Miracle-dz/UniFAR-FTUs huggingface dataset:
SVHAN: Sequential View Based Hierarchical Attention Network for 3D Shape Recognition

As an important field of multimedia, 3D shape recognition has attracted much research attention in recent years. A lot of deep learning models have been proposed for effective 3D shape representation. The view-based methods show the superiority due to the comprehensive exploration of the visual characteristics with the help of established 2D CNN architectures. Generally, the current approaches contain the following disadvantages: First, the most majority of methods lack the consideration for sequential information among the multiple views, which can provide descriptive characteristics for shape representation. Second, the incomprehensive exploration for the multi-view correlations directly affects the discrimination of shape descriptors. Finally, roughly aggregating multi-view features leads to the loss of descriptive information, which limits the shape representation effectiveness. To handle these issues, we propose a novel sequential view based hierarchical attention network (SVHAN) for 3D shape recognition. Specifically, we first divide the view sequence into several view blocks. Then, we introduce a novel hierarchical feature aggregation module (HFAM), which hierarchically exploits the view-level, block-level, and shape-level features, the intra- and inter- view-block correlations are also captured to improve the discrimination of learned features. Subsequently, a novel selective fusion module (SFM) is designed for feature aggregation, considering the correlations between different levels and preserving effective information. Finally, discriminative and informative shape descriptors are generated for the recognition task. We validate the effectiveness of our proposed method on two public databases. The experimental results show the superiority of SVHAN against the current state-of-the-art approaches.
Chemical inhibition of auxin inactivation pathway uncovers the metabolic turnover of auxin homeostasis

The phytohormone auxin, specifically indole-3-acetic acid (IAA) plays a prominent role in plant development. Cellular auxin concentration is coordinately regulated by auxin synthesis, transport, and inactivation to maintain auxin homeostasis; however, the physiological contribution of auxin inactivation to auxin homeostasis has remained elusive. The GH3 genes encode auxin amino acid conjugating enzymes that perform a central role in auxin inactivation. The chemical inhibition of GH3s in planta is challenging because the inhibition of GH3 enzymes leads to IAA overaccumulation that rapidly induces GH3 expression. Here, we developed a potent GH3 inhibitor, designated as kakeimide (KKI), that selectively targets auxin-conjugating GH3s. Chemical knockdown of the auxin inactivation pathway demonstrates that auxin turnover is very rapid (about 10 min), indicating auxin biosynthesis and inactivation dynamically regulate auxin homeostasis.
Intercellular alignment of apical-basal polarity coordinates tissue homeostasis and growth

Maintaining apicobasal polarity (ABP) is crucial for epithelial integrity and homeostasis during tissue development. Although recent studies have greatly advanced our understanding of intracellular mechanisms underlying ABP establishment, it remains largely unknown how the ABP is regulated at the tissue level. Here, we address intercellular mechanisms coordinating ABP using the Drosophila wing imaginal disc. By studying Scribble, a key ABP determinant, we show that ABP is regulated through intercellular alignment, which takes place either progressively or regressively in a context-dependent manner. Cells expressing wild type scribble progressively restore ABP in scribble hypomorphic mutant cells. In contrast, cells with conditional scribble knockdown instigate the regressive loss of polarity in abutting wild type cells. Our data reveal that genetic and physical interactions between Scribble, Septate junction complex and α-Catenin appear to be key for sustaining intercellular network of ABP. Taken together, our findings indicate that the intercellular relay of the status of ABP contributes to the robustness of polarity across the tissue.
G2 stem cells orchestrate time-directed, long-range coordination of calcium signaling during skin epidermal regeneration

Skin epidermal homeostasis is maintained via constant regeneration by stem cells, which must communicate to balance their self-renewal and differentiation. A key molecular pathway, Ca2+ signaling has been implicated as a signal integrator in developing and wounded epithelial tissues[1, 2, 3, 4]. Yet how stem cells carry out this signaling across a regenerative tissue remains unknown due to significant challenges in studying signaling dynamics in live mice, limiting our understanding of the mechanisms of stem cell communication during homeostasis. To interpret high dimensional signals that have complex spatial and temporal patterns, we combined optimized imaging of Ca2+ signaling in thousands of epidermal stem cells in living mice with a new machine learning tool, Geometric Scattering Trajectory Homology (GSTH). Using a combination of signal processing, data geometry, and topology, GSTH captures patterns of signaling at multiple scales, either between direct or distant stem cell neighbors. Here we show that epidermal stem cells display dynamic intercellular Ca2+ signaling among neighborhoods of up to 10 cells that is surprisingly coordinated and directed through time across a pool of thousands of stem cells. We find that this collective coordination is an emergent property of the stem cell compartment, distinct from excitatory quiescent neuronal tissues. We demonstrate that cycling stem cells, specifically G2 cells, govern homeostatic patterns of Ca2+ signaling. Stem cells in different cell cycle stages dynamically regulate localization of the gap junction component Connexin43 (Cx43). Lastly, we uncouple global from local communication and identify Cx43 as the molecular mediator necessary for connectivity between local signaling neighborhoods. This work provides resolution in how stem cells at different stages of the cell cycle communicate and how that diversity of phases is essential for tissue wide communication and signaling flow during epidermal regeneration. Our approach provides a framework to investigate stem cell populations and their signaling dynamics, previously not possible.
A weighted constraint satisfaction approach to human goal-directed decision making

When we plan for long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must also be considered. But when a subgoal must be resolved first, irrelevant future information should not interfere with the processing of more proximal, subgoal-relevant information. We explore the idea that decision making in both situations relies on the flexible modulation of the degree to which different pieces of information under consideration are weighted, rather than explicitly decomposing a problem into smaller parts and solving each part independently. We asked participants to find the shortest goal-reaching paths in mazes and modeled their initial path choices as a noisy, weighted information integration process. In a base task where choosing the optimal initial path required weighting starting-point and goal-proximal factors equally, participants did take both constraints into account, with participants who made more accurate choices tending to exhibit more balanced weighting. The base task was then embedded as an initial subtask in a larger maze, where the same two factors constrained the optimal path to a subgoal, and the final goal position was irrelevant to the initial path choice. In this more complex task, participants’ choices reflected predominant consideration of the subgoal-relevant constraints, but also some influence of the initially-irrelevant final goal. More accurate participants placed much less weight on the optimality-irrelevant goal and again tended to weight the two initially-relevant constraints more equally. These findings suggest that humans may rely on a graded, task-sensitive weighting of multiple constraints to generate approximately optimal decision outcomes in both hierarchical and non-hierarchical goal-directed tasks. Author Summary Different problems require the consideration of different information sources, including often useful long-range, future information that may impact our immediate decisions. However, when future information is irrelevant to a key subgoal, it can be desirable to focus on achieving the subgoal first. We suggest that humans rely on appropriately weighting relevant information over irrelevant information to generate decision outcomes in both types of situations. We conducted behavioral experiments and fitted models of decision processes to understand to what extent people considered various task factors in choosing the initial path in different mazes, both when a simple maze occurred alone or was embedded as an initial part in a larger maze. Our results show that people approximate the optimal decision outcomes in both tasks by modulating the weighting of different factors during planning, and that people who made more accurate initial path choices modulated these weightings more successfully than those who made less accurate choices.
ONTdeCIPHER: An amplicon-based nanopore sequencing pipeline for tracking pathogen variants

Motivation Amplicon-based nanopore sequencing is increasingly used for molecular surveillance during epidemics (e.g. ZIKA, EBOLA) or pandemics (e.g. SARS-CoV-2). However, there is still a lack of versatile and easy-to-use tools that allow users with minimal bioinformatics skills to perform the main steps of downstream analysis, from quality testing to SNPs effect to phylogenetic analysis. Results Here, we present ONTdeCIPHER, an amplicon-based Oxford Nanopore Technology (ONT) sequencing pipeline to analyze the genetic diversity of SARS-CoV-2 and other pathogenes. Our pipeline integrates 13 bioinformatics tools. With a single command line and a simple configuration file, users can pre-process their data and obtain the sequencing statistics, reconstruct the consensus genome, identify variants and their effects for each viral isolate, infer lineage and, finally perform multi-sequence alignments and phylogenetic analyses. Availability and implementation: ONTdeCIPHER is available at https://github.com/emiracherif/ONTdeCIPHER Contact emira.cherif@ird.fr Supplementary information Supplementary data are available at …
Benchmarking functional connectivity by the structure and geometry of the human brain

The brain’s structural connectivity supports the propagation of electrical impulses, manifesting as patterns of co-activation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through poly-synaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to re-weigh and re-express functional connectivity. We find evidence of unexpectedly strong functional connectivity within the canonical intrinsic networks of the brain. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena – functional modules and functional hierarchies – emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain.
Hydrophobicity in clearwing Lepidoptera: impact of scale micro and nanostructure, and trade-off with optical transparency

While scales ensure hydrophobicity in opaque Lepidoptera, their reduction in clearwing species question hydrophobicity and its dependency on wing microstructures (scale presence, morphology, insertion, coloration) and nanostructures (scale ridge features). By measuring static contact angle (CA) of water droplets at different stages of evaporation in opaque and transparent patches of 23 clearwing species with varied microstructure, we establish for the first time that transparency is costly for hydrophobicity, a cost depending on microstructure: transparent patches lose more hydrophobicity with water evaporation than opaque patches, a loss attenuated for higher scale densities, coloured scales (for erect scales), and when combining two types of scales (piliform and lamellar). Nude membranes show lowest hydrophobicity. Models best predicting CA include microstructures for all scales, and nanostructures only for flat scales. We reveal for the first time that wing hydrophobicity negatively relates to optical transparency. Finally, tropical species have more hydrophobic transparent patches but similarly hydrophobic opaque patches compared to temperate species. All findings are consistent with the physics of hydrophobicity, especially on multiscale roughness, and underline that wings are evolutionary compromises.
Novelty and uncertainty interact to regulate the balance between exploration and exploitation in the human brain

Recent evidence suggests that both novelty and uncertainty act as potent features guiding exploration. However, these variables are often conflated with each other experimentally, and an understanding of how these attributes interact to regulate the balance between exploration and exploitation has proved elusive. Using a novel task designed to decouple stimulus novelty and estimation uncertainty, we identify separable behavioral and neural mechanisms by which exploration is colored. We show that uncertainty was avoided except when the information gained through exploration could be reliably exploited in the future. In contrast, and contrary to existing theory, novel options grew increasingly attractive relative to familiar counterparts irrespective of the opportunity to leverage their consequences and despite the uncertainty inherent to novel options. These findings led us to develop a formal computational framework in which uncertainty directed choice adapts to the prospective utility of exploration, while novel stimuli persistently draw favor as a result of inflated reward expectations biasing an exploitative strategy. Crucially, novelty is proposed to actively modulate uncertainty processing, effectively blunting the influence of uncertainty in shaping the subjective utility ascribed to novel stimuli. Both behavioral data and fMRI activity sampled from the ventromedial prefrontal cortex, frontopolar cortex and ventral striatum validate this model, thereby establishing a computational account that can not only explain behavior but also shed light on the functional contribution of these key brain regions to the exploration/exploitation trade-off. Our results point to multiple strategies and neural substrates charged with balancing the explore/exploit dilemma, with each targeting distinct aspects of the decision problem to foster a manageable decomposition of an otherwise intractable task.
Local adaptation and spatiotemporal patterns of genetic diversity revealed by repeated sampling of Caenorhabditis elegans across the Hawaiian Islands

The nematode Caenorhabditis elegans is among the most widely studied organisms, but relatively little is known about its natural ecology. Wild C. elegans have been isolated from both temperate and tropical climates, where they feed on bacteria associated with decomposing plant material. Genetic diversity is low across much of the globe but high in the Hawaiian Islands and across the Pacific Rim. The high genetic diversity found there suggests that: (1) the origin of the species lies in Hawaii or the surrounding Pacific Rim; and (2) the ancestral niche of the species is likely similar to the Hawaiian niche. A recent study of the Hawaiian niche found that genetically distinct groups appeared to correlate with elevation and temperature, but the study had a limited sample size. To better characterize the niche and genetic diversity of C. elegans on the Hawaiian Islands and to explore how genetic diversity might be influenced by local adaptation, we repeatedly sampled nematodes over a three-year period, measured various environmental parameters at each sampling site, and whole-genome sequenced the C. elegans isolates that we identified. We found that the typical Hawaiian C. elegans niche is moderately moist native forests at high elevations (500 to 1500 meters) where temperatures are cool (15 to 20°C). We measured levels of genetic diversity and differentiation among Hawaiian strains and found evidence of seven genetically distinct groups distributed across the islands. Then, we scanned these genomes for signatures of local adaptation and identified 18 distinct regions that overlap with hyperdivergent regions, which are likely maintained by balancing selection and enriched for genes related to environmental sensing, xenobiotic detoxification, and pathogen resistance. These results provide strong evidence of local adaptation among Hawaiian C. elegans and a possible genetic basis for this adaptation.
Debunking the “junk”: Unraveling the role of lncRNA–miRNA–mRNA networks in fetal hemoglobin regulation

Fetal hemoglobin (HbF) induction is considered to be a promising therapeutic strategy to ameliorate the clinical severity of β-hemoglobin disorders, and has gained a significant amount of attention in recent times. Despite the enormous efforts towards the pharmacological intervention of HbF reactivation, progress has been stymied due to limited understanding of γ-globin gene regulation. In this study, we intended to investigate the implications of lncRNA-associated competing endogenous RNA (ceRNA) interactions in HbF regulation. Probe repurposing strategies for extraction of lncRNA signatures and subsequent in silico analysis on publicly available datasets (GSE13284, GSE71935 and GSE7874) enabled us to identify 46 differentially expressed lncRNAs (DElncRNAs). Further, an optimum set of 11 lncRNAs that could distinguish between high HbF and normal conditions were predicted from these DElncRNAs using supervised machine learning and a stepwise selection model. The candidate lncRNAs were then linked with differentially expressed miRNAs and mRNAs to identify lncRNA-miRNA-mRNA ceRNA networks. The network revealed that 2 lncRNAs (UCA1 and ZEB1-AS1) and 4 miRNAs (hsa-miR-19b-3p,hsa-miR-3646,hsa-miR-937 and hsa-miR-548j) sequentially mediate cross-talk among different signaling pathways which provide novel insights into the lncRNA-mediated regulatory mechanisms, and thus lay the foundation of future studies to identify lncRNA-mediated therapeutic targets for HbF reactivation.
Deep learning models of cognitive processes constrained by human brain connectomes

Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity over many trials. Recently, brain decoding with graph neural networks has been shown to scale at fine temporal resolution and on the full brain, achieving state-of-the-art performance on the human connectome project benchmark. The reason behind this success is likely the strong inductive connectome prior that enables the integration of distributed patterns of brain activity. Yet, the nature of such inductive bias is still poorly understood. In this work, we investigate the impact of the inclusion of multiple path lengths (through high-order graph convolution), the homogeneity of brain parcels (graph nodes), and the type of interactions (graph edges). We evaluate the decoding models on a large population of 1200 participants, under 21 different experimental conditions, acquired from the Human Connectome Project database. Our findings reveal that the optimal choice for large-scale cognitive decoding is to propagate neural dynamics within empirical functional connectomes and integrate brain dynamics using high-order graph convolutions. In this setting, the model exhibits high decoding accuracy and robustness against adversarial attacks on the graph architecture, including randomization in functional connectomes and lesions in targeted brain regions and networks. The trained model relies on biologically meaningful features for the prediction of cognitive states and generates task-specific graph representations resembling task-evoked activation maps. These results demonstrate that a full-brain integrative model is critical for the large-scale brain decoding. Our study establishes principles of how to effectively leverage human connectome constraints in deep graph neural networks, providing new avenues to study the neural substrates of human cognition at scale.
Intermanual transfer of visuomotor learning is facilitated by a cognitive strategy

Humans continuously adapt their movement to a novel environment by recalibrating their sensorimotor system. Recent evidence, however, shows that explicit planning to compensate for external changes, i.e. a cognitive strategy, can also aid performance. If such a strategy is indeed planned in external space, it should improve performance in an effector independent manner. We tested this hypothesis by examining whether promoting a cognitive strategy during a visual-force adaptation task performed in one hand can facilitate learning for the opposite hand. Participants rapidly adjusted the height of visual bar on screen to a target level by isometrically exerting force on a handle using their right hand. Visuomotor gain increased during the task and participants learned the increased gain. Visual feedback was continuously provided for one group, while for another group only the endpoint of the force trajectory was presented. The latter has been reported to promote cognitive strategy use. We found that endpoint feedback produced stronger intermanual transfer of learning and slower response times than continuous feedback. In a separate experiment, we confirmed that the aftereffect is indeed reduced when only endpoint feedback is provided, a finding that has been consistently observed when cognitive strategies are used. The results suggest that intermanual transfer can be facilitated by a cognitive strategy. This indicates that the behavioral observation of intermanual transfer can be achieved either by forming an effector-independent motor representation, or by sharing an effector-independent cognitive strategy between the hands. New and noteworthy The causes and consequences of cognitive strategy use for motor learning are poorly understood. We tested whether a visuomotor task learned using a strategy generalizes across effectors. Visual feedback was manipulated to enhance the use of a cognitive strategy. Learning using a cognitive strategy for one hand transferred to the task performed by the un-learned hand. Our result suggests that intermanual transfer can also result from a common cognitive strategy used to control both hands.