The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas.

Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 µm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 µm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).

Ensuring best practice in genomics education: A theory- and empirically informed evaluation framework.

Implementation of genomic medicine into healthcare requires a workforce educated through effective educational approaches. However, ascertaining the impact of genomics education activities or resources is limited by a lack of evaluation and inconsistent descriptions in the literature. We aim to support those developing genomics education to consider how best to capture evaluation data that demonstrate program outcomes and effectiveness within scope. Here, we present an evaluation framework that is adaptable to multiple settings for use by genomics educators with or without education or evaluation backgrounds. The framework was developed as part of a broader program supporting genomic research translation coordinated by the Australian Genomics consortium. We detail our mixed-methods approach involving an expert workshop, literature review and iterative expert input to reach consensus and synthesis of a new evaluation framework for genomics education. The resulting theory-informed and evidence-based framework encompasses evaluation across all stages of education program development, implementation and reporting, and acknowledges the critical role of stakeholders and the effects of external influences.

Evolution of system connectivity to support food production in the Indus Basin in Pakistan.

Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.

The Hill function is the universal Hopfield barrier for sharpness of input-output responses.

The Hill functions, [Formula: see text], have been widely used in biology for over a century but, with the exception of [Formula: see text], they have had no justification other than as a convenient fit to empirical data. Here, we show that they are the universal limit for the sharpness of any input-output response arising from a Markov process model at thermodynamic equilibrium. Models may represent arbitrary molecular complexity, with multiple ligands, internal states, conformations, coregulators, etc, under core assumptions that are detailed in the paper. The model output may be any linear combination of steady-state probabilities, with components other than the chosen input ligand held constant. This formulation generalizes most of the responses in the literature. We use a coarse-graining method in the graph-theoretic linear framework to show that two sharpness measures for input-output responses fall within an effectively bounded region of the positive quadrant, [Formula: see text], for any equilibrium model with [Formula: see text] input binding sites. [Formula: see text] exhibits a cusp which approaches, but never exceeds, the sharpness of [Formula: see text], but the region and the cusp can be exceeded when models are taken away from thermodynamic equilibrium. Such fundamental thermodynamic limits are called Hopfield barriers, and our results provide a biophysical justification for the Hill functions as the universal Hopfield barriers for sharpness. Our results also introduce an object, [Formula: see text], whose structure may be of mathematical interest, and suggest the importance of characterizing Hopfield barriers for other forms of cellular information processing.

Enhancing ion selectivity by tuning solvation abilities of covalent-organic-framework membranes.

Understanding the molecular-level mechanisms involved in transmembrane ion selectivity is essential for optimizing membrane separation performance. In this study, we reveal our observations regarding the transmembrane behavior of Li+ and Mg2+ ions as a response to the changing pore solvation abilities of the covalent-organic-framework (COF) membranes. These abilities were manipulated by adjusting the lengths of the oligoether segments attached to the pore channels. Through comparative experiments, we were able to unravel the relationships between pore solvation ability and various ion transport properties, such as partitioning, conduction, and selectivity. We also emphasize the significance of the competition between Li+ and Mg2+ with the solvating segments in modulating selectivity. We found that increasing the length of the oligoether chain facilitated ion transport; however, it was the COF membrane with oligoether chains containing two ethylene oxide units that exhibited the most pronounced discrepancy in transmembrane energy barrier between Li+ and Mg2+, resulting in the highest separation factor among all the evaluated membranes. Remarkably, under electro-driven binary-salt conditions, this specific COF membrane achieved an exceptional Li+/Mg2+ selectivity of up to 1352, making it one of the most effective membranes available for Li+/Mg2+ separation. The insights gained from this study significantly contribute to advancing our understanding of selective ion transport within confined nanospaces and provide valuable design principles for developing highly selective COF membranes.

Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization.

Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.

Predicting TCR sequences for unseen antigen epitopes using structural and sequence features.

T-cell receptor (TCR) recognition of antigens is fundamental to the adaptive immune response. With the expansion of experimental techniques, a substantial database of matched TCR-antigen pairs has emerged, presenting opportunities for computational prediction models. However, accurately forecasting the binding affinities of unseen antigen-TCR pairs remains a major challenge. Here, we present convolutional-self-attention TCR (CATCR), a novel framework tailored to enhance the prediction of epitope and TCR interactions. Our approach utilizes convolutional neural networks to extract peptide features from residue contact matrices, as generated by OpenFold, and a transformer to encode segment-based coded sequences. We introduce CATCR-D, a discriminator that can assess binding by analyzing the structural and sequence features of epitopes and CDR3-ß regions. Additionally, the framework comprises CATCR-G, a generative module designed for CDR3-ß sequences, which applies the pretrained encoder to deduce epitope characteristics and a transformer decoder for predicting matching CDR3-ß sequences. CATCR-D achieved an AUROC of 0.89 on previously unseen epitope-TCR pairs and outperformed four benchmark models by a margin of 17.4%. CATCR-G has demonstrated high precision, recall and F1 scores, surpassing 95% in bidirectional encoder representations from transformers score assessments. Our results indicate that CATCR is an effective tool for predicting unseen epitope-TCR interactions. Incorporating structural insights enhances our understanding of the general rules governing TCR-epitope recognition significantly. The ability to predict TCRs for novel epitopes using structural and sequence information is promising, and broadening the repository of experimental TCR-epitope data could further improve the precision of epitope-TCR binding predictions.

Airborne disease transmission during indoor gatherings over multiple time scales: Modeling framework and policy implications.

Indoor superspreading events are significant drivers of transmission of respiratory diseases. In this work, we study the dynamics of airborne transmission in consecutive meetings of individuals in enclosed spaces. In contrast to the usual pairwise-interaction models of infection where effective contacts transmit the disease, we focus on group interactions where individuals with distinct health states meet simultaneously. Specifically, the disease is transmitted by infected individuals exhaling droplets (contributing to the viral load in the closed space) and susceptible ones inhaling the contaminated air. We propose a modeling framework that couples the fast dynamics of the viral load attained over meetings in enclosed spaces and the slow dynamics of disease progression at the population level. Our modeling framework incorporates the multiple time scales involved in different setups in which indoor events may happen, from single-time events to events hosting multiple meetings per day, over many days. We present theoretical and numerical results of trade-offs between the room characteristics (ventilation system efficiency and air mass) and the group's behavioral and composition characteristics (group size, mask compliance, testing, meeting time, and break times), that inform indoor policies to achieve disease control in closed environments through different pathways. Our results emphasize the impact of break times, mask-wearing, and testing on facilitating the conditions to achieve disease control. We study scenarios of different break times, mask compliance, and testing. We also derive policy guidelines to contain the infection rate under a certain threshold.

KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design.

Family-based designs can eliminate confounding due to population substructure and can distinguish direct from indirect genetic effects, but these designs are underpowered due to limited sample sizes. Here, we propose KnockoffTrio, a statistical method to identify putative causal genetic variants for father-mother-child trio design built upon a recently developed knockoff framework in statistics. KnockoffTrio controls the false discovery rate (FDR) in the presence of arbitrary correlations among tests and is less conservative and thus more powerful than the conventional methods that control the family-wise error rate via Bonferroni correction. Furthermore, KnockoffTrio is not restricted to family-based association tests and can be used in conjunction with more powerful, potentially nonlinear models to improve the power of standard family-based tests. We show, using empirical simulations, that KnockoffTrio can prioritize causal variants over associations due to linkage disequilibrium and can provide protection against confounding due to population stratification. In applications to 14,200 trios from three study cohorts for autism spectrum disorders (ASDs), including AGP, SPARK, and SSC, we show that KnockoffTrio can identify multiple significant associations that are missed by conventional tests applied to the same data. In particular, we replicate known ASD association signals with variants in several genes such as MACROD2, NRXN1, PRKAR1B, CADM2, PCDH9, and DOCK4 and identify additional associations with variants in other genes including ARHGEF10, SLC28A1, ZNF589, and HINT1 at FDR 10%.

OPUS-Fold3: a gradient-based protein all-atom folding and docking framework on TensorFlow.

For refining and designing protein structures, it is essential to have an efficient protein folding and docking framework that generates a protein 3D structure based on given constraints. In this study, we introduce OPUS-Fold3 as a gradient-based, all-atom protein folding and docking framework, which accurately generates 3D protein structures in compliance with specified constraints, such as a potential function as long as it can be expressed as a function of positions of heavy atoms. Our tests show that, for example, OPUS-Fold3 achieves performance comparable to pyRosetta in backbone folding and significantly better in side-chain modeling. Developed using Python and TensorFlow 2.4, OPUS-Fold3 is user-friendly for any source-code level modifications and can be seamlessly combined with other deep learning models, thus facilitating collaboration between the biology and AI communities. The source code of OPUS-Fold3 can be downloaded from http://github.com/OPUS-MaLab/opus_fold3. It is freely available for academic usage.

Fuse feeds as one: cross-modal framework for general identification of AMPs.

Antimicrobial peptides (AMPs) are promising candidates for the development of new antibiotics due to their broad-spectrum activity against a range of pathogens. However, identifying AMPs through a huge bunch of candidates is challenging due to their complex structures and diverse sequences. In this study, we propose SenseXAMP, a cross-modal framework that leverages semantic embeddings of and protein descriptors (PDs) of input sequences to improve the identification performance of AMPs. SenseXAMP includes a multi-input alignment module and cross-representation fusion module to explore the hidden information between the two input features and better leverage the fusion feature. To better address the AMPs identification task, we accumulate the latest annotated AMPs data to form more generous benchmark datasets. Additionally, we expand the existing AMPs identification task settings by adding an AMPs regression task to meet more specific requirements like antimicrobial activity prediction. The experimental results indicated that SenseXAMP outperformed existing state-of-the-art models on multiple AMP-related datasets including commonly used AMPs classification datasets and our proposed benchmark datasets. Furthermore, we conducted a series of experiments to demonstrate the complementary nature of traditional PDs and protein pre-training models in AMPs tasks. Our experiments reveal that SenseXAMP can effectively combine the advantages of PDs to improve the performance of protein pre-training models in AMPs tasks.

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming.

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information.

Visual encoding models often use deep neural networks to describe the brain's visual cortex response to external stimuli. Inspired by biological findings, researchers found that large receptive fields built with large convolutional kernels improve convolutional encoding model performance. Inspired by scaling laws in recent years, this article investigates the performance of large convolutional kernel encoding models on larger parameter scales. This paper proposes a large-scale parameters framework with a sizeable convolutional kernel for encoding visual functional magnetic resonance imaging activity information. The proposed framework consists of three parts: First, the stimulus image feature extraction module is constructed using a large-kernel convolutional network while increasing channel numbers to expand the parameter size of the framework. Second, enlarging the input data during the training stage through the multi-subject fusion module to accommodate the increase in parameters. Third, the voxel mapping module maps from stimulus image features to functional magnetic resonance imaging signals. Compared to sizeable convolutional kernel visual encoding networks with base parameter scale, our visual encoding framework improves by approximately 7% on the Natural Scenes Dataset, the dedicated dataset for the Algonauts 2023 Challenge. We further analyze that our encoding framework made a trade-off between encoding performance and trainability. This paper confirms that expanding parameters in visual coding can bring performance improvements.

Tetrahedral DNA loaded siCCR2 restrains M1 macrophage polarization to ameliorate pulmonary fibrosis in chemoradiation-induced murine model.

Idiopathic pulmonary fibrosis (IPF) is a chronic lethal disease in the absence of demonstrated efficacy for preventing progression. Although macrophage-mediated alveolitis is determined to participate in myofibrotic transition during disease development, the paradigm of continuous macrophage polarization is still under-explored due to lack of proper animal models. Here, by integrating 2.5 U/kg intratracheal Bleomycin administration and 10 Gy thorax irradiation at day 7, we generated a murine model with continuous alveolitis-mediated fibrosis, which mimics most of the clinical features of our involved IPF patients. In combination with data from scRNA-seq of patients and a murine IPF model, a decisive role of CCL2/CCR2 axis in driving M1 macrophage polarization was revealed, and M1 macrophage was further confirmed to boost alveolitis in leading myofibroblast activation. Multiple sticky-end tetrahedral framework nucleic acids conjunct with quadruple ccr2-siRNA (FNA-siCCR2) was synthesized in targeting M1 macrophages. FNA-siCCR2 successfully blocked macrophage accumulation in pulmonary parenchyma of the IPF murine model, thus preventing myofibroblast activation and leading to the disease remitting. Overall, our studies lay the groundwork to develop a novel IPF murine model, reveal M1 macrophages as potential therapeutic targets, and establish new treatment strategy by using FNA-siCCR2, which are highly relevant to clinical scenarios and translational research in the field of IPF.

Breakdown of utilitarian moral judgement after basolateral amygdala damage.

Most of us would regard killing another person as morally wrong, but when the death of one saves multiple others, it can be morally permitted. According to a prominent computational dual-systems framework, in these life-and-death dilemmas, deontological (nonsacrificial) moral judgments stem from a model-free algorithm that emphasizes the intrinsic value of the sacrificial action, while utilitarian (sacrificial) moral judgments are derived from a model-based algorithm that emphasizes the outcome of the sacrificial action. Rodent decision-making research suggests that the model-based algorithm depends on the basolateral amygdala (BLA), but these findings have not yet been translated to human moral decision-making. Here, in five humans with selective, bilateral BLA damage, we show a breakdown of utilitarian sacrificial moral judgments, pointing at deficient model-based moral decision-making. Across an established set of moral dilemmas, healthy controls frequently sacrifice one person to save numerous others, but BLA-damaged humans withhold such sacrificial judgments even at the cost of thousands of lives. Our translational research confirms a neurocomputational hypothesis drawn from rodent decision-making research by indicating that the model-based algorithm which underlies outcome-based, utilitarian moral judgements in humans critically depends on the BLA.

Electro-Induced Phase Transformation of a Conductive Metal-Organic Framework Film for Nonlinear Optical Switching.

Achieving metal-organic frameworks (MOFs) with nonlinear optical (NLO) switching is profoundly important. Herein, the conductive MOFs Cu-TCNQ phase I (Ph-I) and phase II (Ph-II) films were prepared using the liquid-phase-epitaxial layer-by-layer spin-coating method and steam heating method, respectively. Electronic experiments showed that the Ph-II film could be changed into the Ph-I film under an applied electric field. The third-order NLO results revealed that the Ph-I film had a third-order nonlinear reverse saturation absorption (RSA) response and the Ph-II film displayed a third-order nonlinear saturation absorption (SA) response. With increases in the heating time and applied voltage, the third-order NLO response realized the reversible transition between SA and RSA. The theoretical calculations indicated that Ph-I possessed more interlayer charge transfer, resulting in a third-order nonlinear RSA response that was stronger than that of Ph-II. This work applies phase-transformed MOFs to third-order NLO switching and provides new insights into the nonlinear photoelectric applications of MOFs.

A Hydrogen-Bonded Organic Framework-Based Mitochondrion-Targeting Bioorthogonal Platform for the Modulation of Mitochondrial Epigenetics.

Bioorthogonal chemistry represents a powerful tool in chemical biology, which shows great potential in epigenetic modulation. As a proof of concept, the epigenetic modulation model of mitochondrial DNA (mtDNA) is selected because mtDNA establishes a relative hypermethylation stage under oxidative stress, which impairs the mitochondrion-based therapeutic effect during cancer therapy. Herein, we design a new biocompatible hydrogen-bonded organic framework (HOF) for a HOF-based mitochondrion-targeting bioorthogonal platform TPP@P@PHOF-2. PHOF-2 can activate a prodrug (pro-procainamide) in situ, which can specifically inhibit DNA methyltransferase 1 (DNMT1) activity and remodel the epigenetic modification of mtDNA, making it more susceptible to ROS damage. In addition, PHOF-2 can also catalyze artemisinin to produce large amounts of ROS, effectively damaging mtDNA and achieving better chemodynamic therapy demonstrated by both in vitro and in vivo studies. This work provides new insights into developing advanced bioorthogonal therapy and expands the applications of HOF and bioorthogonal catalysis.

Ultrafast Ion Transfer of Metal-Organic Framework Interface for Highly Efficient Energy Storage.

Flexible supercapacitors are favorable for wearable electronics. However, their high-rate capability and mechanical properties are limited because of unsatisfactory ion transfer kinetics and interfacial modulus mismatch inside devices. Here, we develop a metal-organic framework interface with superior electrical and mechanical properties for supercapacitors. The interfacial mechanism facilitates ultrafast ion transfer with an energy barrier reduction of 43% compared with that of conventional transmembrane transport. It delivers high specific capacity at a wide rate range and exhibits ultrastability beyond 30000 charge-discharge cycles. Furthermore, meliorative modulus mismatch benefited from ultrathin interface design that improves mechanical properties of flexible supercapacitors. It delivers a stable energy supply under various mechanical conditions like bending and twisting status and displays ultrastable mechanical properties with performance retention of 95.5% after 10 000 bending cycles. The research paves the way for interfacial engineering for ultrastable electrochemical devices.

"All in One" Strategy for Achieving Superprotonic Conductivity by Incorporating Strong Acids into a Robust Imidazole-Linked Covalent Organic Framework.

The fabrication of solid-state proton-conducting electrolytes possessing both high performance and long-life reusability is significant but challenging. An "all-in-one" composite, H3PO4@PyTFB-1-SO3H, including imidazole, sulfonic acid, and phosphoric acid, which are essential for proton conduction, was successfully prepared by chemical post-modification and physical loading in the rationally pre-synthesized imidazole-based nanoporous covalent organic framework (COF), PyTFB-1. The resultant H3PO4@PyTFB-1-SO3H exhibits superhigh proton conductivity with its value even highly up to 1.15 × 10-1 S cm-1 at 353 K and 98% relative humidity (RH), making it one of the highest COF-based composites reported so far under the same conditions. Experimental studies and theoretical calculations further confirmed that the imidazole and sulfonic acid groups have strong interactions with the H3PO4 molecules and the synergistic effect of these three groups dramatically improves the proton conductivity properties of H3PO4@PyTFB-1-SO3H. This work demonstrated that by aggregating multiple proton carriers into one composite, effective proton-conducting electrolyte can be feasibly achieved.

Two-Dimensional Sulfonate-Functionalized Metal-Organic Framework Membranes for Efficient Lithium-Ion Sieving.

Two-dimensional (2D) membranes have shown promising potential for ion-selective separation but often suffer from the trade-off between permeability and selectivity. Herein, we report an ultrathin 2D sulfonate-functionalized metal-organic framework (MOF) membrane for efficient lithium-ion sieving. The narrow pores with angstrom precision in the MOF assist hydrated ions to partially remove the hydration shell, according to different hydration energies. The abundant sulfonate groups in the MOF channels serve as hopping sites for fast lithium-ion transport, contributing to a high Li-ion permeability. Then, the difference in affinity of the Li+, Na+, K+, and Mg2+ ions to the terminal sulfonate groups further enhances the Li-ion selectivity. The reported ultrathin MOF membrane overcomes the trade-off between permeability and selectivity and opens up a new avenue for highly permselective membranes.