Mirazul Islam, Yilin Yang, Alan J. Simmons, Vishal M. Shah, Krushna Pavan Musale, Yanwen Xu, Naila Tasneem, Zhengyi Chen, Linh T. Trinh, Paola Molina, Marisol A. Ramirez-Solano, Iannish D. Sadien, Jinzhuang Dou, Andrea Rolong, Ken Chen, Mark A. Magnuson, Jeffrey C. Rathmell, Ian G. Macara, Douglas J. Winton, Qi Liu, Hamim Zafar, Reza Kalhor, George M. Church, Martha J. Shrubsole, Robert J. Coffey, Ken S. Lau <<<

Xiekui Cui, Han Yang, Charles Cai, Cooper Beaman, Xiaoyu Yang, Hongjiang Liu, Xingjie Ren, Zachary Amador, Ian R. Jones, Kathleen C. Keough, Meng Zhang, Tyler Fair, Armen Abnousi, Shreya Mishra, Zhen Ye, Ming Hu, Alex A. Pollen, Katherine S. Pollard, Yin Shen <<<

AbstractTabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science1,2. The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories3–5, gradient-boosted decision trees6–9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains. <<<

Eslem Ben Arous, James A. Blinkhorn, Sarah Elliott, Christopher A. Kiahtipes, Charles D. N’zi, Mark D. Bateman, Mathieu Duval, Patrick Roberts, Robert Patalano, Alexander F. Blackwood, Khady Niang, Eugénie Affoua Kouamé, Edith Lebato, Emily Hallett, Jacopo N. Cerasoni, Erin Scott, Jana Ilgner, Maria Jesús Alonso Escarza, Francois Yodé Guédé, Eleanor M. L. Scerri <<<

Abstract Humans emerged across Africa shortly before 300 thousand years ago (ka)1–3. Although this pan-African evolutionary process implicates diverse environments in the human story, the role of tropical forests remains poorly understood. Here we report a clear association between late Middle Pleistocene material culture and a wet tropical forest in southern Côte d’Ivoire, a region of present-day rainforest. Twinned optically stimulated luminescence and electron spin resonance dating methods constrain the onset of human occupations at Bété I to around 150 ka, linking them with Homo sapiens. Plant wax biomarker, stable isotope, phytolith and pollen analyses of associated sediments all point to a wet forest environment. The results represent the oldest yet known clear association between humans and this habitat type. The secure attribution of stone tool assemblages with the wet forest environment demonstrates that Africa’s forests were not a major ecological barrier for H. sapiens as early as around 150 ka. <<<

Yangyang Shao, Ning Lu, Zhenfang Wu, Chen Cai, Shanshan Wang, Ling-Li Zhang, Fan Zhou, Shijun Xiao, Lin Liu, Xiaofei Zeng, Huajun Zheng, Chen Yang, Zhihu Zhao, Guoping Zhao, Jin-Qiu Zhou, Xiaoli Xue, Zhongjun Qin <<<

Chengzhi Jiao, Xiaoming Xie, Chenyang Hao, Liyang Chen, Yuxin Xie, Vanika Garg, Li Zhao, Zihao Wang, Yuqi Zhang, Tian Li, Junjie Fu, Annapurna Chitikineni, Jian Hou, Hongxia Liu, Girish Dwivedi, Xu Liu, Jizeng Jia, Long Mao, Xiue Wang, Rudi Appels, Rajeev K. Varshney, Weilong Guo, Xueyong Zhang <<<

Zhaolian Lu, Shanlan Mo, Duo Xie, Xiangwei Zhai, Shanjun Deng, Kantian Zhou, Kun Wang, Xueling Kang, Hao Zhang, Juanzhen Tong, Liangzhen Hou, Huijuan Hu, Xuefei Li, Da Zhou, Leo Tsz On Lee, Li Liu, Yaxi Zhu, Jing Yu, Ping Lan, Jiguang Wang, Zhen He, Xionglei He, Zheng Hu <<<

AbstractOlfaction is a fundamental sensory modality that guides animal and human behaviour1,2. However, the underlying neural processes of human olfaction are still poorly understood at the fundamental—that is, the single-neuron—level. Here we report recordings of single-neuron activity in the piriform cortex and medial temporal lobe in awake humans performing an odour rating and identification task. We identified odour-modulated neurons within the piriform cortex, amygdala, entorhinal cortex and hippocampus. In each of these regions, neuronal firing accurately encodes odour identity. Notably, repeated odour presentations reduce response firing rates, demonstrating central repetition suppression and habituation. Different medial temporal lobe regions have distinct roles in odour processing, with amygdala neurons encoding subjective odour valence, and hippocampal neurons predicting behavioural odour identification performance. Whereas piriform neurons preferably encode chemical odour identity, hippocampal activity reflects subjective odour perception. Critically, we identify that piriform cortex neurons reliably encode odour-related images, supporting a multimodal role of the human piriform cortex. We also observe marked cross-modal coding of both odours and images, especially in the amygdala and piriform cortex. Moreover, we identify neurons that respond to semantically coherent odour and image information, demonstrating conceptual coding schemes in olfaction. Our results bridge the long-standing gap between animal models and non-invasive human studies and advance our understanding of odour processing in the human brain by identifying neuronal odour-coding principles, regional functional differences and cross-modal integration. <<<

Anna Goldie, Azalia Mirhoseini, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, Eric Johnson, Omkar Pathak, Azade Nova, Jiwoo Pak, Andy Tong, Kavya Srinivasa, William Hang, Emre Tuncer, Quoc V. Le, James Laudon, Richard Ho, Roger Carpenter, Jeff Dean <<<

AbstractExtracting the underlying temporal structure of experience is a fundamental aspect of learning and memory that allows us to predict what is likely to happen next. Current knowledge about the neural underpinnings of this cognitive process in humans stems from functional neuroimaging research1–5. As these methods lack direct access to the neuronal level, it remains unknown how this process is computed by neurons in the human brain. Here we record from single neurons in individuals who have been implanted with intracranial electrodes for clinical reasons, and show that human hippocampal and entorhinal neurons gradually modify their activity to encode the temporal structure of a complex image presentation sequence. This representation was formed rapidly, without providing specific instructions to the participants, and persisted when the prescribed experience was no longer present. Furthermore, the structure recovered from the population activity of hippocampal–entorhinal neurons closely resembled the structural graph defining the sequence, but at the same time, also reflected the probability of upcoming stimuli. Finally, learning of the sequence graph was related to spontaneous, time-compressed replay of individual neurons’ activity corresponding to previously experienced graph trajectories. These findings demonstrate that neurons in the hippocampus and entorhinal cortex integrate the ‘what’ and ‘when’ information to extract durable and predictive representations of the temporal structure of human experience. <<<

Rachael B. Chanin, Patrick T. West, Jakob Wirbel, Matthew O. Gill, Gabriella Z. M. Green, Ryan M. Park, Nora Enright, Arjun M. Miklos, Angela S. Hickey, Erin F. Brooks, Krystal K. Lum, Ileana M. Cristea, Ami S. Bhatt <<<

Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis <<<

An essential prerequisite for evolution by natural selection is variation among individuals in traits that affect fitness. The ability of a system to produce selectable variation, known as evolvability, thus markedly affects the rate of evolution. Although the immune system is among the fastest-evolving components in mammals, the sources of variation in immune traits remain largely unknown. Here we show that an important determinant of the immune system's evolvability is its organization into interacting modules represented by different immune cell types. By profiling immune cell variation in bone marrow of 54 genetically diverse mouse strains from the Collaborative Cross, we found that variation in immune cell frequencies is polygenic and that many associated genes are involved in homeostatic balance through cell-intrinsic functions of proliferation, migration and cell death. However, we also found genes associated with the frequency of a particular cell type that are expressed in a different cell type, exerting their effect in what we term cyto-trans. The vertebrate evolutionary record shows that genes associated in cyto-trans have faced weaker negative selection, thus increasing the robustness and hence evolvability of the immune system. This phenomenon is similarly observable in human blood. Our findings suggest that interactions between different components of the immune system provide a phenotypic space in which mutations can produce variation with little detriment, underscoring the role of modularity in the evolution of complex systems. <<<

Michael S Haney, Róbert Pálovics, Christy Nicole Munson, Chris Long, Patrik K Johansson, Oscar Yip, Wentao Dong, Eshaan Rawat, Elizabeth West, Johannes C M Schlachetzki, Andy Tsai, Ian Hunter Guldner, Bhawika S Lamichhane, Amanda Smith, Nicholas Schaum, Kruti Calcuttawala, Andrew Shin, Yung-Hua Wang, Chengzhong Wang, Nicole Koutsodendris, Geidy E Serrano, Thomas G Beach, Eric M Reiman, Christopher K Glass, Monther Abu-Remaileh, Annika Enejder, Yadong Huang, Tony Wyss-Coray <<<

Several genetic risk factors for Alzheimer's disease implicate genes involved in lipid metabolism and many of these lipid genes are highly expressed in glial cells. However, the relationship between lipid metabolism in glia and Alzheimer's disease pathology remains poorly understood. Through single-nucleus RNA sequencing of brain tissue in Alzheimer's disease, we have identified a microglial state defined by the expression of the lipid droplet-associated enzyme ACSL1 with ACSL1-positive microglia being most abundant in patients with Alzheimer's disease having the APOE4/4 genotype. In human induced pluripotent stem cell-derived microglia, fibrillar Aβ induces ACSL1 expression, triglyceride synthesis and lipid droplet accumulation in an APOE-dependent manner. Additionally, conditioned media from lipid droplet-containing microglia lead to Tau phosphorylation and neurotoxicity in an APOE-dependent manner. Our findings suggest a link between genetic risk factors for Alzheimer's disease with microglial lipid droplet accumulation and neurotoxic microglia-derived factors, potentially providing therapeutic strategies for Alzheimer's disease. <<<

AbstractStable diffusion revolutionized image creation from descriptive text. GPT-2 (ref. 1), GPT-3(.5) (ref. 2) and GPT-4 (ref. 3) demonstrated high performance across a variety of language tasks. ChatGPT introduced such language models to the public. It is now clear that generative artificial intelligence (AI) such as large language models (LLMs) is here to stay and will substantially change the ecosystem of online text and images. Here we consider what may happen to GPT-{n} once LLMs contribute much of the text found online. We find that indiscriminate use of model-generated content in training causes irreversible defects in the resulting models, in which tails of the original content distribution disappear. We refer to this effect as ‘model collapse’ and show that it can occur in LLMs as well as in variational autoencoders (VAEs) and Gaussian mixture models (GMMs). We build theoretical intuition behind the phenomenon and portray its ubiquity among all learned generative models. We demonstrate that it must be taken seriously if we are to sustain the benefits of training from large-scale data scraped from the web. Indeed, the value of data collected about genuine human interactions with systems will be increasingly valuable in the presence of LLM-generated content in data crawled from the Internet. <<<

Kateryna D Makova, Brandon D Pickett, Robert S Harris, Gabrielle A Hartley, Monika Cechova, Karol Pal, Sergey Nurk, DongAhn Yoo, Qiuhui Li, Prajna Hebbar, Barbara C McGrath, Francesca Antonacci, Margaux Aubel, Arjun Biddanda, Matthew Borchers, Erich Bornberg-Bauer, Gerard G Bouffard, Shelise Y Brooks, Lucia Carbone, Laura Carrel, Andrew Carroll, Pi-Chuan Chang, Chen-Shan Chin, Daniel E Cook, Sarah J C Craig, Luciana de Gennaro, Mark Diekhans, Amalia Dutra, Gage H Garcia, Patrick G S Grady, Richard E Green, Diana Haddad, Pille Hallast, William T Harvey, Glenn Hickey, David A Hillis, Savannah J Hoyt, Hyeonsoo Jeong, Kaivan Kamali, Sergei L Kosakovsky Pond, Troy M LaPolice, Charles Lee, Alexandra P Lewis, Yong-Hwee E Loh, Patrick Masterson, Kelly M McGarvey, Rajiv C McCoy, Paul Medvedev, Karen H Miga, Katherine M Munson, Evgenia Pak, Benedict Paten, Brendan J Pinto, Tamara Potapova, Arang Rhie, Joana L Rocha, Fedor Ryabov, Oliver A Ryder, Samuel Sacco, Kishwar Shafin, Valery A Shepelev, Viviane Slon, Steven J Solar, Jessica M Storer, Peter H Sudmant, Sweetalana, Alex Sweeten, Michael G Tassia, Françoise Thibaud-Nissen, Mario Ventura, Melissa A Wilson, Alice C Young, Huiqing Zeng, Xinru Zhang, Zachary A Szpiech, Christian D Huber, Jennifer L Gerton, Soojin V Yi, Michael C Schatz, Ivan A Alexandrov, Sergey Koren, Rachel J O'Neill, Evan E Eichler, Adam M Phillippy <<<

Apes possess two sex chromosomes-the male-specific Y chromosome and the X chromosome, which is present in both males and females. The Y chromosome is crucial for male reproduction, with deletions being linked to infertility. The X chromosome is vital for reproduction and cognition. Variation in mating patterns and brain function among apes suggests corresponding differences in their sex chromosomes. However, owing to their repetitive nature and incomplete reference assemblies, ape sex chromosomes have been challenging to study. Here, using the methodology developed for the telomere-to-telomere (T2T) human genome, we produced gapless assemblies of the X and Y chromosomes for five great apes (bonobo (Pan paniscus), chimpanzee (Pan troglodytes), western lowland gorilla (Gorilla gorilla gorilla), Bornean orangutan (Pongo pygmaeus) and Sumatran orangutan (Pongo abelii)) and a lesser ape (the siamang gibbon (Symphalangus syndactylus)), and untangled the intricacies of their evolution. Compared with the X chromosomes, the ape Y chromosomes vary greatly in size and have low alignability and high levels of structural rearrangements-owing to the accumulation of lineage-specific ampliconic regions, palindromes, transposable elements and satellites. Many Y chromosome genes expand in multi-copy families and some evolve under purifying selection. Thus, the Y chromosome exhibits dynamic evolution, whereas the X chromosome is more stable. Mapping short-read sequencing data to these assemblies revealed diversity and selection patterns on sex chromosomes of more than 100 individual great apes. These reference assemblies are expected to inform human evolution and conservation genetics of non-human apes, all of which are endangered species. <<<

Sankaraleengam Alagapan, Ki Sueng Choi, Stephen Heisig, Patricio Riva-Posse, Andrea Crowell, Vineet Tiruvadi, Mosadoluwa Obatusin, Ashan Veerakumar, Allison C Waters, Robert E Gross, Sinead Quinn, Lydia Denison, Matthew O'Shaughnessy, Marissa Connor, Gregory Canal, Jungho Cha, Rachel Hershenberg, Tanya Nauvel, Faical Isbaine, Muhammad Furqan Afzal, Martijn Figee, Brian H Kopell, Robert Butera, Helen S Mayberg, Christopher J Rozell <<<

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD). However, achieving stable recovery is unpredictable, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment. <<<