Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictors of a complicated course after surgical repair of tetralogy of Fallot.

Background In this study, we aimed to evaluate the patient and surgical factors affecting prolonged hospital stay and major adverse events after surgical repair of tetralogy of Fallot and to identify the predictors of a complicated course after surgical repair. Methods A total of 170 consecutive patients (96 males, 74 females; median age 12 months; range, 1 to 192 months) who underwent surgical repair of tetralogy of Fallot between January 2015 and April 2018 were retrospectively reviewed. A mechanical ventilation duration of >24 h, an intensive care unit stay of >3 days, and a hospital stay of >7 days were considered as prolonged. Unplanned reoperation, complete heart block requiring a permanent pacemaker implantation, renal failure, diaphragmatic paralysis, neurological deficit, sudden circulatory arrest, need for extracorporeal membrane oxygenation, and death were considered as major adverse events. Results The median time to hospital discharge was 8.0 (range, 5.0 to 40.0) days. Higher preoperative hematocrit levels prolonged the length of hospital stay (odds ratio: 1.12, 95% confidence interval 1.1-1.2, p<0.001). A total of 28 major adverse events were observed in 17 patients (10%). Lower pulmonary artery annulus Z-score (odds ratio: 0.5, 95% confidence interval 0.3-0.9, p=0.01) and residual ventricular septal defects (odds ratio: 54.6, 95% confidence interval 1.6-1,874.2, p=0.03) were found to increase mortality. Residual ventricular septal defect was also a risk factor for major adverse events (odds ratio: 12.4, 95% confidence interval 1.5-99.9, p=0.02). Conclusion Preoperative and operative factors such as high preoperative hematocrit, low preoperative oxygen saturation, low pulmonary annulus Z-score, Down syndrome, residual ventricular septal defects, and the use transannular patch were found to be associated with prolonged length of hospital stay, prolonged mechanical ventilation, prolonged intensive care unit stay, and increased development of major adverse events.
An Elasticity Study of Distributed Graph Processing

Graphs are a natural fit for modeling concepts used in solving diverse problems in science, commerce, engineering, and governance. Responding to the variety of graph data and algorithms, many parallel and distributed graph processing systems exist. However, until now these platforms use a static model of deployment: they only run on a pre-defined set of machines. This raises many conceptual and pragmatic issues, including misfit with the highly dynamic nature of graph processing, and could lead to resource waste and high operational costs. In contrast, in this work we explore a dynamic model of deployment. We first characterize workload dynamicity, beyond mere active-vertex variability. Then, to conduct an in-depth elasticity study of distributed graph processing, we build a prototype, JoyGraph, which is the first such system that implements complex, policy-based, and fine-grained elasticity. Using the state-of-the-art LDBC Graphalytics benchmark and the SPEC Cloud Group's elasticity metrics, we show the benefits of elasticity in graph processing: (i) improved resource utilization, (ii) reduced operational costs, and (iii) aligned operation-workload dynamicity. Furthermore, we explore the cost of elasticity in graph processing. We identify a key drawback: although elasticity does not degrade application throughput, graph-processing workloads are sensitive to data movement while leasing or releasing resources.
CUDAsmith: A Fuzzer for CUDA Compilers

CUDA is a parallel computing platform and programming model for the graphics processing unit (GPU) of NVIDIA. With CUDA programming, general purpose computing on GPU (GPGPU) is possible. However, the correctness of CUDA programs relies on the correctness of CUDA compilers, which is difficult to test due to its complexity. In this work, we propose CUDAsmith, a fuzzing framework for CUDA compilers. Our tool can randomly generate deterministic and valid CUDA kernel code with several different strategies. Moreover, it adopts random differential testing and EMI testing techniques to solve the test oracle problems of CUDA compiler testing. In particular, we lift live code injection to CUDA compiler testing to help generate EMI variants. Our fuzzing experiments with both the NVCC compiler and the Clang compiler for CUDA have detected thousands of failures, some of which have been confirmed by compiler developers. Finally, the cost-effectiveness of CUDAsmith is also thoroughly evaluated in our fuzzing experiment.
Comparison Between Admittance and Impedance Control of a Multi-Finger-Arm Robot using the Guaranteed Manipulability Method

- In previous studies, we proposed cooperative control algorithms for a finger-arm robot comprising a 3-DOF finger robot and a 6-DOF arm robot. In these algorithms, a finger-arm robot completes a constrained task by integrating admittance and impedance control with manipulability control of the finger. Finger manipulability is controlled by both an approximate global search, i.e., the top search method, and a local optimization method. In this study, the methods that have previously controlled only one finger are extended to control multiple fingers. When the method is extended to multiple fingers, it acquires different characteristics from the case when it is used with only one finger. Therefore, in this paper the differences between the algorithms of these methods are described. Because admittance and impedance control methods offer different characteristics to the manipulability control of the finger, it is necessary to compare these methods from the standpoint of the arm and the finger and organize them for more advanced study. Moreover, when these control methods are adapted for multi-finger robots, it is necessary to consider problems that arise with respect to manipulability and whole motion control of the arm and fingers. This paper compares cooperative admittance and impedance control for a multi-finger-arm robot with manipulability control of the fingers. Experimental results are presented for the simultaneous achievement manipulability, admittance, and impedance control of a multi-finger-arm robot in a three-dimensional space. The proposed method demonstrated a strong performance of singularity avoidance, even when a dynamic external force was applied to the multi-finger device.
Project Plan of Generating Comics from Natural Language Description

As creation needs a thorough understanding of the subject being created and also the techniques such as designing and drawing [2], it’s difficult for people who have a great idea but no previous designing experience to change his/her idea to real artwork. It is a common problem among online writers that they have finished their works but cannot create their own cover images due to unfamiliarity of design or drawing. But for computers, they have both the ability to understand natural language and also the deep knowledge in many fields, generating images based on the natural language description, which is called creation, will be a feasible task for them and help people who have the passion but no ability to create.
Is Your Anchor Going Up or Down? Fast and Accurate Supervised Topic Models

Topic models provide insights into document collections, and their supervised extensions also capture associated document-level metadata such as sentiment. However, inferring such models from data is often slow and cannot scale to big data. We build upon the “anchor” method for learning topic models to capture the relationship between metadata and latent topics by extending the vector-space representation of word-cooccurrence to include metadataspecific dimensions. These additional dimensions reveal new anchor words that reflect specific combinations of metadata and topic. We show that these new latent representations predict sentiment as accurately as supervised topic models, and we find these representations more quickly without sacrificing interpretability.
Molecular identification of Salmonella Typhimurium from village chickens based on invA and spvC genes

Aim: This study aimed to identify Salmonella enterica serovars by polymerase chain reaction (PCR) based on virulence genes invasion A (invA) and Salmonella plasmid virulence C (spvC). Materials and Methods: DNA extraction of eight bacteria isolates was done using the PowerSoil® DNA Isolation Kit. The amplification of invA and spvC genes was done using conventional PCR. The positive PCR products were purified using the GeneJET Purification Kit and then sequenced using ABI 3730 XL automated genetic analyzer. The sequences obtained were compared for similarities with other Salmonella serovars deposited on the NCBI GenBank using BLASTN. Results: Four out of eight samples were amplified by primers FS139/RS141 that target invA gene with products of about 284 bp, and three out of four of the same invA positive samples were also amplified by primers FSPV-1/RSPV-2 targeting spvC with a product of about 571 bp. One sample was not amplified by primers FSPV-1/RSPV-2 as it lacked virulence plasmid. Analysis of sequences indicated 100% homology with closely related serovars of S. enterica subspecies enterica serovar Typhimurium. Conclusion: Salmonella Typhimurium that contained invA and spvC genes are pathogenic and virulent strains.
Addressing the Path-Length-Dependency Confound in White Matter Tract Segmentation

We derive the Iterative Confidence Enhancement of Tractography (ICE-T) framework to address the problem of path-length dependency (PLD), the streamline dispersivity confound inherent to probabilistic tractography methods. We show that PLD can arise as a non-linear effect, compounded by tissue complexity, and therefore cannot be handled using linear correction methods. ICE-T is an easy-to-implement framework that acts as a wrapper around most probabilistic streamline tractography methods, iteratively growing the tractography seed regions. Tract networks segmented with ICE-T can subsequently be delineated with a global threshold, even from a single-voxel seed. We investigated ICE-T performance using ex vivo pig-brain datasets where true positives were known via in vivo tracers, and applied the derived ICE-T parameters to a human in vivo dataset. We examined the parameter space of ICE-T: the number of streamlines emitted per voxel, and a threshold applied at each iteration. As few as 20 streamlines per seed-voxel, and a robust range of ICE-T thresholds, were shown to sufficiently segment the desired tract network. Outside this range, the tract network either approximated the complete white-matter compartment (too low threshold) or failed to propagate through complex regions (too high threshold). The parameters were shown to be generalizable across seed regions. With ICE-T, the degree of both near-seed flare due to false positives, and of distal false negatives, are decreased when compared with thresholded probabilistic tractography without ICE-T. Since ICE-T only addresses PLD, the degree of remaining false-positives and false-negatives will consequently be mainly attributable to the particular tractography method employed. Given the benefits offered by ICE-T, we would suggest that future studies consider this or a similar approach when using tractography to provide tract segmentations for tract based analysis, or for brain network analysis.
A model of the headturn preference procedure: Linking cognitive processes to overt behaviour

The study of first language acquisition still strongly relies on behavioural methods to measure underlying linguistic abilities. In the present paper, we closely examine and model one such method, the headturn preference procedure (HPP), which is widely used to measure infant speech segmentation and word recognition abilities Our model takes real speech as input, and only uses basic sensory processing and cognitive capabilities to simulate observable behaviour.We show that the familiarity effect found in many HPP experiments can be simulated without using the phonetic and phonological skills necessary for segmenting test sentences into words. The explicit modelling of the process that converts the result of the cognitive processing of the test sentences into observable behaviour uncovered two issues that can lead to null-results in HPP studies. Our simulations show that caution is needed in making inferences about underlying language skills from behaviour in HPP experiments. The simulations also generated questions that must be addressed in future HPP studies.
Understanding the Hierarchical Structural Evolution in a Scientiﬁc Discipline: A Case Study of Artiﬁcial Intelligence

Detecting what kind of knowledge constitutes the discipline, tracking how the knowledge changes, and understanding why the changes are triggered are the key issues in the analysis of scientiﬁc development from a macro perspective, usually analyzed by topic evolution . However, the traditional methods assume that the disciplinary structure is ﬂat with only one-layer topics, rather than the tree-like structure with hierarchical topics in reality, leading to the inability of existing methods to eﬀectively examine the details of evolutions, such as the interactions between diﬀerent research directions. In this paper, we take the artiﬁcial intelligence (AI) as a case to study its hierarchical structural evolution, more precisely inspecting the disciplinary development, by analyzing 65,887 AI-related research papers published during a 10-year period from 2009 to 2018. From a hierarchical topic model that can construct a topic-tree with multi-layer organizations, we design a visual analysis model for the topic-tree to systematically and visually investigate how the knowledge transfers along the topic-tree and how the topic-tree changes over time. Moreover, some assistant indicators are employed to help the exploration of the complicated structural evolution. Then we discover the latent relationship between the sub-structures within a topic as well as the triggering reason of knowledge migration. Based on these results, we summarize that diﬀerent topics have diﬀerent development patterns, and the recent artiﬁcial intelligence revolution stems from the interactions among diﬀerent topics.
RED: a Rich Epinions Dataset for Recommender Systems

Recommender Systems require speci c datasets to evaluate their approach. They do not require the same information: descriptions of users or items or users interactions may be necessary, which is not gathered in today datasets. In this paper, we provide a dataset containing reviews from users on items, trust values between users, items category, categories hierarchy and users expertise on categories. This dataset can be used to evaluate various Recommender Systems using Collaborative Filtering, Content-Based or Trust-Based.
Strategies for reducing per‐sample costs in target capture sequencing for phylogenomics and population genomics in plants

The reduced cost of high‐throughput sequencing and the development of gene sets with wide phylogenetic applicability has led to the rise of sequence capture methods as a plausible platform for both phylogenomics and population genomics in plants. An important consideration in large targeted sequencing projects is the per‐sample cost, which can be inflated when using off‐the‐shelf kits or reagents not purchased in bulk. Here, we discuss methods to reduce per‐sample costs in high‐throughput targeted sequencing projects. We review the minimal equipment and consumable requirements for targeted sequencing while comparing several alternatives to reduce bulk costs in DNA extraction, library preparation, target enrichment, and sequencing. We consider how each of the workflow alterations may be affected by DNA quality (e.g., fresh vs. herbarium tissue), genome size, and the phylogenetic scale of the project. We provide a cost calculator for researchers considering targeted sequencing to use when designing projects, and identify challenges for future development of low‐cost sequencing in non‐model plant systems.
Human Organ-Specific 3D Cancer Models Produced by the Stromal Self-Assembly Method of Tissue Engineering for the Study of Solid Tumors

Cancer research has considerably progressed with the improvement of in vitro study models, helping to understand the key role of the tumor microenvironment in cancer development and progression. Over the last few years, complex 3D human cell culture systems have gained much popularity over in vivo models, as they accurately mimic the tumor microenvironment and allow high-throughput drug screening. Of particular interest, in vitrohuman 3D tissue constructs, produced by the self-assembly method of tissue engineering, have been successfully used to model the tumor microenvironment and now represent a very promising approach to further develop diverse cancer models. In this review, we describe the importance of the tumor microenvironment and present the existing in vitro cancer models generated through the self-assembly method of tissue engineering. Lastly, we highlight the relevance of this approach to mimic various and complex tumors, including basal cell carcinoma, cutaneous neurofibroma, skin melanoma, bladder cancer, and uveal melanoma.
Adipose-derived stem cells improve grafted burn wound healing by promoting wound bed blood flow

Abstract Background Researchers have explored the use of adipose-derived stem cells (ASCs) as a cell-based therapy to cover wounds in burn patients; however, underlying mechanistic aspects are not completely understood. We hypothesized that ASCs would improve post-burn wound healing after eschar excision and grafting by increasing wound blood flow via induction of angiogenesis-related pathways. Methods To test the hypothesis, we used an ovine burn model. A 5 cm2 full thickness burn wound was induced on each side of the dorsum. After 24 hours, the burned skin was excised and a 2 cm2 patch of autologous donor skin was grafted. The wound sites were randomly allocated to either topical application of 7 million allogeneic ASCs or placebo treatment (phosphate-buffered saline [PBS]). Effects of ASCs culture media was also compared to those of PBS. Wound healing was assessed at one and two weeks following the application of ASCs. Allogeneic ASCs were isolated, cultured and characterized from non-injured healthy sheep. The identity of the ASCs was confirmed by flow cytometry analysis, differentiation into multiple lineages and gene expression via real-time polymerase chain reaction. Wound blood flow, epithelialization, graft size and take and the expression of vascular endothelial growth factor (VEGF) were determined via enzyme-linked immunosorbent assay and Western blot. Results Treatment with ASCs accelerated the patch graft growth compared to the control (p < 0.05). Topical application of ASCs significantly increased wound blood flow (p < 0.05). Expression of VEGF was significantly higher in the wounds treated with ASCs compared to control (p < 0.05). Conclusions ASCs accelerated grafted skin growth possibly by increasing the blood flow via angiogenesis induced by a VEGF-dependent pathway.
An efficient low‐cost xylem sap isolation method for bacterial wilt assays in tomato

Premise A portable, simple, yet efficient method was developed for the rapid extraction of xylem sap from the stems and petioles of tomato plants for diagnostic and quantification assays of the xylem‐colonizing wilt bacterium Ralstonia solanacearum. Methods and Results Xylem saps were extracted from tomato stem sections using negative pressure generated from handheld needleless syringes. The samples were collected from plants grown under different soil moisture levels at four days after inoculation with the pathogen. Pipette tips were modified to serve as adapters for the stem sections. The quantification of the bacterial load in the extracted sap was performed by plating sap dilutions in Kelman's triphenyltetrazolium chloride (TTC) medium. Pathogen identity was further confirmed by performing a PCR using R. solanacearum‐specific primers. Conclusions Due to its simplicity, portability, and thoroughness of extraction from predetermined tissue sizes, the method can potentially facilitate high‐throughput onsite sampling from a large number of samples in a short time, which cannot be achieved with other available techniques.
Nisin Z Production by Wild Strains of Lactococcus lactis Isolated from Brazilian (Italian Type) Fermented Sausage

In this study, five bacteriocin-producing Lactococcus lactis strains were identified from different naturally fermented Brazilian sausages. Ion exchange and reversed-phase chromatographies were used to purify the bacteriocins from culture supernatant of the five strains. Mass spectrometry (MALDI-TOF/TOF) showed that the molecular masses of the bactericoins from L. lactis ID1.5, ID3.1, ID8.5, PD4.7, and PR3.1 were 3330.567 Da, 3330.514 Da, 3329.985 Da, 3329.561 Da, and 3329.591 Da, respectively. PCR product sequence analysis confirmed that the structural genes of bacteriocins produced by the five isolates are identical to the lantibiotic nisin Z. Optimal nisin Z production was achieved in tryptone and casein peptone, at pH 6.0 or 6.5. The most favorable temperatures for nisin Z production were 25°C and 30°C, and its production was better under aerobic than anaerobic condition. The type of carbon source appeared to be an important factor for nisin Z production. While sucrose was found to be the most efficient carbon source for nisin Z production by four L. lactis isolates, fructose was the best for one isolate. Lactose was also a good energy source for nisin Z production. Surprisingly, glucose was clearly the poorest carbon source for nisin Z production. The five isolates produced different amounts of the bacteriocin, L. lactis ID1.5 and ID8.5 isolates being the best nisin Z producers. DNA sequence analysis did not reveal any sequence differences in the nisZ and nisF promoter regions that could explain the differences in nisin Z production, suggesting that there should be other factors responsible for differential nisin Z production by the isolates.
Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals

The proliferation of physiological signals acquisition and monitoring system, has led to an explosion in physiological signals data. Additionally, RFID systems, blockchain technologies, and the fog computing mechanisms have significantly increased the availability of physiological signal information through big data research. The driver for the development of hybrid systems is the continuing effort in making health-care services more efficient and sustainable. Implantable medical devices (IMD) are therapeutic devices that are surgically implanted into patients' body to continuously monitor their physiological parameters. Patients treat cardiac arrhythmia due to IMD therapeutic and life-saving benefits. We focus on hybrid systems developed for patient physiological signals for collection, storage protection, and monitoring in critical care and clinical practice. In order to provide medical data privacy protection and medical decision support, the hybrid systems are presented, and RFID, blockchain, and big data technologies are used to analyse physiological signals.