Differentiating neutrophils undergo large-scale changes in nuclear morphology. How such alterations in structure are established and modulated upon exposure to microbial agents is largely unknown. Here, we found that prior to encounter with bacteria, an armamentarium of inflammatory genes was positioned in a transcriptionally passive environment suppressing premature transcriptional activation. Upon microbial exposure, however, human neutrophils rapidly (<3 h) repositioned the ensemble of proinflammatory genes toward the transcriptionally permissive compartment. We show that the repositioning of genes was closely associated with the swift recruitment of cohesin across the inflammatory enhancer landscape, permitting an immediate transcriptional response upon bacterial exposure. We found that activated enhancers, marked by increased deposition of H3K27Ac, were highly enriched for cistromic elements associated with PU.1, CEBPB, TFE3, JUN, and FOSL2 occupancy. These data reveal how upon microbial challenge the cohesin machinery is recruited to an activated enhancer repertoire to instruct changes in chromatin folding, nuclear architecture, and to activate an inflammatory gene program. <<<

An ultrasonic imaging technique has been developed to investigate the internal changes of pouch cells nondestructively. The local ultrasonic transmittance of pouch cells has been measured and used for imaging with a new ultrasonic scanning machine designed and built in-house. The wetting process of the cells is clearly observed via such ultrasonic imaging techniques. Furthermore, ultrasonic transmission images of fresh cells and aged cells with different electrolytes and cycling conditions exhibit very different ultrasonic transmittance, which can be caused by electrolyte dry-out or “unwetting” due to cell swelling. The ultrasonic imaging technique is a very sensitive method to probe failure mechanisms in Li-ion pouch cells. <<<

Rongjie Cheng, Fanying Li, Maolei Zhang, Xin Xia, Jianzhuang Wu, Xinya Gao, Huangkai Zhou, Zhi Zhang, Nunu Huang, Xuesong Yang, Yaliang Zhang, Shunli Shen, Tiebang Kang, Zexian Liu, Feizhe Xiao, Hongwei Yao, Jianbo Xu, Chao Yan, Nu Zhang <<<

Mutations of the RAS oncogene are found in around 30% of all human cancers yet direct targeting of RAS is still considered clinically impractical except for the KRAS mutant. Here we report that RAS-ON (RASON), a novel protein encoded by the long intergenic non-protein coding RNA 00673 (LINC00673), is a positive regulator of oncogenic RAS signaling. RASON is aberrantly overexpressed in pancreatic ductal adenocarcinoma (PDAC) patients, and it promotes proliferation of human PDAC cell lines in vitro and tumor growth in vivo. CRISPR/Cas9-mediated knockout of Rason in mouse embryonic fibroblasts inhibits KRAS-mediated tumor transformation. Genetic deletion of Rason abolishes oncogenic KRAS-driven pancreatic and lung cancer tumorigenesis in LSL-Kras; Trp53 mice. Mechanistically, RASON directly binds to KRAS and inhibits both intrinsic and GTPase activating protein (GAP)-mediated GTP hydrolysis, thus sustaining KRAS in the GTP-bound hyperactive state. Therapeutically, deprivation of RASON sensitizes KRAS mutant pancreatic cancer cells and patient-derived organoids to EGFR inhibitors. Our findings identify RASON as a critical regulator of oncogenic KRAS signaling and a promising therapeutic target for KRAS mutant cancers. <<<

We explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking. For instance, prompting a 540B-parameter language model with just eight chain of thought exemplars achieves state of the art accuracy on the GSM8K benchmark of math word problems, surpassing even finetuned GPT-3 with a verifier. <<<

Recently, significant progress has been made in time series classification with deep learning. However, using deep learning models to solve time series classification generally suffers from expensive calculations and difficulty of data labeling. In this work, we study self-supervised time series pre-training to overcome these challenges. Compared with the existing works, we focus on the universal and unlabeled time series pretraining. To this end, we propose a novel end-to-end neural network architecture based on self-attention, which is suitable for capturing long-term dependencies and extracting features from different time series. Then, we propose two different self-supervised pretext tasks for time series data type: Denoising and Similarity Discrimination based on DTW (Dynamic Time Warping). Finally, we carry out extensive experiments on 85 time series datasets (also known as UCR2015 [2]). Empirical results show that the time series model augmented with our proposed self-supervised pretext tasks achieves state-of-the-art / highly competitive results. <<<

Telomere length in humans is associated with lifespan and severe diseases, yet the genetic determinants of telomere length remain incompletely defined. Here we performed genome-wide CRISPR-Cas9 functional telomere length screening and identified thymidine (dT) nucleotide metabolism as a limiting factor in human telomere maintenance. Targeted genetic disruption using CRISPR-Cas9 revealed multiple telomere length control points across the thymidine nucleotide metabolism pathway: decreasing dT nucleotide salvage via deletion of the gene encoding nuclear thymidine kinase (TK1) or de novo production by knockout of the thymidylate synthase gene (TYMS) decreased telomere length, whereas inactivation of the deoxynucleoside triphosphohydrolase-encoding gene SAMHD1 lengthened telomeres. Remarkably, supplementation with dT alone drove robust telomere elongation by telomerase in cells, and thymidine triphosphate stimulated telomerase activity in a substrate-independent manner in vitro. In induced pluripotent stem cells derived from patients with genetic telomere biology disorders, dT supplementation or inhibition of SAMHD1 promoted telomere restoration. Our results demonstrate a critical role of thymidine metabolism in controlling human telomerase and telomere length, which may be therapeutically actionable in patients with fatal degenerative diseases. <<<

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners. <<<

Huili Zhang, Feifei Yu, Peng Xie, Shengyuan Sun, Xinhua Qiao, Sanyuan Tang, Chengxuan Chen, Sen Yang, Cuo Mei, Dekai Yang, Yaorong Wu, Ran Xia, Xu Li, Jun Lu, Yuxi Liu, Xiaowei Xie, Dongmei Ma, Xing Xu, Zhengwei Liang, Zhonghui Feng, Xiahe Huang, Hong Yu, Guifu Liu, Yingchun Wang, Jiayang Li, Qifa Zhang, Chang Chen, Yidan Ouyang, Qi Xie <<<

The use of alkaline salt lands for crop production is hindered by a scarcity of knowledge and breeding efforts for plant alkaline tolerance. Through genome association analysis of sorghum, a naturally high-alkaline-tolerant crop, we detected a major locus, (), specifically related to alkaline-salinity sensitivity. An allele with a carboxyl-terminal truncation increased sensitivity, whereas knockout of increased tolerance to alkalinity in sorghum, millet, rice, and maize. encodes an atypical G protein γ subunit that affects the phosphorylation of aquaporins to modulate the distribution of hydrogen peroxide (HO) These processes appear to protect plants against oxidative stress by alkali. Designing knockouts of homologs or selecting its natural nonfunctional alleles could improve crop productivity in sodic lands. <<<

The role of the heart in the experience of time has been long theorized but empirical evidence is scarce. Here, we examined the interaction between fine-grained cardiac dynamics and the momentary experience of subsecond intervals. Participants performed a temporal bisection task for brief tones (80-188 ms) synchronized with the heart. We developed a cardiac Drift-Diffusion Model (cDDM) that embedded contemporaneous heart rate dynamics into the temporal decision model. Results revealed the existence of temporal wrinkles-dilation or contraction of short intervals-in synchrony with cardiac dynamics. A lower prestimulus heart rate was associated with an initial bias in encoding the millisecond-level stimulus duration as longer, consistent with facilitation of sensory intake. Concurrently, a higher prestimulus heart rate aided more consistent and faster temporal judgments through more efficient evidence accumulation. Additionally, a higher speed of poststimulus cardiac deceleration, a bodily marker of attention, was associated with a greater accumulation of sensory temporal evidence in the cDDM. These findings suggest a unique role of cardiac dynamics in the momentary experience of time. Our cDDM framework opens a new methodological avenue for investigating the role of the heart in time perception and perceptual judgment. <<<

AbstractFor both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output — a challenge that is known ascredit assignment. How the brain solves credit assignment is a key question in neuroscience, and also of significant importance for artificial intelligence. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. However, it has been questioned whether it is possible for the brain to implement backpropagation and learning in the brain may actually be more efficient and effective than backpropagation. Here, we set out a fundamentally different principle on credit assignment, calledprospective configuration. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms, and (3) reproduces surprising patterns of neural activity and behaviour observed in diverse human and animal learning experiments. Our findings establish a new foundation for learning beyond backpropagation, for both understanding biological learning and building artificial intelligence. <<<

BACKGROUND: Clinical staging of gastric cancer (GC) before treatment is essential. Endoscopic ultrasound (EUS) is a recommended staging tool, but its efficacy remains controversial. Our previous prospective study evaluated the potential value of EUS for T staging and presented discrepancies. In this study, we aimed to evaluate the efficacy of EUS in T staging by comparing it with pathological staging. We analyze the factors that can potentially affect accuracy to identify suitable subgroups for EUS staging.METHODS: Data from a total of 1763 consecutive patients with GC from January 2015 to December 2017 were analyzed. Results from EUS and pathological T staging were compared. The factors that might affect EUS's accuracy were analyzed.RESULTS: The sensitivity, specificity, positive predictive value, and negative predictive value of EUS in patients with early GC were 62.08%, 96.13%, 90.94%, and 80.21%, respectively. The accuracy rates of uT1, uT2-uT4, and uT3-uT4 were 90.94%, 79.02%, and 78.39%, respectively. In multivariate analysis, underestimation was more likely to be observed in patients with tumors located in the middle or upper third of the stomach. Overestimation was more likely to be observed in patients with tumors located in the lower third or those without ulcer. Other factors affecting accuracy included ulcer, differentiation, larger size and undergoing surgery.CONCLUSION: Our findings highlight the role of EUS in determining the T staging of GC. Overestimation and underestimation in T-staging were significantly associated with the tumor location in early GC, and a decision-making algorithm was proposed for clinical practice in early cancers based on these findings. <<<

Maria M Szwarc, Anna L Guarnieri, Molishree Joshi, Huy N Duc, Madison C Laird, Ahwan Pandey, Santosh Khanal, Emily Dohm, Aimee K Bui, Kelly D Sullivan, Matthew D Galbraith, Zdenek Andrysik, Joaquin M Espinosa <<<

Inactivation of the p53 tumor suppressor, either by mutations or through hyperactivation of repressors such as MDM2 and MDM4, is a hallmark of cancer. Although many inhibitors of the p53-MDM2/4 interaction have been developed, such as Nutlin, their therapeutic value is limited by highly heterogeneous cellular responses. We report here a multi-omics investigation of the cellular response to MDM2/4 inhibitors, leading to identification of FAM193A as a widespread regulator of p53 function. CRISPR screening identified FAM193A as necessary for the response to Nutlin. FAM193A expression correlates with Nutlin sensitivity across hundreds of cell lines. Furthermore, genetic codependency data highlight FAM193A as a component of the p53 pathway across diverse tumor types. Mechanistically, FAM193A interacts with MDM4, and FAM193A depletion stabilizes MDM4 and inhibits the p53 transcriptional program. Last, FAM193A expression is associated with better prognosis in multiple malignancies. Altogether, these results identify FAM193A as a positive regulator of p53. <<<

Shannon information theory provides various measures of so-called "syntactic information", which reflect the amount of statistical correlation between systems. In contrast, the concept of "semantic information" refers to those correlations which carry significance or "meaning" for a given system. Semantic information plays an important role in many fields, including biology, cognitive science, and philosophy, and there has been a long-standing interest in formulating a broadly applicable and formal theory of semantic information. In this paper we introduce such a theory. We define semantic information as the syntactic information that a physical system has about its environment which is causally necessary for the system to maintain its own existence. "Causal necessity" is defined in terms of counter-factual interventions which scramble correlations between the system and its environment, while "maintaining existence" is defined in terms of the system's ability to keep itself in a low entropy state. We also use recent results in nonequilibrium statistical physics to analyze semantic information from a thermodynamic point of view. Our framework is grounded in the intrinsic dynamics of a system coupled to an environment, and is applicable to any physical system, living or otherwise. It leads to formal definitions of several concepts that have been intuitively understood to be related to semantic information, including "value of information", "semantic content", and "agency". <<<

In an ever-growing need for data storage capacity, the Deoxyribonucleic Acid (DNA) molecule gains traction as a new storage medium with a larger capacity, higher density, and a longer lifespan over conventional storage media. To effectively use DNA for data storage, it is important to understand the different methods of encoding information in DNA and compare their effectiveness. This requires evaluating which decoded DNA sequences carry the most encoded information based on various attributes. However, navigating the field of coding theory requires years of experience and domain expertise. For instance, domain experts rely on various mathematical functions and attributes to score and evaluate their encodings. To enable such analytical tasks, we provide an interactive and visual analytical framework for multi-attribute ranking in DNA storage systems. Our framework follows a three-step view with user-settable parameters. It enables users to find the optimal en-/de-coding approaches by setting different weights and combining multiple attributes. We assess the validity of our work through a task-specific user study on domain experts by relying on three tasks. Results indicate that all participants completed their tasks successfully under two minutes, then rated the framework for design choices, perceived usefulness, and intuitiveness. In addition, two real-world use cases are shared and analyzed as direct applications of the proposed tool. DNAsmart enables the ranking of decoded sequences based on multiple attributes. In sum, this work unveils the evaluation of en-/de-coding approaches accessible and tractable through visualization and interactivity to solve comparison and ranking tasks. <<<

Fusobacterium nucleatum (Fn) is a dominant bacterial species in colorectal cancer (CRC) tissue that is associated with cancer progression and poorer patient prognosis. Following a small-molecule inhibitor screen of 1,846 bioactive compounds against a Fn CRC isolate, we find that 15% of inhibitors are antineoplastic agents including fluoropyrimidines. Validation of these findings reveals that 5-fluorouracil (5-FU), a first-line CRC chemotherapeutic, is a potent inhibitor of Fn CRC isolates. We also identify members of the intratumoral microbiota, including Escherichia coli, that are resistant to 5-FU. Further, CRC E. coli isolates can modify 5-FU and relieve 5-FU toxicity toward otherwise-sensitive Fn and human CRC epithelial cells. Lastly, we demonstrate that ex vivo patient CRC tumor microbiota undergo community disruption after 5-FU exposure and have the potential to deplete 5-FU levels, reducing local drug efficacy. Together, these observations argue for further investigation into the role of the CRC intratumoral microbiota in patient response to chemotherapy. <<<

Acidobacteriota are widespread and often abundant in marine sediments, yet their metabolic and ecological properties are poorly understood. Here, we examined metabolisms and distributions of Acidobacteriota in marine sediments of Svalbard by functional predictions from metagenome-assembled genomes (MAGs), amplicon sequencing of 16S rRNA and dissimilatory sulfite reductase (dsrB) genes and transcripts, and gene expression analyses of tetrathionate-amended microcosms. Acidobacteriota were the second most abundant dsrB-harboring (averaging 13%) phylum after Desulfobacterota in Svalbard sediments, and represented 4% of dsrB transcripts on average. Meta-analysis of dsrAB datasets also showed Acidobacteriota dsrAB sequences are prominent in marine sediments worldwide, averaging 15% of all sequences analysed, and represent most of the previously unclassified dsrAB in marine sediments. We propose two new Acidobacteriota genera, Candidatus Sulfomarinibacter (class Thermoanaerobaculia, "subdivision 23") and Ca. Polarisedimenticola ("subdivision 22"), with distinct genetic properties that may explain their distributions in biogeochemically distinct sediments. Ca. Sulfomarinibacter encode flexible respiratory routes, with potential for oxygen, nitrous oxide, metal-oxide, tetrathionate, sulfur and sulfite/sulfate respiration, and possibly sulfur disproportionation. Potential nutrients and energy include cellulose, proteins, cyanophycin, hydrogen, and acetate. A Ca. Polarisedimenticola MAG encodes various enzymes to degrade proteins, and to reduce oxygen, nitrate, sulfur/polysulfide and metal-oxides. 16S rRNA gene and transcript profiling of Svalbard sediments showed Ca. Sulfomarinibacter members were relatively abundant and transcriptionally active in sulfidic fjord sediments, while Ca. Polarisedimenticola members were more relatively abundant in metal-rich fjord sediments. Overall, we reveal various physiological features of uncultured marine Acidobacteriota that indicate fundamental roles in seafloor biogeochemical cycling. <<<

Peng Liao, Weimin Wang, Weichao Wang, Ilona Kryczek, Xiong Li, Yingjie Bian, Amanda Sell, Shuang Wei, Sara Grove, Jeffrey K Johnson, Paul D Kennedy, Miguel Gijón, Yatrik M Shah, Weiping Zou <<<

Tumor cell intrinsic ferroptosis-initiating mechanisms are unknown. Here, we discover that T cell-derived interferon (IFN)γ in combination with arachidonic acid (AA) induces immunogenic tumor ferroptosis, serving as a mode of action for CD8 T cell (CTL)-mediated tumor killing. Mechanistically, IFNγ stimulates ACSL4 and alters tumor cell lipid pattern, thereby increasing incorporations of AA into C16 and C18 acyl chain-containing phospholipids. Palmitoleic acid and oleic acid, two common C16 and C18 fatty acids in blood, promote ACSL4-dependent tumor ferroptosis induced by IFNγ plus AA. Moreover, tumor ACSL4 deficiency accelerates tumor progression. Low-dose AA enhances tumor ferroptosis and elevates spontaneous and immune checkpoint blockade (ICB)-induced anti-tumor immunity. Clinically, tumor ACSL4 correlates with T cell signatures and improved survival in ICB-treated cancer patients. Thus, IFNγ signaling paired with selective fatty acids is a natural tumor ferroptosis-promoting mechanism and a mode of action of CTLs. Targeting the ACSL4 pathway is a potential anti-cancer approach. <<<

Zongli Zheng, Matthew Liebers, Boryana Zhelyazkova, Yi Cao, Divya Panditi, Kerry D Lynch, Juxiang Chen, Hayley E Robinson, Hyo Sup Shim, Juliann Chmielecki, William Pao, Jeffrey A Engelman, A John Iafrate, Long Phi Le <<<

We describe a rapid target enrichment method for next-generation sequencing, termed anchored multiplex PCR (AMP), that is compatible with low nucleic acid input from formalin-fixed paraffin-embedded (FFPE) specimens. AMP is effective in detecting gene rearrangements (without prior knowledge of the fusion partners), single nucleotide variants, insertions, deletions and copy number changes. Validation of a gene rearrangement panel using 319 FFPE samples showed 100% sensitivity (95% confidence limit: 96.5-100%) and 100% specificity (95% confidence limit: 99.3-100%) compared with reference assays. On the basis of our experience with performing AMP on 986 clinical FFPE samples, we show its potential as both a robust clinical assay and a powerful discovery tool, which we used to identify new therapeutically important gene fusions: ARHGEF2-NTRK1 and CHTOP-NTRK1 in glioblastoma, MSN-ROS1, TRIM4-BRAF, VAMP2-NRG1, TPM3-NTRK1 and RUFY2-RET in lung cancer, FGFR2-CREB5 in cholangiocarcinoma and PPL-NTRK1 in thyroid carcinoma. AMP is a scalable and efficient next-generation sequencing target enrichment method for research and clinical applications. <<<

Ana Uzquiano, Amanda J Kedaigle, Martina Pigoni, Bruna Paulsen, Xian Adiconis, Kwanho Kim, Tyler Faits, Surya Nagaraja, Noelia Antón-Bolaños, Chiara Gerhardinger, Ashley Tucewicz, Evan Murray, Xin Jin, Jason Buenrostro, Fei Chen, Silvia Velasco, Aviv Regev, Joshua Z Levin, Paola Arlotta <<<

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development. <<<

Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit. <<<