For a long time, many climate scientists and advocates held onto an optimistic belief that once the impacts of climate change became undeniable, people and governments would act. But whereas the predictions of climate models have increasingly borne out, the assumptions about human behavior have not. Even as disasters mount, climate change remains low on voters' priority lists, and policy responses remain tepid. To me, this gap reflects a deeper failure--not just in policy or communication, but in how we understand human adaptability. When I began my career as a computational cognitive scientist, I was drawn to a defining strength of human cognition--a marked ability to adapt.

Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks; however, there remains a critical need for an intelligent agent that can navigate the drug discovery process. Towards this end, we introduce LIDDiA, an autonomous agent capable of intelligently navigating the drug discovery process in silico. By leveraging the reasoning capabilities of large language models, LIDDiA serves as a low-cost and highly-adaptable tool for autonomous drug discovery. We comprehensively examine LIDDiA, demonstrating that (1) it can generate molecules meeting key pharmaceutical criteria on over 70% of 30 clinically relevant targets, (2) it intelligently balances exploration and exploitation in the chemical space, and (3) it can identify promising novel drug candidates on EGFR, a critical target for cancers.

Humor is a fundamental aspect of human communication and cognition, as it plays a crucial role in social engagement. Although theories about humor have evolved over centuries, there is still no agreement on a single, comprehensive humor theory. Likewise, computationally recognizing humor remains a significant challenge despite recent advances in large language models. Moreover, most computational approaches to detecting humor are not based on existing humor theories. This paper contributes to bridging this long-standing gap between humor theory research and computational humor detection by creating an interpretable framework for humor classification, grounded in multiple humor theories, called THInC (Theory-driven Humor Interpretation and Classification). THInC ensembles interpretable GA2M classifiers, each representing a different humor theory. We engineered a transparent flow to actively create proxy features that quantitatively reflect different aspects of theories. An implementation of this framework achieves an F1 score of 0.85. The associative interpretability of the framework enables analysis of proxy efficacy, alignment of joke features with theories, and identification of globally contributing features. This paper marks a pioneering effort in creating a humor detection framework that is informed by diverse humor theories and offers a foundation for future advancements in theory-driven humor classification. It also serves as a first step in automatically comparing humor theories in a quantitative manner.

We propose the first comprehensive approach for modeling and analyzing the spatiotemporal shape variability in tree-like 4D objects, i.e., 3D objects whose shapes bend, stretch, and change in their branching structure over time as they deform, grow, and interact with their environment. Our key contribution is the representation of tree-like 3D shapes using Square Root Velocity Function Trees (SRVFT). By solving the spatial registration in the SRVFT space, which is equipped with an L2 metric, 4D tree-shaped structures become time-parameterized trajectories in this space. This reduces the problem of modeling and analyzing 4D tree-like shapes to that of modeling and analyzing elastic trajectories in the SRVFT space, where elasticity refers to time warping. In this paper, we propose a novel mathematical representation of the shape space of such trajectories, a Riemannian metric on that space, and computational tools for fast and accurate spatiotemporal registration and geodesics computation between 4D tree-shaped structures. Leveraging these building blocks, we develop a full framework for modelling the spatiotemporal variability using statistical models and generating novel 4D tree-like structures from a set of exemplars. We demonstrate and validate the proposed framework using real 4D plant data.

Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence (AI) in fields such as cybercrime, the use of deepfakes and fake news to deceive or manipulate, and displacement of human jobs across various market sectors. Here, we consider both current and future positive advances and possibilities with synthetic datasets. Synthetic data offers significant benefits, particularly in data privacy, research, in balancing datasets and reducing bias in machine learning models. Generative AI is an artificial intelligence genre capable of creating text, images, video or other data using generative models. The recent explosion of interest in GenAI was heralded by the invention and speedy move to use of large language models (LLM). These computational models are able to achieve general-purpose language generation and other natural language processing tasks and are based on transformer architectures, which made an evolutionary leap from previous neural network architectures. Fuelled by the advent of improved GenAI techniques and wide scale usage, this is surely the time to consider how synthetic data can be used to advance infectious disease research. In this commentary we aim to create an overview of the current and future position of synthetic data in infectious disease research.

Assistive or enabling technologies aim to create more accessible and inclusive solutions for people living with disabilities. This is critical, since many such users rely on technology for daily activities such as mobility and communication. While the problems are global, there are unique challenges that exist in the Asia Pacific region when it comes to developing assistive technologies, particularly assistive devices. The United Nations Economic and Social Commission for Asia and the Pacific (UN ESCAP) estimates that 650 million people in the Asia-Pacific region live with a disability.4 It is also well understood that disability statistics in the region could be significantly underreported.

TileDB, the database for any complex data and compute, announced the launch of TileDB-SOMA, the first collection of software libraries that implement the open-source SOMA API specification. SOMA and TileDB-SOMA are the result of a collaboration between the Chan Zuckerberg Initiative and TileDB to accelerate single-cell research by eliminating data silos and enable large-scale computations that are otherwise too challenging to execute on commodity hardware. "By streamlining access to enormous datasets, powerful new tools like TileDB-SOMA will accelerate the research efforts of single-cell biologists" New technologies and analysis tools have led to the exponential growth of single-cell RNA sequencing (scRNA-seq) data, requiring new solutions that can accommodate datasets at scale. Advancements in genomics technologies have also enabled researchers to combine multiple modalities of data collected from the same cell samples, increasing the complexity and impact of single-cell analysis. "The unsaid assumption in single-cell research is that dataset size is bound by RAM, but instead of asking researchers to change their computational tools, we're rethinking how the data model itself could do more heavy lifting for scientists," said Stavros Papadopoulos, Founder & CEO, TileDB, Inc. "With TileDB-SOMA for R and Python, computational biologists can work across programming languages and combine data that was previously formatted specifically for Seurat, Anndata/Scanpy or Bioconductor. This breaks down data silos, and allows scientists to collaborate without the hassle of converting or duplicating data. Everyone can access the dataset, stored locally or in the cloud, at any scale."

This will help scientists to understand the basic principles by which signals travel through the brain at the neural level and lead to behaviour and learning. An organism's nervous system, including the brain, is made up of neurons that are connected to each other via synapses. Information in the form of chemicals passes from one neuron to another through these contact points. The map of the 3016 neurons that make up the larva of the fruit fly Drosophila melanogaster's brain, and the detailed circuitry of neural pathways within it, is known as a'connectome'. This is the largest complete brain connectome ever to have been mapped.

Proteins are complex and dynamic molecules, encoded by our genes, that are responsible for many of the varied and fundamental processes of life. They have an astounding range of roles in biology. The rods and cones in our eyes that sense light and make it possible for us to see, the molecular sensors that underlie hearing and our sense of touch, the complex molecular machines that convert sunlight into chemical energy in plants, the motors that drive motion in microbes and our muscles, enzymes that break down plastic, antibodies that protect us from disease, and molecular circuits that cause disease when they fail -- are all proteins. Metagenomics, one of the new frontiers in the natural sciences, uses gene sequencing to discover proteins in samples from environments across the earth, from microbes living in the soil, deep in the ocean, in extreme environments like hydrothermal vents, and even in our guts and on our skin. The natural world contains a vast number of proteins beyond the ones that have been cataloged and annotated in well-studied organisms.

The transition of architectural tools from traditional to modern computational has focused on efficiency and productivity. This is so as many of the computational tools used by architects have their legacy in industrial disciplines such as automobile and manufacturing. CATIA, for example, a software used by Frank Gehry and his team to achieve complex designs, was initially developed for the aeronautical industry. As a result of being inherited from these fields, the values and standards of these other disciplines have discretely trickled into architecture. Consequently, notions of optimization, standardization, and efficiency, all qualities of vertical thinking, or industrial era reasoning, are inevitably prioritized in these tools over lateral thinking qualities like inaccuracies, uncertainty, and accidents, qualities often associated with artistic practices.