Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotation of Low-mass Stars in Taurus with K2

We present an analysis of K2 light curves (LCs) from Campaigns 4 and 13 for members of the young (∼3 Myr) Taurus association, in addition to an older (∼30 Myr) population of stars that is largely in the foreground of the Taurus molecular clouds. Out of 156 of the highest-confidence Taurus members, we find that 81% are periodic. Our sample of young foreground stars is biased and incomplete, but nearly all stars (37/38) are periodic. The overall distribution of rotation rates as a function of color (a proxy for mass) is similar to that found in other clusters: the slowest rotators are among the early M spectral types, with faster rotation toward both earlier FGK and later M types. The relationship between period and color/mass exhibited by older clusters such as the Pleiades is already in place by Taurus age. The foreground population has very few stars but is consistent with the USco and Pleiades period distributions. As found in other young clusters, stars with disks rotate on average slower, and few with disks are found rotating faster than ∼2 days. The overall amplitude of the LCs decreases with age, and higher-mass stars have generally lower amplitudes than lower-mass stars. Stars with disks have on average larger amplitudes than stars without disks, though the physical mechanisms driving the variability and the resulting LC morphologies are also different between these two classes.
Risk assessment and biosafety recommendations in Belgium

Transmissible spongiform encephalopathy (TSE) is a family of neurodegenerative diseases found in several animal species including human (Table 1). Among these, the bovine spongiform encephalopathy (BSE) came first to the attention of the agricultural and scientific community in 1986 with the appearance of a new form of neurological disease in cattle in the United Kingdom. The origin of the BSE disease was a mystery but it propagated rapidly in the UK and later in different countries around the world. The rapid spread was linked to feeding to cattle, sheep, and goats meat and bone meat (MBM) contaminated with high risk tissues from BSE affected cattle. Ten years after the beginning of BSE crisis, commonly named “mad cow disease crisis,” a new form or variant of Creutzfeldt-Jacob disease (vCJD) emerged in human which was causally linked to BSE. In January 2012, a total of 176 cases of vCJD were reported in the UK and although to a lesser extent cases were described in other countries. The mad cow disease crisis represented an important issue for public health as well as for trade involving movements of animals. Safety measures and rules to control and prevent the transmission of the disease by feed and food were adopted first in the UK and subsequently in the European Union. These measures included 3 major action lines: the removal of Specified Risk Material (SRM), the ban on feeding of MBM and processed animal protein, and the establishment of monitoring programs. A European framework of surveillance of TSE in cattle, sheep, and goat was established which allowed to determine the risk status of EU member states on a regular time frame and to follow the evolution of TSE prevalence over the years. In parallel, a European Creutzfeldt-Jacob Disease Surveillance Network (EuroCJD and later on NeuroCJD) which encompasses centers in European and non-European countries (Australia, Japan, Canada) allows data comparison for all CJDs since 1993 with the objective to further develop the surveillance of vCJD and to identify novel forms of
Estrógeno e pré-condicionamento : efeitos neuroprotetores e possíveis mecanismos de ação em modelos in vitro e in vivo de isquemia cerebral

IV APRESENTAÇÃO . V LISTA DE FIGURAS . VI LISTA DE TABELAS. VIII LISTA DE ABREVIATURAS. IX INTRODUÇÃO . 1 1. ISQUEMIA CEREBRAL E MORTE CELULAR.2 1.1. EVENTOS QUE CAUSAM A MORTE CELULAR NA ISQUEMIA.3 1.2. MODELOS DE LESÃO ISQUÊMICA. 5 1.2.1. MODELOS EXPERIMENTAIS IN VIVO . 5 1.2.1.1. Isquemia Global. 5 1.2.1.2. Isquemia Focal . 5 1.2.1.3. Hipóxia-Isquemia. 5 1.2.2. MODELOS EXPERIMENTAIS IN VITRO . 6 1.2.2.1. Culturas Organotípicas. 6 1.2.2. 2. Fatias Cerebrais . 7 2. ESTRÓGENOS COMO NEUROPROTETORES.8 2.1. MECANISMOS DE NEUROPROTEÇÃO PELOS ESTRÓGENOS . 9 2.1.1. RECEPTORES DE ESTRÓGENOS . 10 2.1.2. SINALIZAÇÃO ESTROGÊNICA NA NEUROPROTEÇÃO . 11 2.1.3. EFEITOS ANTIOXIDANTES DOS ESTRÓGENOS. 12 3. PRÉ-CONDICIONAMENTO HIPÓXICO-ISQUÊMICO . 13 4. TRANSPORTADORES DE GLUTAMATO . 14 OBJETIVOS . 17 CAPÍTULO 1 . 19 CAPÍTULO 2 . 29 CAPÍTULO 3 . 52
Reciprocity-gap misfit functional for Distributed Acoustic Sensing, combining teleseismic and exploration data

We use data from Distributed Acoustic Sensing (DAS) to perform quantitative imaging of sub-surface Earth's properties in elastic media. We design a new misfit functional based upon the reciprocity-gap, that takes products of displacement and strain (measured by DAS), mixing an observation with a simulation. Our misfit functional requires minimal information regarding the acquisition setup of the measurements, and enable the combination of exploration data with those from teleseismic events for a multi-resolution reconstruction, without extending the computational domain. The teleseismic data contain the low-frequency information which is missing in the exploration ones, allowing for the recovery of the long spatial wavelength, and are used to build initial models. Consequently, we define the elastic Full Reciprocity-gap Waveform Inversion (FRgWI) method, and demonstrate its performance with an elastic isotropic experiment.
Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences

Attention is a commonly used mechanism in sequence processing, but it is of O(n^2) complexity which prevents its application to long sequences. The recently introduced neural Shuffle-Exchange network offers a computation-efficient alternative, enabling the modelling of long-range dependencies in O(n log n) time. The model, however, is quite complex, involving a sophisticated gating mechanism derived from the Gated Recurrent Unit. In this paper, we present a simple and lightweight variant of the Shuffle-Exchange network, which is based on a residual network employing GELU and Layer Normalization. The proposed architecture not only scales to longer sequences but also converges faster and provides better accuracy. It surpasses the Shuffle-Exchange network on the LAMBADA language modelling task and achieves state-of-the-art performance on the MusicNet dataset for music transcription while being efficient in the number of parameters. We show how to combine the improved Shuffle-Exchange network with convolutional layers, establishing it as a useful building block in long sequence processing applications.
On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems

In this work we present a formal theoretical framework for assessing and analyzing two classes of malevolent action towards generic Artificial Intelligence (AI) systems. Our results apply to general multi-class classifiers that map from an input space into a decision space, including artificial neural networks used in deep learning applications. Two classes of attacks are considered. The first class involves adversarial examples and concerns the introduction of small perturbations of the input data that cause misclassification. The second class, introduced here for the first time and named stealth attacks, involves small perturbations to the AI system itself. Here the perturbed system produces whatever output is desired by the attacker on a specific small data set, perhaps even a single input, but performs as normal on a validation set (which is unknown to the attacker). We show that in both cases, i.e., in the case of an attack based on adversarial examples and in the case of a stealth attack, the dimensionality of the AI’s decision-making space is a major contributor to the AI’s susceptibility. For attacks based on adversarial examples, a second crucial parameter is the absence of local concentrations in the data probability distribution, a property known as Smeared Absolute Continuity. According to our findings, robustness to adversarial examples requires either (a) the data distributions in the AI’s feature space to have concentrated probability density functions or (b) the dimensionality of the AI’s decision variables to be sufficiently small. We also show how to construct stealth attacks on high-dimensional AI systems that are hard to spot unless the validation set is made exponentially large.
Graph Highway Networks

Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency. However, they suffer from the notorious over-smoothing problem, in which the learned representations of densely connected nodes converge to alike vectors when many (>3) graph convolutional layers are stacked. In this paper, we argue that there-normalization trick used in GCN leads to overly homogeneous information propagation, which is the source of over-smoothing. To address this problem, we propose Graph Highway Networks(GHNet) which utilize gating units to automatically balance the trade-off between homogeneity and heterogeneity in the GCN learning process. The gating units serve as direct highways to maintain heterogeneous information from the node itself after feature propagation. This design enables GHNet to achieve much larger receptive fields per node without over-smoothing and thus access to more of the graph connectivity information. Experimental results on benchmark datasets demonstrate the superior performance of GHNet over GCN and related models.
Deep Learning and Open Set Malware Classification: A Survey

As the Internet is growing rapidly these years, the variant of malicious software, which often referred to as malware, has become one of the major and serious threats to Internet users. The dramatic increase of malware has led to a research area of not only using cutting edge machine learning techniques classify malware into their known families, moreover, recognize the unknown ones, which can be related to Open Set Recognition (OSR) problem in machine learning. Recent machine learning works have shed light on Open Set Recognition (OSR) from different scenarios. Under the situation of missing unknown training samples, the OSR system should not only correctly classify the known classes, but also recognize the unknown class. This survey provides an overview of different deep learning techniques, a discussion of OSR and graph representation solutions and an introduction of malware classification systems.
DPVis: Visual Analytics With Hidden Markov Models for Disease Progression Pathways

Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this article, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.
Continuous and Discrete-Time Analysis of Stochastic Gradient Descent for Convex and Non-Convex Functions.

This paper proposes a thorough theoretical analysis of Stochastic Gradient Descent (SGD) with decreasing step sizes. First, we show that the recursion defining SGD can be provably approximated by solutions of a time inhomogeneous Stochastic Differential Equation (SDE) in a weak and strong sense. Then, motivated by recent analyses of deterministic and stochastic optimization methods by their continuous counterpart, we study the long-time convergence of the continuous processes at hand and establish non-asymptotic bounds. To that purpose, we develop new comparison techniques which we think are of independent interest. This continuous analysis allows us to develop an intuition on the convergence of SGD and, adapting the technique to the discrete setting, we show that the same results hold to the corresponding sequences. In our analysis, we notably obtain non-asymptotic bounds in the convex setting for SGD under weaker assumptions than the ones considered in previous works. Finally, we also establish finite time convergence results under various conditions, including relaxations of the famous Łojasiewicz inequality, which can be applied to a class of non-convex functions.
Morphological adaptations associated with mate-guarding behaviour in the genus Hericia (Acari: Algophagidae).

Males of an undescribed astigmatic species of the algophagid genus Hericia demonstrate precopulatory contact mate-guarding behaviour. Adults are sexually dimorphic, and most of the morphological differences found in males are associated with guarding behaviour. In this regard, coxae IV of the male are located ventrally and mesiad of coxae III, thereby resulting in legs IV taking a position directly below legs III. Legs IV are also bowed ventrally and can be rotated forward to form a ‘basket’ under the idiosoma. Males use their baskets for ‘scooping’ up quiescent tritonymphal females and holding them securely under their idiosomas. Males thereby guard immature females until they ecdyse to adults, at which time mating takes place. Some males were observed guarding adult females for extended periods of time, presumably after copulation. It is therefore possible that this species also exhibits post-insemination mate-guarding behaviour, however details concerning this behavior are poorly known.
Biofilm on Ventriculo-Peritoneal Shunt Tubing as a Cause of Treatment Failure in Coccidioidal Meningitis

We describe a case of recurrent coccidioidal meningitis in which a fungal biofilm on the tip of ventriculo-peritoneal shunt tubing was likely responsible for a 4-year persistence of Coccidioides immitis, despite the patient’s taking an adequate dosage of fluconazole. Fungal biofilms should be considered as a cause for treatment failure and fungal persistence, especially when artificial prostheses or indwelling catheters are present.
Interpersonal brain synchronization with instructor compensates for learner’s sleep deprivation in interactive learning

Recent advances shifted the focus on single-brain functioning toward two-brain communication during learning interactions, following the demonstration that interpersonal brain synchronization (IBS) can track instructor-learner information exchange. Here, we investigated (i) whether sleep deprivation (SD) that potentially impacts both social interactions and learning abilities modulates IBS, and (ii) conversely whether and to what extent IBS might compensate for SD-related learning deficits. Instructors (always with regular sleep, RS) were asked to teach numerical reasoning strategies to learners (either SD or RS), during which the activity of both brains was simultaneously recorded using functional near-infrared spectroscopy (fNIRS). SD learners initially performed below their baseline level, worse than RS learners, but learning improvement was comparable between RS and SD conditions after learning with the instructor. IBS within the instructor-learner dyads was higher in the SD (vs. RS) condition in the left inferior frontal cortex. In addition, clustered IBS (estimated by nonnegative matrix factorization) was correlated with performance improvement. Finally, Granger Causality analyses revealed biased causality with higher instructor-to-learner than learner-to-instructor directionality in brain signal processing. Together, these results indicate that SD-related learning deficits can to some extent be compensated via interactions with an instructor, as reflected by increased IBS and preserved learning ability. It suggests an essential role of the instructor in driving synchrony between teaching and SD learning brains during interactions.
Stroma transcriptomic and proteomic profile of prostate cancer metastasis xenograft models reveals conservation of bone microenvironment signatures

Prostate cancer (PCa) is the second leading cause of cancer-associated death in men with therapy resistance acquisition to androgen deprivation treatment and metastasis progression. Understanding the mechanisms of tumor progression to metastatic stage is necessary for the design of therapeutic and prognostic schemes. The main objective of the current study is to determine, using transcriptomic and proteomic analyses on patient derived-xenograft models, whether differentially aggressive PCa tumors predispose their microenvironment (stroma) to a metastatic gene expression pattern, and how this information could be applied in prognostics. Transcriptomic profiling (RNA Sequencing) was performed on PCa PDX models representing different disease stages; BM18 (androgen dependent bone metastasis) and LAPC9 (androgen independent bone metastasis). Using organism-specific reference databases, the human-specific transcriptome, representing the tumor, was identified and separated from the mouse-specific transcriptome (representing the contributing stroma counterpart) from the same PDX tumor samples. To identify proteome changes in the tumor (human) versus the stroma (mouse), we performed human and mouse cell separation using the MACS mouse depletion sorting kit, and subjected protein lysates to quantitative TMT labeling and mass spectrometry. We show that tenascin C is one of the most abundant stromal genes in bone metastasis PCa PDXs, is modulated by androgen levels in vivo and is highly expressed in castration resistant LAPC9 PDX compared to castration sensitive BM18 PDX. Tissue microarray of primary PCa samples (N=210) was used to evaluate the potential of TNC to act as a metastasis prognosis marker. Low number of TNC-positive cells were associated with statistically significant clinical progression to local recurrence or metastasis, compared to high TNC-positive group. Our data showed that metastatic PCa PDXs that differ in androgen sensitivity trigger a differential stroma response suggesting that stroma was influenced by tumor cues. Selected stromal markers of osteoblastic PCa induced bone metastases, were induced in the microenvironment of the host organism in metastatic xenografts, although implanted in a non-bone site, indicating a conserved mechanism of tumor cells to induce a stromal pre-metastatic signature with high potential prognostic or diagnostic value.
Advances in genetic and molecular understanding of Omenn syndrome - implications for the future

ABSTRACT Introduction: Omenn syndrome is a rare form of leaky severe combined immunodeficiency, characterized by a widespread thickened exfoliating, protein-losing erythrodermic rash, with scaling with severe alopecia associated with widespread lymphadenopathy and hepatosplenomegaly. Unravelling the pathophysiology has revealed new understandings of the immune system. Areas covered: The description of the clinical phenotype was followed by case descriptions of the immunopathology, description of the genetic defects and realization that Omenn syndrome was a form of severe combined immunodeficiency, with features of severe autoimmunity due to a loss of central and peripheral tolerance. Differences in recombinase activity lead to variable immunological and clinical phenotypes. Expert opinion: As Omenn syndrome is a form of severe combined immunodeficiency, universal introduction of newborn screening will have a significant impact on outcome, as earlier diagnosis will favor better transplant results. Natural killer cells display an immature phenotype, contain a higher content of perforin and demonstrate enhanced degranulation which may mediate allograft rejection, and so addition of serotherapy directed against Natural Killer cells to conditioning protocols may improve outcome. Finally, murine models utilizing genetic correction of autologous stem cells have demonstrated efficacy in curing disease and clinical trials are now in progress.
Multi-dimensional tuning in motor cortical neurons during active behavior

A region within songbird cortex, AId (dorsal intermediate arcopallium), is functionally analogous to motor cortex in mammals and has been implicated in vocal learning during development. AId thus serves as a powerful model for investigating motor cortical contributions to developmental skill learning. We made extracellular recordings in AId of freely behaving juvenile zebra finches and evaluated neural activity during diverse motor behaviors throughout entire recording sessions, including song production as well as hopping, pecking, preening, fluff-ups, beak interactions with cage objects, scratching, and stretching. A large population of single neurons showed significant modulation of activity during singing relative to quiescence. In addition, AId neurons demonstrated heterogeneous response patterns that were evoked during multiple movements, with single neurons often demonstrating excitation during one movement type and suppression during another. Lesions of AId do not disrupt vocal motor output or impair generic movements, suggesting that the responses observed during active behavior do not reflect direct motor drive. Consistent with this idea, we found that some AId neurons showed differential activity during pecking movements depending on the context in which pecks occurred, suggesting that AId circuitry encodes diverse inputs beyond generic motor parameters. Moreover, we found evidence of neurons that did not respond during discrete movements but were nonetheless modulated during active behavioral states compared to quiescence. Taken together, our results support the idea that AId neurons are involved in sensorimotor integration of external sensory inputs and/or internal feedback cues to help modulate goal-directed behaviors. SIGNIFICANCE STATEMENT Motor cortex across taxa receives highly integrated, multi-modal information and has been implicated in both execution and acquisition of complex motor skills, yet studies of motor cortex typically employ restricted behavioral paradigms that target select movement parameters, preventing wider assessment of the diverse sensorimotor factors that can affect motor cortical activity. Recording in AId of freely behaving juvenile songbirds that are actively engaged in sensorimotor learning offers unique advantages for elucidating the functional role of motor cortical neurons. The results demonstrate that a diverse array of factors modulate motor cortical activity and lay important groundwork for future investigations of how multi-modal information is integrated in motor cortical regions to contribute to learning and execution of complex motor skills.