Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multivariate Behavior Genetic Investigation of Dual-Systems Models of Alcohol Involvement.

OBJECTIVE Dual-systems models hypothesize that individuals who tend to be drawn to risky behavior and are low in self-control are at greatest risk for alcohol use disorder (AUD). Importantly, these models assume that behavioral approach tendencies and self-control are distinct. This study investigated hypotheses and assumptions central to dual-systems models. METHOD Participants were 3,509 members of a national twin registry (58% female). Structured interviews assessed alcohol use and AUD symptoms. Self-report questionnaires assessed individual differences in approach tendencies, namely for general risky behavior (sensation seeking) and substance use (positive expectancies), and behavioral control. Regression models tested nonadditive, interaction effects on alcohol involvement, as proposed by the dual-systems model. Multivariate behavior genetic models investigated the incremental validity of these interaction effects and whether approach tendencies and behavioral control explain distinct variance in alcohol involvement. RESULTS In regression models, we found interaction effects consistent with the dual-systems model for women but in the opposite direction for men. After accounting for additive main effects in behavior genetic models, however, these interaction effects played a negligible role phenotypically and genetically. Further, sensation seeking and positive expectancies explained phenotypic and genetic variance in alcohol involvement that was distinct from behavioral control. Behavioral control, however, did not explain distinct variance in alcohol involvement. CONCLUSIONS Contrary to dual-systems models, this study suggests that all of the variance in alcohol involvement explained by behavioral control is also shared with the tendency to engage in risky behavior (sensation seeking) and substance use (positive expectancies). Further, interaction effects central to dual-systems models failed to explain additional variance beyond basic main effects. Thus, more parsimonious models may better explain AUD.
Susceptibility Testing of Bacteria Using Maldi-Tof Mass Spectrometry

Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was introduced into the microbiological routine more than 10 years ago. Since then it has almost replaced biochemical identification. It is unrivaled in terms of accuracy and cost. From a laboratory's perspective it would be an ideal method to replace classic susceptibility testing, that is Kirby-Baur agardiffusion or determination of minimal inhibitory concentrations (MICs). First reports on possible assays for susceptibility testing are more than 10 years old. However, the developments during the last 5 years were substantial. This review focuses with some exceptions on the progress, which was achieved during the last decade.
Helix: Accelerating Human-in-the-loop Machine Learning

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows---by modifying the data pre-processing, model training, and postprocessing steps---via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up one-shot execution of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose H elix , a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. H elix minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized---trading off the cost of materialization for potential future benefits---to speed up future iterations. Additionally, H elix offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of H elix 's programming interface and the speed and ease of iterative development using H elix . In our evaluations, H elix achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools.
Early Action Prediction by Soft Regression

We propose a novel approach for predicting on-going action with the assistance of a low-cost depth camera. Our approach introduces a soft regression-based early prediction framework. In this framework, we estimate soft labels for the subsequences at different progress levels, jointly learned with an action predictor. Our formulation of soft regression framework 1) overcomes a usual assumption in existing early action prediction systems that the progress level of on-going sequence is given in the testing stage; and 2) presents a theoretical framework to better resolve the ambiguity and uncertainty of subsequences at early performing stage. The proposed soft regression framework is further enhanced in order to take the relationships among subsequences and the discrepancy of soft labels over different classes into consideration, so that a Multiple Soft labels Recurrent Neural Network (MSRNN) is finally developed. For real-time performance, we also introduce a new RGB-D feature called “local accumulative frame feature (LAFF)”, which can be computed efficiently by constructing an integral feature map. Our experiments on three RGB-D benchmark datasets and an unconstrained RGB action set demonstrate that the proposed regression-based early action prediction model outperforms existing models significantly and also show that the early action prediction on RGB-D sequence is more accurate than that on RGB channel.
Seasonal variability of exacerbations of severe, uncontrolled eosinophilic asthma and clinical benefits of benralizumab.

BACKGROUND Benralizumab is a humanized, afucosylated, monoclonal antibody that targets interleukin-5 receptor alpha and induces direct, rapid, and nearly complete depletion of eosinophils via enhanced antibody-dependent cell-mediated cytotoxicity. In the United States, benralizumab is indicated for add-on maintenance treatment of patients ≥12 years old with severe asthma and an eosinophilic phenotype. OBJECTIVE This study evaluated the effect of benralizumab treatment on seasonal asthma exacerbation rates for patients with severe, uncontrolled asthma. METHODS This was a post hoc analysis of pooled data from the phase III SIROCCO (ClinicalTrials.gov identifier: NCT01928771) and CALIMA (NCT01914757) trials. The primary analysis population was patients ages 12-75 years treated with high-dosage inhaled corticosteroids and long-acting beta-2 agonists who had baseline blood eosinophil counts of ≥300 cells/μL. Patients received benralizumab 30 mg subcutaneously every 4 weeks or every 8 weeks (the first three doses every 4 weeks) or placebo every 4 weeks. Crude exacerbation rates (asthma exacerbations per patient-year) were determined for each month and season. Marginal asthma exacerbation rates and exacerbation rate ratios were estimated by season or month by using a negative binomial model that included covariates for study code, treatment, region, use of maintenance oral corticosteroids, and number of exacerbations in the previous year. Hemispheric seasons were accounted for by normalizing the study site locations. RESULTS Observed crude exacerbation rates were higher in the fall and winter than in the spring and summer for all the patients. For the patients who received placebo, benralizumab every 4 weeks, and benralizumab every 8 weeks, crude exacerbation rates were the following: fall, 1.52, 0.86, and 0.81, respectively; winter, 1.44, 0.91, and 0.82, respectively; spring, 1.11, 0.66, and 0.52, respectively; and summer, 1.02, 0.55, and 0.51, respectively. Rate reductions in seasonal marginal annual exacerbation rates were 37-50% versus placebo at each season (p < 0.001). CONCLUSION Benralizumab significantly and consistently reduced asthma exacerbations across all seasons versus placebo for patients with severe, uncontrolled eosinophilic asthma.
Automated Analysis for Retinopathy of Prematurity by Deep Neural Networks

Retinopathy of Prematurity (ROP) is a retinal vasproliferative disorder disease principally observed in infants born prematurely with low birth weight. ROP is an important cause of childhood blindness. Although automatic or semi-automatic diagnosis of ROP has been conducted, most previous studies have focused on “plus” disease, which is indicated by abnormalities of retinal vasculature. Few studies have reported methods for identifying the “stage” of the ROP disease. Deep neural networks have achieved impressive results in many computer vision and medical image analysis problems, raising expectations that it might be a promising tool in the automatic diagnosis of ROP. In this paper, convolutional neural networks with a novel architecture are proposed to recognize the existence and severity of ROP disease per-examination. The severity of ROP is divided into mild and severe cases according to the disease progression. The proposed architecture consists of two sub-networks connected by a feature aggregate operator. The first sub-network is designed to extract high-level features from images of the fundus. These features from different images in an examination are fused by the aggregate operator, then used as the input for the second sub-network to predict its class. A large data set imaged by RetCam 3 is used to train and evaluate the model. The high classification accuracy in the experiment demonstrates the effectiveness of the proposed architecture for recognizing the ROP disease.
Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor

Environment analysis of automatic vehicles needs the detection from 3-D point cloud information. This paper addresses this task when only partial scanning data are available. Our method develops the detection capabilities of autonomous vehicles equipped with 3-D range sensors for navigation purposes. In industrial practice, the safety scanners of automated guided vehicles (AGVs) and a localization technology provide an additional possibility to gain 3-D point clouds from planar contour points or low vertical resolution. Based on this data and a suitable evaluation algorithm, intelligence of vehicles can be significantly increased without the need for installation of additional sensors. In this paper, we propose a solution for an obstacle categorization problem for partial point clouds without shape modeling. The approach is tested for a known database, as well as for real-life scenarios. In case of AGVs, real-time run is provided by on-board computers of usual complexity.
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction

In this paper, through multi-task ensemble framework we address three problems of emotion and sentiment analysis i.e. "emotion classification & intensity", "valence, arousal & dominance for emotion" and "valence & arousal} for sentiment". The underlying problems cover two granularities (i.e. coarse-grained and fine-grained) and a diverse range of domains (i.e. tweets, Facebook posts, news headlines, blogs, letters etc.). The ensemble model aims to leverage the learned representations of three deep learning models (i.e. CNN, LSTM and GRU) and a hand-crafted feature representation for the predictions. Experimental results on the benchmark datasets show the efficacy of our proposed multi-task ensemble frameworks. We obtain the performance improvement of 2-3 points on an average over single-task systems for most of the problems and domains.
An Analysis of Complex Industrial Test Code Using Clone Analysis

Many companies, including Ericsson, experience increased software verification costs. Agile cross-functional teams find it easy to make new additions of test cases for every change and fix. The consequence of this phenomenon is duplications of test code. In this paper, we perform an industrial case study that aims at better understanding such duplicated test fragments or as we call them, clones. In our study, 49% (LOC) of the entire test code are clones. The reported results include figures about clone frequencies, types, similarity, fragments, and size distributions, and the number of line differences in cloned test cases. It is challenging to keep clones consistent and remove unnecessary clones during the entire testing process of large-scale commercial software.
Efficacy of retreatment with immunomodulatory drugs and proteasome inhibitors following daratumumab monotherapy in relapsed and refractory multiple myeloma patients

This single‐centre retrospective observational study analysed the efficacy of retreatment with immunomodulatory agents (IMiDs) and proteasome inhibitors (PIs) after treatment with daratumumab monotherapy in patients with relapsed and/or refractory multiple myeloma (RRMM). In total 55 patients were treated with daratumumab monotherapy between 2010 and 2017. From this group 29 (53%) IMiD‐refractory patients were retreated with an IMiD after daratumumab and 6 (11%) PI‐refractory patients were retreated with a PI‐based regimen. For the IMiD‐refractory patients the overall response rate (ORR) was 52% (15/29 patients, partial response or better) upon IMiD retreatment, whereas the ORR to PI retreatment was 67% (4/6 patients) in the PI‐refractory group. The immunomodulatory effects of daratumumab may play a role in these high response rates in previously refractory patients. Due to the >6 month‐long persistence of daratumumab in the plasma the subsequent therapies can effectively be considered as combination therapy. Furthermore, the excellent tolerability of daratumumab treatment may enable patients to recover from prior lines of treatment and receive full dosing of subsequent therapies. In conclusion, a high proportion of RRMM patients benefitted from retreatment with IMiDs and PIs after daratumumab treatment. These retreatment options should therefore be explored in RRMM patients progressing on daratumumab monotherapy.
An Energy-Efficient Asymmetric Multi-Processor for HPC Virtualization

The Asymmetric Multiprocessor (AMP) architecture brings new opportunities to achieve better trade-offs between performance and operational/financial costs. This paper presents the case of an AMP to address poor I/O performance in a virtualized HPC system, by using small side-cores to offload I/O processing. We use full machine simulations to explore the micro-architectural parameter space in detail and perform an energy-delay-area analysis, taking into account the relationship between size and access delay in the caches. The simulation side-core model has been validated on the Atom processor, with performance counter metrics being within 11%. study focuses on TLBs and caches which our results show to have a remarkable impact on performance. Compared with a previous AMP study considering only performance and limited to existing hardware, our results confirm the broad nature of that design, including the preference for an asymmetric 2-way CPU pipeline. Our improved methodology also boosts the degree of confidence in these results. We however show that the optimal features of an efficient side-core are smaller and simpler L1/L2 caches (16KB 4-way and 16KB 2-way I/D caches and a 128KB 4-way L2 cache) and L1/L2 TLBs (32/48 entry fully associative L1 I/D LBs and 256 entry 4-way L2 I/D TLBs). Meanwhile, our analysis reveals that a processor module consisting of two big cores and a small side-core of our design can reduce average power, energy, and area by 9.2%, 8%, and 24.4%, respectively, compared with a module of three big cores (the AMD K10), while retaining performance (at the cost of 1.3% performance loss).
A stereological study of the effects of mercury inhalation on the cerebellum

ABSTRACT Mercury in the environment that arises from organic and inorganic sources can cause irreversible damage to the nervous system. Toxicity may be direct or may arise from interactions with other metals in the environment. We evaluated the possible effects of mercury vapor on rat cerebellum. Twelve adult female rats were divided into control and experimental groups. The rats in the experimental group were exposed to mercury vapor for 9 h/day for 45 days. Cerebellar tissue samples were evaluated using stereology and for histopathology. The total number of Purkinje cells was estimated using a physical disector method. We found that in the experimental group, overall volume decreased and the number of Purkinje cells was reduced. We also found cellular damage including pycnotic nuclei, eosinophilic cytoplasm and vacuolization; these features were absent in the control group. We found that chronic exposure to inorganic mercury vapor is toxic to the cerebellum.
Iron concentration in exhaled breath condensate decreases in ever-smokers and COPD patients

Investigation employing bronchoalveolar lavage supports both increased and decreased iron concentrations in the epithelial lining fluid (ELF) of smokers. Exhaled breath condensate (EBC) is an alternative approach to sampling the ELF. We evaluated for an association between iron homeostasis and both smoking and a diagnosis of chronic obstructive pulmonary disease (COPD) by measuring metal concentrations in EBC samples from non-smoker controls, smoker controls, and individuals diagnosed with COPD. The total number of EBC specimens was 194. EBC iron and zinc concentrations (mean ± standard error) in the total study population were 0.610 ± 0.025 and 40.73 ± 1.79 ppb respectively. In linear regressions, total cigarette smoking in pack years showed a significant (negative) relationship with EBC iron concentration but not with EBC zinc concentration. Iron concentrations in EBC from GOLD stage II, III, and IV patients were all significantly decreased relative to those from non-smoker and smoker controls. In contrast to iron, zinc concentrations in EBC were not significantly different than those from non-smoker and smoker controls. It is concluded that smoking decreases EBC iron concentrations and patients diagnosed with COPD have significantly lower EBC iron concentrations. These results likely reflect an increased burden of cigarette smoke particles in the lower respiratory tract of ever-smokers and patients with COPD and the capacity of components in this particle to complex iron.
DNA damage and protein oxidation associated with ageing correlate with cognitive dysfunction in a Malaysian population

Abstract Ageing is associated with increased oxidative stress accompanied by cognitive decline. The aim of this study was to evaluate oxidative stress biomarkers and their possible relationship with cognitive performances during ageing among the Malay population. Approximately 160 healthy Malay adults aged between 28 and 79 years were recruited around Selangor and Klang Valley. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA), forward digit span (FDS), backward digit span (BDS), digit symbol, Rey Auditory Verbal Learning Test immediate recalled [RAVLT(I)] and delayed recalled [RAVLT(D)], and visual reproduction immediate recalled (VR-I) and delayed recalled (VR-II). DNA damage, plasma protein carbonyl and malondialdehyde (MDA) levels were also determined. Cognitive function test showed significant lower scores of MoCA, BDS, RAVLT(I), RAVLT(D), digit symbol, VR-I, and VR-II in the older age group (60 years old) compared with the 30-, 40-, and 50-year-old group. The extent of DNA damage was sequential with age: 60 > 50 > 40 > 30, whereas protein carbonyl was higher in 40-, 50-, and 60-year-old groups compared with the youngest group (30 years old). However, the MDA level was observed unchanged in all age groups. Approximately 21.88% of the participants had cognitive impairment. Multiple logistic regression analysis revealed that DNA damage and protein carbonyl levels are predictors for cognitive impairment in healthy Malays. In conclusion, cognitive decline occurred in healthy adult Malay population at an early age of 30 years old with corresponding higher DNA damage and protein oxidation.
Buccolingual inclination of molars in untreated children and adults: A cone beam computed tomography study.

OBJECTIVES: To evaluate and compare the buccolingual inclinations of maxillary and mandibular first molars in untreated children and adults. MATERIALS AND METHODS: One hundred and thirty-eight subjects were selected and divided into three groups, as follows: (1) age 6-9 years, N = 46; (2) age 10-19 years, N = 56; and (3) age 25-35 years, N = 36. For each subject, existing cone beam computed tomography images were used, and the long axis for each maxillary and mandibular first molar was determined. The converge angles formed by the long axis of left and right maxillary first molars and by the long axis of left and right mandibular first molars were measured. RESULTS: The maxillary molars exhibited buccal inclination, with the converge angle of 21.1° ± 9.5° in group 1, 17.3° ± 8.6° in group 2, and 9.3° ± 7.3° in group 3. Statistically significant differences were found between groups 1 and 3 and groups 2 and 3, but not between groups 1 and 2. The mandibular molars exhibited lingual inclination, with the converge angle of 34.9° ± 11° for group 1, 26.6° ± 9.2° for group 2, and 26.1° ± 7.7° for group 3. Statistically significant differences were found between groups 1 and 2 and groups 1 and 3, but not between groups 2 and 3. CONCLUSIONS: Maxillary first molars exhibited buccal inclination. Adults displayed less inclination than did children. Mandibular first molars exhibited lingual inclination. Adults displayed less inclination than did children. Some degree of curve of Wilson should be maintained at the end of orthodontic treatment to fulfill physiologic needs and stability.
Age-related epigenome-wide DNA methylation and hydroxymethylation in longitudinal mouse blood

ABSTRACT DNA methylation at cytosine-phosphate-guanine (CpG) dinucleotides changes as a function of age in humans and animal models, a process that may contribute to chronic disease development. Recent studies have investigated the role of an oxidized form of DNA methylation – 5-hydroxymethylcytosine (5hmC) – in the epigenome, but its contribution to age-related DNA methylation remains unclear. We tested the hypothesis that 5hmC changes with age, but in a direction opposite to 5-methylcytosine (5mC), potentially playing a distinct role in aging. To characterize epigenetic aging, genome-wide 5mC and 5hmC were measured in longitudinal blood samples (2, 4, and 10 months of age) from isogenic mice using two sequencing methods – enhanced reduced representation bisulfite sequencing and hydroxymethylated DNA immunoprecipitation sequencing. Examining the epigenome by age, we identified 28,196 unique differentially methylated CpGs (DMCs) and 8,613 differentially hydroxymethylated regions (DHMRs). Mouse blood showed a general pattern of epigenome-wide hypermethylation and hypo-hydroxymethylation with age. Comparing age-related DMCs and DHMRs, 1,854 annotated genes showed both differential 5mC and 5hmC, including one gene – Nfic – at five CpGs in the same 250 bp chromosomal region. At this region, 5mC and 5hmC levels both decreased with age. Reflecting these age-related epigenetic changes, Nfic RNA expression in blood decreased with age, suggesting that age-related regulation of this gene may be driven by 5hmC, not canonical DNA methylation. Combined, our genome-wide results show age-related differential 5mC and 5hmC, as well as some evidence that changes in 5hmC may drive age-related DNA methylation and gene expression.