Miracle-dz/UniFAR-FTUs huggingface dataset:
Lower 6-MMP/6-TG Ratio May Be a Therapeutic Target in Pediatric Autoimmune Hepatitis

Background: Azathioprine (AZA) is the mainstay of maintenance therapy in pediatric autoimmune hepatitis (AIH). The use of thiopurines metabolites to individualize therapy and avoid toxicity has not, however, been clearly defined. Methods: Retrospective analysis of children ⩽18 years diagnosed with AIH between January 2001 and 2016. Standard definitions were used for treatment response and disease flare. Thiopurine metabolite levels were correlated with the corresponding liver function test. Results: A total of 56 children (32 girls) were diagnosed with AIH at a median age of 11 years (interquartile range [IQR] 9). No difference in 6-thioguanine-nucleotide (6-TG) levels (271[IQR 251] pmol/8 × 108 red blood cell vs 224 [IQR 147] pmol/8 × 108 red blood cell, P = 0.06) was observed in children in remission when compared with those who were not in remission. No correlation was observed between the 6-TG and alanine aminotransferase levels (r = −0.179, P = 0.109) or between 6-methyl-mercaptopurine (6-MMP) and alanine aminotransferase levels (r = 0.139, P = 0.213). The 6-MMP/6-TG ratio was significantly lower in patients who were in remission (2[7] vs 5 (10), P = 0.04). Using a quartile analysis, we found that having a ratio of <4 was significantly associated with being in remission with OR 2.50 (95% confidence interval 1.02–6.10), P = 0.047. Use of allopurinol with low-dose AZA in 6 children with preferential 6-MMP production brought about remission in 5/6 (83.3%). Conclusions: Thiopurine metabolite levels should be measured in patients with AIH who have experienced a loss of remission. A 6-MMP/6-TG ratio of <4 with the addition of allopurinol could be considered in these patients.
Pitching Performance After Ulnar Collateral Ligament Reconstruction at a Single Institution in Major League Baseball Pitchers

Background: Ulnar collateral ligament (UCL) reconstruction (UCLR) has shown reliable rates of return to play, with conflicting results on pitching performance after players’ return. Purpose: To evaluate Major League Baseball (MLB) pitching performance before and after UCLR performed at a single institution. Study Design: Cohort study; Level of evidence, 3. Methods: MLB pitchers (minor league players were excluded) who underwent UCLR at our institution between 2002 and 2016 were identified. Player information and return to play were determined including pitching level (MLB vs minor league) and total number of years played after surgery. Pitching performance statistics were evaluated for 3 years before surgery and for 3 years after returning to play including earned run average (ERA), walks plus hits per inning pitched (WHIP), innings pitched, wins above replacement, runs above replacement, and pitch velocity. Results: A total of 54 MLB pitchers were identified, with 46 primary and 8 revision reconstructions. The mean time to return to play was 13.8 months (primary reconstruction: 13.7 months). The majority returned to play with a 94% return rate (primary reconstruction: 96%), and 80% returned to MLB play (primary reconstruction: 82%). Three primary reconstructions required revision surgery. Pitchers played a mean of 3.2 years in MLB and 4.6 years total after surgery (39% still playing). Pitching workload and performance were maintained or improved after surgery. The preoperative ERA was 4.63 versus 4.13 after returning to play (P = .268). Fastball velocity (P = .032), ERA (P = .003), and WHIP (P = .001) worsened the first year after surgery and then improved the second year (ERA: 4.63 to 4.06 [P = .380]; WHIP: 1.35 to 1.32 [P = .221]; fastball velocity: 91.3 to 91.9 mph [P = .097]). Compared with a matched control group, pitchers who had UCLR showed improvements or maintained performance after reconstruction including improved ERA (P = .007), WHIP (P = .025), and fastball velocity (P = .006). Conclusion: MLB players experienced a high rate of return to play and previous level of play after UCLR. Pitching workload was maintained after returning to play. Pitching performance initially decreased the first year after returning and then improved or reverted to previous levels after the first year from UCLR.
On Secondary Development of PTV-VISSIM for Traffic Optimization

Traffic microscopic simulation has been widely used in traffic network performance analysis. Microscopic simulation is safer, cheaper and faster than implementing and testing in real traffic environment. One of the difficulties in traffic simulation is how to verify the effectiveness of the traffic optimization model. The effect of the control and optimization algorithm can be achieved by using VISSIM microscopic simulation software. The internal functions of the microscopic simulation software are relatively simple. The basic traffic simulation parameters can be set in advance to simulate the conditions of the traffic network, cause the parameters cannot be effectively controlled constantly. Thus secondary development of VISSIM is necessary. This article study the secondary development of VISSIM software with Visual Studio(C#), and the Component Object Model(COM). This method is intended to provide useful implementation for the secondary development of VISSIM micro-simulation based on the C# compilation environment. A traffic simulation platform integrated with VISSIM and Visual Studio(C#) for traffic optimization simulation was proposed. The development of a traffic simulation platform displaying road network parameters constantly. It makes significance help to the further research and testing of intelligent traffic. And the platform can greatly reduce programming complexity and save programming time, making it easy to master. This article also presents examples of signal timing optimization based on single-intersection simulations.
A Cell Equalization Method Based on Resonant Switched Capacitor Balancing for Lithium Ion Batteries

This paper presents the concept of a cell balancing method for Lithium Ion (Li-ion) batteries for Electrical Vehicles (EV), based on Resonant Switched Capacitor Converter (RSCC) balancing technique. It equalizes eight number of cells in a series. In Li-ion batteries, voltage differences always exist between cells due to charging and discharging process, therefore a battery management system (BMS) is required to ensure that all cells are equally charged or discharged and it also increases the life cycle of the battery. An equalizing method is essential to achieve the best performance. A number of cell balancing method have been presented. Among them, Switched Capacitor Converter (SCC) is more effective compared to other methods because it can be implemented easily. Low volume, low cost, high power density are its features. However, low balancing speed is an important problem of the SCC. In this paper, a resonant switching capacitor is proposed to reduce the balancing time and reduce the energy loss. The simulation results are performed to verify the feasibility of the system.
In vitro Biological Evaluation of Small Intestinal Submucosa - Chitosan / Hyaluronic Acid Tridimensional Scaffolds as Deep Wound Healing Dressings

In the U.S. more than 6 million patients suffer from chronic wounds (e.g., diabetic, pressure or vascular ulcers), which represent a major public health issue and an important healthcare expenditure. Chronic deep wounds lose the ability to repair without the external signals responsible for enhancing extracellular matrix (ECM) restoration and promoting sustained healing. We have previously prepared Small Intestinal Submucosa-Chitosan (SIS-Ch) and Small Intestinal SubmucosaHyaluronic Acid (SIS-HA) 3D scaffolds, in the form of porous sponges and hydrogels. Physicochemical and mechanical properties in vitro confirmed their potential as wound dressings. Here we conducted in vitro biological tests of SIS-Ch (n=3) and SIS-HA (n=3) scaffolds that included MTT assays to determined cytotoxicity and hemolysis assay to assess the tendency to damage red blood cells. Cell viability of SIS-Ch and SIS-HA sponges exceeded 80%. Nevertheless, SIS-HA hydrogels exhibited the greatest cell viability values of all formulations most likely due to the HA natural function in extracellular matrix that promotes cell migration and proliferation. The presence of Ch and HA in the scaffolds correlated with higher cell metabolic rates when compared with formulations based on SIS only (p<0.05). Hemolytic indexes below 5% were found for all formulations except for SIS_Ch Low and SIS_HA High for porous sponges, confirming thereby their limited tendency to promote hemolysis. Our in vitro biological findings suggest that both SIS-Ch and SISHA scaffolds have the characteristics to be evaluated in preclinical in vivo testing.
Bid Privacy Preservation in Matching-Based Multiradio Multichannel Spectrum Trading

Spectrum trading with secondary users (SUs) for monetary gains improves the inefficient spectrum utilization of primary users (PUs) in the cognitive radio networks (CRNs). It is an efficient way of dynamic spectrum sharing that enhances accessing opportunities of SUs and escalates revenue of PUs. In the spectrum trading market, bidding privacy is a serious concern for SUs, since untrustworthy PUs or spectrum traders may leverage bidding information to manipulate the trading or leak SU's bids to other SU competitors for illegal gains. Current centralized privacy preserving spectrum trading designs may incur extra infrastructure deployment, miss many instantaneous spectrum accessing opportunities, and have scalability issues. In this paper, we propose a privacy-preserving semidistributed spectrum trading scheme for multiradio multichannel CRNs, which has joint consideration of spectrum reuse, SUs’ bidding privacy preservation, and PUs’ revenue maximization. We use the conflict graph to characterize SUs’ interference relationships, and jointly employ the Paillier Cryptosystem and dynamic matching to conduct the spectrum trading in a semidistributed manner. Through security, complexity and performance analysis, we show that the proposed scheme 1) effectively preserves the privacy of SUs’ bidding values, and 2) notably increases the PUs’ revenue and improves spectrum utilization with limited overhead.
Improved Stacking Model Fusion Based on Weak Classifier and Word2vec

Stacking model Fusion is a combination classification method for natural language processing and text categorization. Compared to a single weak classifier, model Fusion has the advantage of combining the classification strengths of multiple classifiers, so the combination classifier is often more accurate than a single classifier, and the research of this field has been developed rapidly in recent years, and the combination classifier has been applied in various natural language processing tasks. But only by using the prediction results of the first layer weak classifier to train the second layer classifier, it has a strong limitation, only considers the training of the classification result and ignores the semantic information. We think that the method of training the weak classifier by TFIDF to the document, the expression of the document is not enough, only the information about the frequency of the document and the document is lack of the semantic information of the word2vector. In this paper, a new combination classification method is proposed, which combines the various weak classifiers trained by TFIDF and Word2vector to express the documents in many aspects, and the feature expression can fully utilize the information provided by the document. It has better classification effect than individual word2vector expression and classification and simple weak classifier combination classification.
Aligning Sentences in English-Bengali Corpora

For different areas of Natural Language Processing (NLP) research parallel corpora are an important resource. Parallel corpora aligned at sentence level is more efficient and useful than parallel corpora which are not aligned for various applications like Cross-Language Information Retrieval and Statistical Machine Translation. Although there exist many sources for bilingual corpora they do not appear in sentence aligned form. So developing an efficient method to align the sentences in such parallel corpora is an important step for NLP research. Researchers of NLP invested much effort to develop efficient methods for aligning sentences in such corpora and several methods have been developed which have been proved to be effective for different language pairs. As far as we are concerned till now no previous work has been done for aligning sentences in English-Bengali parallel corpora which is lagging us behind in NLP research. So our goal was to develop an efficient method for aligning sentences from English-Bengali parallel corpora. We evaluated the performance of some existing methods for our intended language pair and choose the best one for our work. We upgraded the selected method to make it exploit lexical information of the language pair to attain a better result.
A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.
Distance-based Clustering of Moving Objects’ Trajectories from Spatiotemporal Big Data*

With the rapid development of smart sensors, smartphones and social media, distributed sensors and tracking systems are generating overwhelming amounts of high velocity spatiotemporal big data. Clustering spatiotemporal data is an important way to mine hidden information behind moving object sampling data, such as understanding trends in movement patterns, gaining high popularity in geographic information and so on. However, the current approaches for clustering trajectory data generally do not apply for excessive costs in both scalability and computing performance for spatiotemporal big data. To find the clusters of moving objects’ trajectories, the issue is to find a distance measurement method that respects the geographic distance and the semantic similarity for each trajectory. Therefore, a distance measure to compute the spatial similarity between trajectories based on both geographical features and semantic features of motion is proposed in this research, then a framework for clustering moving objects trajectories is also designed to find out the groups of similar paths from big spatiotemporal data. The cluster quality of the proposed method is validated by means of external and internal validation criteria and is practically evaluated by TDrive datasets which is real trajectory dataset.
Liquid Metal Magnetohydrodynamic Pump for Junction Temperature Control of Power Modules

Power modules are the most common components to fail in power converters that are employed in mass transportation systems, thus leading to high unscheduled maintenance cost. While operating, high junction temperature swings occur that result in high thermomechanical stress within the structure of the power module, reducing the lifetime of the module. Liquid metals as a cooling medium received so far little attention in the area of power semiconductor cooling, despite being able to remove high heat fluxes. This paper shows for the first time how liquid metal is used to reduce actively the junction temperature swing. A magnetohydrodynamic (MHD) pump has been designed for this purpose allowing active control of the flow rate of the liquid metal that impinges against the baseplate of the module. The pump has been 3-D printed and is attached directly to the power module. A closed-loop temperature control system is implemented, able to estimate the insulated-gate bipolar transistor's junction temperature, and thus, controls the MHD power. This paper presents simulation and experimental results showing reductions in the temperature swing over the full load cycle with 12 °C as the highest observed reduction rate. This paper also shows detailed designs of the MHD pump and the controller hardware.
Association between mild thyroid dysfunction and clinical outcome in acute coronary syndrome undergoing percutaneous coronary intervention.

BACKGROUND Thyroid hormones profoundly influence the cardiovascular system, but the effects of mild thyroid dysfunction on the clinical outcome of acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) are not well defined. This study aimed to determine the effect of mild thyroid dysfunction on 12-month prognosis in ACS patients undergoing PCI. METHODS In this prospective cohort study with a 12-month follow-up, 1560 individuals were divided into four groups based on thyroid hormone levels upon admission: euthyroidism (used as a reference group), subclinical hypothyroidism, subclinical hyperthyroidism, and low triiodothyronine syndrome (low T3 syndrome). The outcomes measured were all-cause mortality, cardiac mortality, nonfatal reinfarction, and unplanned repeat revascularization. RESULTS In this study, the prevalence of mild thyroid dysfunction was 10.8%. Multivariate analysis showed that low T3 syndrome, but not subclinical hypothyroidism or subclinical hyperthyroidism, was associated with a higher rate of all-cause (HR 2.553, 95% CI 1.093-5.964, p = 0.030) and cardiac mortality (HR 2.594, 95% CI 1.026-6.559, p = 0.034), compared with the euthyroidism group. CONCLUSIONS Mild thyroid dysfunction was frequent in patients with ACS undergoing PCI. Low T3 syndrome was the predominant feature and was associated with 12-month adverse outcomes in these patients.
Speech Recognition of Confusable Word Based on Ideal Distance Matrix

In order to improve the recognition efficiency of classical DTW algorithm in speech recognition of confusable word, a new method for recognition of confusable word is proposed in this paper. The distance between the measured speech and the template speech is calculated by the general distance matrix and the ideal state distance matrix. The Offset-Loss is introduced and it is used to evaluate the similarity between the measured speech and the template speech. The experimental results show that it improves the matching speed and the accuracy of the improved algorithm is almost the same as the classical DTW algorithm.
JPSS-1/NOAA-20 VIIRS early on-orbit geometric performance

The first NOAA/NASA Join Polar Satellite System (JPSS-1) satellite was successfully launched on November 18, 2017, becoming NOAA-20. Instruments on-board NOAA-20 satellite include the Visible Infrared Imaging Radiometer Suite (VIIRS). This instrument is the second build of VIIRS, with the first flight instrument on-board NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite operating since October 2011. The purpose of these VIIRS instruments is to continue the long-term measurements of biogeophysical variables for multiple applications including weather forecasting, rapid response and climate research. The geometric performance of VIIRS is essential to retrieving accurate biogeophysical variables. This paper describes the early on-orbit geometric performance of the JPSS-1/NOAA-20 VIIRS. It first discusses the on-orbit orbit and attitude performance, a key input needed for accurate geolocation. It then discusses the on-orbit geometric characterization and calibration of VIIRS geometry and an initial assessment of the geometric accuracy. This section includes a discussion of an improvement in the geometric model that corrects small geometrical artifacts that appear in the along-scan direction. Finally, this paper discusses on-orbit measurements of the focal length and the impact of this on the scan-to-scan underlap/overlap.
Reinforcement Learning with Non-uniform State Representations for Adaptive Search

Efficient spatial exploration is a key aspect of search and rescue. In this paper, we present a search algorithm that generates efficient trajectories that optimize the rate at which probability mass is covered by a searcher. This should allow an autonomous vehicle find one or more lost targets as rapidly as possible. We do this by performing non-uniform sampling of the search region. The path generated minimizes the expected time to locate the missing target by visiting high probability regions using non-myopic path generation based on reinforcement learning. We model the target probability distribution using a classic mixture of Gaussians model with means and mixture coefficients tuned according to the location and time of sightings of the lost target. Key features of our search algorithm are the ability to employ a very general nondeterministic action model and the ability to generate action plans for any new probability distribution using the parameters learned on other similar looking distributions. One of the key contributions of this paper is the use of non-uniform state aggregation for policy search in the context of robotics. We compare the paths generated by our algorithm with other accepted spatial coverage techniques such as distribution independent boustrophedonic coverage and model dependent spiral search. We present a proof showing that rewarding for clearing probability mass instead of locating the target does not bias the objective function. The experiments show that the learned policy outperforms several well-known baselines even in scenarios different from the one it has been trained on.
Transient Structural health monitoring of The Test Bridges Using Finite Element Method

Failure of the bridges is the biggest threat to human life as well as the assets. To avoid the structural failure, Transient Structural Health Monitoring (SHM) of bridges could be carried out during its service life, in order to meet the service, safety and reliability requirements. Hence in this research, transient structural analysis (SHM) using Finite Element Method (FEM) is conducted of intact as well as damaged test bridge that are having the same physical parameters to analyze non-linear behavior change of the damage that is generated in the test bridge, when some loading conditions are applied using FEM. The authenticity of this analysis can be clearly shown, when the transient structural parameters are been compared between the two test bridges. Variations in the parameters like equivalent stresses, principle stresses, deformation and maximum equivalent strain are analyzed for the structural condition monitoring of the test bridges. These two test bridges, represents the actual bridges that are being used in day to day lives. So, in real case scenario, like these two test bridges, the simulation of the actual bridges can be conducted by comparing the actual bridge models.