Path integration, the ability to sense self-motion for keeping track of changes in orientation and position, constitutes a fundamental mechanism of spatial navigation and a keystone for the development of cognitive maps. Whereas animal path integration is predominantly supported by the head-direction, grid, and place cell systems, the neural foundations are not well understood in humans. Here we used functional magnetic resonance imaging and a virtual rendition of a triangle completion paradigm to test whether human path integration recruits a cortical system similar to that of rodents and nonhuman primates. Participants traveled along two legs of a triangle before pointing toward the starting location. In accordance with animal models, stronger right hippocampal activation predicted more accurate updating of the starting location on a trial-by-trial basis. Moreover, between-subjects fluctuations in response consistency were negatively correlated with bilateral hippocampal and medial prefrontal activation, and bilateral recruitment of the human motion complex (hMT+) covaried with individual path integration capability. Given that these effects were absent in a perceptual control task, the present study provides the first evidence that visual path integration is related to the dynamic interplay of self-motion processing in hMT+, higher-level spatial processes in the hippocampus, and spatial working memory in medial prefrontal cortex. <<<

Parkinson's disease (PD) is a common neurodegenerative disease. Here, the purpose of the study was to explore the function of long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) in PD and its underlying mechanism. An in vivo 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-hydrochloride (MPTP)-induced mouse model of PD was generated and the SH-SY5Y cells were treated with MPP to induce neuronal damage in vitro. Quantitative real-time polymerase chain reaction (QRT-PCR) and Western blot were used to detect the expression of HOTAIR, miR-221-3p, α-synuclein and apoptosis-related genes. MTT, flow cytometry and TUNEL assay was used to detect cell viability and apoptosis, respectively. The levels of inflammatory cytokines TNF-α,IL-1β and IL-6 were detected by ELISA assay. The levels of lactate dehydrogenase (LDH), reactive oxygen species (ROS), and superoxide dismutase (SOD) were determined using the appropriate assay kits. The interactions between miR-221-3p and HOTAIR or α-synuclein were determined by dual luciferase assay and RNA binding protein immunoprecipitation (RIP). Co-localization of HOTAIR and miR-221-3p was also proved by immunofluorescence staining. The results showed that HOTAIR was highly expressed, while miR-221-3p expression was decreased in PD model in vivo and in vitro. In SH-SY5Y cells treated with MPP, the knockdown of HOTAIR increased cell viability and reduced cell apoptosis, the secretion of inflammatory cytokines and oxidative stress reaction, while HOTAIR overexpression led to opposite effects. Furthermore, HOTAIR sponged miR-221-3p which directly targeted α-synuclein and thus regulated the expression of α-synuclein. Meanwhile, inhibiting miR-221-3p could partially reverse the neuroprotective effects of HOTAIR knockdown. In conclusion, HOTAIR attenuated the injury of SH-SY5Y cells induced by MPP via miR-221-3p/α-synuclein axis, suggesting the potential therapeutic value of HOTAIR in PD. <<<

Quantum Natural Language Processing (QNLP) deals with the design and implementation of NLP models intended to be run on quantum hardware. In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum (NISQ) computers for datasets of size greater than 100 sentences. Exploiting the formal similarity of the compositional model of meaning by Coecke, Sadrzadeh, and Clark (2010) with quantum theory, we create representations for sentences that have a natural mapping to quantum circuits. We use these representations to implement and successfully train NLP models that solve simple sentence classification tasks on quantum hardware. We conduct quantum simulations that compare the syntax-sensitive model of Coecke et al. with two baselines that use less or no syntax; specifically, we implement the quantum analogues of a “bag-of-words” model, where syntax is not taken into account at all, and of a word-sequence model, where only word order is respected. We demonstrate that all models converge smoothly both in simulations and when run on quantum hardware, and that the results are the expected ones based on the nature of the tasks and the datasets used. Another important goal of this paper is to describe in a way accessible to AI and NLP researchers the main principles, process and challenges of experiments on quantum hardware. Our aim in doing this is to take the first small steps in this unexplored research territory and pave the way for practical Quantum Natural Language Processing. <<<

Kimberly Martin, Pe’er Dar, Cora MacPherson, Melissa Egbert, Zachary Demko, Sheetal Parmar, Katelyn Hashimoto, Sina Haeri, Fergal Malone, Ronald J. Wapner, Ashley S. Roman, Asma Khalil, Revital Faro, Rajeevi Madankumar, Noel Strong, Robert M. Silver, Nidhi Vohra, Jon Hyett, Matt Rabinowitz, Charlly Kao, Hakon Hakonarson, Bo Jacobsson, Mary E. Norton <<<

Human social networks are overwhelmingly homophilous: individuals tend to befriend others who are similar to them in terms of a range of physical attributes (e.g., age, gender). Do similarities among friends reflect deeper similarities in how we perceive, interpret, and respond to the world? To test whether friendship, and more generally, social network proximity, is associated with increased similarity of real-time mental responding, we used functional magnetic resonance imaging to scan subjects' brains during free viewing of naturalistic movies. Here we show evidence for neural homophily: neural responses when viewing audiovisual movies are exceptionally similar among friends, and that similarity decreases with increasing distance in a real-world social network. These results suggest that we are exceptionally similar to our friends in how we perceive and respond to the world around us, which has implications for interpersonal influence and attraction. <<<

Current computational workflows for comparative analyses of single-cell datasets typically use discrete clusters as input when testing for differential abundance among experimental conditions. However, clusters do not always provide the appropriate resolution and cannot capture continuous trajectories. Here we present Milo, a scalable statistical framework that performs differential abundance testing by assigning cells to partially overlapping neighborhoods on a k-nearest neighbor graph. Using simulations and single-cell RNA sequencing (scRNA-seq) data, we show that Milo can identify perturbations that are obscured by discretizing cells into clusters, that it maintains false discovery rate control across batch effects and that it outperforms alternative differential abundance testing strategies. Milo identifies the decline of a fate-biased epithelial precursor in the aging mouse thymus and identifies perturbations to multiple lineages in human cirrhotic liver. As Milo is based on a cell-cell similarity structure, it might also be applicable to single-cell data other than scRNA-seq. Milo is provided as an open-source R software package at https://github.com/MarioniLab/miloR . <<<

Christina Koupourtidou, Veronika Schwarz, Hananeh Aliee, Simon Frerich, Judith Fischer-Sternjak, Riccardo Bocchi, Tatiana Simon-Ebert, Xianshu Bai, Swetlana Sirko, Frank Kirchhoff, Martin Dichgans, Magdalena Götz, Fabian J Theis, Jovica Ninkovic <<<

Traumatic brain injury leads to a highly orchestrated immune- and glial cell response partially responsible for long-lasting disability and the development of secondary neurodegenerative diseases. A holistic understanding of the mechanisms controlling the responses of specific cell types and their crosstalk is required to develop an efficient strategy for better regeneration. Here, we combine spatial and single-cell transcriptomics to chart the transcriptomic signature of the injured male murine cerebral cortex, and identify specific states of different glial cells contributing to this signature. Interestingly, distinct glial cells share a large fraction of injury-regulated genes, including inflammatory programs downstream of the innate immune-associated pathways Cxcr3 and Tlr1/2. Systemic manipulation of these pathways decreases the reactivity state of glial cells associated with poor regeneration. The functional relevance of the discovered shared signature of glial cells highlights the importance of our resource enabling comprehensive analysis of early events after brain injury. <<<

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms. <<<

Mahdi Moqri, Chiara Herzog, Jesse R Poganik, Biomarkers of Aging Consortium, Jamie Justice, Daniel W Belsky, Albert Higgins-Chen, Alexey Moskalev, Georg Fuellen, Alan A Cohen, Ivan Bautmans, Martin Widschwendter, Jingzhong Ding, Alexander Fleming, Joan Mannick, Jing-Dong Jackie Han, Alex Zhavoronkov, Nir Barzilai, Matt Kaeberlein, Steven Cummings, Brian K Kennedy, Luigi Ferrucci, Steve Horvath, Eric Verdin, Andrea B Maier, Michael P Snyder, Vittorio Sebastiano, Vadim N Gladyshev <<<

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice. <<<

Genome duplication (generating polyploids) is an engine of novelty in eukaryotic evolution and a promising crop improvement tool. Yet newly formed polyploids often have low fertility. Here we report that a severe fertility-compromising defect in pollen tube tip growth arises in new polyploids of . Pollen tubes of newly polyploid grow slowly, have aberrant anatomy and disrupted physiology, often burst prematurely, and have altered gene expression. These phenotypes recover in evolved polyploids. We also show that gametophytic (pollen tube) genotypes of two tip-growth genes under selection in natural tetraploid are strongly associated with pollen tube performance in the tetraploid. Our work establishes pollen tube tip growth as an important fertility challenge for neo-polyploid plants and provides insights into a naturally evolved multigenic solution. <<<

Jae-Hyun Yang, Motoshi Hayano, Patrick T. Griffin, João A. Amorim, Michael S. Bonkowski, John K. Apostolides, Elias L. Salfati, Marco Blanchette, Elizabeth M. Munding, Mital Bhakta, Yap Ching Chew, Wei Guo, Xiaojing Yang, Sun Maybury-Lewis, Xiao Tian, Jaime M. Ross, Giuseppe Coppotelli, Margarita V. Meer, Ryan Rogers-Hammond, Daniel L. Vera, Yuancheng Ryan Lu, Jeffrey W. Pippin, Michael L. Creswell, Zhixun Dou, Caiyue Xu, Sarah J. Mitchell, Abhirup Das, Brendan L. O’Connell, Sachin Thakur, Alice E. Kane, Qiao Su, Yasuaki Mohri, Emi K. Nishimura, Laura Schaevitz, Neha Garg, Ana-Maria Balta, Meghan A. Rego, Meredith Gregory-Ksander, Tatjana C. Jakobs, Lei Zhong, Hiroko Wakimoto, Jihad El Andari, Dirk Grimm, Raul Mostoslavsky, Amy J. Wagers, Kazuo Tsubota, Stephen J. Bonasera, Carlos M. Palmeira, Jonathan G. Seidman, Christine E. Seidman, Norman S. Wolf, Jill A. Kreiling, John M. Sedivy, George F. Murphy, Richard E. Green, Benjamin A. Garcia, Shelley L. Berger, Philipp Oberdoerffer, Stuart J. Shankland, Vadim N. Gladyshev, Bruce R. Ksander, Andreas R. Pfenning, Luis A. Rajman, David A. Sinclair <<<

The conformational landscape of proteins is crucial to understanding theirfunctionality in complex biological processes. Traditional physics-basedcomputational methods, such as molecular dynamics (MD) simulations, suffer fromrare event sampling and long equilibration time problems, hindering theirapplications in general protein systems. Recently, deep generative modelingtechniques, especially diffusion models, have been employed to generate novelprotein conformations. However, existing score-based diffusion methods cannotproperly incorporate important physical prior knowledge to guide the generationprocess, causing large deviations in the sampled protein conformations from theequilibrium distribution. In this paper, to overcome these limitations, wepropose a force-guided SE(3) diffusion model, ConfDiff, for proteinconformation generation. By incorporating a force-guided network with a mixtureof data-based score models, ConfDiff can can generate protein conformationswith rich diversity while preserving high fidelity. Experiments on a variety ofprotein conformation prediction tasks, including 12 fast-folding proteins andthe Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our methodsurpasses the state-of-the-art method. <<<

Growing evidence indicates that collagen-binding integrins are important costimulatory molecules of effector T cells. In this study, we demonstrate that the major collagen-binding integrin expressed by human Th17 cells is alpha2beta1 (α2β1) or VLA-2, also known as the receptor for collagen I on T cells. Our results show that human naïve CD4(+) T cells cultured under Th17 polarization conditions preferentially upregulate α2β1 integrin rather than α1β1 integrin, which is the receptor for collagen IV on T cells. Double staining analysis for integrin receptors and intracellular IL-17 showed that α2 integrin but not α1 integrin is associated with Th17 cells. Cell adhesion experiments demonstrated that Th17 cells attach to collagen I and collagen II using α2β1 integrin but did not attach to collagen IV. Functional studies revealed that collagens I and II but not collagen IV costimulate the production of IL-17A, IL-17F and IFN-γ by human Th17 cells activated with anti-CD3. These results identify α2β1 integrin as the major collagen receptor expressed on human Th17 cells and suggest that it can be an important costimulatory molecule of Th17 cell responses. <<<

Kathie Y Sun, Xiaodong Bai, Siying Chen, Suying Bao, Chuanyi Zhang, Manav Kapoor, Joshua Backman, Tyler Joseph, Evan Maxwell, George Mitra, Alexander Gorovits, Adam Mansfield, Boris Boutkov, Sujit Gokhale, Lukas Habegger, Anthony Marcketta, Adam E Locke, Liron Ganel, Alicia Hawes, Michael D Kessler, Deepika Sharma, Jeffrey Staples, Jonas Bovijn, Sahar Gelfman, Alessandro Di Gioia, Veera M Rajagopal, Alexander Lopez, Jennifer Rico Varela, Jesús Alegre-Díaz, Jaime Berumen, Roberto Tapia-Conyer, Pablo Kuri-Morales, Jason Torres, Jonathan Emberson, Rory Collins, Regeneron Genetics Center, RGC-ME Cohort Partners, Michael Cantor, Timothy Thornton, Hyun Min Kang, John D Overton, Alan R Shuldiner, M Laura Cremona, Mona Nafde, Aris Baras, Gonçalo Abecasis, Jonathan Marchini, Jeffrey G Reid, William Salerno, Suganthi Balasubramanian <<<

Rare coding variants that substantially affect function provide insights into the biology of a gene. However, ascertaining the frequency of such variants requires large sample sizes. Here we present a catalogue of human protein-coding variation, derived from exome sequencing of 983,578 individuals across diverse populations. In total, 23% of the Regeneron Genetics Center Million Exome (RGC-ME) data come from individuals of African, East Asian, Indigenous American, Middle Eastern and South Asian ancestry. The catalogue includes more than 10.4 million missense and 1.1 million predicted loss-of-function (pLOF) variants. We identify individuals with rare biallelic pLOF variants in 4,848 genes, 1,751 of which have not been previously reported. From precise quantitative estimates of selection against heterozygous loss of function (LOF), we identify 3,988 LOF-intolerant genes, including 86 that were previously assessed as tolerant and 1,153 that lack established disease annotation. We also define regions of missense depletion at high resolution. Notably, 1,482 genes have regions that are depleted of missense variants despite being tolerant of pLOF variants. Finally, we estimate that 3% of individuals have a clinically actionable genetic variant, and that 11,773 variants reported in ClinVar with unknown significance are likely to be deleterious cryptic splice sites. To facilitate variant interpretation and genetics-informed precision medicine, we make this resource of coding variation from the RGC-ME dataset publicly accessible through a variant allele frequency browser. <<<

Abstract Background Telomeres form repeated DNA sequences at the ends of chromosomes, which shorten with each cell division. Yet, factors modulating telomere attrition and the health consequences thereof are not fully understood. To address this, we leveraged data from 326,363 unrelated UK Biobank participants of European ancestry. Results Using linear regression and bidirectional univariable and multivariable Mendelian randomization (MR), we elucidate the relationships between leukocyte telomere length (LTL) and 142 complex traits, including diseases, biomarkers, and lifestyle factors. We confirm that telomeres shorten with age and show a stronger decline in males than in females, with these factors contributing to the majority of the 5.4% of LTL variance explained by the phenome. MR reveals 23 traits modulating LTL. Smoking cessation and high educational attainment associate with longer LTL, while weekly alcohol intake, body mass index, urate levels, and female reproductive events, such as childbirth, associate with shorter LTL. We also identify 24 traits affected by LTL, with risk for cardiovascular, pulmonary, and some autoimmune diseases being increased by short LTL, while longer LTL increased risk for other autoimmune conditions and cancers. Through multivariable MR, we show that LTL may partially mediate the impact of educational attainment, body mass index, and female age at childbirth on proxied lifespan. Conclusions Our study sheds light on the modulators, consequences, and the mediatory role of telomeres, portraying an intricate relationship between LTL, diseases, lifestyle, and socio-economic factors. <<<

Fiber photometry is a key technique for characterizing brain-behavior relationships in vivo. Initially, it was primarily used to report calcium dynamics as a proxy for neural activity via genetically encoded indicators. This generated new insights into brain functions including movement, memory, and motivation at the level of defined circuits and cell types. Recently, the opportunity for discovery with fiber photometry has exploded with the development of an extensive range of fluorescent sensors for biomolecules including neuromodulators and peptides that were previously inaccessible in vivo. This critical advance, combined with the new availability of affordable "plug-and-play" recording systems, has made monitoring molecules with high spatiotemporal precision during behavior highly accessible. However, while opening exciting new avenues for research, the rapid expansion in fiber photometry applications has occurred without coordination or consensus on best practices. Here, we provide a comprehensive guide to help end-users execute, analyze, and suitably interpret fiber photometry studies. <<<