Miracle-dz/UniFAR-FTUs huggingface dataset:
Low Self-control and Legal Cynicism among At-Risk Youth: An Investigation into Direct and Vicarious Police Contact

Objectives: This study explores the nexus between low self-control and legal cynicism among a recent sample of at-risk youth while accounting for various features of direct and vicarious police stops. Methods: Analyses are based on data from the Fragile Families and Child Wellbeing Study, which employs a national sample of urban-born, at-risk youth. Results: A uniquely potent association between low self-control and legal cynicism emerged across samples with and without exposure to vicarious or direct police stops. Furthermore, among youth exposed to police stops, the link between low self-control and legal cynicism was largely robust to perceptions/features of these stops, including the degree of officer intrusiveness, arrest, perceptions of procedural justice, and youth feelings of social stigma following the stop. Conclusions: Programmatic efforts that both enhance the early development of self-control through mindfulness and curriculum-based interventions (e.g., Promoting Alternative THinking Strategies) and facilitate trauma-informed policing may be beneficial in curtailing the development of legal cynicism.
Optic Nerve Crush Induces Selective Retinal Ganglion Cell Degeneration in the Rat Retina

Author(s): Ahn, Daniel Sung J. | Abstract: Glaucoma is a common cause of blindness characterized by the degeneration of optic nerve and retinal ganglion cells (RGC). A longstanding question in glaucoma research is whether certain RGC types are more susceptible to glaucoma than other RGC types. It is well understood that the mammalian retina contains a diverse population of RGCs - humans have 20 distinct RGC types; each cell type exhibits unique morphological and physiological properties. Given the diversity of RGC types in the retina, it is plausible that different cell types respond differently to glaucoma and thus, certain cell types may degenerate at earlier time points than others. Determining whether specific RGC types degenerate at earlier time points can have important clinical impact on early detection and treatment of glaucoma. To study whether certain RGC types degenerate at earlier time points, we used a large-scale multi-electrode system to record the action potentials of hundreds of RGCs simultaneously in healthy and optic nerve crushed rat retinas. 9 distinct RGC types were identified in the healthy rat retina based on unique light response and intrinsic spiking properties. The results in the healthy rat retina were compared to the optic nerve crushed rat retina to assess whether certain RGC types degenerated at earlier time points following optic nerve crush. The current results show that 2 RGC types degenerate selectively at 10 days after optic nerve crush
A comparison of relationship stability among African American parents in cohabiting versus marital relationships

vii CHAPTER 1: INTRODUCTION 1 CHAPTER 2: LITERATURE REVIEW 3 CHAPTER 3: METHOD 20 CHAPTER 4: RESULTS 29 CHAPTER 5: DISCUSSION 43 APPENDIX A: INSTITUTIONAL REVIEW BOARD EXEMPTION 51 APPENDIX B: STRUCTURED INTERVIEW QUESTIONNAIRE 52 APPENDIX C: ADDITIONAL TABLES 76
An Introduction to the Aquatic Insects of North America

An Introduction to Aquatic Toxicology-Mikko Nikinmaa 2014-07-01 An Introduction to Aquatic Toxicology is an introductory reference for all aspects of toxicology pertaining to aquatic environments. As water sources diminish, the need to understand the effects that contaminants may have on aquatic organisms and ecosystems increases in importance. This book will provide you with a solid understanding of aquatic toxicology, its past, its cutting-edge present and its likely future. An Introduction to Aquatic Toxicology will introduce you to the global issue of aquatic contamination, detailing the major sources of contamination, from where they originate, and their effects on aquatic organisms and their environment. State-of-the-art toxicological topics covered include nanotoxicology, toxicogenomics, bioinformatics, transcriptomics, metabolomics, as well as water management and the toxicological effects of major environmental issues such as algal blooms, climate change and ocean acidification. This book is intended for anyone who wants to know more about the impact of toxicants on aquatic organisms and ecosystems, or to keep up to date with recent and future developments in the field. Provides with the latest perspectives on the impacts of toxicants on aquatic environments, such as nanotoxicology, toxicogenomics, ocean acidification and eutrophication Offers a complete overview, beginning with the origins of aquatic toxicology and concluding with potential future challenges Includes guidance on testing methods and a glossary of aquatic toxicology terms.
Learning Effective Road Network Representation with Hierarchical Graph Neural Networks

Road network is the core component of urban transportation, and it is widely useful in various traffic-related systems and applications. Due to its important role, it is essential to develop general, effective, and robust road network representation models. Although several efforts have been made in this direction, they cannot fully capture the complex characteristics of road networks. In this paper, we propose a novel Hierarchical Road Network Representation model, named HRNR, by constructing a three-level neural architecture, corresponding to "functional zone", "structural regions" and "road segments", respectively. To associate the three kinds of nodes, we introduce two matrices consisting of probability distributions for modeling segment-to-region assignment or region-to-zone assignment. Based on the two assignment matrices, we carefully devise two reconstruction tasks, either based on network structure or human moving patterns. In this way, our node presentations are able to capture both structural and functional characteristics. Finally, we design a three-level hierarchical update mechanism for learning the node embeddings through the entire network. Extensive experiment results on three real-world datasets for four tasks have shown the effectiveness of the proposed model.
The conserved ER-transmembrane protein TMEM39 coordinates with COPII to promote collagen secretion and prevent ER stress

Dysregulation of collagen production and secretion contributes to aging and tissue fibrosis of major organs. How premature collagen proteins in the endoplasmic reticulum (ER) route as specialized cargos for secretion remains to be fully elucidated. Here, we report that TMEM39, an ER-localized transmembrane protein, regulates production and secretory cargo trafficking of procollagen. We identify the C. elegans ortholog TMEM-39 from an unbiased RNAi screen and show that deficiency of tmem-39 leads to striking defects in cuticle collagen production and constitutively high ER stress response. RNAi knockdown of the tmem-39 ortholog in Drosophila causes similar defects in collagen secretion from fat body cells. The cytosolic domain of human TMEM39A binds to Sec23A, a vesicle coat protein that drives collagen secretion and vesicular trafficking. TMEM-39 regulation of collagen secretion is independent of ER stress response and autophagy. We propose that roles of TMEM-39 in collagen secretion and preventing ER stress are likely evolutionarily conserved.
HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival

The estimated time of arrival (ETA) is a critical task in the intelligent transportation system, which involves the spatiotemporal data. Despite a significant amount of prior efforts have been made to design efficient and accurate systems for ETA task, few of them take structural graph data into account, much less the heterogeneous information network. In this paper, we propose HetETA to leverage heterogeneous information graph in ETA task. Specifically, we translate the road map into a multi-relational network and introduce a vehicle-trajectories based network to jointly consider the traffic behavior pattern. Moreover, we employ three components to model temporal information from recent periods, daily periods and weekly periods respectively. Each component comprises temporal convolutions and graph convolutions to learn representations of the spatiotemporal heterogeneous information for ETA task. Experiments on large-scale datasets illustrate the effectiveness of the proposed HetETA beyond the state-of-the-art methods, and show the importance of representation learning of heterogeneous information networks for ETA task.
Synthesis and in vitro Activity of Eugenyl Benzoate Derivatives as BCL-2 Inhibitor in Colorectal Cancer with QSAR and Molecular Docking Approach

Eugenol derivatives can inhibit BCL-2 in HT29 colorectal cancer cells. This study is aimed to acquiring new compounds of Eugenyl benzoate (2-methoxy-4-(prop-2-en-1-yl)phenyl benzoate) derivatives that can inhibit HT29 colorectal cancer cells. In this research, we used several chemical reactions to synthesize novel compounds, such as Esterification, Demethylation, Halohydrin, and Sharpless reaction. Cytotoxicity assays were performed to test the inhibitory activity of compounds against HT29 colon cancer cells. QSAR analysis were carried out to analyse the relationship of chemical structure of the novel compounds with their cytotoxic activity. Ten novel compounds were successfully synthesized and tested in vitro against the HT29 cell. The IC50 of the novel compounds were between 26.56 μmol/ml - 286.81 μmol/ml which compound 4-[(2S)-2,3-dihydroxypropyl]-2-methoxyphenyl 2-hydroxybenzoate (9) showed as best active compound as BCL-2 inhibitors better than other synthesized compounds and Eugenol as well. QSAR analysis of the in vitro results gave a Log equation: 1/IC50 = −0.865-0.210 (LogP)2 + 1,264 (logP)-0.994 CMR (n = 10; r = 0.706; SE: 0.21; F = 0.497, sig = 7.86). The equation shows the log variable P and CMR affect IC50. The properties of hydrophobicity (log P) are more instrumental than the ones of steric (CMR).
Representing Temporal Attributes for Schema Matching

Temporal data are prevalent, where one or several time attributes present. It is challenging to identify the temporal attributes from heterogeneous sources. The reason is that the same attribute could contain distinct values in different time spans, whereas different attributes may have highly similar timestamps and alike values. Existing studies on schema matching seldom explore the temporal information for matching attributes. In this paper, we argue to order the values in an attribute A by some time attribute T as a time series. To learn deep temporal features in the attribute pair (T, A), we devise an auto-encoder to embed the transitions of values in the time series into a vector. The temporal attribute matching (TAM) is thus to evaluate matching distance of two temporal attribute pairs by comparing their transition vectors. We show that computing the optimal matching distance is NP-hard, and present an approximation algorithm. Experiments on real datasets demonstrate the superiority of our proposal in matching temporal attributes compared to the generic schema matching approaches.
Data Sketching for Real Time Analytics: Theory and Practice

Speed, cost, and scale. These are 3 of the biggest challenges in analyzing big data. While modern data systems continue to push the boundaries of scale, the problems of speed and cost are fundamentally tied to the size of data being scanned or processed. Processing thousands of queries that each access terabytes of data with sub-second latency remains infeasible. Data sketching techniques provide means to drastically reduce this size, allowing for real-time or interactive data analysis with reduced costs but with approximate answers. This tutorial covers a number of useful data sketching and sampling methods and demonstrate their use using the Apache DataSketches project. We focus particularly on common problems in analytic problems such as counting distinct items, quantiles, histograms, heavy hitters, and aggregations with large group bys. For these, we covers algorithms, techniques, and theory that can aid both practitioners and theorists in constructing sketches and designing systems that achieve desired error guarantees. For practitioners and implementers, we show how some of these sketches can be easily instantiated using the Apache Datasketches project.
Cutting Piece and CAD Matching Method Based on Feature Retrieval and Shape Segmentation

In order to accurately measure the deviation between car seat cutting pieces and CAD templates, and then evaluate the production quality of car seat cutting pieces, this paper proposes a matching algorithm of car seat cutting pieces and CAD based on feature retrieval and shape segmentation. The processing object of this algorithm is the cutting piece images collected by the acquisition system that combines the backlight board and CCD camera. Firstly, according to the geometric characteristics of CAD, a CAD retrieval method based on image edge shape features was proposed. Then, in view of the flexible characteristics of car seat cutting piece, a matching algorithm of car seat cutting piece and CAD based on shape segmentation was proposed. Finally, the coordinate system of the cutting piece and CAD is unified by affine transformation, and the deviation between the two is calculated. A large number of experiments are performed in a field of view of 700x 500mm, and the results show that the method proposed in this paper can effectively improve the matching accuracy of the cutting piece and CAD. Experimental results verify the effectiveness of the proposed method.
Spike protein mutational landscape in India: Could Muller’s ratchet be a future game-changer for COVID-19?

The dire need of effective preventive measures and treatment approaches against SARS-CoV-2 virus, causing COVID-19 pandemic, calls for an in-depth understanding of its evolutionary dynamics with attention to specific geographic locations, since lockdown and social distancing to prevent the virus spread could lead to distinct localized dynamics of virus evolution within and between countries owing to different environmental and host-specific selection pressures. To decipher any correlation between SARS-CoV-2 evolution and its epidemiology in India, we studied the mutational diversity of spike glycoprotein, the key player for the attachment, fusion and entry of virus to the host cell. For this, we analyzed the sequences of 630 Indian isolates as available in GISAID database till June 07, 2020, and detected the spike protein variants to emerge from two major ancestors – Wuhan-Hu-1/2019 and its D614G variant. Average stability of the docked spike protein – host receptor (S-R) complexes for these variants correlated strongly (R2=0.96) with the fatality rates across Indian states. However, while more than half of the variants were found unique to India, 67% of all variants showed lower stability of S-R complex than the respective ancestral variants, indicating a possible fitness loss in recently emerged variants, despite a continuous increase in mutation rate. These results conform to the sharply declining fatality rate countrywide (>7-fold during April 11 – June 28, 2020). Altogether, while we propose the potential of S-R complex stability to track disease severity, we urge an immediate need to explore if SARS-CoV-2 is approaching mutational meltdown in India. Significance Epidemiological features are intricately linked to evolutionary diversity of rapidly evolving pathogens, and SARS-CoV-2 is no exception. Our work suggests the potential of average stability of complexes formed by the circulating spike mutational variants and the human host receptor to track the severity of SARS-CoV-2 infection in a given region. In India, the stability of these complexes for recent variants tend to decrease relative to their ancestral ones, following countrywide declining fatality rate, in contrast to an increasing mutation rate. We hypothesize such a scenario as nascent footprints of Muller’s ratchet, proposing large-scale population genomics study for its validation, since this understanding could lead to therapeutic approaches for facilitating mutational meltdown of SARS-CoV-2, as experienced earlier for influenza A virus.
A mesoscopic simulator to uncover heterogeneity and evolutionary dynamics in tumors

Increasingly complex in-silico modeling approaches offer a way to simultaneously access cancerous processes at different spatio-temporal scales. High-level models, such as those based on partial differential equations, are computationally affordable and allow large tumor sizes and long temporal windows to be studied, but miss the discrete nature of many key underlying cellular processes. Individual-based approaches provide a much more detailed description of tumors, but have difficulties when trying to handle full-sized real cancers. Thus, there exists a trade-off between the integration of macroscopic and microscopic information, now widely available, and the ability to attain clinical tumor sizes. In this paper we put forward a stochastic mesoscopic simulation framework that incorporates key cellular processes during tumor progression while keeping computational costs to a minimum. Our framework captures a physical scale that allows both the incorporation of microscopic information, tracking the spatio-temporal emergence of tumor heterogeneity and the underlying evolutionary dynamics, and the reconstruction of clinically sized tumors from high-resolution medical imaging data, with the additional benefit of low computational cost. We illustrate the functionality of our modeling approach for the case of glioblastoma, a paradigm of tumor heterogeneity that remains extremely challenging in the clinical setting. Author summary Computer simulation based on mathematical models provides a way to improve the understanding of complex processes in oncology. In this paper we develop a stochastic mesoscopic simulation approach that incorporates key cellular processes while keeping computational costs to a minimum. Our methodology captures the development of tumor heterogeneity and the underlying evolutionary dynamics. The physical scale considered allows microscopic information to be included, tracking the spatio-temporal evolution of tumor heterogeneity and reconstructing clinically sized tumors from high-resolution medical imaging data, with a low computational cost. We illustrate the functionality of the modeling approach for the case of glioblastoma, an epitome of heterogeneity in tumors.
CoRel: Seed-Guided Topical Taxonomy Construction by Concept Learning and Relation Transferring

Taxonomy is not only a fundamental form of knowledge representation, but also crucial to vast knowledge-rich applications, such as question answering and web search. Most existing taxonomy construction methods extract hypernym-hyponym entity pairs to organize a "universal" taxonomy. However, these generic taxonomies cannot satisfy user's specific interest in certain areas and relations. Moreover, the nature of instance taxonomy treats each node as a single word, which has low semantic coverage for people to fully understand. In this paper, we propose a method for seed-guided topical taxonomy construction, which takes a corpus and a seed taxonomy described by concept names as input, and constructs a more complete taxonomy based on user's interest, wherein each node is represented by a cluster of coherent terms. Our framework, CoRel, has two modules to fulfill this goal. A relation transferring module learns and transfers the user's interested relation along multiple paths to expand the seed taxonomy structure in width and depth. A concept learning module enriches the semantics of each concept node by jointly embedding the taxonomy and text. Comprehensive experiments conducted on real-world datasets show that CoRel generates high-quality topical taxonomies and outperforms all the baselines significantly.
Forecasting the Evolution of Hydropower Generation

Hydropower is the largest renewable energy source for electricity generation in the world, with numerous benefits in terms of: environment protection (near-zero air pollution and climate impact), cost-effectiveness (long-term use, without significant impacts of market fluctuation), and reliability (quickly respond to surge in demand). However, the effectiveness of hydropower plants is affected by multiple factors such as reservoir capacity, rainfall, temperature and fluctuating electricity demand, and particularly their complicated relationships, which make the prediction/recommendation of station operational output a difficult challenge. In this paper, we present DeepHydro, a novel stochastic method for modeling multivariate time series (e.g., water inflow/outflow and temperature) and forecasting power generation of hydropower stations. DeepHydro captures temporal dependencies in co-evolving time series with a new conditioned latent recurrent neural networks, which not only considers the hidden states of observations but also preserves the uncertainty of latent variables. We introduce a generative network parameterized on a continuous normalizing flow to approximate the complex posterior distribution of multivariate time series data, and further use neural ordinary differential equations to estimate the continuous-time dynamics of the latent variables constituting the observable data. This allows our model to deal with the discrete observations in the context of continuous dynamic systems, while being robust to the noise. We conduct extensive experiments on real-world datasets from a large power generation company consisting of cascade hydropower stations. The experimental results demonstrate that the proposed method can effectively predict the power production and significantly outperform the possible candidate baseline approaches.
Amplitude Consistent Enhancement for Speech Dereverberation

The mapping and masking methods based on deep learning are both essential methods for speech dereverberation at present, which typically enhance the amplitude of the reverberant speech while letting the reverberant phase unprocessed. The reverberant phase and enhanced amplitude are used to synthesize the target speech. However, because the overlapping frames interfere with each other during the superposition process (overlap-and-add), the final synthesized speech signal will deviate from the ideal value. In this paper, we propose an amplitude consistent enhancement method (ACE) to solve this problem. With ACE to train the deep neural networks (DNNs), we use the difference between amplitudes of the synthesized and clean speech as the loss function. Also, we propose a method of adding an adjustment layer to improve the regression accuracy of DNN. The speech dereverberation experiments show that the proposed method has improved the PESQ and SNR by 5% and 15% compared with the traditional signal approximation method.