Miracle-dz/UniFAR-FTUs huggingface dataset:
Myotis petax (Chiroptera, Vespertilionidae) Preys on Pelagic Amphipoda (Crustacea, Gammaroidea) of Lake Baikal

In the present paper, we document a previously unknown phenomenon — the feeding of Myotis petax on fresh water pelagic crustaceans. A series of photographs are presented demonstrating the different phases of successful attacks of the bats on the amphipod Macrohectopus branickii. This crustacean, endemic to Lake Baikal, performs vertical migrations in the water column at night and sometimes forms mass accumulations near its surface. The observations presented expand the prey list of the ‘trawling Myotis’ and also supplement the picture of trophic relationships of the pelagic organisms of the ancient and deep Lake Baikal with terrestrial vertebrates.
Alternatives In The Implementation Of Internet–Enabled Laboratory Experiments In Undergraduate Civil Engineering Courses

Abstr act Recent developments in information technology have generated considerable interest in the field of internet-enab led remote observation and control of laboratory experiments. This paper describes two alternative frameworks required to adapt two laboratory experiments, one each in the areas of structural and geotechnical engineering, for remote control and observation including protection from misuse. Faculty and students will be able to observe, conduct, control, and analyze experimental data either in or outside of a classroom. In the face of continuously evolving distance education programs, more and more educators recognize the importance of a complete educational experience involving theory as well as hands-on experiments. In general, a large number of students lack opportunities to gain hands-on experiences due to the availability of only a few experimental facilities nationwide. Opportunities can be created by the availability of such facilities via remote access and control within a distance learning environment. Even for on-campus students, the possibility of experimental training can be unrealized due to economic constraints in developing a large number of experimental set-ups for simultaneous use in a laboratory environment within a limited allotted time. Simulations (virtual experiment) are often used to integrate classroom lecture with some kind of hands-on experience. However, educators have pointed out that simulation systems, while important, cannot completely substitute for experience with actual materials. One of the two implementations considered in this paper uses LABVIEW for not only data acquisition and control but also for providing web access and for developing features to safeguard against the possibility of damage to the experimental setup if left unmonitored. The other implementation considers the use of equipment-specific software for control in conjunction with MS-Netmeeting for web accessibility. The advantages and limitations associated with each implementation are discussed with respect to the experiments being adapted. Different options available within LABVIEW are also presented and our experiences on the advantages and limitations of these options discussed.
The Hidden Convex Optimization Landscape of Regularized Two-Layer ReLU Networks: an Exact Characterization of Optimal Solutions

We prove that finding all globally optimal two-layer ReLU neural networks can be performed by solving a convex optimization program with cone constraints. Our analysis is novel, characterizes all optimal solutions, and does not leverage duality-based analysis which was recently used to lift neural network training into convex spaces. Given the set of solutions of our convex optimization program, we show how to construct exactly the entire set of optimal neural networks. We provide a detailed characterization of this optimal set and its invariant transformations. As additional consequences of our convex perspective, (i) we establish that Clarke stationary points found by stochastic gradient descent correspond to the global optimum of a subsampled convex problem (ii) we provide a polynomial-time algorithm for checking if a neural network is a global minimum of the training loss (iii) we provide an explicit construction of a continuous path between any neural network and the global minimum of its sublevel set and (iv) characterize the minimal size of the hidden layer so that the neural network optimization landscape has no spurious valleys. Overall, we provide a rich framework for studying the landscape of neural network training loss through convexity.
Inductive Relation Prediction Using Analogy Subgraph Embeddings

Prevailing methods for relation prediction in heterogeneous graphs including knowledge graphs aim at learning the latent representations (i.e., embeddings) of observed nodes and relations, and are thus limited to the transductive setting where the relation types must be known during training. In this paper, we propose ANalogy SubGraph Embedding Learning (GraphANGEL), a novel relation prediction framework that predicts relations between each node pair by checking whether the subgraphs containing the pair are similar to other subgraphs containing the considered relation. Each graph pattern explicitly represents a specific logical rule, which contributes to an inductive bias that facilitates generalization to unseen relation types and leads to more explainable predictive models. Our model consistently outperforms existing models in terms of heterogeneous graph based recommendation as well as knowledge graph completion. We also empirically demonstrate the capability of our model in generalizing to new relation types while producing explainable heat maps of attention scores across the discovered logics.
Exploring a Pre-trained Model for Re-Matching News Texts and Images

We investigate the use of a pre-trained model to address the task of re-matching images and texts collected from online news sites. Our aim is to explore the potential of pre-training in learning the connection between the visual and textual modalities. Online news is challenging because it covers a large number of semantic concepts and also the correlation between the modalities can be weak. The results show that the proposed method has good performance in text-image retrieval. The performance was 46.58% (R@100).
EVIEW Pediatric Cardiology in the Genomic Era

While congenital heart disease, cardiomyopathy, arrhythmias and acquired cardiac diseases are common causes of mortality and morbidity in infants and children, the basic underlying mechanisms of many specific pediatric cardiovascular diseases still remains undetermined. Breakthroughs in molecular genetic technology have just begun to be applied in pediatric cardiology stemming from the use of chromosomal mapping and the identification of genes involved in both the primary etiology and as significant risk factors in the development of cardiac and vascular abnormalities. This review will focus on information ob-tained thus far by molecular genetic analysis in the diagnosis, treatment and overall understanding of pediatric cardiovascular disease pathogenesis examining both the more prevalent congenital/inherited heart defects, arrhythmias and cardiomyopathies, as well as sporadic and acquired disorders. In addition, a survey of the pediatric cardiologist’s armamentarium with regards to molecular and genetic analysis is presented highlighting the current use of molecular diagnostic methods including microarray, gene-mapping, proteomic, transgenic and stem cell technologies as well as future directions in both clinical application and research.
Understanding the Variance Collapse of SVGD in High Dimensions

Stein variational gradient descent (SVGD) is a deterministic inference algorithm that evolves a set of particles to fit a target distribution. Despite its computational efficiency, SVGD often underestimates the variance of the target distribution in high dimensions. In this work we attempt to explain the variance collapse in SVGD. On the qualitative side, we compare the SVGD update with gradient descent on the maximum mean discrepancy (MMD) objective; we find that the variance collapse phenomenon relates to the bias from deterministic updates present in the “driving force” of SVGD, and empirically verify that removal of such bias leads to more accurate variance estimation. On the quantitative side, we demonstrate that the variance collapse of SVGD can be accurately predicted in the proportional asymptotic limit, i.e., when the number of particles n and dimensions d diverge at the same rate. In particular, for learning high-dimensional isotropic Gaussians, we derive the exact equilibrium variance for both SVGD and MMD-descent, under certain empirically verified near-orthogonality condition on the converged particles, and confirm that SVGD suffers from the “curse of dimensionality”.
An Empirical Study of Requirements Elaboration

This paper describes an empirical study undertaken to investigate the quantitative aspects of the phenomenon of requirements elaboration which deals with the transformation of high-level goals into low-level requirements. Prior knowledge of the magnitude of requirements elaboration is instrumental in developing early estimates of a project’s cost and schedule. This study examines the data on capability goals and capability requirements of 20 real-client, MS-student, team projects done at USC. Metrics for data collection and analysis are described along with the utility of the results they produce. These results suggest some relationship between the nature of projects and the size of requirements elaboration.
A Zest of LIME: Towards Architecture-Independent Model Distances

Definitions of the distance between two machine learning models either characterize the similarity of the models’ predictions or of their weights. While similarity of weights is attractive because it implies similarity of predictions in the limit, it suffers from being inapplicable to comparing models with different architectures. On the other hand, the similarity of predictions is broadly applicable but depends heavily on the choice of model inputs during comparison. In this paper, we instead propose to compute distance between black-box models by comparing their Local Interpretable Model-Agnostic Explanations (LIME). To compare two models, we take a reference dataset, and locally approximate the models on each reference point with linear models trained by LIME. We then compute the Cosine distance between the concatenated weights of the linear models. This yields an approach that is both architecture-independent and possesses the benefits of comparing models in weight space. We empirically show that our method, which we call Zest, helps in several tasks that require measurements of model similarity: verifying machine unlearning, and detecting many forms of model reuse, such as model stealing, knowledge distillation, and transfer learning.1
Confidence of probabilistic predictions modulates the cortical response to pain

Pain typically evolves over time and the brain needs to learn this temporal evolution to predict how pain is likely to change in the future and orient behavior. This process is termed temporal statistical learning (TSL). Recently, it has been shown that TSL for pain sequences can be achieved using optimal Bayesian inference, which is encoded in somatosensory processing regions. Here, we investigate whether the confidence of these probabilistic predictions modulates the EEG response to noxious stimuli, using a TSL task. Confidence measures the uncertainty about the probabilistic prediction, irrespective of its actual outcome. Bayesian models dictate that the confidence about probabilistic predictions should be integrated with incoming inputs and weight learning, such that it modulates the early components of the EEG responses to noxious stimuli, and this should be captured by a negative correlation: when confidence is higher, the early neural responses are smaller as the brain relies more on expectations/predictions and less on sensory inputs (and vice versa). We show that participants were able to predict the sequence transition probabilities using Bayesian inference, with some forgetting. Then, we find that the confidence of these probabilistic predictions was negatively associated with the amplitude of the N2 and P2 components of the Vertex Potential: the more confident were participants about their predictions, the smaller was the Vertex Potential. These results confirm key predictions of a Bayesian learning model and clarify the functional significance of the early EEG responses to nociceptive stimuli, as being implicated in confidence-weighted statistical learning. SIGNIFICANCE The functional significance of EEG responses to pain has long been debated because of their dramatic variability. This study indicates that such variability can be partly related to the confidence of probabilistic predictions emerging from sequences of pain inputs. The confidence of pain predictions is negatively associated with the cortical EEG responses to pain. This indicates that the brain relies less on sensory inputs when confidence is higher and shows us that confidence-weighted statistical learning modulates the cortical response to pain.
A structural connectivity atlas of limbic brainstem nuclei

Background Understanding the structural connectivity of key brainstem nuclei with limbic cortical regions is essential to the development of therapeutic neuromodulation for depression, chronic pain, addiction, anxiety and movement disorders. Several brainstem nuclei have been identified as the primary central nervous system (CNS) source of important monoaminergic ascending fibers including the noradrenergic locus coeruleus, serotonergic dorsal raphe nucleus, and dopaminergic ventral tegmental area. However, due to practical challenges to their study, there is limited data regarding their in vivo anatomic connectivity in humans. Objective To evaluate the structural connectivity of the following brainstem nuclei with limbic cortical areas: locus coeruleus, ventral tegmental area, periaqueductal grey, dorsal raphe nucleus, and nucleus tractus solitarius. Additionally, to develop a group average atlas of these limbic brainstem structures to facilitate future analyses. Methods Each nucleus was manually masked from 197 Human Connectome Project (HCP) structural MRI images using FSL software. Probabilistic tractography was performed using FSL’s FMRIB Diffusion Toolbox. Connectivity with limbic cortical regions was calculated and compared between brainstem nuclei. Results were aggregated to produce a freely available MNI structural atlas of limbic brainstem structures. Results A general trend was observed for a high probability of connectivity to the amygdala, hippocampus and DLPFC with relatively lower connectivity to the orbitofrontal cortex, nucleus accumbens, hippocampus and insula. The locus coeruleus and nucleus tractus solitarius demonstrated significantly greater connectivity to the DLPFC than amygdala while the periaqueductal grey, dorsal raphe nucleus, and ventral tegmental area did not demonstrate a significant difference between these two structures. Conclusion Monoaminergic and other modulatory nuclei in the brainstem project widely to cortical limbic regions. We describe the structural connectivity across the several key brainstem nuclei theorized to influence emotion, reward, and cognitive functions. An increased understanding of the anatomic basis of the brainstem’s role in emotion and other reward-related processing will support targeted neuromodulatary therapies aimed at alleviating the symptoms of neuropsychiatric disorders. Highlights The brainstem plays a key role in the processing of emotional stimuli and is intricately linked with the limbic system. Anatomic data for these connections is limited in humans We describe the structural connectivity of five brain stem nuclei (locus coeruleus, ventral tegmental area, periaqueductal grey, dorsal raphe nucleus, and nucleus tractus solitarius) in relation to limbic circuits Our results present a comprehensive delineation of the brainstem-limbic structural connectivity of these nuclei and are compiled into a freely available tractographic atlas Applications include future targeting of these structures for neuropsychiatric conditions
Proteome-scale induced proximity screens reveal highly potent protein degraders and stabilizers

Targeted protein degradation and stabilization are promising therapeutic modalities due to their potency and versatility. However, only few E3 ligases and deubiquitinases have been harnessed for this purpose. Moreover, there may be other protein classes that could be exploited for protein stabilization or degradation. Here, we used a proteome-scale platform to identify hundreds of human proteins that can promote the degradation or stabilization of a target protein in a proximity-dependent manner. This allowed us to comprehensively compare the activities of human E3s and deubiquitinases, characterize non-canonical protein degraders and stabilizers, and establish that effectors have vastly different activities against diverse targets. Notably, the top degraders were more potent against multiple therapeutically relevant targets than the currently used E3s CBRN and VHL. Our study provides a functional catalogue of effectors for targeted protein degradation and stabilization and highlights the potential of induced proximity screens for discovery of novel proximity-dependent protein modulators.
Negative interactions and virulence differences drive the dynamics in multispecies bacterial infections

Bacterial infections are often polymicrobial, leading to intricate pathogen-pathogen and pathogen-host interactions. There is increasing interest in studying the molecular basis of pathogen interactions and how such mechanisms impact host morbidity. However, much less is known about the ecological dynamics between pathogens and how they affect virulence and host survival. Here we address these open issues by co-infecting larvae of the insect model host Galleria mellonella with one, two, three or four bacterial species, all of which are opportunistic human pathogens. We found that host mortality was always determined by the most virulent species regardless of the number of species and pathogen combinations injected. In certain combinations, the more virulent pathogen simply outgrew the less virulent pathogen. In other combinations, we found evidence for negative interactions between pathogens inside the host, whereby the more virulent pathogen typically won a competition. Taken together, our findings reveal positive associations between a pathogen’s growth inside the host, its competitiveness towards other pathogens, and its virulence. Beyond being generalizable across species combinations, our findings predict that treatments against polymicrobial infections should target the most virulent species to reduce host morbidity, a prediction we validated experimentally. Importance There is increasing evidence that polymicrobial infections are common and that host morbidity is impacted by interactions between the co-infecting pathogens. In this study, we infected the larvae of the insect host Galleria mellonella with four opportunistic human pathogens as mono or multispecies bacterial infections in all possible combinations and followed host survival and pathogen interactions over time. We discovered that species followed the exact same rank order in terms of their virulence, their growth inside the host, and their competitive ability against co-infecting pathogens. As consequence of this consistent rank order, we found that the most virulent species determines host survival dynamics in mixed infections. Beyond revealing a generalizable predictive pattern for virulence outcomes, our findings also predict that treatments should target the most virulent species in polymicrobial infections, a prediction we validated experimentally.
Brainstem modulation of nociception by periaqueductal gray neurons expressing the mu-opioid receptor

Pharmacological manipulations directed at the periaqueductal gray (PAG) have revealed the importance of the mu-opioid receptor in the endogenous pain-modulatory system. Despite the clear role for opioidergic signaling within the PAG for the descending modulation of pain, the molecular and anatomical characterizations of neurons containing the mu-opioid receptor remain elusive. Using combinatorial anatomical, optogenetic, and chemogenetic approaches, we delineate a supraspinal pathway centered on PAGMOR neurons in the modulation of pain and itch behaviors. We found that chemogenetic manipulations of PAGMOR neurons in assays of nociception unveiled complex results; whereas activation of these neurons generally facilitated responses to noxious stimuli and jumping behaviors on the hotplate assay, opposing patterns were observed with reflexive responses to sensory testing. Activation of PAGMOR neurons also robustly inhibited itch. These dichotomous findings across distinct types of sensory testing emphasize the contextual behavioral expression of nociception using reflexive and noxious paradigms. Lastly, we uncovered the role for PBN projections in the PAG that modulate pain in an uninjured, post-surgical state of latent sensitization.
Transcriptome and chromatin accessibility mapping reveals a type I Interferon response triggered by Mycobacterium tuberculosis infection

Tuberculosis, a deadly infectious lung disease caused by Mycobacterium tuberculosis (Mtb), remains the leading cause of bacterial disease-related deaths worldwide. The success of Mtb as a human pathogen depends on its ability to manipulate host immune response pathways, many of which are regulated by epigenetic mechanisms that control the accessibility of chromatin to the transcriptional machinery. Recent reports suggest that host phosphatases, such as PPM1A, may play a role in the regulation of chromatin accessibility during bacterial infections. However, changes in genome-wide chromatin accessibility during Mtb infection and whether PPM1A plays a role in this process remains unknown. Using combinatorial chromatin accessibility (ATAC-seq) and transcriptomics (RNA-seq) profiling of wild-type (WT), PPM1A knockout (ΔPPM1A) and PPM1A overexpressing (PPM1A+) macrophages, we demonstrate that Mtb infection induces global chromatin remodeling consistent with changes in gene expression signatures. The strongest concordant chromatin accessibility and gene expression signature triggered by Mtb infection was enriched for genes involved in the type I interferon (IFN) signaling pathways. Modulation of PPM1A expression results in altered chromatin accessibility signatures during Mtb infection that are reflected in the total number, chromosome location and directionality of change. Transcription factor motif analysis revealed an enrichment for transcription factors involved in the type I IFN pathway during Mtb infection, including IRF4, MEF2A, and JDP2. In contrast, both deletion and overexpression of PPM1A produced unique transcription factor enrichment signatures linked to the genomic regions with altered chromatin accessibility. Our study demonstrates that altered type I IFN responses in Mtb-infected macrophages occurs as a result of genome-wide changes in chromatin accessibility, and that PPM1A likely plays a role in a subset of these signatures.