Miracle-dz/UniFAR-FTUs huggingface dataset:
Aldosterone LC-MS/MS Assay-Specific Threshold Values in Screening and Confirmatory Testing for Primary Aldosteronism

Context Current threshold values for primary aldosteronism (PA) diagnostic testing are based on measuring aldosterone (PAC) using immunoassays. Quantification of PAC by liquid chromatography-tandem mass spectrometry (LC-MS/MS) yields lower values. Objective To compare aldosterone measurement by radioimmunoassay (RIA) with LC-MS/MS and evaluate performances of proposed LC-MS/MS-specific cutoffs for PA screening and confirmatory testing. Patients and Intervention Forty-one patients underwent aldosterone/renin ratio (ARR) testing to screen for, and fludrocortisone suppression testing (FST) to confirm or exclude, PA. Renin (DRC) was measured by chemiluminescent immunoassay. Results Median serum PACLC-MS/MS was 27.8% lower (P < 0.05) than plasma PACRIA in 164 pairs of FST samples. A positive correlation (Spearman coefficient, 0.894, P < 0.01; Pearson r coefficient, 0.861, P < 0.01) was observed between the two assays. Thirty-seven patients showed consistent FST diagnoses (29 positive, 8 negative), whereas four showed inconsistent FSTs by the two assays. Good agreement (κ coefficient, 0.736; P < 0.01) was observed between the current FST diagnostic PACRIA cutoff of 165 pmol/L and the proposed PACLC-MS/MS cutoff of 133 pmol/L. Among 37 patients with consistent FST results, no differences were observed in sensitivity (89.7% vs 93.1%) or specificity (87.5% vs 87.5%) for PA screening between the current ARR cutoff of 70 pmol/mU (PACRIA/DRC) and the proposed cutoff of 55 pmol/mU (PACLC-MS/MS/DRC). Conclusions Adjustment of the current cutoffs for PA diagnostic testing is necessary if PAC is measured by LC-MS/MS. Our preliminary results suggest that the proposed LC-MS/MS cutoffs for ARR and FST perform as well as current RIA cutoffs.
An Automated Approach to Estimating Code Coverage Measures via Execution Logs

Software testing is a widely used technique to ensure the quality of software systems. Code coverage measures are commonly used to evaluate and improve the existing test suites. Based on our industrial and open source studies, existing state-of-the-art code coverage tools are only used during unit and integration testing due to issues like engineering challenges, performance overhead, and incomplete results. To resolve these issues, in this paper we have proposed an automated approach, called LogCoCo, to estimating code coverage measures using the readily available execution logs. Using program analysis techniques, LogCoCo matches the execution logs with their corresponding code paths and estimates three different code coverage criteria: method coverage, statement coverage, and branch coverage. Case studies on one open source system (HBase) and five commercial systems from Baidu and systems show that: (1) the results of LogCoCo are highly accurate (> 96% in seven out of nine experiments) under a variety of testing activities (unit testing, integration testing, and benchmarking); and (2) the results of LogCoCo can be used to evaluate and improve the existing test suites. Our collaborators at Baidu are currently considering adopting LogCoCo and use it on a daily basis.
The effectiveness of conservative and surgical treatment for shoulder stiffness: a systematic review of current literature

Introduction Currently, no therapeutic intervention is universally accepted, and the most effective management for restoring motion and diminishing pain in patients with shoulder stiffness has yet to be defined. This systematic review analyses outcomes of conservative and surgical interventions to treat shoulder stiffness. Source of data A systematic review of literature according to the PRISMA guidelines was performed. A comprehensive search of PubMed, Medline, CINAHL, Cochrane, Embase, Ovid and Google Scholar databases using various combinations of the keywords 'shoulder', 'shoulder stiffness', 'stiff shoulder', 'conservative', since inception of databases to June 2018 was performed. Areas of agreement Shoulder stiffness could be treated with conservative means including nonsteroidal anti-inflammatory medications, corticosteroid injections, or transcutaneous electrical nerve stimulation, manipulation under anaesthesia, and arthroscopic capsular release. Areas of controversy No therapeutic intervention is universally accepted, and the most effective management to restore motion and diminish pain in patients with shoulder stiffness has yet to be defined. Growing points The rate of failure after treatment for stiff shoulder is higher in the surgical group than in the conservative group. Areas timely for developing research There is insufficient evidence to establish whether surgical or conservative management is the best choice to manage shoulder stiffness. Prospective, randomized studies are needed to establish whether surgical or conservative management produce a clinically relevant difference in functional outcome.
Ligand‐Driven Conformational Dynamics Influences Selectivity of UbiX

Up until now, it has remained elusive as to why the flavin prenyltransferase UbiX requires dimethylallyl monophosphate (DMAP) as one of its cosubstrates instead of dimethylallyl pyrophosphate (DMAPP), even though the former is not used in metabolic pathways, while the latter is a common isoprenoid precursor. Herein, mainly on the basis of molecular dynamics (MD) simulations, we demonstrate that the selectivity of UbiX may be governed by its conformational dynamics. The hydrogen‐bonding network of UbiX does not facilitate a proper encompassing of DMAPP. This induces significant conformational changes of the enzyme that result mostly in unreactive trajectories, whereas DMAP remains at a catalytically competent position throughout the performed simulations. Within the presented study, we provide a justification for the atypical selectivity of UbiX.
Effective Treatment of Chronic Proliferative Cholangitis by Local Gentamicin Infusion in Rabbits

Background Hepatolithiasis is highly prevalent in East Asia characterized by the presence of gallstones in the biliary ducts of the liver. Surgical resection is the potentially curative treatment but bears a high risk of stone recurrence and biliary restenosis. This is closely related to the universal presence of chronic proliferative cholangitis (CPC) in the majority of patients. Recent evidence has indicated the association of bacterial infection with the development of CPC in hepatolithiasis. Thus, this study aims to investigate the feasibility and efficacy of local infusion of gentamicin (an antibiotic) for the treatment of CPC in a rabbit model. Methods The rabbit CPC model was established based on previously published protocols. Bile duct samples were collected from gentamicin-treated or control animals for pathological and molecular characterization. Results Histologically, the hyperplasia of biliary epithelium and submucosal glands were inhibited and the thickness of the bile duct wall was significantly decreased after gentamicin therapy. Consistently, the percentage of proliferating cells marked by ki67 was significantly reduced by the treatment. More importantly, this treatment inhibited interleukin 2 production, an essential inflammatory cytokine, and the enzyme activity of endogenous β-Glucuronidase, a key factor in the formation of bile pigment. Conclusions Local gentamicin infusion effectively inhibits the inflammation, cell proliferation, and lithogenesis in a rabbit model of CPC. This approach represents a potential treatment for CPC and thus prevents recurrent hepatolithiasis.
Virtually Being Lenin Enhances Presence and Engagement in a Scene From the Russian Revolution

Virtual Reality (VR) has been widely applied to cultural heritage such as the reconstruction of ancient sites and artifacts. It has hardly been applied to the reprise of specific important moments in history. On the other hand immersive journalism does attempt to recreate current events in VR, but such applications typically give the viewer a disembodied non-participatory role in the scene of interest. Here we show how VR was used to reconstruct a specific historical event, where a famous photograph was brought to life, showing Lenin, the leader of the 1917 October Russian Revolution, giving a speech to Red Army recruits in Moscow 1920. We carried out a between groups experimental study with three conditions: Embodied—where the participant was first embodied as Lenin and then later in the audience watching Lenin; Included—where the participant was not embodied as Lenin but was embodied as part of the audience; and Observing—where the participant mainly viewed the scene from a disembodied third person point of view. Twenty participants were assigned to each of the three conditions in a between-groups design. We found that the level of presence was greatest in the Embodied and Included conditions, and that participants were least likely to later follow up information about the Russian Revolution in the Observing condition. Our conclusion is that if the VR setup allows for a period of embodiment as a character in the scenario then this should be employed in order to maximize the chance of participant presence and engagement with the story.
Effective API Recommendation without Historical Software Repositories

It is time-consuming and labor-intensive to learn and locate the correct API for programming tasks. Thus, it is beneficial to perform API recommendation automatically. The graph-based statistical model has been shown to recommend top-10 API candidates effectively. It falls short, however, in accurately recommending an actual top-1 API. To address this weakness, we propose RecRank, an approach and tool that applies a novel ranking-based discriminative approach leveraging API usage path features to improve top-1 API recommendation. Empirical evaluation on a large corpus of (1385+8) open source projects shows that RecRank significantly improves top-1 API recommendation accuracy and mean reciprocal rank when compared to state-of-the-art API recommendation approaches.
Edge‐Epitaxial Growth of 2D NbS2‐WS2 Lateral Metal‐Semiconductor Heterostructures

2D metal‐semiconductor heterostructures based on transition metal dichalcogenides (TMDs) are considered as intriguing building blocks for various fields, such as contact engineering and high‐frequency devices. Although, a series of p–n junctions utilizing semiconducting TMDs have been constructed hitherto, the realization of such a scheme using 2D metallic analogs has not been reported. Here, the synthesis of uniform monolayer metallic NbS2 on sapphire substrate with domain size reaching to a millimeter scale via a facile chemical vapor deposition (CVD) route is demonstrated. More importantly, the epitaxial growth of NbS2‐WS2 lateral metal‐semiconductor heterostructures via a “two‐step” CVD method is realized. Both the lateral and vertical NbS2‐WS2 heterostructures are achieved here. Transmission electron microscopy studies reveal a clear chemical modulation with distinct interfaces. Raman and photoluminescence maps confirm the precisely controlled spatial modulation of the as‐grown NbS2‐WS2 heterostructures. The existence of the NbS2‐WS2 heterostructures is further manifested by electrical transport measurements. This work broadens the horizon of the in situ synthesis of TMD‐based heterostructures and enlightens the possibility of applications based on 2D metal‐semiconductor heterostructures.
Learning Sentiment Memories for Sentiment Modification without Parallel Data

The task of sentiment modification requires reversing the sentiment of the input and preserving the sentiment-independent content. However, aligned sentences with the same content but different sentiments are usually unavailable. Due to the lack of such parallel data, it is hard to extract sentiment independent content and reverse the sentiment in an unsupervised way. Previous work usually can not reconcile sentiment transformation and content preservation. In this paper, motivated by the fact the non-emotional context (e.g., “staff”) provides strong cues for the occurrence of emotional words (e.g., “friendly”), we propose a novel method that automatically extracts appropriate sentiment information from learned sentiment memories according to the specific context. Experiments show that our method substantially improves the content preservation degree and achieves the state-of-the-art performance.
Noise and Heterogeneity in Historical Build Data: An Empirical Study of Travis CI

Automated builds, which may pass or fail, provide feedback to a development team about changes to the codebase. A passing build indicates that the change compiles cleanly and tests (continue to) pass. A failing (a.k.a., broken) build indicates that there are issues that require attention. Without a closer analysis of the nature of build outcome data, practitioners and researchers are likely to make two critical assumptions: (1) build results are not noisy; however, passing builds may contain failing or skipped jobs that are actively or passively ignored; and (2) builds are equal; however, builds vary in terms of the number of jobs and configurations. To investigate the degree to which these assumptions about build breakage hold, we perform an empirical study of 3.7 million build jobs spanning 1,276 open source projects. We find that: (1) 12% of passing builds have an actively ignored failure; (2) 9% of builds have a misleading or incorrect outcome on average; and (3) at least 44% of the broken builds contain passing jobs, i.e., the breakage is local to a subset of build variants. Like other software archives, build data is noisy and complex. Analysis of build data requires nuance.
Deep Learning Based Feature Envy Detection

Software refactoring is widely employed to improve software quality. A key step in software refactoring is to identify which part of the software should be refactored. To facilitate the identification, a number of approaches have been proposed to identify certain structures in the code (called code smells) that suggest the possibility of refactoring. Most of such approaches rely on manually designed heuristics to map manually selected source code metrics to predictions. However, it is challenging to manually select the best features, especially textual features. It is also difficult to manually construct the optimal heuristics. To this end, in this paper we propose a deep learning based novel approach to detecting feature envy, one of the most common code smells. The key insight is that deep neural networks and advanced deep learning techniques could automatically select features (especially textual features) of source code for feature envy detection, and could automatically build the complex mapping between such features and predictions. We also propose an automatic approach to generating labeled training data for the neural network based classifier, which does not require any human intervention. Evaluation results on open-source applications suggest that the proposed approach significantly improves the state-of-the-art in both detecting feature envy smells and recommending destinations for identified smelly methods.
Hierarchical bi-directional attention-based RNNs for supporting document classification on protein–protein interactions affected by genetic mutations

Abstract In this paper, we describe a hierarchical bi-directional attention-based Re-current Neural Network (RNN) as a reusable sequence encoder architecture, which is used as sentence and document encoder for document classification. The sequence encoder is composed of two bi-directional RNN equipped with an attention mechanism that identifies and captures the most important elements, words or sentences, in a document followed by a dense layer for the classification task. Our approach utilizes the hierarchical nature of documents which are composed of sequences of sentences and sentences are composed of sequences of words. In our model, we use word embeddings to project the words to a low-dimensional vector space. We leverage word embeddings trained on PubMed for initializing the embedding layer of our network. We apply this model to biomedical literature specifically, on paper abstracts published in PubMed. We argue that the title of the paper itself usually contains important information more salient than a typical sentence in the abstract. For this reason, we propose a shortcut connection that integrates the title vector representation directly to the final feature representation of the document. We concatenate the sentence vector that represents the title and the vectors of the abstract to the document feature vector used as input to the task classifier. With this system we participated in the Document Triage Task of the BioCreative VI Precision Medicine Track and we achieved 0.6289 Precision, 0.7656 Recall and 0.6906 F1-score with the Precision and F1-score be the highest ranking first among the other systems. Database URL: https://github.com/afergadis/BC6PM-HRNN
Position Locationing for Millimeter Wave Systems

The vast amount of spectrum available for millimeter wave (mmWave) wireless communication systems will support accurate real-time positioning concurrent with communication signaling. This paper demonstrates that accurate estimates of the position of an unknown node can be determined using estimates of time of arrival (ToA), angle of arrival (AoA), as well as data fusion or machine learning. Real-world data at 28 GHz and 73 GHz is used to show that AoA-based localization techniques will need to be augmented with other positioning techniques. The fusion of AoA-based positioning with received power measurements for RXs in an office which has dimensions of 35 m by 65.5 m is shown to provide location accuracies ranging from 16 cm to 3.25 m, indicating promise for accurate positioning capabilities in future networks. Received signal strength intensity (RSSI) based positioning techniques that exploit the ordering of the received power can be used to determine rough estimates of user position. Prediction of received signal characteristics is done using 2-D ray tracing.
The INO80 complex activates the transcription of S‐phase genes in a cell cycle‐regulated manner

Chromatin structure is an essential factor in the proper regulation of DNA repair, DNA replication and transcription. The INO80 complex and the SWR complex have been shown to play a fundamental role in transcription regulation through remodeling chromatin at specific genes and loci. Here, we report that the Schizosaccharomyces pombe INO80 complex physically interacts with the mlui‐binding factor (MBF) complex. Furthermore, we are able to detect the INO80 complex in MBF‐regulated promoters. Binding of INO80 to these genes is cell cycle regulated, with a maximum binding preceding their transcription and accumulation of their mRNAs. In fact, the INO80 complex is required to fully and timely activate the transcription of these genes. We also show that the accumulation of acetylated H2A.Z at the +1 nucleosome is cell cycle regulated. Cells in which H2A.Z acetylation is abolished still have some cell cycle‐regulated transcription of MBF‐dependent genes, although to a much lesser extent.
Motor imagery training induces changes in brain neural networks in stroke patients

Motor imagery is the mental representation of an action without overt movement or muscle activation. However, the effects of motor imagery on stroke-induced hand dysfunction and brain neural networks are still unknown. We conducted a randomized controlled trial in the China Rehabilitation Research Center. Twenty stroke patients, including 13 males and 7 females, 32–51 years old, were recruited and randomly assigned to the traditional rehabilitation treatment group (PP group, n = 10) or the motor imagery training combined with traditional rehabilitation treatment group (MP group, n = 10). All patients received rehabilitation training once a day, 45 minutes per session, five times per week, for 4 consecutive weeks. In the MP group, motor imagery training was performed for 45 minutes after traditional rehabilitation training, daily. Action Research Arm Test and the Fugl-Meyer Assessment of the upper extremity were used to evaluate hand functions before and after treatment. Transcranial magnetic stimulation was used to analyze motor evoked potentials in the affected extremity. Diffusion tensor imaging was used to assess changes in brain neural networks. Compared with the PP group, the MP group showed better recovery of hand function, higher amplitude of the motor evoked potential in the abductor pollicis brevis, greater fractional anisotropy of the right dorsal pathway, and an increase in the fractional anisotropy of the bilateral dorsal pathway. Our findings indicate that 4 weeks of motor imagery training combined with traditional rehabilitation treatment improves hand function in stroke patients by enhancing the dorsal pathway. This trial has been registered with the Chinese Clinical Trial Registry (registration number: ChiCTR-OCH-12002238).
Convergence acceleration for observers by gain commutation

ABSTRACT Increasing convergence rates of observers and differentiators for a class of nonlinear systems in the output canonical form (under presence of bounded matched disturbances, Lipschitz uncertainties and measurement noises) is investigated. A supervisory algorithm is designed that switches among different values of observer gains to accelerate the estimation. In the noise-free case, the presented switched-gain observer ensures global uniform time of convergence of the estimation error to the origin. In the presence of noise, the goals of overshoot reducing for the initial phase, acceleration of convergence and improvement of asymptotic precision of estimation are achieved. Efficiency of the proposed switching-gain observer is demonstrated by numerical comparison with a sliding mode and linear high-gain observers.
Dual-Force: Understanding WebView Malware via Cross-Language Forced Execution

Modern Android malwares tend to use advanced techniques to cover their malicious behaviors. They usually feature multi-staged, condition-guarded and environment-specific payloads. An increasing number of them utilize WebView, particularly the two-way communications between Java and JavaScript, to evade detection and analysis of existing techniques. We propose Dual-Force, a forced execution technique which simultaneously forces both Java and JavaScript code of WebView applications to execute along various paths without requiring any environment setup or providing any inputs manually. As such, the hidden payloads of WebView malwares are forcefully exposed. The technique features a novel execution model that allows forced execution to suppress exceptions and continue execution. Experimental results show that Dual-Force precisely exposes malicious payload in 119 out of 150 Web-View malwares. Compared to the state-of-the-art, Dual-Force can expose 23% more malicious behaviors.
A Neuromuscular Interface for Robotic Devices Control

A neuromuscular interface (NI) that can be employed to operate external robotic devices (RD), including commercial ones, was proposed. Multichannel electromyographic (EMG) signal is used in the control loop. Control signal can also be supplemented with electroencephalography (EEG), limb kinematics, or other modalities. The multiple electrode approach takes advantage of the massive resources of the human brain for solving nontrivial tasks, such as movement coordination. Multilayer artificial neural network was used for feature classification and further to provide command and/or proportional control of three robotic devices. The possibility of using biofeedback can compensate for control errors and implement a fundamentally important feature that has previously limited the development of intelligent exoskeletons, prostheses, and other medical devices. The control system can be integrated with wearable electronics. Examples of technical devices under control of the neuromuscular interface (NI) are presented.