Daughter of man infamously shot by Dick Cheney gives VERY sarcastic tribute to the Daily Mail after former VP died aged 84... and reveals what really happened Why Tuesday's races aren't as close as you think: White House analyst CRAIG KESHISHIAN reveals what the polls always miss Dick Cheney dead: Vice President who served with George W. Bush and took leading role in'war on terror' dies at 84 It's the trendy new diagnosis for everything from fatigue to brain fog... but here's the truth about your gut problem - and how to fix it: DR EMILY LEEMING How Charles reacted when'difficult' William asked if he could do fewer engagements: Biographer ROBERT JOBSON reveals the'tension', secrets of Kate's family life - and how couple are making Prince George'strong' Sydney Sweeney sparks outrage after starring on GQ's Men Of The Year cover... as she breaks silence on American Eagle ad Rapper Young Bleed dead at 51 after suffering brain aneurysm as son reflects on'legend' father'She used it to freshen up... it killed her': My wife died of cancer at 63. She never smoked or drank. My ex is the international fugitive Democrat who's fled to Europe with our nine-year-old son. Here are the disgraceful secrets she'd hate the world to know Woke Gen-Z's revenge: Poll reveals staggering number of under-30s who back Mamdani while their parents are terrified of a return to 1980s New York Erika Kirk reveals her 3-year-old daughter's heartbreaking question in first interview since husband Charlie's assassination Father reveals'radical faith' spiral of American son killed in hail of arrows by reclusive tribe as new believers consider following him to isolated island Revealed: Football manager who died mid-match, leaving players in disbelief on the pitch, 'complained about fish he had eaten' hours before heart attack - as devastated star speaks out Dallas Cowboys agree huge trade for rival's defensive captain just hours before the NFL deadline US to test nuclear missile TODAY to show'readiness' amid arms race fears Warren Buffett's $6billion stock exit is his loudest warning yet AMANDA PLATELL: Fergie's delusions have reached a new low. I can't believe Beatrice and Eugenie are egging her on.

As fears of all-out nuclear war intensify, scientists are sounding the alarm that the decision to launch a catastrophic strike could soon rest not with world leaders, but with a machine. In a stark warning, the Stockholm International Peace Research Institute (SIPRI), an independent group that monitors global security issues, reported that the decades-long decline in global nuclear arsenals has come to an end. Instead, nations are now modernizing, expanding, and deploying their stockpiles at a rapid and alarming pace, signaling the onset of a new, high-tech arms race. While AI and similar technologies can accelerate decision-making during crises, scientists warn they also raise the risk of nuclear conflict through miscommunication, misunderstanding, or technical failure, the report stated. In a nuclear standoff, decision-makers often have only minutes to assess threats and respond.

It's been more than a hot minute since we've seen some new Sega Genesis classics in the Nintendo Switch online library, but we're finally getting some today. The Switch Online Expansion Pack brings the frantic platformer VectorMan, the run-n-gunner Wolf of the Battlefield: MERCS and the totally awesome ToeJam & Earl in Panic on Funkotron to the Switch's classic games library. VectorMan from BlueSky Software is a 2D platformer with impressive graphics for its time that often gets overlooked in the long hallway of the Sega Genesis' legacy. The protagonist is a robot made of floating left on a resource ravaged Earth to clean up the mess humanity made when one of the supervisor robots named WarHead connects to a nuclear weapon and holds the planet hostage. VectorMan must blast his way through waves of enemies to free the Earth from WarHead's clutches.

Proteolysis targeting chimeras (PROTACs) are small molecules that trigger the breakdown of traditionally "undruggable" proteins by binding simultaneously to their targets and degradation-associated proteins. A key challenge in their rational design is understanding their structural basis of activity. Due to the lack of crystal structures (18 in the PDB), existing PROTAC docking methods have been forced to simplify the problem into a distance-constrained protein-protein docking task. To address the data issue, we develop a novel pseudo-data generation scheme that requires only binary protein-protein complexes. Its inference speed enables the large-scale screening of PROTAC designs, and computed properties of predicted structures achieve statistically significant correlations with published degradation activities. Targeted protein degradation is an emerging paradigm in rational drug design that induces the breakdown of "undruggable" proteins (Zhao et al., 2022). Proteolysis targeting chimeras (PROTACs) are small molecules that achieve this by simultaneously binding a protein of interest (POI) and a degradation-associated protein (e.g. In contrast to small molecule drugs, which attach to predefined sites on their protein targets, PROTACs operate by inducing a stable, ternary complex between themselves and two proteins which don't typically interact.

Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary. In contrast, chemical language models learn atom-level representations of smaller molecules that include every atom, bond, and ring. In this work, we show that chemical language models can learn atom-level representations of proteins enabling protein generation unconstrained to the standard genetic code and far beyond it. In doing so, we show that language models can generate entire proteins atom by atom -- effectively learning the multiple hierarchical layers of molecular information that define proteins from their primary sequence to their secondary, and tertiary structure. We demonstrate language models are able to explore beyond protein space -- generating proteins with modified sidechains that form unnatural amino acids. Even further, we find that language models can explore chemical space and protein space simultaneously and generate novel examples of protein-drug conjugates. The results demonstrate the potential for biomolecular design at the atom level using language models.

Proteolysis-Targeting Chimeras (PROTACs) represent a novel class of small molecules which are designed to act as a bridge between an E3 ligase and a disease-relevant protein, thereby promoting its subsequent degradation. PROTACs are composed of two protein binding "active" domains, linked by a "linker" domain. The design of the linker domain is challenging due to geometric and chemical constraints given by its interactions, and the need to maximize drug-likeness. To tackle these challenges, we introduce ShapeLinker, a method for de novo design of linkers. It performs fragment-linking using reinforcement learning on an autoregressive SMILES generator. The method optimizes for a composite score combining relevant physicochemical properties and a novel, attention-based point cloud alignment score. This new method successfully generates linkers that satisfy both relevant 2D and 3D requirements, and achieves state-of-the-art results in producing novel linkers assuming a target linker conformation. This allows for more rational and efficient PROTAC design and optimization. Code and data are available at https://github.com/aivant/ShapeLinker.

Iran's supply of drones to Russia for its war effort in Ukraine appears to have escalated in recent months after a study released this week found Tehran has modified its drones to employ maximum damage. A January report by Conflict Armament Research (CAR) released publicly Thursday broke down why the affordably made Iranian Shahed-131 single-use drones have been employed to significant effect in Ukraine. Russia has relied on Iranian-supplied unmanned areial vehicles (UAV) in Ukraine for months to help assist with its diminishing missile stockpiles as the war continues into its 11th month, according to the Pentagon. Multipurpose warhead from a Shahed-131 UAV found in research by Conflict Armament Research on Thursday. Iranian drones have been used to hit civilian structures and target Ukraine's critical infrastructure as Russia looks to bombard the war-torn nation in near-daily strikes.

The United States builds a lot of weapons. Unless a lot of really bad stuff happens all at once, we build more weapons than we can possibly use, and since we keep inventing new ones that are better and doing what weapons do, all the old stuff tends to just pile up. These piles of old explosives aren't aging particularly well, leaving us with few options, which include forgetting about them for longer than is probably safe, or blowing them up. A third option is disassembly and recycling, but that's dangerous for humans, because these weapons can be very old, and very lethal. Sandia National Labs has been helping the Department of Defense deal with some of its stockpile of M26 rockets, which are packed full of tiny little grenades and need to be taken apart very carefully.

The Russian Ministry of Defence has been keen to promote a range of new super weapons currently believed to be in development. President Putin unveiled a catalogue of doomsday weaponry as part of his annual'State of the Nation' speech in March 2018. However, questions remain about the true nature of their capabilities, how far into development the weapons truly are, and when they will be combat-ready. The RS-28 Sarmat is intended to replace the Soviet-designed SS-18 Voyevoda, the world's heaviest ICBM (Intercontinental Ballistic Missile). It is known as'Satan' in the West and carries 10 nuclear warheads.

We consider the problem of how to improve automatic target recognition by fusing the naive sensor-level classification decisions with "intuition," or context, in a mathematically principled way. This is a general approach that is compatible with many definitions of context, but for specificity, we consider context as co-occurrence in imagery. In particular, we consider images that contain multiple objects identified at various confidence levels. We learn the patterns of co-occurrence in each context, then use these patterns as hyper-parameters for a Hierarchical Bayesian Model. The result is that low-confidence sensor classification decisions can be dramatically improved by fusing those readings with context. We further use hyperpriors to address the case where multiple contexts may be appropriate. We also consider the Bayesian Network, an alternative to the Hierarchical Bayesian Model, which is computationally more efficient but assumes that context and sensor readings are uncorrelated.