Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Evaluation of Individualized Treatment Rules

Abstract The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a target population. We propose a new evaluation metric, the population average prescriptive effect (PAPE). The PAPE compares the performance of ITR with that of non-individualized treatment rule, which randomly treats the same proportion of units. Averaging the PAPE over a range of budget constraints yields our second evaluation metric, the area under the prescriptive effect curve (AUPEC). The AUPEC represents an overall performance measure for evaluation, like the area under the receiver and operating characteristic curve (AUROC) does for classification, and is a generalization of the QINI coefficient used in uplift modeling. We use Neyman’s repeated sampling framework to estimate the PAPE and AUPEC and derive their exact finite-sample variances based on random sampling of units and random assignment of treatment. We extend our methodology to a common setting, in which the same experimental data are used to both estimate and evaluate ITRs. In this case, our variance calculation incorporates the additional uncertainty due to random splits of data used for cross-validation. The proposed evaluation metrics can be estimated without requiring modeling assumptions, asymptotic approximation, or resampling methods. As a result, it is applicable to any ITR including those based on complex machine learning algorithms. The open-source software package is available for implementing the proposed methodology. Supplementary materials for this article are available online.
The ANNS approach to DEM reconstruction

This research has 6 fundamental aims: (i) to present a modified version of Taylor's interpolation, one that is more effective and faster than the original; (ii) outline the capability of artificial neural networks (ANNs) to perform an optimal functional approximation of the digital elevation model reconstruction from a satellite map, using a small and independent sample of Global Positioning System observations; (iii) demonstrate experimentally how ANNs outperform the traditional and most used algorithm for the height interpolation (Taylor's interpolation); (iv) introduce a new ANN, the Conic Net, able to outperform the results of the classic and more known multilayer perceptron; (v) determine that Conic Nets, even when using Taylor's modified interpolation as input features, are able to optimally approximate the heights with one order of magnitude more than the original satellite map; and (vi) make evident the possibility to interpolate the DEM heights through an ANN, which learns a data set of known points.
Questionnaire Development Process to Measure the SMEs' Continuous Use behavior towards Cloud Computing Services

In the business world, cloud computing (CC) technologies are emerging computing platforms used to improve the deployment of ICT services with the aim of improving performance and competitive advantage. However, enough attention has not been given to the factors that influence the continuous use behavior of organizations towards CC services. Hence, this study aims to develop a reliable and valid questionnaire for the determination of the continuous use behavior of SMEs towards CC services. The questionnaire used in this study passed through three basic steps in accordance to the established scale development guidelines provided by Moore and Benbasat [1]. The questionnaire included three main sections with 87 questions. The questionnaire serves as a tool that will help SMEs and CC service providers to plan their strategies for providing a supportive sustained use of CC services in Malaysia.
Validation of an individualized reduction of falls intervention program among wheelchair and scooter users with multiple sclerosis

Introduction: Falls are a serious concern for wheelchair and scooter users with multiple sclerosis (MS). Approximately, 75% of the population reports at least one fall in a 6-month period and nearly half report frequent falls. Falls can result in physical injuries and contribute to activity curtailment. Despite the negative consequences, limited evidenced-based fall prevention programs designed specifically for wheelchair and scooter users with MS exist. Purpose: Recognizing the threat falls pose to health and well-being and the dearth of fall prevention programs, the purpose of this study is to perform a structured process evaluation and examine the feasibility and efficacy of a community-based intervention specifically designed to reduce fall incidence among wheelchair and scooter users with MS. Secondary aims of the intervention are to improve functional mobility skills associated with fall risk (e.g., transfer and wheelchair skills, balance), increase knowledge of fall risk factors, decrease fear of falling, and enhance quality of life and community participation. Methods: To evaluate our specific aims, a clinical trial will be performed with 160 wheelchair and scooter users with MS. (ClinicalTrials.gov Identifier: NCT03705364). Participants will be recruited to participate in a small group-style community-based program. The content of the program will be based on factors found to be associated with falls among wheelchair and scooter users with MS. These factors include but are not limited to, wheelchair/scooter related characteristics, transfer activities, impaired seated balance, and environmental factors. A physical or occupational therapist, will implement the intervention, which is comprised of 6 sessions that occur once weekly. The incidence of falls, along with an examination of wheelchair/scooter and transfer skills, seated postural control and knowledge of fall related risk factors will be compared between intervention and control participants, with assessment periods occurring prior to the intervention, 1 to 2 weeks after completion of the 6-week intervention session, and 12 weeks after the intervention period is complete. Conclusion: Results from this study will guide the refinement of the intervention program and inform future research among a large and diverse group of wheelchair and scooter users living with MS.
Massively Multilingual Transfer for NER

In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a “massive” setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model.
An Integrated Analysis of the Rice Transcriptome and Metabolome Reveals Differential Regulation of Carbon and Nitrogen Metabolism in Response to Nitrogen Availability

Nitrogen (N) is an extremely important macronutrient for plant growth and development. It is the main limiting factor in most agricultural production. However, it is well known that the nitrogen use efficiency (NUE) of rice gradually decreases with the increase of the nitrogen application rate. In order to clarify the underlying metabolic and molecular mechanisms of this phenomenon, we performed an integrated analysis of the rice transcriptome and metabolome. Both differentially expressed genes (DEGs) and metabolite Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that carbon and nitrogen metabolism is significantly affected by nitrogen availability. Further analysis of carbon and nitrogen metabolism changes in rice under different nitrogen availability showed that high N inhibits nitrogen assimilation and aromatic metabolism pathways by regulating carbon metabolism pathways such as the tricarboxylic acid (TCA) cycle and the pentose phosphate pathway (PPP). Under low nitrogen, the TCA cycle is promoted to produce more energy and α-ketoglutarate, thereby enhancing nitrogen transport and assimilation. PPP is also inhibited by low N, which may be consistent with the lower NADPH demand under low nitrogen. Additionally, we performed a co-expression network analysis of genes and metabolites related to carbon and nitrogen metabolism. In total, 15 genes were identified as hub genes. In summary, this study reveals the influence of nitrogen levels on the regulation mechanisms for carbon and nitrogen metabolism in rice and provides new insights into coordinating carbon and nitrogen metabolism and improving nitrogen use efficiency in rice.
Finger Motion Estimation Based on Sparse Multi-Channel Surface Electromyography Signals Using Convolutional Neural Network

This paper presents a finger motion estimation based on sparse multi-channel surface electromyography (sEMG) signals using a convolutional neural network (CNN). Although classification with CNNs has achieved high accuracy in gesture recognition, the most cases use a high-density sEMG as the signal acquisition method, which is problematic because this requires many sensors for measuring sEMG signals, resulting in high costs. We therefore propose estimating the finger motion with a sparse multi-channel sEMG method using ring-shaped sensors. The finger motion estimation is performed by classifying images generated from the amplitude variations of sEMG signals, and the image classification is achieved with a simple CNN model featuring two pairs of convolutional and pooling layers and two fully connected layers. Experimental results showed that the test accuracy reached 90% in classifying sEMG signals into four types: thumb opened, thumb closed, fingers (excluding thumb) opened, and fingers (excluding thumb) closed.
The Physiological Roles of Carnosine and β-Alanine in Exercising Human Skeletal Muscle

ABSTRACT Carnosine (β-alanyl-l-histidine) plays an important role in exercise performance and skeletal muscle homeostasis. Dietary supplementation with the rate-limiting precursor β-alanine leads to an increase in skeletal muscle carnosine content, which further potentiates its effects. There is significant interest in carnosine and β-alanine across athletic and clinical populations. Traditionally, attention has been given to performance outcomes with less focus on the underlying mechanism(s). Putative physiological roles in human skeletal muscle include acting as an intracellular pH buffer, modulating energy metabolism, regulating Ca2+ handling and myofilament sensitivity, and scavenging of reactive species. Emerging evidence shows that carnosine could also act as a cytoplasmic Ca2+–H+ exchanger and form stable conjugates with exercise-induced reactive aldehydes. The enigmatic nature of carnosine means there is still much to learn regarding its actions and applications in exercise, health, and disease. In this review, we examine the research relating to each physiological role attributed to carnosine, and its precursor β-alanine, in exercising human skeletal muscle.
Precision Medicine Applications to Manage Multiply Injured Patients With Orthopaedic Trauma

Summary: Precision medicine offers potential for improved outcomes by tailoring interventions based on patient-specific demographics and disease-specific data. Precision methods are relatively unexplored in trauma patients. New research is being looked at for precision methods to treat patients with large extremity wounds, nonunions, and fractures associated with polytrauma. Precision-based clinical decision tools are being validated to optimize timing for open wound definitive closure. Early patient-specific biomarkers to stratify nonunion risk within 1 week of fracture are being explored. Patient-specific data to stage timing of major fracture interventions in multiply injured patients are being interrogated.
Modeling user context for valence prediction from narratives

Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. early diagnosis of mental diseases, supervision of disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect challenge, the task of valence prediction was framed as a three-class classification problem using 8 seconds fragments from individuals' narratives. As such, the task did not allow for exploring contextual information of the narratives. In this work, we investigate the intrinsic information from multiple narratives recounted by the same individual in order to predict their current state-of-mind. Furthermore, with generalizability in mind, we decided to focus our experiments exclusively on textual information as the public availability of audio narratives is limited compared to text. Our hypothesis is, that context modeling might provide insights about emotion triggering concepts (e.g. events, people, places) mentioned in the narratives that are linked to an individual's state of mind. We explore multiple machine learning techniques to model narratives. We find that the models are able to capture inter-individual differences, leading to more accurate predictions of an individual's emotional state, as compared to single narratives.
People of Lower Social Status Are More Sensitive to Hedonic Product Information—Electrophysiological Evidence From an ERP Study

Consumer psychology research has shown that individuals of different social statuses have distinctive purchase intentions for different products. Individuals of a high social status will simultaneously measure the symbolic status meaning and utilitarian value of a product, but they will not show strong preferences for any attributes. However, individuals of a low social status show strong purchasing tendency for hedonic products that are associated with symbolic status meaning and could satisfy their spiritual needs. This phenomenon may be due to self-threat, which caused by hedonic products. Based on the above, this study compares the cognitive processing differences of hedonic and utilitarian label products between high- and low-social-status groups by recording event related potentials (ERPs). The results showed that under the P2, P3, and LPP components, the low-social-status group elicited smaller deflections in hedonic label stimuli than in utilitarian label stimuli. The high-social-status group did not show a significant difference in these components. These results suggested that individuals with a low social status are more sensitive to hedonic product information, because high-status information contained in the hedonic label induces a sense of threat in them and generates certain negative emotions.
A Qualitative Post-Experience Method for Evaluating Changes in VR Presence Experience Over Time

A particular measure to evaluate a head-mounted display (HMD) based experience is the state of feeling present in virtual reality. Interruptions of a presence experience - break in presence (BIP) - appearing over time, need to be detected to assess and improve an application. Existing methods either lack in taking these BIPs into account - questionnaires - or are complex in their application and evaluation - physiological and behavioral measures -. To provide a practical approach, we propose a post-experience method in which the users reflect on their experience by drawing a line, indicating their experienced state of presence, in a paper-based drawing template. The amplitude of the drawn line represents the variation of their presence experience over time. We propose a descriptive model that describes temporal variations in the drawings by the definition of relevant points over time - e.g., putting on the HMD -, phases of the experience - e.g., transition into VR - and parameters - e.g., the transition time -. The descriptive model enables us to objectively evaluate user drawings and represent the course of the drawings by a defined set of parameters. An exploratory user study (N=30) showed that the drawings are very consistent, the method can detect all BIPs and shows good indications for representing the intensity of a BIP. With our method practitioners and researchers can accelerate the evaluation and optimization of experiences by evaluating BIPs. The possibility to store objective parameters paves the way for automated evaluation methods and big data approaches.
RA-XII Suppresses the Development and Growth of Liver Cancer by Inhibition of Lipogenesis via SCAP-dependent SREBP Supression

Lipogenesis plays a critical role in the growth and metastasis of tumors, which is becoming an attractive target for anti-tumor drugs. RA-XII, one of the cyclopeptide glycosides isolated from Rubia yunnanensis, exerts anti-tumor effects on liver cancer. However, the underlying mechanisms are not clear. In the present study, the effects of RA-XII on lipogenesis were evaluated and the underlying mechanisms were investigated. The results indicated that RA-XII strongly inhibited tumor growth and lipogenesis (triglycerides and lipid droplets) in HepG2 cells, and the expression of key factors involved in lipogenesis (SREBP, SCD, FASN) was also obviously downregulated. Further investigation showed that the anti-tumor effects of RA-XII were attenuated by SREBP knockdown. Moreover, RA-XII downregulated the expression of SREBP cleavage-activating protein (SCAP), an upstream regulator of SREBP, and siRNA of SCAP prevented its restrained effects on tumor growth and lipogenesis. In addition, the in vivo experiment showed that RA-XII strongly restrained the lipogenesis and growth of liver tumor in nude mice xenograft model. Taken together, these results indicate that RA-XII suppresses the liver cancer growth by inhibition of lipogenesis via SCAP-dependent SREBP suppression. The findings reveal the potentials of RA-XII to be used in a novel therapeutic approach for treating liver cancer.
Ketamine Infusions for Chronic Pain: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

BACKGROUND IV ketamine is widely used to treat patients with chronic pain, yet the long-term impact remains uncertain. We synthesized evidence from randomized control trials to investigate the effectiveness of IV ketamine infusions for pain relief in chronic conditions and to determine whether any pain classifications or treatment regimens are associated with greater benefit. METHODS We searched Medline, Embase, and Google Scholar, as well as the clinicaltrials.gov website from inception through December 16, 2017 for randomized control trials comparing IV ketamine to placebo infusions for chronic pain that reported outcomes for ≥48 hours after the intervention. Three authors independently screened the studies, pooled the data, and appraised risk of bias. Random-effects model was used to calculate weighted mean differences for pain scores and secondary outcomes. Our primary outcome was the lowest recorded pain score ≥48 hours after cessation of treatment. Secondary outcomes included responder rate and adverse effects. RESULTS Among 696 studies assessed for eligibility, 7 met inclusion criteria. All studies except one were at high risk of bias. These studies randomly assigned 211 patients with neuropathic (n = 2), mixed (n = 2), and nonneuropathic (nociplastic or nociceptive) (n = 3) pain. Three studies reported significant analgesic benefit favoring ketamine, with the meta-analysis revealing a small effect up to 2 weeks after the infusion (mean difference in pain scores, -1.83 points on a 0-10 numerical rating scale; 95% CI, -2.35 to -1.31 points; P < .0001). In the 3 studies that reported responder rates, the proportion with a positive outcome was greater in the ketamine than in the placebo group (51.3% vs 19.4%; relative risk, 2.43; 95% CI, 1.10-5.40; P = .029; I = 0.0%). No differences were noted based on pain classification or condition. Compared to low-dose ketamine studies and investigations that evaluated non-complex regional pain syndrome conditions, a small but nonsignificant greater reduction in pain scores was found among studies that either utilized high-dose ketamine therapy (P = .213) or enrolled complex regional pain syndrome patients (P = .079). CONCLUSIONS Evidence suggests that IV ketamine provides significant short-term analgesic benefit in patients with refractory chronic pain, with some evidence of a dose-response relationship. Larger, multicenter studies with longer follow-ups are needed to better select patients and determine the optimal treatment protocol.
A normally laminated afferent projection to an abnormally laminated cortex: Some olfactory connections in the reeler mouse

The relative positions of pyramidal and polymorphic cell classes are inverted in the central olfactory cortical structures of the reeler mutant mouse. Each cell class is generated at the normal embryonic time. The polymorphic cells of the mutant, like those of the normal, are generated between E11–E13. The pyramidal cells are formed between E11–E16 in both. Despite the anomalous positions of their somata deep in the cortex the apical dendrites of many pyramidal cells reach and ramify at a superficial cortical level subjacent to the lateral olfactory tract. The main and accessory olfactory bulbs are cytoarchitectonically normal in the mutant and project normally upon the anterior olfactory nucleus, the olfactory tubercle, the hippocampal rudiment, the piriform cortex, the amygdaloid region and the entorhinal cortex. As in the normal animal the axons traverse layer Iα, and their terminals are concentrated in the immediately subjacent laminar zone. The rostrally directed cortico‐cortical association system of the piriform cortex projects upon the anterior olfactory nucleus in the mutant just as in the normal with a relative concentration of terminals in a lamina subjacent and complementary to the zone of termination of the lateral olfactory tract. The normal pattern of lamination of these two afferent systems in the abnormally laminated olfactory cortex of the mutant suggests that, in this system at least, the developmental mechanisms which determine relative position of neuron somata and those which govern axon trajectories and the distribution of axon terminals are largely independent.
Nonlinear Behavior of Electrostatically Actuated Microbeams with Coupled Longitudinal–Transversal Vibration

Microelectromechanical switch has become an essential component in a wide variety of applications, ranging from biomechanics and aerospace engineering to consumer electronics. Electrostatically actuated microbeams and microplates are chief parts of many MEMS instruments. In this study, the nonlinear characteristics of coupled longitudinal–transversal vibration are analyzed, while an electrostatically actuated microbeam is designed considering that the frequency ratio is two to one between the first longitudinal vibration and transversal vibration. The nonlinear governing equations are truncated into a set of coupled ordinary differential equations by the Galerkin method. Then the equations are solved using the multiple-scales method and the nonlinear dynamics of the internal resonance is investigated. The influence of bias voltage, longitudinal excitation and frequency detuning parameters are mainly analyzed. Results show that using the pseudo-arclength continuation method, the nonlinear amplitude–response curves can be plotted continuously. The saturation and jump phenomena are greatly affected by the bias voltage and the detuning frequency. Beyond the critical excitation amplitude, the response energy will transfer from the longitudinal motion to the transversal motion, even the excitation is employed on the longitudinal direction. The large-amplitude jump of the low-order vibration mode can be used to detect the variation of the conditions or parameters, which shows great potential in improving precision of MEMS switches.