}\,{\rm{samples}}\,{\rm{in}}\,G$$

Protein expression and purification

Mtb RNAP

Mtb RNAP was purified as previously described66,76. In brief, plasmid pMP61 (wild-type RNAP) or pMP62 (S450L RNAP) was used to overexpress Mtb core RNAP subunits rpoA, rpoZ, a linked rpoBC and a His8 tag. pMP61/pMP62 was grown in E. coli Rosetta2 cells in LB with 50 μg ml−1 kanamycin and 34 μg ml−1 chloramphenicol at 37 °C to an OD600 of 0.3, transferred to room temperature and left shaking to an approximate OD600 of 0.6. RNAP expression was induced by adding IPTG to a final concentration of 0.1 mM, grown for 16 h, and collected by centrifugation (8,000g, 15 min at 4 °C). Collected cells were resuspended in 50 mM Tris-HCl, pH 8.0, 1 mM EDTA, 1 mM PMSF, 1 mM protease inhibitor cocktail, 5% glycerol and lysed by sonication. The lysate was centrifuged (27,000g, 15 min, 4 °C) and polyethyleneimine (PEI, Sigma-Aldrich) added to the supernatant to a final concentration of 0.6% (w/v) and stirred for 10 min to precipitate DNA binding proteins including target RNAP. After centrifugation (11,000g, 15 min, 4 °C), the pellet was resuspended in PEI wash buffer (10 mM Tris-HCl, pH 7.9, 5% v/v glycerol, 0.1 mM EDTA, 5 mM DTT, 300 mM NaCl) to remove non-target proteins. The mixture was centrifuged (11,000g, 15 min, 4 °C), supernatant discarded, then RNAP eluted from the pellet into PEI Elution Buffer (10 mM Tris-HCl, pH 7.9, 5% v/v glycerol, 0.1 mM EDTA, 5 mM DTT, 1 M NaCl). After centrifugation, RNAP was precipitated from the supernatant by adding (NH4)2SO4 to a final concentration of 0.35 g l−1. The pellet was dissolved in Nickel buffer A (20 mM Tris pH 8.0, 5% glycerol, 1 M NaCl, 10 mM imidazole) and loaded onto a HisTrap FF 5 ml column (GE Healthcare Life Sciences). The column was washed with Nickel buffer A and then RNAP was eluted with Nickel elution buffer (20 mM Tris, pH 8.0, 5% glycerol, 1 M NaCl, 250 mM imidazole). Eluted RNAP was subsequently purified by gel filtration chromatography on a HiLoad Superdex 26/600 200 pg in 10 mM Tris pH 8.0, 5% glycerol, 0.1 mM EDTA, 500 mM NaCl, 5 mM DTT. Eluted samples were aliquoted, flash frozen in liquid nitrogen and stored in −80 °C until usage.

Mtb σA–RbpA

Mtb σA–RbpA was purified as previously described76,77. The Mtb σA expression vector pAC2 contains the T7 promoter, ten histidine residues, and a precision protease cleavage site upstream of Mtb σA. The Mtb RbpA vector is derived from the pET-20B backbone (Novagen) and contains the T7 promoter upstream of untagged Mtb RbpA. Both plasmids were co-transformed into E. coli Rosetta2 cells and selected on medium containing kanamycin (50 µg ml−1), chloramphenicol (34 µg ml−1) and ampicillin (100 µg ml−1). Protein expression was induced at OD600 of 0.6 by adding IPTG to a final concentration of 0.5 mM and leaving cells to grow at 30 °C for 4 h. Cells were then collected by centrifugation (4,000g, 20 min at 4 °C). Collected cells were resuspended in 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 5 mM imidazole, 0.1 mM PMSF, 1 mM protease inhibitor cocktail, and 1 mM β-mercaptoethanol, then lysed using a continuous-flow French press. The lysate was centrifuged twice (15,000g, 30 min, 4 °C) and the proteins were purified by Ni2+-affinity chromatography (HisTrap IMAC HP, GE Healthcare Life Sciences) via elution at 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 500 mM imidazole, and 1 mM β-mercaptoethanol. Following elution, the complex was dialysed overnight into 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 5 mM imidazole, and 1 mM β-mercaptoethanol and the His10 tag was cleaved with precision protease overnight at a ratio of 1:30 (protease mass:cleavage target mass). The cleaved complex was loaded onto a second Ni2+-affinity column and was retrieved from the flow-through. The complex was loaded directly onto a size-exclusion column (SuperDex-200 16/16, GE Healthcare Life Sciences) equilibrated with 50 mM Tris-HCl, pH 8, 500 mM NaCl, and 1 mM DTT. The sample was concentrated to 4 mg ml−1 by centrifugal filtration and stored at –80 °C until usage.

Mtb CarD

Mtb CarD was purified as previously described66,76. In brief, Mtb CarD was overexpressed from pET SUMO (Invitrogen) in E. coli BL21(DE3) cells (Novagen) and selected on medium containing 50 µg ml−1 kanamycin. Protein expression was induced by adding IPTG to a final concentration of 1 mM when cells reached an apparent OD600 of 0.6, followed by 4 h of growth at 28 °C, then collected by centrifugation (4,000g, 15 min at 4 °C). Collected cells were resuspended in 20 mM Tris-HCl, pH 8.0, 150 mM potassium glutamate, 5 mM MgCl2, 0.1 mM PMSF, 1 mM protease inhibitor cocktail, and 1 mM β-mercaptoethanol, then lysed using a continuous-flow French press. The lysate was centrifuged twice (16,000g, 30 min, 4 °C) and the proteins were purified by Ni2+-affinity chromatography (HisTrap IMAC HP, GE Healthcare Life Sciences) via elution at 20 mM Tris-HCl, pH 8.0, 150 mM potassium glutamate, 250 mM imidazole, and 1 mM β-mercaptoethanol. Following elution, the complex was dialysed overnight into 20 mM Tris-HCl, pH 8.0, 150 mM potassium glutamate, 5 mM MgCl2, and 1 mM β-mercaptoethanol and the His10 tag was cleaved with ULP-1 protease (Invitrogen) overnight at a ratio of 1/30 (protease mass/cleavage target mass). The cleaved complex was loaded onto a second Ni2+-affinity column and was retrieved from the flow-through. The complex was loaded directly onto a size-exclusion column (SuperDex-200 16/16, GE Healthcare Life Sciences) equilibrated with 20 mM Tris-HCl, pH 8, 150 mM potassium glutamate, 5 mM MgCl2 and 2.5 mM DTT. The sample was concentrated to 5 mg ml−1 by centrifugal filtration and stored at –80 °C.

Wild-type Mtb NusG (+ mutants N65H, R124L and N125S)

Plasmid pAC82 (or mutant variation) was used to overexpress wild-type Mtb NusG13. Plasmids encoding NusG mutants were generated using Q5 Site-directed mutagenesis (NEB) and sequenced to confirm the presence of target mutations. E. coli BL21 cells containing plasmids encoding different versions of Mtb NusG were grown in LB with 50 μg ml−1 kanamycin at 37 °C to an OD600 of 0.4, then transferred to room temperature and left shaking to an OD600 of 0.67. Protein expression was induced by adding IPTG to a final concentration of 0.1 mM, grown for an additional 4 h, then collected by centrifugation (4,000g, 20 min at 4 °C). Collected cells were resuspended in 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 5 mM imidazole, 10% glycerol, 1 mM PMSF, 1 mM protease inhibitor cocktail (Roche), 2 mM β-mercaptoethanol, and lysed by French press. The lysate was centrifuged (4,000 rpm for 20 min, 4 °C) and the supernatant was removed and applied to a HisTrap column pre-washed with 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 10% glycerol, 15 mM imidazole, and 2 mM β-mercaptoethanol. After loading the sample, the column was washed with five volumes of the same buffer, before gradient elution with 50 mM Tris-HCl, pH 8.0, 500 mM NaCl, 10% glycerol, 250 mM imidazole, and 2 mM β-mercaptoethanol. The eluted protein was mixed with precision protease and dialysed overnight at 4 °C in 20 mM Tris-HCl, pH 8.0, 500 mM NaCl, 10 mM β-mercaptoethanol to cleave the N-terminal His10 tag before applying to a HisTrap column to remove the uncleaved protein. The flow-through was collected and glycerol was added to a final concentration of 20% (v/v). Aliquots were flash frozen in liquid nitrogen and stored in –80 °C until use.

Promoter-based in vitro termination assays

The DNA sequence for the Mtb H37Rv 5 S rRNA (rrf gene) intrinsic terminator was taken from Mycobrowser (MTB000021), with genomic coordinates of 1,476,999 to 1,477,077 basepairs. The intrinsic terminator was found by predicting its RNA structure using mfold (RNA folding form v2.3) via the UNAFold Web Server. The intrinsic terminator was cloned downstream of a cytidine-less halt cassette in plasmid pAC7038, a gift of the R. Landick laboratory, using Q5 site-directed mutagenesis (following manufacturer’s protocol – NEB) at an annealing temperature of 59 °C with GC enhancer for the PCR step, with primers 5′-TGGTGTTTTTGTATGTTTATATCGACTCAGCCGCTCGCGCCATGGACGCTCTCCTGA-3′ and 5′-CCGTTACCGGGGGTGTTTTTGTATGTTCGGCGGTGTCCTGGATCCTGGCAGTTCCCT-3′ (synthesized by IDT), to create plasmid pJC1. The 323 base pairs linear DNA fragment used for in vitro transcription assays was PCR amplified using Accuprime Pfx DNA polymerase (Invitrogen) at an annealing temperature of 56.5 °C, with primers 5′-GAATTCAAATATTTGTTGTTAACTCTTGACAAAAGTGTTAAAAGC-3′ and 5′-GTTGCTTCGCAACGTTCAAATCC-3′ (synthesized by IDT), following manufacturer instructions, and PCR purified (using the QIAquick PCR Purification Kit (QIAGEN)) to remove protein contents and buffer exchange into 10 mM Tris-HCl pH 8.5.

pJC1 contains the rrf termination site at approximately +150 bp. This template also contained a C-less cassette (+1 to +26). Core RNAP was incubated for 15 min at 37 °C with σA/RbpA in transcription buffer (20 mM Tris, 25 mM KGlu, 10 mM MgOAc, 1 mM DTT, 5 µg ml−1 BSA) to form holo-RNAP, followed by 10 min incubation with 500 nM CarD at 37 °C. Holo-RNAP (200 nM) was then incubated with template DNA (10 nM) for 15 min at 37 °C. To initiate transcription, the complex was incubated with ATP + GTP (both at 16 µM), UTP (2 µM), and 0.1 µl per reaction [α-32P]UTP for 15 min at 37 °C to form a halted complex at U26. Transcription was restarted by adding a master mix containing NTP mix (A + C + G + U), heparin, and NusG at a final concentration of 150 µM (each NTP), 10 µg ml−1 (heparin), and 1 µM NusG at 23 °C. The reaction was allowed to proceed for 30 min, followed by a ‘chase’ reaction in which all 4 nucleotides were added to a final concentration of 500 µM each. After 10 min, aliquots were removed and added to a 2× Stop buffer (95% formamide, 20 mM EDTA, 0.05% bromophenol blue, 0.05% xylene cyanol). Samples were analysed on an 8% denaturing PAGE (19:1 acrylamide: bis acrylamide, 7 M urea, 1X TBE pH= 8.3) for 1.25 h at 400 V, and the gel was exposed on a Storage Phosphor Screen and imaged using a Typhoon PhosphoImager (GE Healthcare).

Quantification of termination and changes in termination

Synthesized RNA bands on the gel image were quantified using ImageJ software (NIH). Each lane from below the rrf termination site (~150 nt) to above the runoff RNA products (263 nt) was converted to a pseudo-densitometer plot using the ImageJ line function and the relative areas of the termination and runoff bands were measured. Termination efficiency (TE) was calculated as the fraction of the termination (term) peak area relative to total of the termination and runoff (term + runoff) peak areas. Fold changes in termination attributable to each NusG (∆T) were determined as the aggregate of changes in the termination rates kb and kt, as defined by von Hippel and Yager (equations (1) and (2))62,63. Multiple algebraic transforms can yield the aggregate fold changes in termination, ∆T, based on the following equations.

$${\rm{TE}}=\frac{{k}_{t}}{{k}_{t}+{k}_{b}}$$

(1)

$${\rm{TE}}={\left[1+{{\rm{e}}}^{-\Delta \Delta {G}^{\ddagger }/-RT}\right]}^{-1},$$

(2)

where ∆∆G is the difference in activation barriers between termination and bypass, which is most directly related to the energies of RNAP–NusG and internal RNAP interactions that govern termination.

$${\Delta \Delta G}^{\ddagger }=-RT\times {\rm{ln}}\left(\left(1/{\rm{TE}}\right)-1\right)$$

(3)

(equation (2) rearranged).

$$\Delta T={{\rm{e}}}^{\left({\Delta \Delta G}_{2}^{\ddagger }-{\Delta \Delta G}_{1}^{\ddagger }\right)}$$

(4)

(fold change in aggregate termination rates for two conditions, 1 and 2).

$$\Delta T=\frac{\left(\frac{1}{{{\rm{TE}}}_{2}}\right)-1}{\left(\frac{1}{{{\rm{TE}}}_{1}}\right)-1}$$

(5)

(alternative calculation derived from equation (1) assuming NusG only affects kb).

Calculating ∆T using either the combinations of equations (3) and (4) or using equation (5) gives the same results because the ∆T is the same whether conditions differ by aggregate effects on both kb and kt or an effect on only one of them. We calculate ∆T using these approaches rather than the simple difference in energies of activation (\(\Delta \Delta {G}_{2}^{\ddagger }-\Delta \Delta {G}_{1}^{\ddagger }\)) because it allows a clearer graphical depiction of effects without changing the results. Errors in ∆T were calculated using a two-sided, unpaired t-test with no assumptions on variance.

Electrophoretic mobility shift assay

RNAP–NusG complexes were assembled and run on an electrophoretic mobility shift assay to test proper binding of all mutant NusGs. Core RNAP (200 nM) was incubated with the template strand of elongation scaffold DNA13 (50 nM) for 15 min at room temperature. Next, the complex was incubated with the complementary non-template strand (50 nM) for 15 min at room temperature. Finally, the complex was incubated with 1 µM wild-type NusG, N65H NusG, R124L NusG, or N125S NusG for 10 min at room temperature. All complexes were assembled in the following transcription buffer: 20 mM Tris, 25 mM potassium glutamate, 10 mM magnesium acetate, 1 mM DTT, 5 µg ml−1 BSA. Samples were immediately loaded and run on a native PAGE (4.5% acrylamide:bis solution 37.5:1, 4% glycerol, 1× TBE) for 1 h at 15 mA. The gel was run at 4 °C. The gel was first stained with GelRed (Biotium) followed by Coomassie blue for visualization of DNA and protein respectively.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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2024 Cybersecurity trends with the evolution of artificial intelligence

2024 Cybersecurity trends with the evolution of artificial intelligence

As we enter 2024, the cybersecurity landscape is evolving at a rapid pace. With each passing day, the sophistication of cyber threats increases, and the need for robust security measures becomes more pressing. In this ever-changing digital world, it’s imperative for individuals and organizations alike to stay informed and prepared to protect their digital assets. Here are some of the 2024 cybersecurity trends that are expected to dominate this year say researchers at IBM.

  • AI-based threats are anticipated to grow, with AI being used to create more convincing phishing emails.
  • A shift from traditional passwords to passkeys is expected, with the adoption of the FIDO standard, enhancing security and user convenience.
  • Deepfake technology will likely become more sophisticated and widespread, necessitating education and security measures beyond detection.
  • Generative AI may lead to ‘hallucinations’ or inaccuracies in information, which could pose security risks. Technologies like retrieval-augmented generation (RAG) may help improve accuracy.
  • AI will also play a positive role in cybersecurity, aiding in threat anticipation and case summarization, while cybersecurity will be essential to ensure AI’s trustworthiness.
  • Persistent threats include data breaches, with costs continuing to rise, and ransomware attacks becoming faster to execute.
  • Multifactor authentication is becoming more common as a security measure.
  • Internet of Things (IoT) threats have increased, with a significant rise in attacks.
  • Quantum computing remains a potential future threat to cryptography but has not yet had a significant impact.
  • The cybersecurity skills gap has shown some improvement, with a decrease in open positions, but the need for skilled professionals remains high.

One of the most significant developments in the realm of cybersecurity is the use of artificial intelligence (AI). AI is enhancing the capabilities of cyber defense systems, but it’s also being wielded by cybercriminals. They are using AI to create phishing emails that are so well-crafted they can be hard to distinguish from legitimate messages. To combat this, the adoption of AI-powered security systems is essential. These systems can identify and mitigate the threat posed by these advanced phishing attempts.

Another trend that’s gaining traction is the move towards passwordless authentication. The traditional password system is becoming obsolete, making way for more secure methods such as the FIDO standard, which relies on passkeys. These new authentication tools, which can be physical or digital, don’t require users to remember complex passwords and are designed to reduce the risk of security breaches.

The emergence of deepfake technology is another challenge on the horizon. These hyper-realistic audio and video forgeries are becoming more convincing and widespread, posing a serious threat to personal and corporate security. To defend against the malicious use of deepfakes, education and the implementation of advanced security measures are crucial.

2024 Cybersecurity trends

Here are some other articles you may find of interest on the subject of artificial intelligence :

In the fight against misinformation, generative AI plays a dual role. While it can produce content that mimics human writing, it can also be used to generate false or misleading information. Technologies like retrieval-augmented generation (RAG) are being developed to enhance the reliability of generative AI by incorporating accurate data during the content creation process, helping to curb the spread of misinformation.

Despite the potential risks, AI remains an invaluable tool in the arsenal of cyber defense. The challenge lies in ensuring that the AI systems themselves are secure and reliable. As we rely more on these systems, their integrity becomes a cornerstone of our digital security.

The issues of data breaches and ransomware are not new, but they continue to escalate in both frequency and severity. The costs associated with these incidents are soaring, highlighting the importance of robust security protocols and effective incident response strategies.

As we enhance our security measures, multifactor authentication (MFA) is becoming a standard practice. MFA adds an extra layer of protection, which is increasingly necessary in today’s digital environment. However, as the Internet of Things (IoT) expands, so does the number of attacks on these connected devices. This surge in IoT attacks calls for stronger security measures to protect against potential vulnerabilities.

The advent of quantum computing is another factor that could significantly impact cybersecurity. Quantum computing has the potential to break current cryptographic standards, which means there’s an urgent need to develop quantum-resistant encryption methods to safeguard our data in the future.

A persistent issue in the field of cybersecurity is the skills shortage. Although there has been progress in addressing this gap, continuous education and training are necessary. Equipping the workforce with the skills to tackle new cyber threats is a critical step in strengthening our collective cyber defenses.

As we navigate the complex and dynamic world of cybersecurity in 2024, staying vigilant and proactive is more important than ever. Cyber threats are becoming more sophisticated, and our defenses must evolve to match them. By keeping abreast of these trends and challenges, we can better prepare ourselves to defend against the myriad of threats that lurk in the digital realm.

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The Evolution of Cybersecurity | Sourcelearns

The ’90s are known as the virus era, but cybersecurity tools were already beginning to take shape. Firewalls and antivirus software scanned for malware, while immunizers modified programs to prevent attacks.

It was also the time when hacker groups took form. They began monetizing by stealing information. This led to massive data breaches, such as TK Maxx’s 45 million credit card details and accountancy firms’ client files.

The 1960s

The 1960s ushered in groundbreaking digital technologies. These innovations revolutionized how we communicate by creating networks that could be accessed anywhere in the world.

As technology continued to evolve, Cybersecurity began to become a concern. Hacking, cyber espionage, and equipment failures were becoming more common. Even movies like 1983’s WarGames highlighted the potential danger of cyber attacks.

A researcher created a program that could move through ARPANET’s network, leaving a data trail behind. This program was called CREEPER and would later be the inspiration for antivirus software. Viruses and malware were quickly growing, and the need for protection became increasingly urgent. Firewalls and commercial antivirus programs were created in the 1990s to meet this growing demand. They work by using blocklists to identify threats and neutralize them.

The 1970s

Cybersecurity’s birth is primarily attributed to the 1970s. The Advanced Research Projects Agency Network (ARPANET) — a connectivity network developed before the internet — was created in this decade. As more information was digitized, hackers with not-so-great intentions began accessing computers. They used their skills to tamper with systems, steal information, and even hold corporations for ransom. This gave rise to cybersecurity specialists known as white hat hackers, who act as security experts.

By the end of this decade, computers were becoming smaller and less expensive. Locking them up wasn’t feasible or beneficial, so passwords were embraced to access computers. This triggered the arms race between malware and anti-malware. Hackers realized that getting hacked wasn’t just about digital vandalism, making money, and gaining political capital.

The 1980s

The 1980s saw a significant shift in how people used computers. Computers have become commonplace in homes and offices, bringing many benefits but creating new opportunities for cybercriminals. During this decade, viruses like the Morris worm and Melissa virus began damaging networks, and polymorphic viruses and firewall technology came to the fore.

Hackers also entered the mainstream, making media depictions of cyberattacks more realistic. This era also saw email development and a growing reliance on digital communications. As a result, the US government began developing software to protect against hackers, launching an ARPANET project called Protection Analysis to create automated ways of spotting vulnerabilities in computer programs. This cat-and-mouse between hackers and security vendors was the birth of cybersecurity as we know it.

The 1990s

Once computers became commonplace in offices and homes, cybercriminals found new ways to exploit them. Hundreds of millions of credit card data were breached, and hackers started to realize there was real money to be made from ransomware attacks, hacktivism, and other destructive cyberattacks.

As the number of viruses grew, security solutions were forced to evolve as well. Antivirus software developed more sophisticated and started to use heuristic detection, which uses generic code to identify malware even if it hasn’t been detected before.

The 1990s also saw the rise of polymorphic virus risks, which mutate to avoid detection. This ushered in an era of malicious hackers that targeted significant corporations, stealing their valuable information and causing downtime. This prompted companies to make cybersecurity a priority.

The 2000s

With the internet now readily available, more people began putting their personal information online. Organized crime entities saw this as a new source of revenue and started hacking into governments and individuals to steal data. This caused network security threats to increase exponentially, requiring firewalls and antivirus programs to be produced on a mass basis to protect the public.

The 2000s also saw more credit card breaches and hacktivism as bad actors realized there was a lot of money to be made from holding corporations hostage and stealing their data. These cyberattacks shaped cybersecurity as we know it by making it clear that companies had to improve their cybersecurity programs or risk losing valuable information and potentially being shut down altogether.

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The Rise and Evolution of Online Translation Platforms

In today’s globalized world, the ability to communicate across languages and cultures is more crucial than ever. As businesses expand internationally, travelers explore new destinations, and digital content proliferates, the demand for accurate and efficient translation has skyrocketed. Enter online translation platforms, the unsung heroes of our interconnected age.

1. What are Online Translation Platforms?

Online translation platforms are digital tools or services that allow users to translate text or speech from one language to another. These platforms can range from simple text-based translators, like Google Translate, to more sophisticated systems that integrate with websites and applications, offering real-time translation for users.

2. The Evolution of Translation Platforms

The journey of online translation began with basic word-for-word substitutions, which often resulted in translations that were technically correct but lacked context or cultural nuance. However, with the advent of artificial intelligence (AI) and machine learning, these platforms have evolved significantly:

  • Machine Translation (MT): Early systems used rule-based methods, but modern MT employs neural networks and deep learning to produce more accurate and contextually relevant translations.
  • Real-time Translation: Platforms now offer real-time translation for live conversations, be it in messaging apps or video conferences.
  • Integration with Other Services: Many platforms integrate with websites, apps, and content management systems, allowing for seamless translation of digital content.

3. Benefits of Using Online Translation Platforms

  • Accessibility: Anyone with an internet connection can access these platforms, breaking down language barriers.
  • Cost-Effective: Compared to hiring professional translators, online platforms can be a more affordable solution for many tasks.
  • Speed: Instant translation is now a reality, making communication faster than ever.
  • Continuous Improvement: With each translation, many platforms learn and improve, offering better results over time.

4. Limitations and Challenges

While online translation platforms offer numerous benefits, they are not without limitations:

  • Lack of Nuance: Translations can sometimes miss cultural nuances or idiomatic expressions.
  • Data Privacy Concerns: Users might be wary of sharing sensitive information on online platforms.
  • Over-reliance: Sole reliance on machine translation can lead to miscommunication, especially in critical areas like legal or medical translations.

5. The Future of Online Translation

The future looks promising for online translation platforms. With advancements in AI and machine learning, we can expect even more accurate and context-aware translations. Additionally, augmented reality (AR) might play a role, with real-time translations appearing as overlays in AR glasses.

Moreover, as the world becomes more interconnected, the demand for translation services, both online and offline, will continue to grow. This will drive innovation and competition in the sector, leading to even more advanced and user-friendly platforms.

6. The Role of Human Translators in the Digital Age

Despite the rapid advancements in online translation platforms, the role of human translators remains indispensable. Machines, no matter how advanced, lack the human touch, cultural understanding, and emotional intelligence that human translators bring to the table. Here’s why they remain relevant:

  • Cultural Sensitivity: Human translators understand the cultural nuances and can interpret context in ways machines can’t. This is especially crucial for content that requires a deep understanding of local customs, traditions, and idioms.
  • Specialized Fields: Areas like legal, medical, and technical translations often require a specialized knowledge base. Human experts in these fields ensure that translations are not just linguistically accurate but also contextually correct.
  • Quality Assurance: Many businesses and organizations use a hybrid approach. They combine machine translation for speed and scale with human oversight for quality assurance, ensuring the final output is both fast and accurate.

Platforms like Duolingo and Wikipedia have leveraged the power of the community to drive translations. These crowd-sourced models allow for a diverse set of inputs, often resulting in translations that are both accurate and rich in local flavor.

8. Ethical Considerations in Online Translation

As with all technology, online translation platforms come with ethical considerations:

  • Bias and Stereotyping: Algorithms can sometimes perpetuate biases present in the data they were trained on. It’s essential to ensure that these platforms are trained on diverse datasets to avoid reinforcing stereotypes.
  • Job Displacement: While online platforms create new opportunities, there’s also a concern about job displacement in the translation industry. Balancing technological advancement with job preservation is a challenge that the industry must address.

9. Personalized and Adaptive Translation

The future might see translation platforms that adapt to individual users. Just as AI can learn a user’s preferences in music or shopping, future translation tools might adapt to a user’s linguistic style, offering personalized translations based on past interactions.

10. Conclusion: A Collaborative Future

The future of translation is not a choice between humans and machines but a collaboration between the two. Online translation platforms will continue to evolve, becoming more sophisticated and integrated into our daily lives. However, human expertise will always be needed to navigate the complexities of language and culture. Together, humans and technology will work hand in hand to make cross-cultural communication smoother and more accessible to all.