Miracle-dz/UniFAR-FTUs huggingface dataset:
Zero-shot Domain Adaptation of Heterogeneous Graphs via Knowledge Transfer Networks

How can we make predictions for nodes in a heterogeneous graph when an entire type of node (e.g. user) has no labels (perhaps due to privacy issues) at all? Although heterogeneous graph neural networks (HGNNs) have shown superior performance as powerful representation learning techniques, there is no direct way to learn us-ing labels rooted at different node types. Domain adaptation (DA) targets this setting, however, existing DA can not be applied directly to HGNNs. In heterogeneous graphs, the source and target domains have different modalities, thus HGNNs provide different feature extractors to them, while most of DA assumes source and target domains share a common feature extractor. In this work, we address the issue of zero-shot domain adaptation in HGNNs. We ﬁrst theoretically induce a relationship between source and target domain features extracted from HGNNs, then propose a novel domain adaptation method, Knowledge Transfer Networks for HGNNs (HGNN-KTN). HGNN-KTN learns the relationship between source and target features, then maps the target distributions into the source domain. HGNN-KTN outperforms state-of-the-art baselines, showing up to 73 . 3% higher in MRR on 18 different domain adaptation tasks running on real-world benchmark graphs.
R-GCN: The R Could Stand for Random

The inception of the Relational Graph Convolutional Network (R-GCN) marked a milestone in the Semantic Web domain as a widely cited method that generalises end-to-end hierarchical representation learning to Knowledge Graphs (KGs). R-GCNs generate representations for nodes of interest by repeatedly aggregating parameterised, relation-specific transformations of their neighbours. However, in this paper, we argue that the the R-GCN's main contribution lies in this"message passing"paradigm, rather than the learned weights. To this end, we introduce the"Random Relational Graph Convolutional Network"(RR-GCN), which leaves all parameters untrained and thus constructs node embeddings by aggregating randomly transformed random representations from neighbours, i.e., with no learned parameters. We empirically show that RR-GCNs can compete with fully trained R-GCNs in both node classification and link prediction settings.
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
Structural complementarity and similarity: linking relational principles to network motifs

The principle of similarity, or homophily, is often used to explain prevalent patterns observed in complex networks such as transitivity and abundance of triangles (3-cycles). On the other hand, many phenomena, from division of labor to protein-protein interactions (PPI), are driven by complementarity, or differences and synergy. However, the principle of complementarity has not been operationalized in network terms. Here we show that complementarity is linked to the abundance of quadrangles (4-cycles) and the presence of dense bipartite-like subgraphs. Starting from simple geometric arguments we link similarity and complementarity to their characteristic network motifs and introduce two families of coefficients: (1) structural similarity generalizing the notions of local clustering and closure and capturing the full spectrum of similarity-driven structures; (2) analogous complementarity coefficients based on quadrangles instead of triangles. We demonstrate on a variety of social and biological networks that the coefficients capture important structural properties which can be related to meaningful domain-specific phenomena. We show that they can be used to distinguish between different kinds of social relations and measure an increasing structural diversity of PPI networks across the tree of life. Our results suggest that for some types of relations assuming homophily may not be adequate. Moreover, they can be used to inform link prediction methods and decide when assuming triadic or tetradic closure is more appropriate. More generally, we provide a novel set of tools for studying the structure of networks in terms of complementarity between their elements. We also provide an efficient Python implementation of the proposed methods. Keywords— similarity | complementarity | complex networks | network motifs | network geometry
MAML is a Noisy Contrastive Learner in Classification

Model-agnostic meta-learning (MAML) is one of the most popular and widely adopted meta-learning algorithms, achieving remarkable success in various learning problems. Yet, with the unique design of nested inner-loop and outer-loop updates, which govern the task-specific and meta-model-centric learning, respectively, the underlying learning objective of MAML remains implicit and thus impedes a more straightforward understanding of it. In this paper, we provide a new perspective of the working mechanism of MAML. We discover that MAML is analogous to a meta-learner using a supervised contrastive objective. The query features are pulled towards the support features of the same class and against those of different classes. Such contrastiveness is experimentally verified via an analysis based on the cosine similarity. Moreover, we reveal that vanilla MAML has an undesirable interference term originating from the random initialization and the cross-task interaction. We thus propose a simple but effective technique, zeroing trick, to alleviate the interference. Extensive experiments are conducted on both mini-ImageNet and Omniglot datasets to demonstrate the consistent improvement brought by our proposed method, validating its effectiveness.
Construction and effect evaluation of different sciatic nerve injury models in rats

Abstract Background The most commonly used experimental model for preclinical studies on peripheral nerve regeneration is the sciatic nerve injury model. However, no experimental study has been conducted to evaluate acute injury modes at the same time. Objective We conducted sciatic nerve transverse injury, clamp injury, keep epineurium and axon cutting injury, and chemical damage injury in rats to evaluate the degree of damage of the four different injury modes and the degree of self-repair after injury. Methods The sciatic nerve transverse injury model, clamp injury model, keep epineurium injury model, and chemical damage injury model were constructed. Then, the sciatic nerve function was assessed using clinical evaluation methods and electrophysiological examinations, as well as immunofluorescence and axonal counting assessments of the reconstructed nerve pathways. Results The evaluations showed that the transverse group had the lowest muscle action potential, sciatic functional index, nociceptive threshold, mechanical threshold, rate of wet gastrocnemius muscle weight, area of muscle fiber, and numbers of myelinated nerve fibers. The chemical group had the highest, while the clamp group and the keep epineurium group had medium. Conclusion Transverse injury models have the most stable effect among all damage models; chemical injury models self-recover quickly and damage incompletely with poor stability of effect; and clamp injury models and keep epineurium injury models have no significant differences in many ways with medium stability.
The role of soy and soy isoflavones on women's fertility and related outcomes: an update

Abstract Abstract Soy is a key food in human nutrition. It is widely used in eastern traditional cuisine and it has recently diffused among self-conscious and vegetarian diets. The success of soy mainly depends on versatility and supposed healthy properties of soy foods and soy components. Meanwhile, the possible influence on endocrine system, in particular by isoflavones, raised concerns among some researchers. The present paper aims to conduct a review of available data on the effect of soy, soy foods and soy components on women's fertility and related outcomes. Eleven interventional studies, eleven observational studies and one meta-analysis have been selected from the results of queries. A weak, not clinically relevant effect has been highlighted on cycle length and hormonal status. However, a suggestive positive influence has been shown among women with fertility issues and during assisted reproductive technologies. Overall, soy and soy components consumption do not seem to perturb healthy women's fertility and can have a favourable effect among subjects seeking pregnancy. However, because of the paucity of studies exploring the impact of soy intake on women's fertility, as well as the limited population sample size, the frequently incomplete specimens’ collection to investigate all cycle phases and the insufficient characterisation of participants, the evidence is suggestive and it needs further in-depth research taking into account all these aspects.
Late onset neuromyelitis optica spectrum disorder with anti‐aquaporin 4 and anti‐myelin oligodendrocyte glycoprotein antibodies

This study aimed to evaluate the clinical characteristics and prognosis of late onset (≥50 years) neuromyelitis optica spectrum disorder (LO‐NMOSD), and compare them with those of early onset (<50 years) NMOSD (EO‐NMOSD) and NMOSD with various antibody serostatuses.
Team Size, Team Role, Communication Modality, and Team Coordination in the Distributed Operation of Multiple Heterogeneous Unmanned Vehicles

The aim of this study was to investigate the effects of team size, communication modality, and team role on measures of team process over time. As the use of semiautonomous, unmanned vehicles increases, it is pertinent to investigate issues associated with the human teams that will control them, including consideration of team roles and the design of systems to support these roles. Using a 1:35 scale facility for military operations in urban terrain, distributed teams of two or three operators used a simulated, unmanned aerial vehicle and a scaled, unmanned ground vehicle to complete two distinct phases of a mission. The teams used either audio or instant messaging for communication, and the analysis of communication assessed their discussion of target identification, target classification, object localization, and vehicle navigation. Findings indicate that the addition of a third teammate resulted in greater role specificity, and the use of audio increased communication of task-relevant content. The proper assignment of team roles can improve the ability of a team to both acquire and synthesize information from remote environments. Furthermore, proper system design can improve the flow of information between teammates over time.
Design of an islet-on-a-chip device reveals glucose-stimulated respiration is substrate limited by glycolytic flux through PKM2

1.Abstract Pancreatic islets respond metabolically to glucose by closing KATP channels resulting in Ca influx and insulin secretion. Previous work showed that the importance of glycolytic flux in triggering insulin secretion. However, it is unclear whether the triggered (‘first phase’) secretion is further amplified by Ca-stimulation of mitochondrial NADH production and/or oxidative phosphorylation (OxPhos). To tease apart the metabolism of first phase glucose-stimulated insulin secretion, we designed an islet-on-a-chip microfluidic device to simultaneously measure O2-consumption rate (OCR) and Ca -activity of individual islets with high temporal resolution. We used finite element analysis to optimize the placement of sensor in optically clear microwells on a thin glass coverslip. The microfluidic channels were subsequently fabricated using O2impermeable plastic to limit outside-in diffusion and push islets in the device against the microsensor. We validated our device using living mouse islets and well-established modulators of respiration. By inhibiting glycolysis and mitochondrial pyruvate transport, we showed islet OxPhos is limited by substrate-NADH rather than ADP in low and high glucose. We subsequently imaged glucose-stimulated OCR and Ca-influx simultaneously to reveal a biphasic respiratory response that is determined by glycolytic flux through pyruvate kinase (PKM2) and independent of Ca. Ultimately, we validated our device and successfully used it to show islet OxPhos is determined by glycolytic flux and not amplified by Ca-activity.
A promoter-proximal silencer modifies the activity of a shared enhancer to mediate divergent expression of nub and pdm2 paralogs in wing development

Duplication of genes and their associated cis-regulatory elements, or enhancers, is a key contributor to genome evolution and biological complexity. Moreover, many paralogs, particularly tandem duplicates, are fixed for long periods of time under the control of shared enhancers. However, in most cases the mechanism by which gene expression and function diverge following duplication is not known. Here we dissect the regulation and function of the paralogous nubbin/pdm2 genes during wing development in Drosophila melanogaster. We show that these paralogs play a redundant role in the wing and that their expression relies on a single shared wing enhancer. However, the two genes differ in their ability to respond to this enhancer, with nub responding in all wing progenitor cells and pdm2 only in a small subset. This divergence is a result of a pdm2-specific silencer element at the pdm2 promoter that receives repressive input from the transcription factor Rotund. Repression through this silencer also depends on nub, allowing pdm2 to fully respond to the wing enhancer when nub expression is perturbed and functional compensation to occur. Thus, expression divergence downstream of a shared enhancer arises as a consequence of silencing the promoter of one paralog.
Rapid single-molecule characterisation of nucleic-acid enzymes

The activity of enzymes is traditionally characterised through bulk-phase biochemical methods that only report on population averages. Single-molecule methods are advantageous in elucidating kinetic and population heterogeneity but are often complicated, time consuming, and lacking statistical power. We present a highly generalisable and high-throughput single-molecule assay to rapidly characterise proteins involved in DNA metabolism. The assay exclusively relies on changes in total fluorescence intensity of surface-immobilised DNA templates as a result of DNA synthesis, unwinding or digestion. Combined with an automated data-analysis pipeline, our method provides enzymatic activity data of thousands of molecules in less than an hour. We demonstrate our method by characterising three fundamentally different nucleic-acid enzyme activities: digestion by the phage λ exonuclease, synthesis by the phage Phi29 polymerase, and unwinding by the E. coli UvrD helicase. We observe a previously unknown activity of the UvrD helicase to remove proteins tightly bound to the ends of DNA.
Geometric model incorporating prey’s turn and predator attack endpoint explains multiple preferred escape trajectories

The escape trajectory (ET) of prey – measured as the angle relative to the predator’s approach path – plays a major role in avoiding predation. Previous geometric models predict a single ET; however, many species show highly variable ETs with multiple preferred directions. Although such a high ET variability may confer unpredictability to avoid predation, the reasons why animals prefer specific multiple ETs remain unclear. Here, we constructed a novel geometric model that incorporates the time required for prey to turn and the predator’s position at the end of its attack. The optimal ET was determined by maximizing the time difference of arrival at the edge of the safety zone between the prey and predator. By fitting the model to the experimental data of fish Pagrus major we show that the model can clearly explain the observed multiple preferred ETs. By using the same model, we were able to explain different patterns of ETs empirically observed in other species (e.g., insects and frogs): a single preferred ET and multiple preferred ETs at small (20–50°) and large (150–180°) angles from the predator. Our results open new avenues of investigation for understanding how animals choose their ETs from behavioral and neurosensory perspectives.
Mindfulness-Based Cognitive Therapy in Recurrent MDD Patients With Residual Symptoms: Alterations in Resting-State Theta Oscillation Dynamics Associated With Changes in Depression and Rumination

Many patients with major depressive disorder (MDD) suffer from residual symptoms. Rumination is a specific known risk factor for the onset, severity, prolongation, and relapse of MDD. This study aimed to examine the efficacy and EEG substrates of mindfulness-based cognitive therapy (MBCT) in alleviating depression and rumination in an MDD population with residual symptoms. We recruited 26 recurrent MDD individuals who had residual symptoms with their current antidepressants to participate in the 8-week MBCT intervention. We evaluated the efficacy and changes in the dynamics of resting-state theta rhythm after the intervention, as well as the associations between theta alterations and improvements in depression and rumination. The participants showed reduced depression, enhanced adaptive reflective rumination, and increased theta power and phase synchronization after MBCT. The increased theta-band phase synchronizations between the right occipital regions and the right prefrontal, central, and parietal regions were associated with reduced depression, while the increase in theta power in the left parietal region was associated with improvements in reflective rumination. MBCT could alleviate depression and enhance adaptive, reflective rumination in recurrent MDD individuals with residual symptoms through the modulation of theta dynamics in specific brain regions.
Indian Polyvalent Antivenom Accelerates Recovery From Venom-Induced Consumption Coagulopathy (VICC) in Sri Lankan Russell’s Viper (Daboia russelii) Envenoming

Background Venom-induced consumption coagulopathy (VICC) is an important clinical consequence of Russell’s viper (Daboia russelii) envenoming. There is limited evidence for antivenom effectiveness in resolving VICC. We aimed to compare the recovery of VICC in patients who received and did not receive antivenom following Russell’s viper envenoming. Patients and Methods This was a non-randomized observational study comparing patients with VICC from Russell’s viper envenoming given antivenom for systemic envenoming and those not given antivenom. Antivenom administration was decided by the treating physicians. We included 44 patients with confirmed Russell’s viper bites with one or more International Normalized Ratio (INR) value ≥ 1.5 (VICC). We compared five patients who did not receive antivenom with 39 patients who did receive antivenom. The primary outcome was the proportion of patients with an INR < 1.5 by 48 h post-bite. Results The antivenom group had higher peak serum venom concentrations [median (IQR) = 272 (96–1,076) ng/mL versus 21 (8–58) ng/mL] and more severe VICC compared to the no antivenom group. Twenty seven of 39 patients (69%) in the antivenom group had an INR < 1.5 at 48 h post-bite compared to none of the five patients (0%) in the no antivenom group (absolute difference: 69%; 95%CI: 13 to 83%; p = 0.006; Fisher’s exact test). The fibrinogen recovered in 32 of 39 patients (82%) in the antivenom group compared to one of five patients (20%) in the no antivenom group (absolute difference 62%; 95% CI: 28 to 95%; p = 0.001; Fisher’s exact test). Both INR and fibrinogen were significantly improved between 24 and 48 h post-bite in the antivenom group compared to the no antivenom group. Conclusion Antivenom accelerated the recovery of VICC in patients with Russell’s viper envenoming, compared to no recovery in a smaller group of patients with milder VICC not receiving antivenom. This supports the efficacy of antivenom in patients with VICC.
17α-Hydroxyprogesterone Caproate Inhibits Cytokine Production via Suppression of NF-κB Activation

Cytokine release syndrome (CRS) is one of the leading causes of morbidity and mortality in COVID-19 patients with elevated levels of circulating cytokines contributing to various clinical symptoms. Favorable control of CRS represents a promising and effective strategy to mitigate the clinical outcomes of hospitalized patients with moderate to severe pneumonia. Using in vivo cytokine release assay in human peripheral blood mononuclear cell (PBMC)-engrafted immunodeficient mice, we reported that 17α-hydroxyprogesterone caproate (17-OHPC), a synthetic progestogen, exhibited significant inhibition of OKT-3-stimulated production of numerous cytokines including TNF-α, IFN-γ, IL-2, IL-4, IL-6, IL-10, and GM-CSF. Furthermore, 17-OHPC inhibited in vitro production of IFN-γ, IL-1β, IL-2, IL-6, and IL-10 in human PBMCs stimulated with OKT3, while exhibiting down-regulation of the mRNA levels of TNF-α, IFN-γ, IL-2, IL-6, and IL-10. Using the same human PBMCs, additional stimulators anti-CD28 antibody or PHA treatments led to substantial cytokine production, which was also attenuated by 17-OHPC. OKT3-stimulated phosphorylation of IκBα and nuclear translocation of NF-κB p65 in human PBMCs were also reversed by 17-OHPC, suggesting its inhibition on NF-κB signaling in immune cells. Taken together, this work reported both in vivo and in vitro inhibition of cytokine production by 17-OHPC, presumably by virtue of its suppression of NF-κB signaling. These findings provide pharmacological evidence to support the potential application of 17-OHPC in treating CRS associated with COVID-19.