Miracle-dz/UniFAR-FTUs huggingface dataset:
The Quality of Surgical and Pneumonia Care in Minority-Serving and Racially Integrated Hospitals.

OBJECTIVE To explore the association between quality of care for surgical and pneumonia patients and the racial/ethnic composition of hospitals' patients. DATA SOURCE Our primary data were surgical and pneumonia processes of care indicators from the 2012 Medicare Hospital Compare Data. We merged this data with information from the 2011 American Hospital Association Annual Survey of Hospitals. We computed the racial and ethnic composition of hospital patients using 2008 data from the Healthcare Costs and Utilization Project. STUDY DESIGN The sample included 1,198 acute care general hospitals from 11 states: AZ, CA, FL, IA, MA, MD, NC, NJ, NY, WA, and WI. We compared quality across minority-serving, racially integrated, and majority-white hospitals using unconditional quantile regression models controlling for hospital and market characteristics. PRINCIPAL FINDINGS We found quality differences between the lowest performing minority-serving, racially integrated, and majority-white hospitals. As we moved from 10th to 90th quantile, the quality differences between hospitals by patients' racial composition disappeared. In other words, the best minority-serving and racially integrated hospitals performed as well as the best majority hospitals. CONCLUSIONS Efforts to improve quality of care for patients in minority-serving and racially integrated hospitals should focus on the lowest performers.
Bio-Logic Builder: A Non-Technical Tool for Building Dynamical, Qualitative Models

Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion. As part of the user interface, generalized “bio-logic” modules have been defined to provide users with the building blocks for many biological processes. To build/modify computational models, experimentalists provide purely qualitative information about a particular regulatory mechanisms as is generally found in the laboratory. The Bio-Logic Builder subsequently converts the provided information into a mathematical representation described with Boolean expressions/rules. We used this tool to build a number of dynamical models, including a 130-protein large-scale model of signal transduction with over 800 interactions, influenza A replication cycle with 127 species and 200+ interactions, and mammalian and budding yeast cell cycles. We also show that any and all qualitative regulatory mechanisms can be built using this tool.
Transfer Learning for Cross-Platform Software Crowdsourcing Recommendation

Recently, with the development of software crowdsourcing industry, an increasing number of users joined the software crowdsourcing platforms to publish software project tasks or to seek proper work opportunities. One of competitive functions of these platforms is to recommend proficient projects to developers. However, in such recommender system, there exists a serious platform cold-start problem, especially for new software crowdsourcing platforms, as they usually have too little cumulative data to support accurate model training and prediction. This paper focuses on solving the platform cold-start problem in software crowdsourcing recommendation system by transfer learning technologies. We proposed a novel cross-platform recommendation method for new software crowdsourcing platforms, whose idea is trying to transfer data and knowledge from other mature software crowdsourcing platforms (source domains) to solve the insufficient recommendation model training problem in a new platform (target domain). The proposed method maps different kinds of features both in the source domain and the target domain after a certain transformation and combination to a latent space by learning the correspondences between features. Specifically, our method is an instance of content-based recommendation, which uses tags and keywords extracted from project description in crowdsourcing platforms as features, and then set weights for each feature to reflect its importance. Then, Weight-SCL is proposed to merge and distinguish tag features and keyword features before doing feature mapping and data migration to implement knowledge transformation. Finally, we use the data from two famous software crowdsourcing platform as dataset, and a series of experiments are conducted to evaluate the performance of the multi-source recommendation system in comparison with the baseline methods, and get 1.2X performance promotion.
Revisiting Pivot Language Approach for Machine Translation

This paper revisits the pivot language approach for machine translation. First, we investigate three different methods for pivot translation. Then we employ a hybrid method combining RBMT and SMT systems to fill up the data gap for pivot translation, where the source-pivot and pivot-target corpora are independent. Experimental results on spoken language translation show that this hybrid method significantly improves the translation quality, which outperforms the method using a source-target corpus of the same size. In addition, we propose a system combination approach to select better translations from those produced by various pivot translation methods. This method regards system combination as a translation evaluation problem and formalizes it with a regression learning model. Experimental results indicate that our method achieves consistent and significant improvement over individual translation outputs.
Temporal Dynamic Matrix Factorization for Missing Data Prediction in Large Scale Coevolving Time Series

Data missing in collections of time series occurs frequently in practical applications and turns out to be a major menace to precise data analysis. However, most of the existing methods either might be infeasible or could be inefficient to predict the missing values in large-scale coevolving time series. Also, the evolving of time series needs to be handled properly to adapt to the temporal characteristic. Furthermore, more massive volume of data is generated in many areas than ever before. In this paper, we have taken up the challenge of missing data prediction in coevolving time series by employing temporal dynamic matrix factorization techniques. First, our approaches are optimally designed to largely utilize both the interior patterns of each time series and the information of time series across multiple sources to build an initial model. Based on the idea, we have imposed hybrid regularization terms to constrain the objective functions of matrix factorization. Then, temporal dynamic matrix factorization is proposed to effectively update the initial already trained models. In the process of dynamic matrix factorization, batch updating and fine-tuning strategies are also employed to build an effective and efficient model. Extensive experiments on real-world data sets and synthetic data set demonstrate that the proposed approaches can effectively improve the performance of missing data prediction. Even when the missing ratio reaches as high as 90%, our proposed methods still show low prediction errors. Dynamic performance demonstrates that the methods can obtain satisfactory effectiveness and efficiency. Furthermore, we have also demonstrated how to take advantage of the high processing power of Apache Spark to perform missing data prediction in large-scale coevolving time series.
Cortical thickness alterations linked to somatoform and psychological dissociation in functional neurological disorders

Links between dissociation and functional neurological disorder (FND)/conversion disorder are well‐established, yet the pathophysiology of dissociation remains poorly understood. This MRI study investigated structural alterations associated with somatoform and psychological dissociation in FND. We hypothesized that multimodal, paralimbic cingulo‐insular regions would relate to the severity of somatoform dissociation in patients with FND.
High-Resolution X-Ray Tomography: A 3D Exploration Into the Skeletal Architecture in Mouse Models Submitted to Microgravity Constraints

Bone remodeling process consists in a slow building phase and in faster resorption with the objective to maintain a functional skeleton locomotion to counteract the Earth gravity. Thus, during spaceflights, the skeleton does not act against gravity, with a rapid decrease of bone mass and density, favoring bone fracture. Several studies approached the problem by imaging the bone architecture and density of cosmonauts returned by the different spaceflights. However, the weaknesses of the previously reported studies was two-fold: on the one hand the research suffered the small statistical sample size of almost all human spaceflight studies, on the other the results were not fully reliable, mainly due to the fact that the observed bone structures were small compared with the spatial resolution of the available imaging devices. The recent advances in high-resolution X-ray tomography have stimulated the study of weight-bearing skeletal sites by novel approaches, mainly based on the use of the mouse and its various strains as an animal model, and sometimes taking advantage of the synchrotron radiation support to approach studies of 3D bone architecture and mineralization degree mapping at different hierarchical levels. Here we report the first, to our knowledge, systematic review of the recent advances in studying the skeletal bone architecture by high-resolution X-ray tomography after submission of mice models to microgravity constrains.
Real‐time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder

Attention Deficit Hyperactivity Disorder (ADHD) is associated with poor self‐control, underpinned by inferior fronto‐striatal deficits. Real‐time functional magnetic resonance neurofeedback (rtfMRI‐NF) allows participants to gain self‐control over dysregulated brain regions. Despite evidence for beneficial effects of electrophysiological‐NF on ADHD symptoms, no study has applied the spatially superior rtfMRI‐NF neurotherapy to ADHD. A randomized controlled trial tested the efficacy of rtfMRI‐NF of right inferior prefrontal cortex (rIFG), a key region that is compromised in ADHD and upregulated with psychostimulants, on improvement of ADHD symptoms, cognition, and inhibitory fMRI activation. To control for region‐specificity, an active control group received rtfMRI‐NF of the left parahippocampal gyrus (lPHG). Thirty‐one ADHD boys were randomly allocated and had to learn to upregulate their target brain region in an average of 11 rtfMRI‐NF runs over 2 weeks. Feedback was provided through a video‐clip of a rocket that had to be moved up into space. A transfer session without feedback tested learning retention as a proximal measure of transfer to everyday life. Both NF groups showed significant linear activation increases with increasing number of runs in their respective target regions and significant reduction in ADHD symptoms after neurotherapy and at 11‐month follow‐up. Only the group targeting rIFG, however, showed a transfer effect, which correlated with ADHD symptom reductions, improved at trend level in sustained attention, and showed increased IFG activation during an inhibitory fMRI task. This proof‐of‐concept study demonstrates for the first time feasibility, safety, and shorter‐ and longer‐term efficacy of rtfMRI‐NF of rIFG in adolescents with ADHD. Hum Brain Mapp 38:3190–3209, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.
Wirelessly Powered Crowd Sensing: Joint Power Transfer, Sensing, Compression, and Transmission

Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performing data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms.
Thorough in silico and in vitro cDNA analysis of 21 putative BRCA1 and BRCA2 splice variants and a complex tandem duplication in BRCA2 allowing the identification of activated cryptic splice donor sites in BRCA2 exon 11

For 21 putative BRCA1 and BRCA2 splice site variants, the concordance between mRNA analysis and predictions by in silico programs was evaluated. Aberrant splicing was confirmed for 12 alterations. In silico prediction tools were helpful to determine for which variants cDNA analysis is warranted, however, predictions for variants in the Cartegni consensus region but outside the canonical sites, were less reliable. Learning algorithms like Adaboost and Random Forest outperformed the classical tools. Further validations are warranted prior to implementation of these novel tools in clinical settings. Additionally, we report here for the first time activated cryptic donor sites in the large exon 11 of BRCA2 by evaluating the effect at the cDNA level of a novel tandem duplication (5′ breakpoint in intron 4; 3′ breakpoint in exon 11) and of a variant disrupting the splice donor site of exon 11 (c.6841+1G > C). Additional sites were predicted, but not activated. These sites warrant further research to increase our knowledge on cis and trans acting factors involved in the conservation of correct transcription of this large exon. This may contribute to adequate design of ASOs (antisense oligonucleotides), an emerging therapy to render cancer cells sensitive to PARP inhibitor and platinum therapies.
On-the-fly Prototyping: Designing, Testing, and Evolving an Interactive Public Installation in Loco

In this paper, we describe the experience of deploying an interactive public installation adopting aspects of in-the-wild study and evolutionary design, described here as on-the-fly prototyping. This approach enables continuous design improvements while learning from evaluating the user interaction with the prototype as it evolves in the wild. This approach offers much potential use benefits for researchers and practitioners, minimizing the need to conduct multiple experiments that demand time and resources. We describe our lessons learned and directions to apply this approach and evaluate in-the-wild. We emphasize the need of a pilot study in situ and a team committed to collect and analyze data, discuss the insights and changes, given the time pressure and new scenarios that can emerge. In addition, the system design must support predictable, orderly and managed evolution. Finally, we show that learning and iterating in the wild is feasible, contributing to the study of ubiquitous computing.
An analysis of rock climbing sport regarding performance, sponsorship, and health

The basis of this work was to extract knowledge using data mining techniques over 2 million records from rock climbing competitions all over the world. The first phase of the project involved heavy data cleaning and preprocessing procedures to prepare the data for the mining models. After the first phase was completed, we explored three main questions: which factors can predict the performance of a competitor, which factors will likely lead to a sponsorship of a competitor and is it possible to predict healthiness of a competitor. This research will not only help rock climbers gain significant insights about their performance, it will also help sports sponsors choose the best candidates to assign their brand to.
[Inhibition of CaMKII alleviates myocardial ischemia?reperfusion injury by reducing mitochondrial oxidative stress in isolated perfused rat heart].

OBJECTIVE To investigate the role of calcium/calmodulin-dependent protein kinase II (CaMKII) in myocardial ischemia-reperfusion (IR) injury in isolated perfused rat heart and explore the underlying mechanisms. METHODS An ischemia-reperfusion (IR) model was prepared using isolated rat hearts perfused with Krebs-Henseleit solution were randomly divided into control group, 2.5 µmol/L KN-93 group, IR (induced by ischemia for 45 min followed by reperfusion for 120 min) group and KN-93+IR group. The myocardial performance was evaluated by assessing the left ventricular pressure. Lactate dehydrogenase (LDH) activity and cTnI content in the coronary flow and the infarct size were determined to evaluate the myocardial injury. The phosphorylation of CaMKII (p-CaMKII) and PLN (p-PLN) and oxidation of CaMKII (ox--CaMKII) were measured with Western blotting. The activity of mitochondrial superoxide dismutase (SOD) and the content of malondialdehyde (MDA) were determined using ELISA. RESULTS Compared with the control group, KN-93 treatment at 2.5 µmol/L produced no significant effects on cardiac function or performance in rat hearts without IR injury. Myocardial IR injury significantly decreased myocardial performance and mitochondrial SOD activity in the perfused hearts (P<0.01) and caused significantly increased infarct size, LDH activity, cTnI content, expressions of p-CaMKII, ox-CaMKII and p-PLN, and also increased mitochondrial MDA content (P<0.01). KN-93 treatment at 2.5 µmol/L administered before ischemia and before reperfusion markedly attenuated such changes induced by ischemia and reperfusion (P<0.01). CONCLUSION CaMKII participates in myocardial IR injury in isolated rat heart, and inhibiting CaMKII can alleviate myocardial injury by relieving mitochondrial oxidation stress.
A novel planning solution for semi‐autonomous aspiration of Baker's cysts

A Baker's cyst is a pathological structure located near a kneepit, which causes discomfort and reduces mobility of the knee. It is commonly treated with aspiration, which often requires MRI scanning and US guidance. The aim of this study was to propose a novel planning solution for semi‐autonomous aspiration of the Baker's cyst using only MRI imaging.
In Utero Administration of Drugs Targeting Microglia Improves the Neurodevelopmental Outcome Following Cytomegalovirus Infection of the Rat Fetal Brain

Congenital cytomegalovirus (CMV) infections represent one leading cause of neurodevelopmental disorders. Recently, we reported on a rat model of CMV infection of the developing brain in utero, characterized by early and prominent infection and alteration of microglia—the brain-resident mononuclear phagocytes. Besides their canonical function against pathogens, microglia are also pivotal to brain development. Here we show that CMV infection of the rat fetal brain recapitulated key postnatal phenotypes of human congenital CMV including increased mortality, sensorimotor impairment reminiscent of cerebral palsy, hearing defects, and epileptic seizures. The possible influence of early microglia alteration on those phenotypes was then questioned by pharmacological targeting of microglia during pregnancy. One single administration of clodronate liposomes in the embryonic brains at the time of CMV injection to deplete microglia, and maternal feeding with doxycyxline throughout pregnancy to modify microglia in the litters' brains, were both associated with dramatic improvements of survival, body weight gain, sensorimotor development and with decreased risk of epileptic seizures. Improvement of microglia activation status did not persist postnatally after doxycycline discontinuation; also, active brain infection remained unchanged by doxycycline. Altogether our data indicate that early microglia alteration, rather than brain CMV load per se, is instrumental in influencing survival and the neurological outcomes of CMV-infected rats, and suggest that microglia might participate in the neurological outcome of congenital CMV in humans. Furthermore this study represents a first proof-of-principle for the design of microglia-targeted preventive strategies in the context of congenital CMV infection of the brain.
FGF23 Neutralizing Antibody Partially Improves Bone Mineralization Defect of HMWFGF2 Isoforms in Transgenic Female Mice

Mice overexpressing high molecular weight FGF2 isoforms (HMWTg) in osteoblast lineage phenocopy human X‐linked hypophosphatemic rickets (XLH) and a Hyp murine model of XLH demonstrating increased FGF23/FGF receptor signaling and hypophosphatemic rickets/osteomalacia. Because HMWFGF2 was upregulated in bones of Hyp mice and abnormal FGF23 signaling is important in XLH, HMWTg mice were used to examine the effect of the FGF23 neutralizing antibody (FGF23Ab). Eight‐week‐old female Vector control mice and HMWTg mice were treated with FGF23Ab or control IgG. A single injection of FGF23Ab rescued abnormal hypophosphatemia in HMWTg. The decreased type II sodium‐dependent phosphate co‐transporter (Npt2a) was rescued by FGF23Ab treatment. Inappropriately low serum 1,25(OH)2D in HMWTg mice was normalized by FGF23Ab treatment, which is accompanied by increased anabolic vitamin D hydroxylase Cyp27b1 and decreased catabolic vitamin D hydroxylase Cyp24 mRNA in kidney. Long‐term treatment with FGF23Ab normalized femur length and significantly increased vertebrae BMD and BMC, and femur BMC in HMWTg mice compared to IgG‐treated HMWTg mice. Micro–computed tomography (μCT) revealed increased cortical porosity and decreased cortical apparent density in the HMWTg‐IgG group compared with the Vector‐IgG group; however, FGF23Ab treatment rescued defective cortical mineralization, decreased porosity, and increased apparent density in HMWTg mice. Bone histomorphometry analysis showed FGF23Ab treatment decreased osteoid volume, increased intra‐label thickness, mineralization apposition rate, and bone formation rate in HMWTg mice. FGF23Ab improved disorganized double labeling in femurs from HMWTg mice. Quantitative real‐time PCR analysis of tibia shafts showed FGF23Ab treatment normalized the osteocalcin (Ocn) mRNA expression in HMWTg mice, but further increased expression of SIBLING protein–related and pyrophosphate‐related genes that are important in matrix mineralization, suggesting that HMWFGF2 modulates these genes independent of FGF23. We conclude that FGF23Ab partially rescued hypophosphatemic osteomalacia in HMWTg. However, long‐term treatment with FGF23Ab further increased SIBLING protein–related genes and pyrophosphate‐related genes in bone that could contribute to incomplete rescue of the mineralization defect. © 2018 American Society for Bone and Mineral Research.