Miracle-dz/UniFAR-FTUs huggingface dataset:
Methane Production Explained Largely by Water Content in the Heartwood of Living Trees in Upland Forests

Most forests worldwide are located in upland landscapes. Previous studies have mainly focused on ground methane (CH4) flux in upland forests, and living tree stem‐based CH4 processes and fluxes are thus relatively poorly understood. This study investigated the relationship between CH4 concentration and water content in the heartwood of living trees in midtemperate, warm temperate, and subtropical upland forests and also measured seasonal changes of in situ stem CH4 flux and the CH4 concentration and water content in the heartwood of Populus davidiana Dode in a warm temperate upland forest. We found that approximately 4–13% of tree stems or approximately 8–31% of tree species had a substantial CH4 concentration of ≥10,000 μL L−1 in their heartwood across the three types of upland forests. The heartwood CH4 concentration was related to water content by a power function. A threshold of water content occurred beyond which CH4 was produced at high levels in the heartwood. The CH4 emissions from the breast height stems of P. davidiana ranged from 202.1 to 331.6 μg m−2 h−1 on a stem surface area basis during July–October 2016 and were significantly linearly correlated with the CH4 concentration or water content in the heartwood throughout the experimental period, but the linear correlation was not significant at daily and monthly scales. Temperature was not a limiting factor for CH4 production during July–October 2016, and thus, most of the CH4 production may be explained by water content in the heartwood of living trees in upland forests.
Pathway-enhanced Transformer-based model for robust enumeration of cell types from the cell-free transcriptome

Analysis cell types origin of cell-free RNA can enhance the resolution of liquid biopsies, thereby deepening the understanding of molecular and cellular changes in development and disease processes. Existing deconvolution methods typically rely on meticulously curated gene expression profiles or employ deep neural network with vast and complex solution spaces that are difficult to interpret. These approaches fail to leverage the synergistic and co-expression effects among genes in biological signaling pathways, compromising their generalizability and robustness. To address this issue, we have developed ‘Deconformer’, a Transformer-based deconvolution model that integrates biological signaling pathways at the embedding stage. Compared to popular methods on multiple datasets, Deconformer demonstrates superior performance and robustness, and is capable of tracking the developmental process of the placenta. Additionally, Deconformer’s self-attention mechanism has identified a connection between platelet activation, dependencies with other pathways, and the severity of COVID-19. We believe that Deconformer will enable and accelerate the precise analysis of a wide range of cell-free RNA, describing disease progression and severity from the perspectives of originating cell fractions and pathway dependencies.
Decoding the spatiotemporal dynamic neural representation of repetitive facial expression imitation

Imitating facial emotion expressions can facilitate social interactions, although the behavioral and neural spatiotemporal dynamics is unclear. Here participants (N=100) imitated facial emotions repeatedly over one month (16 times in total) with neural activity measured on three occasions using functional near-infrared spectroscopy. Additionally, the transfer effect of repeated imitation on emotional face and scene perception was assessed by fMRI with multivariate pattern analysis. Valence specific imitation performance was facilitated by the alterations in the similarity of spatiotemporal patterns evoked in the mirror neuron system (MNS) with information flow moving progressively towards the inferior frontal gyrus as the as the number of times of imitation increase. Furthermore, MNS representation predictive patterns of processing emotional faces, but not scenes, were enhanced. Overall, these findings provide a neural changes of information flow within MNS and advance our understanding of the spatiotemporal dynamics from novice to proficient of facial emotion imitation.
Trajectorygeometry suggests cell fate decisions involve branches rather than bifurcations

Differentiation of multipotential progenitor cells is a key process in the development of any multi-cellular organism and often continues throughout its life. It is often assumed that a bi-potential progenitor develops along a (relatively) straight trajectory until it reaches a decision point where the trajectory bifurcates. At this point one of two directions is chosen, each direction representing the unfolding of a new transcriptomic programme. However, we have lacked quantitative means for testing this model. Accordingly, we have developed the R package TrajectoryGeometry. Applying this to published data we find several examples where, rather than bifurcate, developmental pathways branch. That is, the bipotential progenitor develops along a relatively straight trajectory leading to one of its potential fates. A second relatively straight trajectory branches off from this towards the other potential fate. In this sense only cells that branch off to follow the second trajectory make a “decision”. Our methods give precise descriptions of the genes and cellular pathways involved in these trajectories. We speculate that branching may be the more common behaviour and may have advantages from a control-theoretic viewpoint.
Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.
Failed stopping transiently suppresses the electromyogram in task-irrelevant muscles

Selectively stopping individual parts of planned or ongoing movements is an everyday motor skill. For example, while walking in public you may stop yourself from waving at a stranger who you mistook for a friend while continuing to walk. Despite its ubiquity, our ability to selectively stop actions is limited. Canceling one action can delay the execution of other simultaneous actions. This stopping-interference effect on continuing actions during selective stopping may be attributed to a global inhibitory mechanism with widespread effects on the motor system. Previous studies have characterized a transient global reduction in corticomotor excitability by combining brain stimulation with electromyography (EMG). Here, we examined whether global motor inhibition during selective stopping can be measured peripherally and with high temporal resolution using EMG alone. Eighteen participants performed a bimanual anticipatory response inhibition task with their index fingers while maintaining a tonic contraction of the task-irrelevant abductor digiti minimi (ADM) muscles. A time series analysis of the ADM EMG signal revealed transient inhibition during failed stopping compared to go response trials 150 ms to 203 ms following the stop signal. The pattern was observed in both hands during bimanual stop-all trials as well as selective stop-left and stop-right trials of either hand. These results indicate that tonic muscle activity is sensitive to the effects of global motor suppression even when stopping fails. Therefore, EMG can provide a physiological marker of global motor inhibition to probe the time course and extent of stopping processes. Key Points Successfully stopping an initiated response globally suppresses the motor system. Using electromyography of tonic muscle activity, we show inhibition spills over to task-irrelevant muscles during failed stopping. The electromyographic pattern of inhibition is transient, lasting from approximately 150 to 203 ms following a stop signal when stopping fails. The time course of the peripheral suppression of muscle activity may be leveraged to more precisely examine candidate neural mechanisms. This non-invasive measure of motor system inhibition may be useful for tracking inhibitory control deficits in clinical populations.
A non-Hebbian code for episodic memory

Hebbian plasticity has long dominated neurobiological models of memory formation. Yet plasticity rules operating on one-shot episodic memory timescales rarely depend on both pre- and postsynaptic spiking, challenging Hebbian theory in this crucial regime. To address this, we present an episodic memory model governed by a simple non-Hebbian rule depending only on presynaptic activity. We show that this rule, capitalizing on high-dimensional neural activity with restricted transitions, naturally stores episodes as paths through complex state spaces like those underlying a world model. The resulting memory traces, which we term path vectors, are highly expressive and decodable with an odor-tracking algorithm. We show that path vectors are robust alternatives to Hebbian traces when created via spiking and support diverse one-shot sequential and associative recall tasks, and policy learning. Thus, non-Hebbian plasticity is sufficient for flexible memory and learning, and well-suited to encode episodes and policies as paths through a world model.
Deciphering rapid cell signaling and control of cell motility by reverse opto-chemical engineering

Cells transform complex environmental stimuli into physiological responses. For time-varying stimuli or motile cells, the perception of the environment depends on the temporal stimulus pattern and cell motion, respectively. Here we report a concept, “reverse optochemical engineering” (ROCE), that uses temporal light patterns and photo-triggers to expose cells to virtual sensory landscapes while recording in real time their physiological responses and motor behavior. We studied cyclic-nucleotide signaling in cell lines, sperm, olfactory neurons, and cardiomyocytes. The technique can be employed for remote control of motility by light. We reprogrammed sperm from a chemotactic to a ‘phototactic’ cell that is attracted towards light. The method provides new opportunities to decipher the mechanisms and signaling molecules underlying rapid cellular computations, and thus reveal the wire diagram of cellular networks.
Effects of Opening Shapes on Behaviour of Shear Walls Made of AAC Masonry Units

Stiffening walls in masonry structures give support against lateral forces, such as wind or earthquakes. These aspects in brick walls, which have very low compressive and shear strength, require further observations (Jasiński 2017). This paper demonstrates the author’s own results from tests performed on ten masonry units made of autoclaved aerated concrete. The tests were performed in four series of two elements. The first series denoted as HOS-AAC included four elements (the reference elements) without openings, with the length l = 4.43 m, h = 2.43 m, and thickness t = 180 mm. The wall tests were performed under initial compressive stress values σc = 0 N/mm2, 0.75 N/mm2 and 1.0 N/mm2. In remaining three series, we used walls (external dimensions l = 4.43 m, h = 2.43 m, t = 180 mm) with different opening shapes denoted by convention as A, B and C. The models were tested under different values of initial compressive stress σc = 0.1 N/mm2, 0.5 N/mm2 or 1.0 N/mm2. The results were compared with test results for unreinforced walls without openings, tested under the same initial conditions. The failure mechanism and changes in cracking morphology were described. And the analysis was performed for parameters responsible for wall stiffness and changes in values of cracking and failure stresses.
ILLUSORY LIGHT DRIVES PUPIL RESPONSES IN PRIMATES

In humans, the eye pupils respond to both physical light sensed by the retina and mental representations of light produced by the brain. Notably, our pupils constrict when a visual stimulus is illusorily perceived brighter, even if retinal illumination is constant. Yet, it remains unclear whether such perceptual penetrability of pupil responses is an epiphenomenon unique to humans or whether it represents an adaptive mechanism shared with other animals to anticipate variations in retinal illumination between successive eye fixations. To address this issue, we measured the pupil responses of both humans and macaque monkeys exposed to three chromatic versions (cyan, magenta, and yellow) of the Asahi brightness illusion. We found that the stimuli illusorily perceived brighter or darker trigger differential pupil responses that are very similar in macaques and human participants. Additionally, we show that this phenomenon exhibits an analogous cyan bias in both primate species. Beyond evincing the macaque monkey as a relevant model to study the perceptual penetrability of pupil responses, our results suggest that this phenomenon is tuned to ecological conditions since the exposure to a “bright cyan-bluish sky” may be associated with increased risks of dazzle and retinal damages.
Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks

The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the causal significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp EEG) recordings in patients during presurgical intracerebral electrical stimulation (iES). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence of recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show a more functionally integrated processing style than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure.
Nanoscale architecture and dynamics of CaV1.3 channel clusters in cardiac myocytes revealed by single channel nanoscopy

The clustering of L-type calcium channels in cardiac myocytes presents an important mechanism for functional regulation of calcium signaling. Here we applied targeted super-resolution imaging techniques for the study of atrial-specific CaV1.3 channel clusters in human iPSC-derived atrial cardiomyocytes (hiPSC-aCM). We thereby clarified cluster localization, dimensions, architecture, and dynamics, which were largely unexplored previously. Live-cell STimulated Emission Depletion (STED) imaging identified that cell surface-localized clusters contained 9 channel molecules within 120 nm diameter on average. DNA Points Accumulation for Imaging in Nanoscale Topography (DNA-PAINT) optimized for molecular mapping revealed an irregular arrangement of channels with significant spacing. Single Particle Tracking (SPT) further evidenced that clustered channels do not associate into rigidly packed structures (oligomers or lattices), but rather co-diffuse in confined and stationary membrane nanodomains. Immunofluorescence showed consistent cell-surface colocalization with Ryanodine Receptor type 2 and Junctophilin-2 forming stable calcium release units, similar to dyadic junctions containing CaV1.2 in ventricular cardiomyocytes. Lastly, novel genetic constructs for live-cell imaging showed that the cytosolic C-terminal tail of CaV1.3 by itself is sufficient for cluster formation. In conclusion, a novel strategy for LTCC clustering studies in atrial cells was established, suitable for a wide range of super-resolution imaging techniques. Based on live-cell STED, DNA-PAINT and SPT data, we propose that CaV1.3 channel clusters consist of mobile individual channels inside defined membrane nanodomains.
Low microbial abundance and community diversity within the egg capsule of the oviparous cloudy catshark (Scyliorhinus torazame) during oviposition

Vertebrate embryos are protected from bacterial infection by various maternally derived immune factors before the embryonic organs are fully developed. However, the defense mechanisms employed by elasmobranch embryos during development remain poorly understood. This study attempted to elucidate the embryonic defense mechanism of elasmobranchs by investigating the intracapsular environment of freshly laid eggs of the oviparous cloudy catshark (Scyliorhinus torazame). The egg capsule of oviparous elasmobranchs is tightly sealed until pre-hatching (early opening of the egg capsule), after which seawater flows into the capsule and the embryos are consequently exposed to the surrounding seawater. We first experimentally examined the resistance of embryos to potential bacterial infections and found that the early embryos were highly vulnerable to environmental pathogens, suggesting that the embryos are protected from the threat of infection before pre-hatching. Indeed, the intracapsular environment of freshly laid eggs exhibited a significantly low bacterial density that was maintained until pre-hatching. Furthermore, the microbiome inside eggs just after oviposition differed markedly from the microbiomes of rearing seawater and adult oviducal gland epithelia; these eggs were predominantly populated by an unidentified genus of Sphingomonadaceae. Overall, this study provides compelling evidence that early embryos of oviparous cloudy catshark are incubated in a clean intracapsular environment that potentially plays a significant role in embryonic development in oviparous elasmobranchs. Our results suggest that maintenance of this clean condition might be attributable to bactericidal or bacteriostatic activities associated with the egg jelly and/or oviducal gland.
Fatigue Crack Propagation and Fracture in Sour Dilute Brine

Fatigue propagation behavior was investigated for low alloy steels of types commonly used in sour dilute brines. Test variables included hydrogen sulfide partial pressure, total pressure, stress ratio, fatigue frequency, steel grade and test temperature. Sour fatigue propagation rates were much higher than propagation rates in air and the sour rates showed pronounced frequency effects at higher stress intensity factor ranges. Increased hydrogen sulfide partial pressure increased propagation but total pressure did not. Implications for operations in wet, sour environments are discussed.
The Aquila Digital Community The Aquila Digital Community

Decentralized partially-observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. Multi-agent reinforcement learning (MARL) based approaches have been recently proposed for distributed solution of Dec-POMDPs without full prior knowledge of the model, but these methods assume that conditions during learning and policy execution are identical. In some practical scenarios this may not be the case. We propose a novel MARL approach in which agents are allowed to rehearse with information that will not be available during policy execution. The key is for the agents to learn policies that do not explicitly rely on these rehearsal features . We also establish a weak convergence result for our algorithm, RLaR, demonstrating that RLaR converges in probability when certain conditions are met. We show experimentally that incorporating rehearsal features can enhance the learning rate compared to non-rehearsal-based learners, and demonstrate fast, (near) optimal performance on many existing benchmark Dec-POMDP problems. We also compare RLaR against an existing approximate Dec-POMDP solver which, like RLaR, does not assume a priori knowledge of the model. While RLaR’s policy representation is not as scalable, we show that RLaR produces higher quality policies for most problems and horizons studied.
SAMv2: A Unified Framework for Learning Appearance, Semantic and Cross-Modality Anatomical Embeddings

—Identifying anatomical structures ( e.g. , lesions or landmarks) in medical images plays a fundamental role in medical image analysis. As an exemplar-based landmark detection method, Self-supervised Anatomical eMbedding (SAM) learns a discriminative embedding for each voxel in the image and has shown promising re-sults on various tasks. However, SAM still faces challenges in: (1) differentiating voxels with similar appearance but different semantic meanings ( e.g. , two adjacent structures without clear borders); (2) matching voxels with similar semantics but markedly different appearance ( e.g. , the same vessel before and after contrast injection); and (3) cross-modality matching ( e.g. , CT-MRI registration). To overcome these challenges, we propose SAMv2, which is a unified framework designed to learn appearance, semantic, and cross-modality anatomical embeddings. Specifically, SAMv2 incorporates three key innovations: (1) semantic embedding learning with prototypical contrastive loss; (2) a fixed-point-based matching strategy; and (3) an iterative approach for cross-modality embedding learning. We thoroughly evaluated SAMv2 across three tasks, including one-shot landmark detection, lesion tracking on longitudinal CT scans, and CT-MRI affine/rigid registration with varying field of view. Our results suggest that SAMv2 outperforms SAM and other state-of-the-art meth-ods, offering a robust and versatile approach for landmark based medical image analysis tasks. Code and trained models are available at: https://github.com/alibaba-damo-academy/self-supervised-anatomical-embedding-v2.