Miracle-dz/UniFAR-FTUs huggingface dataset:
Antagonistic effects of growing season and autumn temperatures on the timing of leaf coloration in winter deciduous trees

Autumn phenology remains a relatively neglected aspect in climate change research, which hinders an accurate assessment of the global carbon cycle and its sensitivity to climate change. Leaf coloration, a key indicator of the growing season end, is thought to be triggered mainly by high or low temperature and drought. However, how the control of leaf coloration is split between temperature and drought is not known for many species. Moreover, whether growing season and autumn temperatures interact in influencing the timing of leaf coloration is not clear. Here, we revealed major climate drivers of leaf coloration dates and their interactions using 154 phenological datasets for four winter deciduous tree species at 89 stations, and the corresponding daily mean/minimum air temperature and precipitation data across China's temperate zone from 1981 to 2012. Results show that temperature is more decisive than drought in causing leaf coloration, and the growing season mean temperature plays a more important role than the autumn mean minimum temperature. Higher growing season temperature and lower autumn minimum temperature would induce earlier leaf coloration date. Moreover, the mean temperature over the growing season correlates positively with the autumn minimum temperature. This implies that growing season mean temperature may offset the requirement of autumn minimum temperature in triggering leaf coloration. Our findings deepen the understanding of leaf coloration mechanisms in winter deciduous trees and suggest that leaf life‐span control depended on growing season mean temperature and autumn low temperature control and their interaction are major environmental cues. In the context of climate change, whether leaf coloration date advances or is delayed may depend on intensity of the offset effect of growing season temperature on autumn low temperature.
A Regulation Policy of EV Discharging Price for Demand Scheduling

The widespread penetration of electric vehicles (EVs) will cause an increase in energy demand. To achieve an effective demand response approach, the participation of EV users is a crucial factor. Nevertheless, the discharging price found in the literature is generally set in accordance with the spot electricity price. A fair pricing strategy is thus desirable to motivate EV users to regulate their charging behavior for less bill payment, and at the same time to achieve load regulation as well as benefit for power companies. The charging and discharging prices should be cohesively equilibrated to achieve the task. This paper presents a new V2G (Vehicle-to-Grid) pricing policy by incorporating the system load condition, maximum power limit, and price rate for user load in a fair manner. The price setting follows a hierarchical optimization procedure between the operator and end users with the aim to maximize operator's profit and balance end users’ bill and comfort level. The optimization for the operator is achieved via genetic algorithm whereas the energy management of each user is decomposed as a single power optimization problem. Simulation results have confirmed the benefit of liberating the discharging price from the user load tariff and have verified the effectiveness of the proposed method.
DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings

We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost thermal camera, we track a person's breathing patterns as temperature changes around his/her nostril. The paper's technical contribution is threefold. First of all, instead of creating handcrafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexity of the breathing dynamics. Second, a spatial pattern analysis based on a deep Convolutional Neural Network (CNN) is directly applied to the spectrogram sequences without the need of hand-crafting features. Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e.g., up to a few hours). The model is trained and tested with data collected from people exposed to two types of cognitive tasks (Stroop Colour Word Test, Mental Computation test) with sessions of different difficulty levels. Using normalised self-report as ground truth, the CNN reaches 84.59% accuracy in discriminating between two levels of stress and 56.52% in discriminating between three levels. In addition, the CNN outperformed powerful shallow learning methods based on a single layer neural network. Finally, the dataset of labelled thermal images will be open to the community.
Mortality and Length of Stay Trends Following Implementation of a Rapid Response System and Real-Time Automated Clinical Deterioration Alerts

A study was performed to determine the potential influence of a rapid response system (RRS) employing real-time clinical deterioration alerts (RTCDAs) on patient outcomes involving 8 general medicine units. Introduction of the RRS occurred in 2006 with staged addition of the RTCDAs in 2009. Statistically significant year-to-year decreases in mortality were observed through 2014 (r = −.794; P = .002). Similarly, year-to-year decreases in the number of cardiopulmonary arrests (CPAs; r = −.792; P = .006) and median lengths of stay (r = −.841; P = .001) were observed. There was a statistically significant year-to-year increase in the number of RRS activations for these units (r = .939; P < .001) that was inversely correlated with the occurrence of CPAs (r = −.784; P = .007). In this single-institution retrospective study, introduction of a RRS employing RTCDAs was associated with lower hospital mortality, CPAs, and hospital length of stay.
Objective Model Selection for Identifying the Human Feedforward Response in Manual Control

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: “false-positive” feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.
Online Learning with an Unknown Fairness Metric

We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric. These constraints demand that we select similar actions or individuals with approximately equal probability (arXiv:1104.3913), which may be at odds with optimizing reward, thus modeling settings where profit and social policy are in tension. We assume we learn about an unknown Mahalanobis similarity metric from only weak feedback that identifies fairness violations, but does not quantify their extent. This is intended to represent the interventions of a regulator who "knows unfairness when he sees it" but nevertheless cannot enunciate a quantitative fairness metric over individuals. Our main result is an algorithm in the adversarial context setting that has a number of fairness violations that depends only logarithmically on $T$, while obtaining an optimal $O(\sqrt{T})$ regret bound to the best fair policy.
Associations Between Three CTLA-4 Polymorphisms and Hashimoto's Thyroiditis Risk: An Updated Meta-Analysis with Trial Sequential Analysis.

AIMS In this article, we conducted an updated meta-analysis with trial sequential analysis (TSA) to refine the associations between three common single nucleotide polymorphisms (SNPs) in the CTLA-4 gene (+49A/G, CT60, and -318C/T) and Hashimoto's thyroiditis (HT). METHODS Statistical association analyses were performed using four genetic models, including the allelic, codominant, dominant, and recessive models with the Revman 5.3, Stata 14.0, and TSA 0.9 software. For quality evaluation, the Newcastle-Ottawa Scale was used. RESULTS Our meta-analysis included 29 independent studies with low risk of bias that involved 3614 cases and 8839 controls. The pooled results indicated a significant association between the +49A/G polymorphism and an increased risk of HT in all four genetic models. Furthermore, the TSA demonstrated that the evidence of this association was robust and credible. Subgroup analysis revealed a significantly higher risk of HT in Asians compared with Caucasians associated with the +49A/G polymorphism. Surprisingly, in contrast to the results with adults, we did not find any significant association when analyzing the pediatric subgroup. For the CT60 polymorphism, a significant association with risk of HT was detected overall, and subgroup analysis revealed that this association was significant in the Asian subgroup, but not in the Caucasian subgroup. No statistically significant associations were detected in any of the investigated genetic models for the -318C/T polymorphism. However, the results of the TSA suggested that the sample sizes used for the CTLA-4 CT60 and -318C/T SNPs were insufficient. CONCLUSION Our meta-analysis showed significant associations between the risk of HT and both the +49A/G and CT60 polymorphisms, but not the -318C/T polymorphism. In addition, the TSA results indicated that CTLA-4 +49A/G should be considered as a biomarker for HT, whereas both the CT60 and -318C/T SNPs warrant confirmation by further studies.
Primary Ovarian Insufficiency

Abstract Primary ovarian insufficiency (POI), also known as premature ovarian failure or premature menopause, is defined as cessation of menstruation before the expected age of menopause. Potential etiologies for POI can be divided into genetic, autoimmune, and iatrogenic categories. This review will try to summarize the genetic basis of POI focusing on recent data that are available using newer genetic techniques such as genome-wide association studies, whole-exome sequencing (WES), or next-generation sequencing techniques. By using these techniques, many genes have arisen that play some role in the pathophysiology of POI. Some of them have been replicated in other studies; however, the majority has not been proven yet to be unequivocally causative through functional validation studies. Elucidating the genetic and molecular basis of POI is of paramount importance not only in understanding ovarian physiology but also in providing genetic counseling and fertility guidance. Once additional variants are detected, it might become possible to predict the age of (premature) menopause in women at risk for POI. Women having certain perturbations of POI can be offered the option of oocyte cryopreservation, with later thawing and use in assisted reproductive technology at an appropriate age.
Mechanical work and metabolic cost of walking after weight loss in obese adolescents.

PURPOSE This study was performed to investigate whether changes in biomechanical parameters of walking explain the reduction in net metabolic cost after weight loss in obese adolescents. METHODS Body composition and metabolic and mechanical energy costs of walking at 1.25 m·s(-1) were assessed in 16 obese adolescents before and after a weight loss. Center of mass (COM) and foot accelerations were measured using two inertial sensors and integrated twice to determine COM and foot velocities and displacements. Potential and kinetic energy fluctuations of the COM and the external mechanical work were calculated. Lateral leg swing was calculated from foot displacements. RESULTS As expected, the decrease in net metabolic cost was greater, which would have been expected on the basis of the amount of weight loss. The smaller lateral leg swing after weight loss did not explain part of the decrease in net metabolic cost. The reduced body mass required less leg muscle work to raise and accelerate the COM as well as to support body weight. The decrease in body mass seems also associated with a lesser leg muscle work required to raise the COM because of smaller vertical motions. As a result of the inverted pendulum mechanism, the decrease in vertical motions (hence in potential energy fluctuations) was probably related to the decrease in mediolateral kinetic energy fluctuations. Moreover, the lesser amount of fat mass in the gynoid region seems related to the decrease in net metabolic cost of walking. CONCLUSIONS The reduction in net metabolic cost of walking after weight loss in weight-reduced adolescents is associated with changes in the biomechanical parameters of walking.
Selection in Health Insurance Markets and Its Policy Remedies

Selection (adverse or advantageous) is the central problem that inhibits the smooth, efficient functioning of competitive health insurance markets. Even—and perhaps especially—when consumers are well-informed decision makers and insurance markets are highly competitive and offer choice, such markets may function inefficiently due to risk selection. Selection can cause markets to unravel with skyrocketing premiums and can cause consumers to be under- or overinsured. In its simplest form, adverse selection arises due to the tendency of those who expect to incur high health care costs in the future to be the most motivated purchasers. The costlier enrollees are more likely to become insured rather than to remain uninsured, and conditional on having health insurance, the costlier enrollees sort themselves to the more generous plans in the choice set. These dual problems represent the primary concerns for policymakers designing regulations for health insurance markets. In this essay, we review the theory and evidence concerning selection in competitive health insurance markets and discuss the common policy tools used to address the problems it creates. We emphasize the two markets that seem especially likely to be targets of reform in the short and medium term: Medicare Advantage (the private plan option available under Medicare) and the state-level individual insurance markets.
Opposing roles of KIT and ABL1 in the therapeutic response of gastrointestinal stromal tumor (GIST) cells to imatinib mesylate

Most gastrointestinal stromal tumors (GISTs) are caused by activating mutations of the KIT receptor tyrosine kinase. The small molecule inhibitor imatinib mesylate was initially developed to target the ABL1 kinase, which is constitutively activated through chromosomal translocation in BCR-ABL1-positive chronic myeloid leukemia. Because of cross-reactivity of imatinib against the KIT kinase, the drug is also successfully used for the treatment of GIST. Although inhibition of KIT clearly has a major role in the therapeutic response of GIST to imatinib, the contribution of concomitant inhibition of ABL in this context has never been explored. We show here that ABL1 is expressed in the majority of GISTs, including human GIST cell lines. Using siRNA-mediated knockdown, we demonstrate that depletion of KIT in conjunction with ABL1 – hence mimicking imatinib treatment – leads to reduced apoptosis induction and attenuated inhibition of cellular proliferation when compared to depletion of KIT alone. These results are explained by an increased activity of the AKT survival kinase, which is mediated by the cyclin-dependent kinase CDK2, likely through direct phosphorylation. Our results highlight that distinct inhibitory properties of targeted agents can impede antitumor effects and hence provide insights for rational drug development. Novel KIT-targeted agents to treat GIST should therefore comprise an increased specificity for KIT while at the same time displaying a reduced ability to inhibit ABL1.
Characteristics and Associated Comorbidities of Pediatric Dental Patients Treated under General Anesthesia.

OBJECTIVE To describe characteristics and identify common comorbidities of children receiving dental treatment under general anesthesia at Children's Hospital of New York-Presbyterian. STUDY DESIGN Electronic medical records of all children that received dental treatment under general anesthesia through the Division of Pediatric Dentistry from 2012-2014 were reviewed. Data describing patient characteristics (age, sex, race/ethnicity, insurance carrier, and American Society of Anesthesiologists physical status classification system), medical history, and justification for treatment were collected. Descriptive statistics, including frequencies, percentages and t-tests, were calculated. RESULTS A total of 298 electronic medical records were reviewed, of which 50 records were excluded due to missing information. Of the 248 electronic medical records included, the average age was 5-years-old and 58% were male. The most common reason for dental treatment under general anesthesia was extent and severity of dental disease (53%), followed by significant medical history (47%) and behavior/pre-cooperative age (39%). Those who were ASA III or IV were older (6.6-years) (p<.001). Common medical comorbidities appear evenly distributed: autism (12%), cardiac anomalies (14%), developmental delay (14%), genetic syndromes/chromosomal disorders (13%), and neurological disorders (12%). Younger age groups (1 to 2 years and 3 to 5 years) had a high percentage of hospitalizations due to the extent and severity of the dental disease (83%) and behavior (77%) (p<0.001). CONCLUSIONS No single comorbidity was seen more often than others in this patient population. The range of medical conditions in this population may be a reflection of the range of pediatric specialty services at Children's Hospital of NewYork-Presbyterian.
Overlap Detection for a Genome Assembly Based on Genomic Signal Processing

Although the genome sequences of most studied organisms, like human, E. coli, and others are already known, de novo genome sequencing remains popular as a majority of genomes remains unknown. Unfortunately, sequencing machines are able to read only short fragments of DNA. Therefore, one of the basic steps in reconstructing novel genomes lies in putting these pieces of DNA, called reads, together into complete genome sequences using a process known as genome assembly. Reads joining, however, requires efficient detection of their overlaps. This is commonly performed by comparing the particular characters (A, C, G, T) of the reads using string processing techniques. In this paper, we present an alternative way of detecting overlaps using genomic signal processing. Unlike string comparison, numerical phase signals reflect the complementarity of double stranded DNA making the signal ideal for effective strand independent overlap detection using covariance with high accuracy.
A multiple vehicle group modelling and computation framework for guidance of an autonomous road vehicle

Predictive autonomous vehicle guidance schemes can be configured to embed human driver-like decisions regarding multiple dynamic obstacle vehicle groups prevalent in public traffic, especially on highways. This paper proposes a vehicle grouping model and computation algorithm that facilitates dynamic grouping of surrounding object vehicles. This grouping serves to compute the time varying areas that are to be occupied by vehicle groups in the predicted motion plan so that those areas including the undesired local minimums can be excluded for the non-convex motion planning problem. To reduce the computational burden of the grouping and boundary generation for online implementation, supervised learning methods are applied to train the neural networks that compute the optimal boundary of the group (s) that maximizes the available planning field. The proposed algorithm is incorporated in a predictive guidance scheme and its performance and computational details are illustrated via simulations of an autonomously controlled vehicle in public highway traffic scenarios involving multiple other object vehicles.
Developmental Trends in Flexibility and Automaticity of Social Cognition.

Age-related changes in flexibility and automaticity of reasoning about social situations were investigated. Children (N = 101; age range = 7;8-17;7) were presented with the flexibility and automaticity of social cognition (FASC), a new measure of social cognition in which cartoon vignettes of social situations are presented and participants explain what is happening and why. Scenarios vary on whether the scenario is socially ambiguous and whether or not language is used. Flexibility is determined by the number of unique, plausible explanations, and automaticity is indicated by speed of response. Overall, both flexibility and automaticity increased significantly with age. Language and social ambiguity influenced performance. Future work should investigate differences in FASC in older populations and clinical groups.
Anxiety disorders and CRP in a population cohort study with 54,326 participants: The LifeLines study

Abstract Objectives: Growing evidence indicates that inflammatory processes may play a role in the pathogenesis of anxiety disorders. Nevertheless, much remains to be learned about the involvement of inflammation, including C-reactive protein (CRP), in specific anxiety disorders. This study examines the relation between anxiety disorders and CRP. Methods: Associations of serum CRP with anxiety disorders were determined in a large population study (n = 54,326 participants, mean age = 47 years; 59% female), the LifeLines cohort. Depressive and anxiety disorders (generalized anxiety disorder, social anxiety phobia, panic disorder with or without agoraphobia and agoraphobia without panic disorder) were assessed using the Mini-International Neuropsychiatric Interview. Results: Anxiety disorders, with the exception of social anxiety disorder, were significantly associated with increased CRP. After adjusting for demographics, life style factors, health factors, medication use, depression, and psychological stressors, CRP remained significantly associated with panic disorder with agoraphobia (β = 0.01, P = .013). Moreover, CRP levels were significantly higher in people with panic disorder with agoraphobia compared to other anxiety disorders, independent of all covariates (F = 3.00, df = 4, P = .021). Conclusions: Panic disorder with agoraphobia is associated with increased CRP, although the effect size of this association is small. This indicates that neuroinflammatory mechanisms may play a potential role in its pathophysiology.