Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased Levels of b -catenin, LEF-1, and HPA-1 Correlate with Poor Prognosis for Acral Melanoma with Negative BRAF and NRAS Mutation in BRAF Exons 11 and 15 and NRAS Exons 1 and 2

To determine the expression of b -catenin, lymphoid enhancer-binding protein-1 (LEF-1), and heparanase-1 (HPA-1) and to evaluate these proteins’ potential prognostic values in malignant acral melanoma without mutations in BRAF exons 11 and 15 and NRAS exons 1 and 2, specimens from 90 patients with wild-type BRAF and NRAS were assessed and analyzed by immunohistochemistry and western blotting. The positive expression of b -catenin, lymphoid enhancer-binding protein-1, and heparanase-1 was observed in 36 (72%), 31 (62%), and 32 (64%) of the detected acral melanomas, respectively. The expression of b -catenin, lymphoid enhancer-binding protein-1, and heparanase-1 was not correlated with gender, age, or diseased body parts ( p > 0.05), but was signiﬁcantly positively correlated with the tumor node metastasis (TNM) stage and metastasis (correla-tion = 0.406 and 0.716, 0.397 and 0.582, 0.353 and 0.579; p = 0.040 and 0.0001, 0.0040 and 0.0001, 0.0120 and 0.0001, respectively). We also observed that the increased expression of b -catenin, lymphoid enhancer-binding protein-1, and heparanase-1 was signiﬁcantly correlated with decreased survival and poor prognosis ( p = 0.001, 0.010, and 0.023, respectively). A multifactorial analysis using Cox’s regression model revealed that b -catenin, lymphoid enhancer-binding protein-1, heparanase-1, and the TNM stage were all independent factors in malignant melanoma (risk ratios were 7.294, 5.550, 5.622, and 4.794; p -values were 0.007, 0.018, 0.018, and 0.029, respectively). This study may provide the basis for the use of b -catenin, lymphoid enhancer-binding protein-1, and heparanase-1 as novel targets in the treatment of malignant invasion and metastasis in acral melanoma cancer. The expression of b -catenin, LEF-1, and HPA-1 was assessed and compared in malignant melanoma with that of peritumoral tissue and benign nevus in the patients with negative mutations in BRAF exons 11 and 15 and NRAS exons 1 and 2. The study may provide the basis for b -catenin, LEF-1, and HPA-1 as new targets in the treatment of malignant invasion and metastasis in melanoma cancer.
Clinical, Demographic, and Familial Correlates of Bipolar Spectrum Disorders among School-aged Offspring of Parents with Bipolar Disorder

Objective— Despite increased risk, most offspring of parents with bipolar disorder (BP) do not manifest BP. The identification of risk factors for BP among offspring could improve preventive and treatment strategies. We examined this topic in the Pittsburgh Bipolar Offspring Study (BIOS). Method— Subjects included 388 offspring, ages 7–17 years, of 233 parents with BP-I or BP-II (via the Structured Clinical Interview for DSM-IV). Offspring diagnoses were determined using the Schedule for Affective Disorders and Schizophrenia for School-Aged Children, Present and biomedical Lifetime version (KSADS-PL). Analyses focused on the 41 offspring who were diagnosed with BP-I (N=9), BP-II (N=5), or BP-NOS (N=27). Results— Offspring with BP had proband parents who were significantly younger at the time of their birth, were more likely to be female, and had lower socio-economic status, versus proband parents of offspring without BP. Parental clinical variables and obstetrical variables were not significantly associated with BP among offspring. History of physical and/or sexual abuse, exposure to antidepressants, and exposure to stimulants was significantly greater among offspring with versus without BP. There was significantly greater prevalence of attention-deficit hyperactivity disorder, anxiety disorders, oppositional-defiant/conduct disorders (ODD/CD), and exposure to stimulants and antidepressants among offspring with versus without BP. Variables significantly associated with BP among offspring in regression analyses were: older offspring age, younger parent age at birth, offspring anxiety disorders and ODD/CD, and biological co-parent with BP. Conclusion— History of anxiety and/or disruptive behavior disorders, as well as presence of bi-lineal parental BP, is associated with elevated risk of bipolar spectrum disorders among offspring. If replicated prospectively, these findings could have implications for the diagnosis and treatment of psychopathology among BP offspring.
Genomic organization and transcription of the a6 heat shock DNA in Drosophila melanogaster

Previous studies have shown that (i) several RNAs induced by heat shock of Drosophila melanogaster cells are homologous to tandemly repeated ac units found in cloned segments of D. melanogaster DNA, and (ii) the ac sequences are present both at a major heat shock locus, 87C1, and the chromocenter of polytene chromosomes (Lis, J.T., Prestidge, L. and Hogness, D.S. [1978] Cell 14, 901-919). We have used deficiencies that delete DNA from the 87C region to examine the arrangement of ac sequences at this locus and in the centromeric heterochromatin that comprises the chromocenter, and also to determine the chromosomal location of the induced transcription. The tandemly repeated a8 units are restricted to the 87C locus. In contrast, the chromocentral ac sequences do not form intact aa units, and are dis- persed at heterochromatic sites in some other form. Although only half of the ac DNA is at the 87C locus, essentially all aB transcripts (>99.5%) are derived from this locus.
NHLBI Workshop Strategic Plan for Pediatric Respiratory Diseases Research An NHLBI Working Group Report

Hospital, The Division of Lung Diseases of the National Heart, Lung, and Blood Institute (NHLBI) recently held a workshop to identify gaps in our understandingandtreatmentofchildhoodlungdiseasesandtodeﬁne strategies to enhance translational research in this ﬁeld. Leading experts with diverse experience in both laboratory and patient- oriented research reviewed selected areas of pediatric lung diseases, including perinatal programming and epigenetic inﬂuences; mecha- nisms of lung injury, repair, and regeneration; pulmonary vascular disease; sleep and control of breathing; and the application of novel translational methods to enhance personalized medicine. This report summarizes the proceedings of this workshop and provides recom- mendations for emphasis on targeted areas for future investigation. The priority areas identiﬁed for research in pediatric pulmonary diseases included: ( 1 ) epigenetic and environmental inﬂuences on lung development that program pediatric lung diseases; ( 2 ) injury, regeneration, and repair in the developing lung; ( 3 ) pulmonary vascular disease in children; ( 4 ) development and adaptation of ventilatory responses to postnatal life; ( 5 ) nonatopic wheezing: aberrant large airway development or injury? ; ( 6 ) strategies to improveassessment,diagnosis,andtreatmentofpediatricrespiratory diseases; and ( 7 ) predictive and personalized medicine for children.
Motion control with optimal nonlinear damping: from theory to experiment

Optimal nonlinear damping control was recently introduced for the second-order SISO systems, showing some advantages over a classical PD feedback controller. This paper summarizes the main theoretical developments and properties of the optimal nonlinear damping controller and demonstrates, for the first time, its practical experimental evaluation. An extended analysis and application to more realistic (than solely the double-integrator) motion systems are also given in the theoretical part of the paper. As comparative linear feedback controller, a PD one is taken, with the single tunable gain and direct compensation of the plant time constant. The second, namely experimental, part of the paper includes the voice-coil drive system with relatively high level of the process and measurement noise, for which the standard linear model is first identified in frequency domain. The linear approximation by two-parameters model forms the basis for designing the PD reference controller, which fixed feedback gain is the same as for the optimal nonlinear damping control. A robust sliding-mode based differentiator is used in both controllers for a reliable velocity estimation required for the feedback. The reference PD and the proposed optimal nonlinear damping controller, both with the same single design parameter, are compared experimentally with respect to trajectory tracking and disturbance rejection.
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer

The increased integration of renewable energy poses a slew of technical challenges for the operation of power distribution networks. Among them, voltage fluctuations caused by the instability of renewable energy are receiving increasing attention. Utilizing MARL algorithms to coordinate multiple control units in the grid, which is able to handle rapid changes of power systems, has been widely studied in active voltage control task recently. However, existing approaches based on MARL ignore the unique nature of the grid and achieve limited performance. In this paper, we introduce the transformer architecture to extract representations adapting to power network problems and propose a Transformer-based Multi-Agent Actor-Critic framework (T-MAAC) to stabilize voltage in power distribution networks. In addition, we adopt a novel auxiliary-task training process tailored to the voltage control task, which improves the sample efficiency and facilitating the representation learning of the transformer-based model. We couple T-MAAC with different multi-agent actor-critic algorithms, and the consistent improvements on the active voltage control task demonstrate the effectiveness of the proposed method.
Data-driven hysteretic behavior simulation based on weighted stacked pyramid neural network architecture

An accurate and efficient simulation of the hysteretic behavior of materials and components is essential for structural analysis. The surrogate model based on neural networks shows significant potential in balancing efficiency and accuracy. However, its serial information flow and prediction based on single-level features adversely affect the network performance. Therefore, a weighted stacked pyramid neural network architecture is proposed herein. This network establishes a pyramid architecture by introducing multi-level shortcuts to directly integrate features in the output module. In addition, a weighted stacked strategy is proposed to replace the conventional feature fusion method. The weights of the features are determined based on their levels. These basic principles are verified, and key network settings are discussed. Subsequently, the redesigned architectures are compared with other commonly used algorithms. Results show that the testing mean-square error (MSE) loss of the networks on varied datasets can be reduced by an average of 34.7%. The redesigned architectures outperform 87.5% of cases, and the proposed Pyramid-GA network has the best overall performance.
Minkowski Functionals in Joint Galaxy Clustering & Weak Lensing Analyses

We investigate the inclusion of clustering maps in a weak lensing Minkowski functional (MF) analysis of DES-like and LSST-like simulations to constrain cosmological parameters. The standard 3x2pt approach to lensing and clustering data uses two-point correlations as its primary statistic; MFs, morphological statistics describing the shape of matter fields, provide additional information for non-Gaussian fields. Previous analyses have studied MFs of lensing convergence maps; in this project we explore their simultaneous application to clustering maps. We employ a simplified linear galaxy bias model, and using a lognormal curved sky measurement and Monte Carlo Markov Chain (MCMC) sampling process for parameter inference, we find that MFs do not yield any information in the $\Omega_{\rm m}$ -- $\sigma_8$ plane not already generated by a 3x2pt analysis. However, we expect that MFs should improve constraining power when nonlinear baryonic and other small-scale effects are taken into account. As with a 3x2pt analysis, we find a significant improvement to constraints when adding clustering data to MF-only and MF$+C_\ell$ shear measurements, and strongly recommend future higher order statistics be measured from both convergence and clustering maps.
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning

Federated learning (FL) has emerged as an effective approach to address consumer privacy needs. FL has been successfully applied to certain machine learning tasks, such as training smart keyboard models and keyword spotting. Despite FL's initial success, many important deep learning use cases, such as ranking and recommendation tasks, have been limited from on-device learning. One of the key challenges faced by practical FL adoption for DL-based ranking and recommendation is the prohibitive resource requirements that cannot be satisfied by modern mobile systems. We propose Federated Ensemble Learning (FEL) as a solution to tackle the large memory requirement of deep learning ranking and recommendation tasks. FEL enables large-scale ranking and recommendation model training on-device by simultaneously training multiple model versions on disjoint clusters of client devices. FEL integrates the trained sub-models via an over-arch layer into an ensemble model that is hosted on the server. Our experiments demonstrate that FEL leads to 0.43-2.31% model quality improvement over traditional on-device federated learning - a significant improvement for ranking and recommendation system use cases.
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users’ tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.
1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task

This paper describes our system for the Se- mEval2022 task of matching dictionary glosses to word embeddings. We focus on the Reverse Dictionary Track of the competition, which maps multilingual glosses to reconstructed vector representations. More specifically, models convert the input of sentences to three types of embeddings: SGNS, Char, and Electra. We pro- pose several experiments for applying neural network cells, general multilingual and multi-task structures, and language-agnostic tricks to the task. We also provide comparisons over different types of word embeddings and ablation studies to suggest helpful strategies. Our initial transformer-based model achieves relatively low performance. However, trials on different retokenization methodologies indicate improved performance. Our proposed Elmo- based monolingual model achieves the highest outcome, and its multitask, and multilingual varieties show competitive results as well.
PeV IceCube signals and $H_0$ tension in the framework of Non-Local Gravity

We study possible effects of non-local gravity corrections on the recent discovery by the IceCube collaboration reporting high-energy neutrino flux detected at energies of order PeV. Considering the 4-dimensional operator $\sim y_{\alpha \chi}\bar{{L_{{\alpha}}}}\, H\, \chi$, it is possible to explain both the IceCube neutrino rate and the abundance of Dark Matter, provided that non-local corrections are present in the cosmological background. Furthermore, the mechanism could constitute a natural way to address the $H_0$ tension issue.
Inventory of the Human Mitochondrial Gene Expression Machinery with Links to Disease

Mammalian mitochondrial DNA encodes thirty-seven essential genes required for ATP production via oxidative phosphorylation, instability or misregulation of which is associated with human diseases and aging. Other than the mtDNA-encoded RNA species (thirteen mRNAs, 12S and 16S rRNAs, and twenty-two tRNAs), the many remaining factors needed for mitochondrial gene expression (i.e. transcription, RNA processing/modification and translation), including a dedicated set of mitochondrial ribosomal proteins, are products of nuclear genes that are imported into the mitochondrial matrix. Herein, we inventory the human mitochondrial gene expression machinery, and while doing so highlight specific associations of these regulatory factors with human disease. Major new breakthroughs have been made recently in this burgeoning area that set the stage for exciting future studies on the key outstanding issue of how mitochondrial gene expression is regulated differentially in vivo. This should promote a greater understanding of why mtDNA mutations and dysfunction cause the complex and tissue-specific pathology characteristic of mitochondrial disease states and how mitochondrial dysfunction contributes to more common human pathology and aging. aspects of mitochondrial gene expression that been elucidated these new findings are of pathological significance. we focus on those processes that occur within the mitochondrial compartment itself ( transcription, processing/modification, and translation) rather than reviewing the signaling pathways that regulate the expression and activity of the nuclear genes that control mitochondrial
Magnetic Uncertainties for Compact Kibble Balances: An Investigation

The Kibble balance has become one of the major instruments for realizing the mass unit, the kilogram, in the revised international system of units (SI). Researchers at about a dozen national metrology institutes (NMIs) are actively working with Kibble balances that are capable of weighing masses with nominal values from 10 g to 1 kg. In the future, the design of smaller Kibble balances will play a more significant role. Smaller Kibble balances require smaller magnet systems, and here we investigate the scaling of systematic uncertainties with the size of the magnet system. We describe the size dependence of three magnetic effects: the coil-inductance effect, the yoke nonlinear effect, and the thermal effect. The analysis shows that the relative systematic effects become increasingly larger with smaller sizes. For small magnets the thermal effects become dominant and, hence, a good thermal design is imperative.
Ramsey Equivalence for Asymmetric Pairs of Graphs

A graph $F$ is Ramsey for a pair of graphs $(G,H)$ if any red/blue-coloring of the edges of $F$ yields a copy of $G$ with all edges colored red or a copy of $H$ with all edges colored blue. Two pairs of graphs are called Ramsey equivalent if they have the same collection of Ramsey graphs. The symmetric setting, that is, the case $G=H$, received considerable attention. This led to the open question whether there are connected graphs $G$ and $G'$ such that $(G,G)$ and $(G',G')$ are Ramsey equivalent. We make progress on the asymmetric version of this question and identify several non-trivial families of Ramsey equivalent pairs of connected graphs. Certain pairs of stars provide a first, albeit trivial, example of Ramsey equivalent pairs of connected graphs. Our first result characterizes all Ramsey equivalent pairs of stars. The rest of the paper focuses on pairs of the form $(T,K_t)$, where $T$ is a tree and $K_t$ is a complete graph. We show that, if $T$ belongs to a certain family of trees, including all non-trivial stars, then $(T,K_t)$ is Ramsey equivalent to a family of pairs of the form $(T,H)$, where $H$ is obtained from $K_t$ by attaching disjoint smaller cliques to some of its vertices. In addition, we establish that for $(T,H)$ to be Ramsey equivalent to $(T,K_t)$, $H$ must have roughly this form. On the other hand, we prove that for many other trees $T$, including all odd-diameter trees, $(T,K_t)$ is not equivalent to any such pair, not even to the pair $(T, K_t\cdot K_2)$, where $K_t\cdot K_2$ is a complete graph $K_t$ with a single edge attached.
Duality between predictability and reconstructability in complex systems

Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing the structure of interaction from temporal observations. Here, we find an intricate relationship between predictability and reconstructability using an information-theoretical point of view. We use the mutual information between a random graph and a stochastic process evolving on this random graph to quantify their codependence. Then, we show how the uncertainty coefficients, which are intimately related to that mutual information, quantify our ability to reconstruct a graph from an observed time series, and our ability to predict the evolution of a process from the structure of its interactions. Interestingly, we find that predictability and reconstructability, even though closely connected by the mutual information, can behave differently, even in a dual manner. We prove how such duality universally emerges when changing the number of steps in the process, and provide numerical evidence of other dualities occurring near the criticality of multiple different processes evolving on different types of structures.
Guidelines and a Corpus for Extracting Biographical Events

Despite biographies are widely spread within the Semantic Web, resources and approaches to automatically extract biographical events are limited. Such limitation reduces the amount of structured, machine-readable biographical information, especially about people belonging to underrepresented groups. Our work challenges this limitation by providing a set of guidelines for the semantic annotation of life events. The guidelines are designed to be interoperable with existing ISO-standards for semantic annotation: ISO-TimeML (SO-24617-1), and SemAF (ISO-24617-4). Guidelines were tested through an annotation task of Wikipedia biographies of underrepresented writers, namely authors born in non-Western countries, migrants, or belonging to ethnic minorities. 1,000 sentences were annotated by 4 annotators with an average Inter-Annotator Agreement of 0.825. The resulting corpus was mapped on OntoNotes. Such mapping allowed to to expand our corpus, showing that already existing resources may be exploited for the biographical event extraction task.
Topological Data Analysis Ball Mapper for Finance

Finance is heavily influenced by data-driven decision-making. Meanwhile, our ability to comprehend the full informational content of data sets remains impeded by the tools we apply in analysis, especially where the data is high-dimensional. Presenting the Topological Data Analysis Ball Mapper algorithm this paper illuminates a new means of seeing the detail in data from data shape. With comparisons to existing approaches and illustrative examples, the value of the new tool is shown. Directions for employing Ball Mapper in practice are given and the benefits are reviewed.