Miracle-dz/UniFAR-FTUs huggingface dataset:
P1105Recombinant insulin-like growth factor-1 improves cardiac function and symptoms in the patients on the waiting list for heart transplantation with end-stage dilated cardiomyopathy

Background: End-stage heart failure with idiopathic dilated cardiomyopathy (DCM) has no therapeutic options other than heart transplantation. We investigated whether exogenous insulin-like growth factor-1 (IGF-1) could improve cardiac function and symptoms in end-stage DCM patients on the waiting list for heart transplantation. Methods and Results: Recombinant IGF-1 was administered daily for three months to consecutive eight heart-transplant candidates with end-stage DCM. Cardiac catheterization, myocardial biopsy, echocardiography, magnetic resonance imaging, exercise test and neurohumoral blood samplings were conducted at baseline, at the end of three-month subcutaneous IGF-1 treatment and at the three-month follow-up. Ventricular mass index (91 ± 22 to 99 ± 27g/m2, p=0.003) and ejection fraction (18 ± 8 to 23 ± 7%, p=0.016) were increased at the end of treatment, and returned to baseline at the follow-up. However, New York Heart Association functional class (3.4 ± 0.5 to 2.3 ± 0.5, p=0.012) and brain natriuretic peptide (304 ± 264 to 150 ± 168 pg/mL, p=0.012) were improved at the end of treatment, and remained improved (2.1 ± 0.4, 166 ± 131 pg/mL, respectively) even at the follow-up. After treatment, a left ventricular assist device was removed from one patient. Two patients were withdrawn from the waiting list for heart transplantation. Conclusions: A three-month administration of recombinant IGF-1 to the patients on the waiting list with end-stage DCM might result in improvement in cardiac function and symptoms. IGF-1 therapy might be a novel treatment for bridge to transplant or recovery.
A minimal threshold of FANCJ helicase activity is required for its response to replication stress or double-strand break repair

Abstract Fanconi Anemia (FA) is characterized by bone marrow failure, congenital abnormalities, and cancer. Of over 20 FA-linked genes, FANCJ uniquely encodes a DNA helicase and mutations are also associated with breast and ovarian cancer. fancj−/− cells are sensitive to DNA interstrand cross-linking (ICL) and replication fork stalling drugs. We delineated the molecular defects of two FA patient-derived FANCJ helicase domain mutations. FANCJ-R707C was compromised in dimerization and helicase processivity, whereas DNA unwinding by FANCJ-H396D was barely detectable. DNA binding and ATP hydrolysis was defective for both FANCJ-R707C and FANCJ-H396D, the latter showing greater reduction. Expression of FANCJ-R707C or FANCJ-H396D in fancj−/− cells failed to rescue cisplatin or mitomycin sensitivity. Live-cell imaging demonstrated a significantly compromised recruitment of FANCJ-R707C to laser-induced DNA damage. However, FANCJ-R707C expressed in fancj-/- cells conferred resistance to the DNA polymerase inhibitor aphidicolin, G-quadruplex ligand telomestatin, or DNA strand-breaker bleomycin, whereas FANCJ-H396D failed. Thus, a minimal threshold of FANCJ catalytic activity is required to overcome replication stress induced by aphidicolin or telomestatin, or to repair bleomycin-induced DNA breakage. These findings have implications for therapeutic strategies relying on DNA cross-link sensitivity or heightened replication stress characteristic of cancer cells.
Planning Mobile Charging Vehicles Based on User Charging Behavior

With increasing environmental awareness from the society, electric vehicles (EVs) have been taking a steady increasing market share each year. Meanwhile, the overall charging demand usually significantly varies in both spatial and time domains. So the availability of public charging facilities usually do not match the exact demand during different hours of the day. However, the rapid development of driverless technology provides a new possibility to solve this problem. Unmanned EVs not only facilitates the collection of integrated datasets, but also reduce the user's impact on charging events. In this paper, we introduce a new charging facility - mobile charging vehicles (MCVs), which has dedicated personnel on duty and carries batteries that can be used for charging other EVs. We propose three different scheduling strategies of MCVs and evaluate the performance through a real world EVs charging dataset. Our experiment shows that the addition of MCVs to current charging facilities greatly increase the charging efficiency, in both waiting time of EVs and the load rate of stations.
A sparse-to-dense method for 3D optical flow estimation in 3D light-microscopy image sequences

We present a two-stage 3D optical flow estimation method for light microscopy image volumes. The method takes a pair of light microscopy image volumes as input, segments the 2D slices of the source volume in superpixels and sparsely estimates the 3D displacement vectors in the volume pair. A weighted interpolation is then introduced to get a dense 3D flow field. Edges and motion boundaries are considered during the interpolation. Our experimental results show good gain in execution speed, and accuracy evaluated in computer generated 3D data. Promising results on real 3D image sequences are reported.
Good Tolerance of Citrate Accumulation due to Plasma Exchange among Patients with Acute-on-Chronic Liver Failure: A Prospective, Observational Study

Aim To assess the tolerance of citrate accumulation due to plasma exchange (PE) among patients with acute-on-chronic liver failure (ACLF). Methods A prospective, observational study was conducted among patients with ACLF who received heparin anticoagulation during PE-centered therapy without filtration and dialysis. Citrate accumulation was defined as the value of total calcium (Catot) to ionized calcium (Caion) ratio (Catot/Caion) greater than or equal to 2.5 (Catot/Caion ≥ 2.5). Results Fifty-four patients were enrolled. The mean age and MELD score were 50.0 ± 11.3 years old and 25 ± 7, respectively. Thirty-three patients had liver cirrhosis. The total 3-month survival rate was 57.4% (31/54). The mean Catot/Caion at the time before PE was 2.05 ± 0.14. Catot/Caion ≥ 2.5 occurred in 100.0% (54/54) and 29.6% (16/54) of patients with mean Catot/Caion of 4.34 ± 1.52 and 2.36 ± 0.32 immediately after PE and 1 hour after PE, respectively, and these levels were much higher than those before PE (p < 0.01). However, all values returned to lower than 2.5 by the next morning with no difference from those before PE (2.10 ± 0.14 versus 2.05 ± 0.14, p > 0.05). Hypocalcemia (ionized calcium) and mild alkalosis were the main metabolic alterations. No symptoms associated with hypocalcemia occurred. Conclusions Citrate accumulation is well tolerated by patients with ACLF who receive PE-centered therapy without filtration and dialysis. This study is regeristed with ChiCTR-OOC-17013618.
Exploiting Aperiodic Server to Improve Aperiodic Responsiveness for LET-Based Real-Time Systems

Real-time systems often have to satisfy stringent timing constraints. However, to fully utilize system resources for the increasing needs of soft real-time applications, real-time systems are always designed to improve average-case performance instead of achieving predictability. Timing behaviors of systems become difficult to be inferred due to non-deterministic interleaves among concurrent tasks, especially when systems react to unexpected aperiodic requests. In this paper, we propose a component-based execution model, namely Clockwerk, to improve predictability as well as aperiodic responsiveness in real-time systems. Clockwerk extends LET to support the aperiodic tasks by Servants, which propose to implement the program component and runtime scheduling unit. A modified aperiodic server algorithm is applied to enhance the aperiodic responsiveness with no loss of predictability. A prototype of Clockwerk runtime system implements based on a mixture scheduler. We evaluate the implementation by a set of simulated and experimental evaluations. Evaluation results show that the aperiodic responsiveness of Clockwerk obtains 7.7X-16.7X speedup over a conventional work. And low overheads of our implementations demonstrate that Clockwerk is qualified to construct predictable real-time systems as well.
Gene Discovery of Characteristic Metabolic Pathways in the Tea Plant (Camellia sinensis) Using ‘Omics’-Based Network Approaches: A Future Perspective

Characteristic secondary metabolites, including flavonoids, theanine and caffeine, in the tea plant (Camellia sinensis) are the primary sources of the rich flavors, fresh taste, and health benefits of tea. The decoding of genes involved in these characteristic components is still significantly lagging, which lays an obstacle for applied genetic improvement and metabolic engineering. With the popularity of high-throughout transcriptomics and metabolomics, ‘omics’-based network approaches, such as gene co-expression network and gene-to-metabolite network, have emerged as powerful tools for gene discovery of plant-specialized (secondary) metabolism. Thus, it is pivotal to summarize and introduce such system-based strategies in facilitating gene identification of characteristic metabolic pathways in the tea plant (or other plants). In this review, we describe recent advances in transcriptomics and metabolomics for transcript and metabolite profiling, and highlight ‘omics’-based network strategies using successful examples in model and non-model plants. Further, we summarize recent progress in ‘omics’ analysis for gene identification of characteristic metabolites in the tea plant. Limitations of the current strategies are discussed by comparison with ‘omics’-based network approaches. Finally, we demonstrate the potential of introducing such network strategies in the tea plant, with a prospects ending for a promising network discovery of characteristic metabolite genes in the tea plant.
NAI-SEA at SemEval-2018 Task 5: An Event Search System

In this paper, we describe Alibaba’s participating system in the semEval-2018 Task5: Counting Events and Participants in the Long Tail. We designed and implemented a pipeline system that consists of components to extract question properties and document features, document event category classifications, document retrieval and document clustering. To retrieve the majority of the relevant documents, we carefully designed our system to extract key information from each question and document pair. After retrieval, we perform further document clustering to count the number of events. The task contains 3 subtasks, on which we achieved F1 score of 78.33, 50.52, 63.59 , respectively, for document level retrieval. Our system ranks first in all the three subtasks on document level retrieval, and it also ranks first in incident-level evaluation by RSME measure in subtask 3.
Dynamics of multiple resistance mechanisms in plasma DNA during EGFR‐targeted therapies in non‐small cell lung cancer

Tumour heterogeneity leads to the development of multiple resistance mechanisms during targeted therapies. Identifying the dominant driver(s) is critical for treatment decision. We studied the relative dynamics of multiple oncogenic drivers in longitudinal plasma of 50 EGFR‐mutant non‐small‐cell lung cancer patients receiving gefitinib and hydroxychloroquine. We performed digital PCR and targeted sequencing on samples from all patients and shallow whole‐genome sequencing on samples from three patients who underwent histological transformation to small‐cell lung cancer. In 43 patients with known EGFR mutations from tumour, we identified them accurately in plasma of 41 patients (95%, 41/43). We also found additional mutations, including EGFR T790M (31/50, 62%), TP53 (23/50, 46%), PIK3CA (7/50, 14%) and PTEN (4/50, 8%). Patients with both TP53 and EGFR mutations before treatment had worse overall survival than those with only EGFR. Patients who progressed without T790M had worse PFS during TKI continuation and developed alternative alterations, including small‐cell lung cancer‐associated copy number changes and TP53 mutations, that tracked subsequent treatment responses. Longitudinal plasma analysis can help identify dominant resistance mechanisms, including non‐druggable genetic information that may guide clinical management.
Self-Consistency and the Definition of Mate Searching

Hanna Kokko1,2,3 and Bob B. M. Wong1,4,5 1Laboratory of Ecological and Evolutionary Dynamics, Department of Biological and Environmental Science, University of Helsinki, Finland 2E-mail: hanna.kokko@helsinki.fi 3School of Botany and Zoology, Australian National University, ACT 0200, Australia 4School of Biological Sciences, Monash University, VIC 3800, Australia 5E-mail: bob.wong@sci.monash. edu.au
Inhibition of Acetylcholinesterase and Butyrylcholinesterase by a Plant Secondary Metabolite Boldine

Acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) are two enzymes sensitive to various chemical compounds having ability to bind to crucial parts of these enzymes. Boldine is a natural alkaloid and it was mentioned in some older works that it can inhibit some kinds of AChE. We reinvestigated this effect on AChE and also on BChE using acetyl (butyryl) thiocholine and Ellman's reagents as standard substances for spectrophotometric assay. We found out IC50 of AChE equal to 372 μmol/l and a similar level to BChE, 321 μmol/l. We conclude our experiment by a finding that boldine is cholinesterase inhibitor; however we report significantly weaker inhibition than that suggested in literature. Likewise, we tried to investigate the mechanism of inhibition and completed it with in silico study. Potential toxic effect on cholinesterases in real conditions is also discussed.
PRMT1 Plays a Critical Role in Th17 Differentiation by Regulating Reciprocal Recruitment of STAT3 and STAT5

Th17 cells are a class of Th cells that secrete IL-17 and mediate pathogenic immunity responsible for autoimmunity including experimental autoimmune encephalomyelitis, a murine model of multiple sclerosis. Retinoic acid–related orphan receptor γ t (RORγt) is the critical transcription factor that controls the differentiation of Th17 cells. However, little is known about the transcriptional cofactors for RORγt in the regulation of Th17 differentiation. In this study, we demonstrate that protein arginine N-methyltransferase 1 (PRMT1) associates with RORγt and regulates mouse Th17 differentiation. Overexpression of PRMT1 promoted Th17 differentiation, whereas inactivation or knockdown of PRMT1 decreased Th17 differentiation while expanding Foxp3+ regulatory T cells. Consistently, pharmacological inhibition of PRMT1 impaired the generation of Th17 cells and prevented induction of experimental autoimmune encephalomyelitis in mice. Mechanistically, PRMT1-dependent modification of asymmetric histone 4 arginine 3 dimethylation is required to stabilize the stimulatory STAT3 to displace the inhibitory STAT5 at IL-17 locus, resulting in the activation of IL-17 gene. Furthermore, PRMT1-facilitated recruitment of STAT3 overcame the inhibition of Th17 differentiation exerted by IL-2–induced STAT5 activation. PRMT1 thus regulates Th17 differentiation by controlling the reciprocal recruitment of STAT3 and STAT5. Our study thus reveals PRMT1 as a novel target for alleviating Th17-mediated autoimmunity by decreasing RORγt-dependent generation of pathogenic Th17 cells.
Reduced striatal dopamine release during motor skill acquisition in Parkinson’s disease

Background Striatal dopamine is functionally important for the acquisition of motor skills. However, it remains controversial as to whether intrinsic processing of motor learning is impaired in patients with Parkinson’s disease (PD), and if yes, whether the impairment is associated with altered striatal dopamine release. Additionally, most neuro-imaging studies of patients with PD have focused on motor sequence learning. In contrast, skill acquisition, specifically, the reconstruction of muscle control of isolated movements, has barely been studied. Method In this study, we used a repetitive skill training task to measure the peak acceleration of left thumb movement during a process to achieve fine tuning of motor skill. Using 11C-raclopride (RAC) positron emission tomography, we investigated changes in striatal dopamine levels in two conditions of a skill acquisition task: initial skill training (Day 1) and acquired condition (Day 2) with eight patients with PD and age-matched healthy subjects (HS). Result In HS, the mean acceleration of each session improved through repeated training sessions on Day 1. However, in patients with PD, the training-associated increase was less than that for HS, and this suggests that repetitive skill training does not result in the effective improvement of motor performance. The regions of interest (ROI) analysis revealed that the RAC-binding potential (BP) was significantly reduced in the right putamen on Day 1 compared with Day 2 in HS. In patients with PD, BP within the right putamen was unchanged. Further, we found that patients with PD had increased dopamine levels within the right ventral striatum (VST) and right caudate (CAU) on Day 2, which was greater than that in HS. These results suggest the impaired activation of the putamen during skill acquisition in patients with PD and compensated hyperactivation of the VST and CAU for the reduced dopamine release within the dorsal putamen (DPU). Conclusion Our findings suggest that patients with PD had insufficiency in the process to improve motor skills. Different patterns of striatal dopamine release are relevant to the impairment of these motor functions in patients with PD, at the early stage of the disease.
Hyperspectral Image Classification With Deep Feature Fusion Network

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved good performance. In general, deep models adopt a large number of hierarchical layers to extract features. However, excessively increasing network depth will result in some negative effects (e.g., overfitting, gradient vanishing, and accuracy degrading) for conventional convolutional neural networks. In addition, the previous networks used in HSI classification do not consider the strong complementary yet correlated information among different hierarchical layers. To address the above two issues, a deep feature fusion network (DFFN) is proposed for HSI classification. On the one hand, the residual learning is introduced to optimize several convolutional layers as the identity mapping, which can ease the training of deep network and benefit from increasing depth. As a result, we can build a very deep network to extract more discriminative features of HSIs. On the other hand, the proposed DFFN model fuses the outputs of different hierarchical layers, which can further improve the classification accuracy. Experimental results on three real HSIs demonstrate that the proposed method outperforms other competitive classifiers.
A Channel Allocation Scheme for Priority-Based Services in High-Speed Railway Wireless Communication Networks

The channel resource allocation problem in high-speed railway wireless communication networks is one of the key issues in recent years. In this paper, we propose an efficient channel allocation scheme for priority-based services. In such a scenario, when a service request arrives, the proposed scheme will be helpful to provide an optimal decision of how many channel resources should be allocated according to the priority of the service and the available resources currently. The objective is to maximize the long-term system reward. To achieve it, we formulate the service request decision making process as a semi-Markov decision process (SMDP), and solve the optimization problem using an iteration algorithm. Numerical results indicate that the significant performance is gained and system reward can be obtained by the SMDP-based service model compared with the greedy approach.
Space-Efficient DFS and Applications: Simpler, Leaner, Faster

The problem of space-efficient depth-first search (DFS) is reconsidered. A particularly simple and fast algorithm is presented that, on a directed or undirected input graph $G=(V,E)$ with $n$ vertices and $m$ edges, carries out a DFS in $O(n+m)$ time with $n+\sum_{v\in V_{\ge 3}}\lceil{\log_2(d_v-1)}\rceil +O(\log n)\le n+m+O(\log n)$ bits of working memory, where $d_v$ is the (total) degree of $v$, for each $v\in V$, and $V_{\ge 3}=\{v\in V\mid d_v\ge 3\}$. A slightly more complicated variant of the algorithm works in the same time with at most $n+({4/5})m+O(\log n)$ bits. It is also shown that a DFS can be carried out in a graph with $n$ vertices and $m$ edges in $O(n+m\log^*\! n)$ time with $O(n)$ bits or in $O(n+m)$ time with either $O(n\log\log(4+{m/n}))$ bits or, for arbitrary integer $k\ge 1$, $O(n\log^{(k)}\! n)$ bits. These results among them subsume or improve most earlier results on space-efficient DFS. Some of the new time and space bounds are shown to extend to applications of DFS such as the computation of cut vertices, bridges, biconnected components and 2-edge-connected components in undirected graphs.
Learning Prototypical Goal Activities for Locations

People go to different places to engage in activities that reflect their goals. For example, people go to restaurants to eat, libraries to study, and churches to pray. We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). Our research aims to learn goal-acts for specific locations using a text corpus and semi-supervised learning. First, we extract activities and locations that co-occur in goal-oriented syntactic patterns. Next, we create an activity profile matrix and apply a semi-supervised label propagation algorithm to iteratively revise the activity strengths for different locations using a small set of labeled data. We show that this approach outperforms several baseline methods when judged against goal-acts identified by human annotators.
Anthropometry-based Equations to Estimate Body Composition: A Suitable Alternative in Renal Transplant Recipients and Patients With Nondialysis Dependent Kidney Disease?

OBJECTIVE Chronic kidney disease (CKD) patients and renal transplant recipients (RTRs) are characterized by aberrant body composition such as muscle wasting and obesity. It is still unknown which is the most accurate method to estimate body composition in CKD. We investigated the validity of the Hume equation and bioelectrical impedance analysis (BIA) as an estimate of body composition against dual-energy X-ray absorptiometry (DXA) in a cohort of nondialysis dependent (NDD)-CKD and RTRs. DESIGN AND METHODS This was a cross-sectional study with agreement analysis of different assessments of body composition conducted in 61 patients (35 RTRs and 26 NDD-CKD) in a secondary care hospital setting in the UK. Body composition (lean mass [LM], fat mass [FM], and body fat% [BF%]) was assessed using multifrequency BIA and DXA, and estimated using the Hume formula. Method agreement was assessed by intraclass correlation coefficient (ICC), regression, and plotted by Bland and Altman analysis. RESULTS Both BIA and the Hume formula were able to accurately estimate body composition against DXA. In both groups, the BIA overestimated LM (1.7-2.1 kg, ICC .980-.984) and underestimated FM (1.3-2.1 kg, ICC .967-.972) and BF% (3.1-3.8%, ICC .927-.954). The Hume formula also overestimated LM (3.5-3.6 kg, ICC .950-.960) and underestimated BF% (1.9-2.1%, ICC .808-.859). Hume-derived FM was almost identical to DXA in both groups (-0.3 to 0.1 kg, ICC .947-.960). CONCLUSION Our results demonstrate, in RTR and NDD-CKD patients, that the Hume formula, whose estimation of body composition is based only upon height, body mass, age, and sex, may reliably predict the same parameters obtained by DXA. In addition, BIA also provided similar estimates versus DXA. Thus, the Hume formula and BIA could provide simple and inexpensive means to estimate body composition in renal disease.