Miracle-dz/UniFAR-FTUs huggingface dataset:
Longitudinal Reciprocal Associations Between Anxiety, Depression, and Alcohol Use in Adolescent Girls

BACKGROUND Depression, anxiety, and alcohol misuse predict adverse social, academic, and emotional outcomes, and their relations to one another increase during adolescence-particularly in girls. However, evidence on the directions of these relations is mixed. Longitudinal models of internalizing problem-alcohol use links may identify promising prevention targets. Accordingly, we examined reciprocal associations between anxiety severity and alcohol use, as well as between depression severity and alcohol use, in adolescent girls. METHODS Data were drawn from a population-based longitudinal study of female adolescents. The current sample comprised 2,100 participants (57.1% Black, 42.9% White) assessed annually between ages 13 and 17. Girls self-reported depression severity, anxiety severity, and frequency of alcohol use (consumption of ≥1 full drink) in the past year. Primary caregivers reported on socioeconomic and neighborhood factors; these were included with race, early puberty, and conduct problems (youth-report) as covariates. Anxiety and depression severity were included within a single cross-lagged panel model, along with alcohol use, to isolate their independent and reciprocal links to drinking behavior. RESULTS Higher depression severity modestly predicted increased likelihood of subsequent alcohol use from ages 13 to 17. However, inconsistent relations emerged for the reverse pathway: Alcohol use modestly predicted decreased depression severity at ages 14 and 16; associations were nonsignificant in other lagged associations. Anxiety severity and alcohol use were not consistently associated. CONCLUSIONS Results highlight the key role of depression, relative to anxiety, in predicting later alcohol use. Future studies may examine whether depression prevention programs yield secondary reductions in alcohol use in adolescent girls.
Evaluating Transdiagnostic, Evidence-Based Mental Health Care in a Safety-Net Setting Serving Homeless Individuals

Homeless individuals experience higher rates of mental illness than the general population, though this group is less likely to receive evidence-based psychological treatment for these difficulties. One explanation for this science-to-service gap may be that most empirically supported interventions are designed to address a single disorder, which may not map on to the substantial comorbidity present in safety-net samples, and create a high training burden for often underresourced clinicians who must learn multiple protocols to address the needs of their patients. One solution may be to prioritize the dissemination of transdiagnostic interventions that can reduce therapist burden and simultaneously address comorbid conditions. The purpose of the present article is to describe the process of conducting a pilot study administering the Unified Protocol (UP), a transdiagnostic treatment for the range of emotional disorders, at a community-based organization that provides health care and other services to homeless individuals and families in Boston, Massachusetts. Therapists on a specialized behavioral health unit received didactic training in the intervention, followed by weekly consultation while they provided the UP to patients on their caseload. Qualitative and quantitative data were collected from both patients and therapists. Barriers to use of the UP by therapists, as well as to conducting research in this setting, will be discussed, along with the solutions that were used.
Application of conditional filtering to simulation of turbulence, chemistry, and their interactions

Combustion technology has been applied in human society for millennia and, since the industrial revolution, has become an integral part of most energy supply chains. Simulation is an important tool in modern combustor design; this thesis aims to improve the quality of combustion simulation tools, and thereby facilitate the design of improved combustors. More speci cally, it aims to examine how generalizing and/or relaxing the de nition of conditional ltering a common technique in turbulence-chemistry interaction modelling can produce novel turbulence and combustion models. The work begins with an extension of the Conditional Source-term Estimation (CSE) model for turbulence-chemistry interaction modelling. A novel variation of the algorithm, termed CSE with Geometric Conditioning Variables (CSE-GCV) is proposed as a method of circumventing the theoretical and practical issues associated with traditional CSE ensemble division. In CSE-GCV, the concept of the conditional lter is generalized by introducing geometric (position-based) variables as conditioning variables. CSE-GCV is tested and found to be workable; a theoretical analysis demonstrates that CSE-GCV also generally has the advantage of reduced computational complexity compared to traditional CSE. In a separate study, the stabilization procedure employed in traditional dynamic sublter modelling for Large Eddy Simulation (LES) is re-interpreted as a form of conditional ltering based on position. This re-interpretation is used as the starting point for a conditional dynamic sublter model in which the stabilization procedure is based on ltering conditionally on scalar elds. Both the traditional and conditional models are applied to a turbulent ame; results suggest that the two models perform similarly, although performance of both is sub-optimal in the case considered. The nal, two-part, study is based around the suggestion that, with su cient conditioning, conditionallyltered elds should be independent of position. It is found that assuming this uniformity produces a novel turbulence-chemistry interaction model, termed the Uniform Conditional State (UCS) model, in which the conditional scalar dissipation model is the key un-closed parameter. The UCS model is applied to a series of turbulent non-premixed ames, and is found to predict their properties to good accuracy, with details showing some sensitivity to the conditional scalar dissipation model.
When Distress Becomes Somatic: Dementia Family Caregivers' Distress and Genetic Vulnerability to Pain and Sleep Problems.

Background and Objectives Stress can trigger physical pain and disturb sleep. Whether dementia family caregivers experience heightened pain is unknown. Cycles of unwanted thoughts about caregiving stressors and avoidance of these thoughts-that is, caregiving-related distress-may exacerbate both pain and sleep disturbances, and genetic susceptibility to stress may further modulate these associations. Research Design and Methods Dementia caregivers (72 spouses, 58 adult children, ages 34-89) rated the extent to which they experienced unintended thoughts about caregiving and tried to suppress such thoughts. They also reported their pain levels, sleep problems, and depressive symptoms. Peripheral blood leukocytes were genotyped for 5-HTTLPR (serotonin-transporter-linked polymorphic region) and 5-HT1A receptor polymorphism rs6295 on the 5HTR1A locus. Results Short-allele carriers for 5-HTTLPR experienced more pain and sleep problems in association with greater caregiving-related distress than those with other genotypes. For rs6295, C carriers also showed the strongest links between distress and sleep problems. Those who experienced more avoidance and intrusive thoughts about caregiving had more severe depressive symptoms, consistent with past work. Discussion and Implications Caregivers' genetic profiles helped to explain whether caregiving-related distress predicted worse pain and sleep problems. These data reveal new somatic risks of caregiver distress and provide targets for intervention. According to plasticity theories, caregivers genetically predisposed to greater stress reactivity may also respond particularly well to interventions, and many brief treatments may effectively address caregivers' intrusions and avoidance.
HDL cholesterol, size, particle number, and residual vascular risk after potent statin therapy: the JUPITER trial

Background — Chemically-measured high-density lipoprotein cholesterol (HDL-C) may not be the best clinical measure of HDL. Little is known about alternative HDL measures such as HDL size or particle number (HDL-P) as determinants of residual risk after potent statin therapy. Methods and Results — In JUPITER, HDL size and HDL-P were measured by nuclear magnetic resonance spectroscopy, and HDL-C and apolipoprotein A-I (apoA-I) were chemically assayed in 10,886 participants without cardiovascular disease (CVD) before and after random allocation to rosuvastatin 20 mg/day or placebo. Levels were examined with first CVD (N=237). HDL-P correlated better with apoA-I (Spearman r=0.69, p<0.0001) than with HDL-C (r=0.55, p<0.0001). Rosuvastatin lowered LDL cholesterol (49%) and raised HDL-C (6.1%), apoA-I (2.1%), HDL-P (3.8%) and HDL size (1.2%); all p<0.0001. Among placebo-allocated individuals, on-treatment HDL-C, apoA-I, and HDL-P had similar inverse associations with CVD (risk factor-adjusted hazard ratio and 95% CI per 1-SD: 0.79 [0.63-0.98], 0.75 [0.62-0.92], and 0.81 [0.67-0.97], respectively). Among rosuvastatin-allocated individuals, on-treatment HDL-P had a statistically significant and somewhat stronger association with CVD (0.73, 0.57-0.93, p=0.01) than HDL-C (0.82, 0.63-1.08, p=0.16) or apoA-I (0.86, 0.67-1.10, p=0.22). Among rosuvastatin-allocated individuals, on-treatment HDL-P remained significant (0.72, 0.53-0.97, p=0.03) after additionally adjusting for HDL-C. In risk factor-adjusted models, HDL size showed no significant association with CVD. In the setting of potent particle be a better B, and hsCRP, we had adequate power Strengths of this study include the large number of individuals with HDLL mmmeaeesususurereresss assessed both at baseline and on-treatment, the random allocation of a potent statin versus pllacaccebebeboo,o, aaandndnd tthee dddeetetailed information on cardiovvvasaacccular risk factooorsrr andndnd oooutcomes. The present ttudddy y also hass ppoooteenentiitiala lllimimimitittatatatioionsnsns. InIn ppaarticccuullar, JUJUUPIIITTETERRR eexxclclududdeded ininindddivividududualalss wiwiwiththth kknnonowwnwn CVCVVD,D,D, dddiaiabebebetetetes,s, hhhigggh h LDLDLDL L chhhololoesesesteteterorrol,l, oorrr hihihighghgh trtrtigigglyyyceceeriridededess.s. FFFururutththererer,, , ththhe memedididanana HHHDDLDL--C-C iin n JUPITER waasss 49494 mmmg/g/g/dLdLdL aandndnd tthehee eeexpxpxpeccctetetdd d ininincrcrcreaeaeasesese ininin HHHDLDLDL-C-CC wwwititih h rororosususuvavavastststatatininin wwwasasa lless than
Polynomial time algorithms for network code construction

The famous max-flow min-cut theorem states that a source node can send information through a network (V,E) to a sink node at a rate determined by the min-cut separating s and t. Recently, it has been shown that this rate can also be achieved for multicasting to several sinks provided that the intermediate nodes are allowed to re-encode the information they receive. We demonstrate examples of networks where the achievable rates obtained by coding at intermediate nodes are arbitrarily larger than if coding is not allowed. We give deterministic polynomial time algorithms and even faster randomized algorithms for designing linear codes for directed acyclic graphs with edges of unit capacity. We extend these algorithms to integer capacities and to codes that are tolerant to edge failures.
Effect of Blood Agar from Different Animal Blood on Growth Rates and Morphology of Common Pathogenic Bacteria

Sheep and horse blood are the most commonly used blood for the isolation of microorganisms from human tissue and fluids. However, in many developing countries such as Nigeria, expired human blood from blood banks is still used despite the risk of exposure to HIV and other blood-borne infections, because it is easy to obtain. Blood agar made from blood of rams (similar to sheep), cows, chickens and goats, which are very common in Nigeria, were therefore evaluated. The isolation rates, colony size and morphology as well as haemolytic characteristics of common hospital pathogens such as Pseudomonas aeruginosa, Klebsiella pneumonae, Staphylococcus aureus, and Streptococcus spp, were tested on blood agar prepared from the different animal blood types. All reactions were observed at 24 hrs and 48 hrs respectively. Good growth was achieved by all isolates on rabbit, sheep and chicken blood agar though the best growth was achieved on ram blood agar but there was no significant variation in their morphology. There were differences in their abilities to distinguish haemolytic patterns. Beta Haemolytic Streptococci remained the same on all the blood agar, but the haemolysis of Staphylococci aureus and Pseudomonas aeruginosa varied on different media while haemolysis was least consistent on chicken and cow blood agar. Ram blood agar gave the best reactions in terms of good growth rates of organisms, good morphological characterization as well as good haemolytic reactions. Besides, it is easily available and large quantities of blood can be obtained. Despite the good qualities of ram blood agar observed in this study, however, there is a need for it to be tested further for its ability to support more fastidious organisms.
8-Week Basic Military Training Improves Adiponectin Multimer Ratio in Healthy Young Males

Abstract To investigate the effect of exercise on adiponectin in young healthy human males, we examined serum total adiponectin and high-molecular-weight (HMW) adiponectin in newly recruited male soldiers who participated in an 8-week basic military training (BMT). A total of 95 males (mean age, 18.79±1.50 years) were sampled from among 1,100 new male army recruits in China. Participants were separated into 3 groups according to their body mass index (BMI): overweight group (BMI: 24.9 kg/m2 to<30 kg/m2; n=26); normal-weight group (BMI: 18.5 kg/m2 to<24.9 kg/m2; n=40); and underweight group (BMI:<18.5 kg/m2; n=29). Anthropometric measurements, fasting serum total adiponectin, HMW adiponectin, and lipid profiles were recorded at baseline and at the end of the 8-week BMT. After the 8-week BMT, the HMW/total adiponectin ratio (HMW/total ratio) and HDL cholesterol improved significantly (p<0.001 and p<0.001, respectively). HMW/total ratio showed significant correlations with HDL cholesterol. Our study suggests that an 8-week BMT can improve the HMW/total ratio in healthy young males regardless of their BMI and anthropometry. Both HMW/total ratio and HDL cholesterol can serve as potential biomarkers for assessing the efficacy of exercise and may have metabolic benefits for preventing obesity and obesity-related disease.
Impact of pharmacological treatment of diabetes mellitus on dementia risk: systematic review and meta-analysis

Background The association between diabetes mellitus (DM) treatment and dementia is not well understood. Objective To investigate the association between treatment of diabetes, hypoglycemia, and dementia risk. Research design and methods We performed a systematic review and meta-analysis of pharmacological treatment of diabetes and incident or progressive cognitive impairment. We searched Ovid MEDLINE, Embase, Cochrane Central Registry of Controlled Trials, and PsychINFO from inception to 18 October 2017. We included cross-sectional, case–control, cohort, and randomized controlled studies. The study was registered with PROSPERO (ID CRD42017077953). Results We included 37 studies into our systematic review and 13 into our meta-analysis. Ten studies investigated any antidiabetic treatment compared with no treatment or as add-on therapy to prior care. Treatment with an antidiabetic agent, in general, was not associated with incident dementia (risk ratio (RR) 1.01; 95% CI 0.93 to 1.10). However, we found differential effects across drug classes, with a signal of harm associated with insulin therapy (RR 1.21; 95% CI 1.06 to 1.39), but potentially protective effects with thiazolidinedione exposure (RR 0.71; 95% CI 0.55 to 0.93). Severe hypoglycemic episodes were associated with a nearly twofold increased likelihood of incident dementia (RR 1.77; 95% CI 1.35 to 2.33). Most studies did not account for DM duration or severity. Conclusions and limitations The association between treatment for diabetes and dementia is differential according to drug class, which is potentially mediated by hypoglycemic risk. Not accounting for DM duration and/or severity is a major limitation in the available evidence base.
Modular And Self-Adaptable (MASA) strategy for building robots

We argue that hardware modularity plays a key role in the convergence of Robotics and Artificial Intelligence (AI). We introduce a new approach for building robots that leads to more adaptable and capable machines. We present the concept of a self-adaptable robot that makes use of hardware modularity and AI techniques to reduce the effort and time required to be built. We demonstrate in simulation and with a real robot how, rather than programming, training produces behaviors in the robot that generalize fast and produce robust outputs in the presence of noise.
Development of Patch Textile Antenna for Medical Robots

This paper describes the use of application Finite Difference Time Domain (FDTD) technique of computational modeling for development and simulation patch textile antennas for microwave radiometry. The motivation for this work focuses on the development of this new miniature antenna for medical robots. Slot patch textile antenna was selected as optimum using not less than 450 MHz bandwidth and center frequency 3,8 GHz. The new sensor was tested and validated on different phantoms and biological tissues. Results suggest sufficient characteristics of new antenna for potential use in medicine and robotics.
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware

Convolutional Neural Networks (CNN) has become more popular choice for various tasks such as computer vision, speech recognition and natural language processing. Thanks to their large computational capability and throughput, GPUs ,which are not power efficient and therefore does not suit low power systems such as mobile devices, are the most common platform for both training and inferencing tasks. Recent studies has shown that FPGAs can provide a good alternative to GPUs as a CNN accelerator, due to their re-configurable nature, low power and small latency. In order for FPGA-based accelerators outperform GPUs in inference task, both the parameters of the network and the activations must be quantized. While most works use uniform quantizers for both parameters and activations, it is not always the optimal one, and a non-uniform quantizer need to be considered. In this work we introduce a custom hardware-friendly approach to implement non-uniform quantizers. In addition, we use a single scale integer representation of both parameters and activations, for both training and inference. The combined method yields a hardware efficient non-uniform quantizer, fit for real-time applications. We have tested our method on CIFAR-10 and CIFAR-100 image classification datasets with ResNet-18 and VGG-like architectures, and saw little degradation in accuracy.
Image Classification using Backpropagation Algorithm

Automatically Image Classification is one of the challenging problem of recent era. Several diverse research disciplines have been confluent on this important theme for searching powerful solutions. In this paper we have implemented and tested Neural Network Backpropagation algorithm for large image dataset. Backpropagation algorithm is one of the powerful technique that works efficiently for numeric and huge dataset. We also compared the performance of our algorithm with existing Machine Learning algorithm like naive bayes classifier, decision tree, lazy classifier and others. We found Backpropagation is one of the best solution for this problem it give 97.02 % of accuracy.
ADMM-Based Optimal Power Control for Cognitive Satellite Terrestrial Uplink Networks

This paper proposes an underlay power control scheme for cognitive satellite terrestrial uplink networks, where the primary terrestrial mobile network coexists with the secondary satellite communication network. In particular, the schemes are developed to guarantee that both the interference power constraints and interference outage probability constraints (IOPCs) of primary terrestrial users (PTUs) are satisfied. Since the IOPCs of PTUs belong to the chance constraint and the system throughput optimization model is non-convex, we first transform the IOPCs of PTUs into a series of equivalent transmit power constraints by using the Marcum-<inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> functions. Then, the alternating direction method of multipliers (ADMM) is introduced to derive the power allocation by alternatively optimizing the subproblems, each of which has the exact closed-form solution. The global convergence of the ADMM is guaranteed. Simulation results demonstrate the effectiveness of the proposed method. <xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label><p>Simulation codes are provided to reproduce the results presented in this paper: <uri>https://github.com/gaobingaobingaobin/figure3github</uri></p></fn>
The Deep Weight Prior

Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior, that in contrast to previously published techniques, favors empirically estimated structure of convolutional filters e.g., spatial correlations of weights. We define deep weight prior as an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that deep weight priors can improve the performance of Bayesian neural networks on several problems when training data are limited. Also, we found that initialization of weights of conventional networks with samples from deep weight prior leads to faster training.
Alcohol Consumption Among Young Adults Ages 18–24 in the United States: Results from the 2001–2002 NESARC Survey

The high prevalence of drinking in young adults is a serious public health concern. Alcohol use among young adults often is associated with a wide variety of risky behaviors and both immediate and long-term negative consequences. The 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) presents a unique opportunity to examine young adult drinking because it has an excellent response rate, oversamples young adults ages 18–24, and includes college-related group housing. According to the NESARC data, in 2001–2002 over three-quarters of young adults ages 21–24 were current drinkers, as were nearly two-thirds of those ages 18–20, despite the fact that the legal drinking age is 21. More than half of young adult men exceeded the recommended daily drinking limit, as did two-fifths of young adult women. Although the prevalence of exceeding the daily limit is higher for those ages 21–24 than for those ages 18–20, it also is substantial for those ages 18–20. Because drinking more than the recommended per-occasion maximum is likely to impair mental and physical performance, the increase over the past decade in the prevalence among young adults of drinking five or more drinks 12 or more times per year may help explain the increased risk of injury and other acute negative consequences commonly observed among college students ages 18–24.