Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship Between Child Maltreatment and Substance Abuse Treatment Outcomes Among Emerging Adults and Adolescents

Emerging adulthood is the period of greatest risk for problematic substance use. The primary aim of the current study was to examine the relationship between a broad measure of child maltreatment and several key outcomes for a large clinical sample of emerging adults (n = 858) and adolescents (n = 2,697). The secondary aim was to examine the extent to which the relationship between child maltreatment and treatment outcomes differed between emerging adults and adolescents. Multilevel latent growth curve analyses revealed emerging adults and adolescents who experienced child maltreatment reported significantly greater reductions over time on several treatment outcomes (e.g., substance use, substance-related problems, and emotional problems). Overall, analyses did not support differential relationships between child maltreatment and changes over time in these substance use disorder treatment outcomes for emerging adults and adolescents. The one exception was that although emerging adults with child maltreatment did reduce their HIV risk over time, their improvements were not as great as were the improvements in HIV risk reported by adolescents who had experienced child maltreatment.
Bi-level reconfigurations of fault tolerant arrays in bi-modal computational environments

Fault-tolerant architectures and algorithms are studied for processor arrays which are subject to computational loads that alternate between two phases-a strict phase, characterized by a heavy load and strict constraints on response time, and a relaxed phase, characterized by a light load and relatively relaxed constraints on response time. Under this type of load, a bilevel algorithm may be applied to reconfigure the system after faults. Specifically, at one level, called the fast response level, a local distributed fault-tolerant algorithm is used during the strict phase to achieve fast fault recovery at the expense of possible rapid degradation in the potential to tolerate future faults. In order to minimize the effect of this degradation, a second level, called the optimization level, is added. At that level, a global, relatively slow reorganization algorithm is applied during the relaxed phase to restore the system into a shape that ensures adequate fault-tolerance capability in the remaining part of the system's mission. Three examples are given for bilevel reconfiguration algorithms that emphasize three different restoration criteria. <<ETX>>
Zero aliasing compression

A compression technique, called periodic quotient compression, which eliminates the problem of aliasing is presented. The compression in signature analysis is based on polynomial division, where the remainder is the signature and the quotient is discarded. With this technique one looks at both the remainder and the quotient and assumes that the good circuit response is known a-priory during the design of the linear feedback shift register (LFSR). The concept of periodic polynomials is used to completely characterize the quotient, thus eliminating aliasing. The maximum number of bits required to compress an N-b response to achieve zero aliasing is determined. The authors provide an algorithm for constructing an LFSR to achieve this bound for any given circuit under test. <<ETX>>
Suppressing spatiotemporal lasing instabilities with wave-chaotic microcavities

Taming laser instabilities Broad-area and high-power lasers often suffer from instabilities owing to the chaotic interference of multiple modes within the cavity. Such instabilities can ultimately limit the operation of the laser or damage the cavity. The usual approach to minimizing such instabilities is to limit the number of modes in the cavity. Bittner et al. designed a chaotic cavity that disrupts the formation of self-organized structures that lead to instabilities (see the Perspective by Yang). This approach of fighting chaos with chaos by using the boundary condition of the cavity shape may provide a robust route to stabilizing lasers at high operating powers. Science, this issue p. 1225; see also p. 1201 A chaotic cavity design is used to suppress the spatiotemporal instabilities in lasers. Spatiotemporal instabilities are widespread phenomena resulting from complexity and nonlinearity. In broad-area edge-emitting semiconductor lasers, the nonlinear interactions of multiple spatial modes with the active medium can result in filamentation and spatiotemporal chaos. These instabilities degrade the laser performance and are extremely challenging to control. We demonstrate a powerful approach to suppress spatiotemporal instabilities using wave-chaotic or disordered cavities. The interference of many propagating waves with random phases in such cavities disrupts the formation of self-organized structures such as filaments, resulting in stable lasing dynamics. Our method provides a general and robust scheme to prevent the formation and growth of nonlinear instabilities for a large variety of high-power lasers.
Abuse Characteristics and the Concordance of Child Protective Service Determinations and Adolescent Self-Reports of Abuse

This study examines the concordance of abuse self-reported by adolescents at 18 years and child protective service (CPS) determinations and how abuse characteristics predict concordance. It includes 819 youths participating in 18-year interviews of the Longitudinal Study of Child Abuse and Neglect (LONGSCAN). Cross-tabulations revealed low correspondence between self-reports and CPS determinations of physical, sexual, and emotional abuse. Logistic regression identified that among youths with CPS physical abuse determinations, White race, chronicity, and co-occurring neglect were positively associated with corresponding self-reports. Co-occurring CPS-determined emotional abuse was more concordant with self-reports. More frequent self-reported physical abuse was associated with corresponding CPS determinations. Self-reports of childhood emotional abuse and perpetration by nonparental family/other household members were positively associated with corresponding CPS determinations. CPS determination concordance also varied significantly by LONGSCAN site. Results demonstrate differences in abuse characteristics captured by CPS data and youth self-report, which may impact research findings on abuse correlates.
Global extent of rivers and streams

Expanding the role of rivers The surfaces of rivers and streams are interfaces for a host of chemical exchanges with the atmosphere and biosphere. For instance, carbon dioxide outgassing from rivers is estimated to be equivalent to one-fifth of combined emissions from fossil fuel combustion and cement production. Allen and Pavelsky used satellite imagery to estimate the surface area of rivers and streams (see the Perspective by Palmer and Ruhi). The stunning map that they generated results in an upward revision, by about one-third, to the total surface area of rivers and streams on Earth. Science, this issue p. 585; see also p. 546 A global map of rivers and streams from satellite imagery requires an upward revision of 35% to their global surface area. The turbulent surfaces of rivers and streams are natural hotspots of biogeochemical exchange with the atmosphere. At the global scale, the total river-atmosphere flux of trace gasses such as carbon dioxide depends on the proportion of Earth’s surface that is covered by the fluvial network, yet the total surface area of rivers and streams is poorly constrained. We used a global database of planform river hydromorphology and a statistical approach to show that global river and stream surface area at mean annual discharge is 773,000 ± 79,000 square kilometers (0.58 ± 0.06%) of Earth’s nonglaciated land surface, an area 44 ± 15% larger than previous spatial estimates. We found that rivers and streams likely play a greater role in controlling land-atmosphere fluxes than is currently represented in global carbon budgets.
A liquid phase of synapsin and lipid vesicles

Going through a phase Neuronal communication at synapses relies on regulated neurotransmitter secretion. Neurotransmitters are stored in small vesicles that are organized in clusters within nerve terminals. On stimulation, the vesicles fuse with the presynaptic plasma membrane, but despite their tight packing, replacement synaptic vesicles are rapidly recruited. Vesicles newly reformed by membrane recycling randomly intermix with the clusters. Milovanovic et al. show that synapsin, an abundant synaptic vesicle–associated protein, organizes these vesicle clusters by liquid-liquid phase separation—like oil in water (see the Perspective by Boczek and Alberti). Science, this issue p. 604; see also p. 548 Within the synapse, synapsin may form liquid biomolecular condensates that can trap lipid-bound synaptic vesicles. Neurotransmitter-containing synaptic vesicles (SVs) form tight clusters at synapses. These clusters act as a reservoir from which SVs are drawn for exocytosis during sustained activity. Several components associated with SVs that are likely to help form such clusters have been reported, including synapsin. Here we found that synapsin can form a distinct liquid phase in an aqueous environment. Other scaffolding proteins could coassemble into this condensate but were not necessary for its formation. Importantly, the synapsin phase could capture small lipid vesicles. The synapsin phase rapidly disassembled upon phosphorylation by calcium/calmodulin-dependent protein kinase II, mimicking the dispersion of synapsin 1 that occurs at presynaptic sites upon stimulation. Thus, principles of liquid-liquid phase separation may apply to the clustering of SVs at synapses.
Connecting neuronal circuits for movement

Dedicated neuronal circuits mediate execution, choice, and coordination of body action Movement is the most common final output of nervous system activity and is essential for survival. But what makes this seemingly trivial statement so scientifically challenging? Neurons that contribute to when and how our body moves are distributed throughout the nervous system. Thus, even a simple movement such as arm flexion requires the coordinated activation of many different neuronal populations across multiple brain regions. A key question is how the nervous system produces diverse and precise actions aligned with the organisms' behavioral needs. These processes are affected in diseases such as Parkinson's or Huntington's, in which aberrant motor behavior dominates. Recent studies are transformative in how we think about the control of movement. A common denominator of these studies is that brain regions that contribute to motor behavior can no longer be considered as interacting boxes. Instead, deep circuit-level insight based on specific neuronal populations emerges as being critical to revealing motor system organization and understanding its function. It is likely that insights at this level can also help to design more specific and direct interventions for diseases of the motor system and neuroprosthetics applied after injuries.
Mycobacterium tuberculosis Infection Manipulates the Glycosylation Machinery and the N-Glycoproteome of Human Macrophages and Their Microparticles.

Tuberculosis (TB) remains a prevalent and lethal infectious disease. The glycobiology associated with Mycobacterium tuberculosis infection of frontline alveolar macrophages is still unresolved. Herein, we investigated the regulation of protein N-glycosylation in human macrophages and their secreted microparticles (MPs) used for intercellular communication upon M. tb infection. LC-MS/MS-based proteomics and glycomics were performed to monitor the regulation of glycosylation enzymes and receptors and the N-glycome in in vitro-differentiated macrophages and in isolated MPs upon M. tb infection. Infection promoted a dramatic regulation of the macrophage proteome. Most notably, significant infection-dependent down-regulation (4-26 fold) of 11 lysosomal exoglycosidases, e.g., β-galactosidase, β-hexosaminidases and α-/β-mannosidases, was observed. Relative weak infection-driven transcriptional regulation of these exoglycosidases and a stronger augmentation of the extracellular hexosaminidase activity demonstrated that the lysosome-centric changes may originate predominantly from infection-induced secretion of the lysosomal content. The macrophages showed heterogeneous N-glycan profiles and displayed significant up-regulation of complex-type glycosylation and concomitant down-regulation of paucimannosylation upon infection. Complementary intact N-glycopeptide analysis supported a subcellular-specific manipulation of the glycosylation machinery and altered glycosylation patterns of lysosomal N-glycoproteins within infected macrophages. Interestingly, the corresponding macrophage-derived MPs displayed unique N-glycome and proteome signatures supporting a preferential packaging from plasma membranes. The MPs were devoid of infection-dependent N-glycosylation signatures, but interestingly displayed increased levels of the glyco-initiating oligosaccharyltransferase complex and associated α-glucosidases that correlated with increased formation, N-glycan precursor levels and N-glycan density of infected MPs. In conclusion, this system-wide study provides new insight into the host- and pathogen-driven N-glycoproteome manipulation of macrophages in TB.
Mothers, Domestic Violence, and Child Protection

This article explores the relationship between understandings of domestic violence and the child protection response drawing on material gathered in focus groups with workers who support mothers dealing with both domestic violence and child protection issues. The interviewees expressed concern that the dynamics of domestic violence are often misunderstood and inappropriately responded to by child protection workers.This article critically examines the interviewees’ concerns and concludes that to properly protect children, it is crucial that child protection workers have a clear understanding of the dynamics of and issues related to domestic violence.
Victim Advocates’ Perceptions of Legal Work

Past scholarship has weighed the risks and rewards of legal remedies for victims of domestic violence and sexual assault. Missing from this debate, however, is an analysis of the social incentives for victim advocates to offer legal options to their clients. Preliminary findings show that victim advocates perceive that outsiders respect legal work more than their care work with clients (listening, caring, and empathizing). This study offers three explanations for this phenomenon: (1) the devaluation of women’s care work in general, (2) the confidentiality constraints on communicating the value of their care work, and (3) popular assumptions that care work requires professional credentials in order to be legitimate.
Nitrogen isotope evidence for expanded ocean suboxia in the early Cenozoic

Circulation more than temperature Changes in continental configuration and sea level affected the ocean's oxygen levels and the rate of denitrification between 70 and 30 million years ago. That finding by Kast et al. shows a fundamental difference from the modern ocean, in which the extent of suboxia is controlled primarily by global temperature. Changes in the nitrogen isotopic composition of marine organic matter correlate with the collision of India and Asia and the circulation changes that occurred as a result. Later, isotopic composition changed further in response to a fall in sea level as global cooling caused ice sheets to grow. Science, this issue p. 386 The ocean was more suboxic and supported greater denitrification in the Paleocene than during the Oligocene. The million-year variability of the marine nitrogen cycle is poorly understood. Before 57 million years (Ma) ago, the 15N/14N ratio (δ15N) of foraminifera shell-bound organic matter from three sediment cores was high, indicating expanded water column suboxia and denitrification. Between 57 and 50 Ma ago, δ15N declined by 13 to 16 per mil in the North Pacific and by 3 to 8 per mil in the Atlantic. The decline preceded global cooling and appears to have coincided with the early stages of the Asia-India collision. Warm, salty intermediate-depth water forming along the Tethys Sea margins may have caused the expanded suboxia, ending with the collision. From 50 to 35 Ma ago, δ15N was lower than modern values, suggesting widespread sedimentary denitrification on broad continental shelves. δ15N rose at 35 Ma ago, as ice sheets grew, sea level fell, and continental shelves narrowed.
Computational methods for two-level 0-1 programming problems with fuzzy parameters through genetic algorithms

From the observation that possible values of parameters involved in objective functions and constraints of mathematical programming problems are often only imprecisely or ambiguously known to experts, in this paper we consider two-level 0-1 programming problems with fuzzy parameters represented by fuzzy numbers. Using genetic algorithms, we present a computational method for obtaining Stackelberg solutions to the two-level 0-1 programming problem with fuzzy parameters. To demonstrate feasibility of the method, computational experiments are carried out.
KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to gain insight into CPP activity. Experiments can provide detailed insight into the cell-penetration property of CPPs. However, the synthesis and identification of CPPs through wet-lab experiments is both resource- and time-expensive. Therefore, the development of an efficient prediction tool is essential for the identification of unique CPP prior to experiments. To this end, we developed a kernel extreme learning machine (KELM) based CPP prediction model called KELM-CPPpred. The main data set used in this study consists of 408 CPPs and an equal number of non-CPPs. The input features, used to train the proposed prediction model, include amino acid composition, dipeptide amino acid composition, pseudo amino acid composition, and the motif-based hybrid features. We further used an independent data set to validate the proposed model. In addition, we have also tested the prediction accuracy of KELM-CPPpred models with the existing artificial neural network (ANN), random forest (RF), and support vector machine (SVM) approaches on respective benchmark data sets used in the previous studies. Empirical tests showed that KELM-CPPpred outperformed existing prediction approaches based on SVM, RF, and ANN. We developed a web interface named KELM-CPPpred, which is freely available at http://sairam.people. iitgn.ac. in/KELM-CPPpred.html.
Promotion of Latina Health

Latina women in the United States are vulnerable to two intersecting public health concerns: intimate partner violence (IPV) and subsequent risk for HIV/AIDS infection. Examination of the cultural and contextual life factors of this understudied population is crucial to developing culturally relevant HIV interventions. Focus groups with Latinas (15 monolingual; 10 bilingual) who have experienced IPV were conducted. Monolingual and bilingual Latinas endorsed that they were concerned about HIV infection, naming partner infidelity and experiences of forced and coerced sex as primary reasons for their concern. However, monolingual participants had lower levels of HIV knowledge, spending much time discussing myths of HIV infection, whereas bilingual participants spent more time discussing specific prevention techniques, including challenges related to the violence in their relationships. These findings suggest that HIV/AIDS prevention programs for Latinas need to pay close attention to the different historical, contextual, and cultural experiences of this at-risk group of women.
Optimization of interval type-2 fuzzy logic controller using quantum genetic algorithms

A Type-2 Fuzzy logic controller adapted with quantum genetic algorithm, referred to as type-2 quantum fuzzy logic controller (T2QFLC), is presented in this article for robot manipulators with unstructured dynamical uncertainties. Quantum genetic algorithm is employed to tune type-2 fuzzy sets and rule sets simultaneously for effective design of interval type-2 FLCs. Traditional fuzzy logic controllers (FLCs), often termed as type-1 FLCs using type-1 fuzzy sets, have difficulty in modeling and minimizing the effect of uncertainties present in many real time applications. Therefore, manually designed type-2 FLCs have been utilized in many control process due to their ability to model uncertainty and it relies on heuristic knowledge of experienced operators. The type-2 FLC can be considered as a collection of different embedded type-1 FLCs. However, manually designing the rule set and interval type-2 fuzzy set for an interval type-2 FLC to give a good response is a difficult task. The purpose of our study is to make the design process automatic. The type-2 FLCs exhibit better performance for compensating the large amount of uncertainties with severe nonlinearities. Furthermore, the adaptive type-2 FLC is validated through a set of numerical experiments and compared with QGA evolved type-1 FLCs, traditional and neural type-1 FLCs.
An intelligent framework for monitoring student performance using fuzzy rule-based Linguistic Summarisation

Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning in order to better engage students with the subject and improve their understanding of the material being taught. We describe the use of a fuzzy Linguistic Summarisation (LS) technique for extracting linguistically interpretable scaled fuzzy weighted rules from student data describing prominent relationships between activity / engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem based learning tasks. The system can be used to more effectively evaluate new teaching approaches and methodologies, identify weaknesses and provide more personalised feedback on learner's progress. We present a case study and initial experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a Group Performance Model (GPM) to deploy Activity Led Learning (ALL) in a Master-level module. Results show that the fuzzy weighted rules can identify useful relationships between student engagement and performance providing a mechanism allowing educators to transparently evaluate teaching and factors effecting student performance, which can be incorporated as part of an automated intelligent analysis and feedback system.