Miracle-dz/UniFAR-FTUs huggingface dataset:
Mid- to Long-Term Clinical and Echocardiographic Effects of Post-procedural Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis

Aims To date, the prognostic effects of permanent pacemaker implantation (PPI) after transcatheter aortic valve replacement (TAVR) remain controversial. The purpose of this meta-analysis was to investigate the mid- (1 year) to long-term (> 1 year) clinical and echocardiographic effects of post-procedural PPI in patients after TAVR. Methods PubMed, Embase, Web of Science, and Cochrane Library databases were systematically searched from the establishment of databases up to 1 December 2021. Studies comparing clinical and echocardiographic outcomes between patients with and without post-TAVR PPI of ≥ 1-year follow-up were collected for further meta-analysis. Results A total of 39 studies comprising of 83,082 patients were included in this meta-analysis. At mid-term follow-up (1 year), the pooled results demonstrated a higher risk of all-cause mortality in patients with post-procedural PPI than those without following TAVR (relative risk (RR), 1.17; 95% CI, 1.10–1.24; P < 0.00001). No significant differences were observed in cardiovascular mortality (RR, 0.86; 95% CI, 0.71–1.03; P = 0.10) or heart failure rehospitalization (RR, 0.91; 95% CI, 0.58–1.44; P = 0.69) at 1-year follow-up. At long-term follow-up (> 1 year), post-TAVR PPI had negative effects on all-cause mortality (RR, 1.18; 95% CI, 1.09–1.28; P < 0.0001) and heart failure rehospitalization (RR, 1.42; 95% CI, 1.18–1.71; P = 0.0002). There was no difference in long-term cardiovascular mortality between the two groups (RR, 1.15; 95% CI, 0.97–1.36; P = 0.11). Left ventricular ejection fraction (LVEF) was not significantly different at baseline (mean difference, 1.40; 95% CI, –0.13–2.93; P = 0.07), but was significantly lower in the PPI group at 1-year follow-up (mean difference, –3.57; 95% CI, –4.88 to –2.26; P < 0.00001). Conclusion Our meta-analysis provides evidence that post-TAVR PPI has negative clinical and echocardiographic effects on patients at mid- to long-term follow-up. Further studies are urgently needed to explore the cause of these complications and optimize the treatment and management of patients requiring permanent pacing after TAVR. Systematic Review Registration [https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42021289935], identifier [CRD42021289935].
The Effect of Vitamin D Supplementation After Resistance Training on Physiological Characteristics in Futsal Players with Vitamin D Deficiency

Background: Recent research indicates the prevalence of vitamin D deficiency worldwide and is conflicting evidence as to whether vitamin D supplementation actually improves physical performance. Objectives: The purpose of this study was to investigate the effect of vitamin D supplementation on improving muscle strength, muscle volume and cardiorespiratory fitness through resistance training in male athletes with vitamin D deficiency. Methods: This study was conducted with pre-test and post-test series design and quasi-experimental method. The population included 36 male futsal players with vitamin D deficiency that were randomly divided into four groups of nine: exercise (EX), exercise-supplement (EXS), supplement (SUP) and control (CON). SUP and EXS groups received vitamin D3 (50,000 intramuscular injections) every two weeks for 8 weeks and performed three resistance training (RT) sessions per week at a rate of 75% 1RM. Before and after intervention, blood sampling were drawn and measurements performed for 1RM, muscle volume (cm2), and VO2max by standard Bruce test. Correlated t-test was used to compare pre-test and post-test results and to measure the differences between groups, one-way analysis of variance and LSD post hoc test were used using SPSS statistical software. Results: Muscle volume increased significantly (P-value = 0.001) only in EX and EXS groups. Cardio-respiratory fitness did not change significantly in any of the groups (P > 0.05). There were no significant differences between EXS and SUP groups for any of the measured variables (P ≥ 0.05). Conclusions: It seems that simultaneous application of vitamin D supplementation and resistance training for 8 weeks does not have a significant effect on the improvement of the strength and endurance of futsal players.
A Variable Speed Pumped Storage System Based on Droop-Fed Vector Control Strategy for Grid Frequency and AC-Bus Voltage Stability

Harnessing wind energy is the most rapidly growing amongst renewable energy sources. However, because of its intermittency in nature, wind power results in unfavorable influences on power system control, operation and stability. The voltage sag and flicker and grid frequency fluctuation are significant in this regard. To minimize the effect of wind power fluctuations and other contingencies on the grid frequency and AC-bus voltage, this paper presents a droop-fed vector control strategy based variable speed pumped storage (VSPS) system comprising the doubly fed induction machine. Modelling of the system is undertaken based on a phasor model technique. The case study is made by considering the droop-controlled VSPS plant in a grid containing conventional synchronous machines for hydropower and thermal power plants and an induction machine wind farm. The performance is validated and analyzed using a MATLAB/Simulink platform. The proposed droop-fed control model is compared with the conventional control strategy (without being droop-fed) and tested to wind power fluctuations, start-up transients, load variations and three-phase fault. The results show that the droop-fed vector control strategy of the VSPS plant achieves better dynamic and steady state controlling responses for grid frequency and AC-bus voltage in the power system than the conventional vector control scheme during wind power fluctuations and contingencies.
A combined approach of VGG 16 and LSTM transfer learning technique for skin melanoma classification

To distinguish melanoma from other skin illnesses, doctors examine pigmented lesions on the skin. Damaged DNA causes cells to expand uncontrollably, and the rate of growth is currently increasing rapidly. Melanoma is a kind of skin cancer induced by UV radiation from the sun, with a survival rate of about 15-20 percent. Increased UV light on the earth's surface is also aiding the spread of skin cancer throughout the globe. Melanoma is diagnosed late, resulting in severe malignancy and spread to other bodily organs such as the liver, lungs, and brain. Melanoma diagnosis from dermoscopic skin samples automatically is a difficult problem. The purpose of Computer Vision, Machine Learning, and Deep Learning in the era of digital pictures is to extract information from them and develop new knowledge. Deep convolutional neural network (DCNN) models have been extensively studied for skin disease detection, with some achieving diagnostic results that are equivalent to or even better than dermatologists. Pre-processing involves first applying a filter or kernel to reduce noise and artefacts, then trained on a variety of tiny, unbalanced datasets to ensure that the moderately complicated models outperform the bigger models. Finally, to minimise overfitting, regularization DropOut is introduced.
Macroscopic implications from phase space dynamics of tokamak turbulence : relaxation, transport, and flow generation

Aspects of the macroscopic phenomenology of tokamak plasmas - relaxation, transport, and flow generation - are analyzed in the context of phase space dynamics. Particular problems of interest are: i) fluctuation entropy evolution with turbulence driven flows and its application to flow generation by heat flux driven turbulence, and ii) dynamical coupling between phase space structures and zonal flows and its implication for macroscopic relaxation and transport. In chapter 2, intrinsic toroidal rotation drive by heat flux driven turbulence in tokamak is analyzed based on phase space dynamics. In particular, the dynamics of fluctuation entropy with turbulence driven flows is formulated. The entropy budget is utilized to quantify tokamaks as a heat engine system, where heat flux is converted to macroscopic flows. Efficiency of the flow generation process is defined as the ratio of entropy destruction via flow generation to entropy production via heat input. Comparison of the results to experimental scaling is discussed as well. In chapter 3, dynamics of a single phase space structure (drift hole) is discussed for a strongly magnetized 3D plasma. The drift hole is shown to be dynamically coupled to zonal flows by polarization charge scattering. The coupled dynamics of the drift hole and zonal flow is formulated based on momentum budget. As an application, a bound on the self-bound drift hole potential amplitude is derived. The results show that zonal flow damping appears as a controlling parameter. In chapter 4, dynamics of both a single structure and multi- structures in phase space are discussed for a relevant system, i.e. trapped ion driven ion temperature gradient turbulence. The structures are dynamically coupled to zonal flows, since they must scatter polarization charge to satisfy the quasi-neutrality. The coupled evolution of the structures and flows is formulated as a momentum theorem. An implication for transport process is discussed as well. The transport flux is prescribed by dynamical friction exerted by structures on flows. The dynamical friction exerted by zonal flow is a novel effect and reduces transport by algebraically competing against other fluxes, such as a quasilinear diffusive flux
Rel-CNN: Learning Relationship Features in Time Series for Classification

Time series classification has ubiquitous applications in the real world. Owing to its importance, many time series classification techniques have been proposed over years. Among them, neural network based methods have attracted significant research attention due to their ability to automatically extract latent and discriminative features from data. In this paper, we explore relationship features, which provide valuable global information for time series analytics, and propose a general neural network architecture, namely Rel-CNN, to learn both global and local subsequence features for time series classification. Moreover, we provide two detailed model designs, Relationship Feature based Convolution Filtering and Latent Relationship Feature based Convolution Filtering, and address technical issues due to excessive parameters to learn in these models. We evaluate our models and baselines on time series classification, with extensive experiments on the widely-used 85 uni-variate “bake-off” datasets and 8 multi-variate UEA datasets. Experimental results show that our Rel-CNN models are superior to the representative time series classifiers, in terms of average accuracy, average Macro-f1 and ranking metrics. In addition, an ensemble version of Rel-CNN also outperforms the state-of-the-art ensemble classifiers in terms of average rank, average accuracy and average Macro-f1 on the bake-off datasets.
Machine Learning Screens Potential Drugs Targeting a Prognostic Gene Signature Associated With Proliferation in Hepatocellular Carcinoma

Background: This study aimed to screen potential drugs targeting a new prognostic gene signature associated with proliferation in hepatocellular carcinoma (HCC). Methods: CRISPR Library and TCGA datasets were used to explore differentially expressed genes (DEGs) related to the proliferation of HCC cells. Differential gene expression analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct a prognostic gene signature. Then the predictive power of the gene signature was validated in the TCGA and ICGC datasets. Furthermore, potential drugs targeting this gene signature were screened. Results: A total of 640 DEGs related to HCC proliferation were identified. Using univariate Cox analysis and random forest algorithm, 10 hub genes were screened. Subsequently, using multiplex combinatorial screening, five hub genes (FARSB, NOP58, CCT4, DHX37 and YARS) were identified. Taking the median risk score as a cutoff value, HCC patients were divided into high- and low-risk groups. Kaplan-Meier analysis performed in the training set showed that the overall survival of the high-risk group was worse than that of the low-risk group (p < 0.001). The ROC curve showed a good predictive efficiency of the risk score (AUC > 0.699). The risk score was related to gene mutation, cancer cell stemness and immune function changes. Prediction of immunotherapy suggetsted the IC50s of immune checkpoint inhibitors including A-443654, ABT-888, AG-014699, ATRA, AUY-922, and AZ-628 in the high-risk group were lower than those in the low-risk group, while the IC50s of AMG-706, A-770041, AICAR, AKT inhibitor VIII, Axitinib, and AZD-0530 in the high-risk group were higher than those in the low-risk group. Drug sensitivity analysis indicated that FARSB was positively correlated with Hydroxyurea, Vorinostat, Nelarabine, and Lomustine, while negatively correlated with JNJ-42756493. DHX37 was positively correlated with Raltitrexed, Cytarabine, Cisplatin, Tiotepa, and Triethylene Melamine. YARS was positively correlated with Axitinib, Fluphenazine and Megestrol acetate. NOP58 was positively correlated with Vorinostat and 6-thioguanine. CCT4 was positively correlated with Nerabine. Conclusion: The five-gene signature associated with proliferation can be used for survival prediction and risk stratification for HCC patients. Potential drugs targeting this gene signature deserve further attention in the treatment of HCC.
Do speakers converge rhythmically? A study on segmental timing properties of Grison and Zurich German before and after dialogical interactions

This paper reports on the results of a research investigating whether rhythmic features, in terms of segmental timing properties, are object of speaker’s adjustments after the exposure to a conversational partner. In the context of dialects in contact, this is crucial to understand whether rhythmic attributes may bring about language variation and change. In the context of human-machine interactions, this can benefit the design of spoken dialogues systems to achieve human-likeness. To study rhythmic accommodation, we selected a corpus of pre- and post-dialogue recordings, performed by 18 speakers of Grison and Zurich German (henceforth GRG and ZHG), two Swiss German dialects characterised by noticeable segmental and supra-segmental differences. To quantify rhythmic convergence, we designed three measures based on the segmental timing differences between the two dialects. We compared the Euclidean distances in the three measures between GRG and ZHG speakers in a pair before and after two interactions. Results reveal that dyads members do not significantly shift the production of segmental timing features after the dialogues. Neither linguistic nor social factors can account for the observed accommodation pattern. Cross-dialectal segmental timing differences, captured by the three ratio measures, may be either robust against the influence of interlocutors’ acoustic behaviour or too subtle to be perceived or retained after interactions.
Persistency, Consistency, and Polynomial Matrix Models in Least-Squares Identification

Persistency, Consistency, and Polynomial Matrix Models in Least-Squares Identification by Matthew S. Holzel Chair: Dennis S. Bernstein The ultimate goal of system identification is the identification of possibly nonlinear systems in the presence of unknown deterministic and stochastic noise using robust, efficient algorithms that are amenable to recursive implementation. Throughout the dissertation, we will consider incrementally more difficult problems in system identification, with the aim of achieving this goal. Specifically, we begin by considering the simplest case of identifying linear systems with no noise. Afterwards, we allow for deterministic noise, followed by stochastic noise. Finally, we conclude by allowing for an unknown Hammerstein nonlinearity, before attempting to solve the problem of identifying a more general class of nonlinear systems. Our motivation for considering these problems in system identification is control. Specifically, we are interested in implementing control algorithms which require either a polynomial matrix model of the system or the Markov parameters of the system, such as in retrospective cost adaptive control and model predictive control. Furthermore, we seek to develop recursive algorithms for identification to allow for online system identification, such as on aircraft in flight. Thus we focus exclusively on ix least-squares-based estimation of Markov parameters and polynomial matrix models. The novel contributions of this work are: theory and algorithms for relating Markov parameters and polynomial matrix models, a finite-data analysis of persistency in time-domain identification, the zero buffering technique, an analysis of the consistency of μ-Markov-based least-squares estimates in errors-in-variables identification of linear and Hammerstein system identification, an algorithm for solving equality-constrained multivariate polynomial least-squares problems, an algorithm for computing the nullspace of large sparse matrices that retains sparsity, and an SVD-based algorithm for computing the zeros of polynomial matrices.
Generating Questions from Wikidata Triples

Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts. To handle rare entities and generalize to unseen properties, previous work on KBQG resorted to extensive, often ad-hoc pre- and post-processing of the input triple. We revisit KBQG – using pre training, a new (triple, question) dataset and taking question type into account – and show that our approach outperforms previous work both in a standard and in a zero-shot setting. We also show that the extended KBQG dataset (also helpful for knowledge base question answering) we provide allows not only for better coverage in terms of knowledge base (KB) properties but also for increased output variability in that it permits the generation of multiple questions from the same KB triple.
Marine Microeukaryote Metatranscriptomics: Sample Processing and Bioinformatic Workflow Recommendations for Ecological Applications

Microeukaryotes (protists) serve fundamental roles in the marine environment as contributors to biogeochemical nutrient cycling and ecosystem function. Their activities can be inferred through metatranscriptomic investigations, which provide a detailed view into cellular processes, chemical-biological interactions in the environment, and ecological relationships among taxonomic groups. Established workflows have been individually put forth describing biomass collection at sea, laboratory RNA extraction protocols, and bioinformatic processing and computational approaches. Here, we present a compilation of current practices and lessons learned in carrying out metatranscriptomics of marine pelagic protistan communities, highlighting effective strategies and tools used by practitioners over the past decade. We anticipate that these guidelines will serve as a roadmap for new marine scientists beginning in the realms of molecular biology and/or bioinformatics, and will equip readers with foundational principles needed to delve into protistan metatranscriptomics.
Analytical model of the crossed wire particle velocity sensor

The subject of this Bachelor Assignment is acoustic particle velocity sensing based on thermal sensors. An acoustic particle velocity sensor is a sensor which is able to measure sound waves not through variations in pressure but through particle velocity. The particle velocity is measured by variations in resistance due to flow induced changes in temperature. In this report an analytical model for the temperature distribution is developed. From this the temperature perturbation under influence of a flow is calculated, a comparison with measurements shows that the temperature perturbationmodel still needs refinement.
A Comparison Between Complications Following Total Versus Subtotal Thyroidectomy: A Literature Review

Thyroidectomy is a common procedure performed for various medical conditions such as hyperthyroidism, thyroid nodules, and thyroid cancer. As with any other procedure, thyroidectomy has several complications such as Recurrent Laryngeal Nerve (RLN) injury, hypocalcemia, and hematoma. Total thyroidectomy and subtotal thyroidectomy are two main types of procedures performed with each having their own common complications. This literature review aims to compare common complications that occur in total thyroidectomy and subtotal thyroidectomy. Three main complications, RLN injury, hypocalcemia, and hematoma formation, are reviewed using PubMed/MEDLINE and Google Scholar with a Synthesis Without Meta-analysis (SWiM) approach to ensure a clear and reliable reporting of findings. Complications post thyroidectomy may be life threatening where prompt attention and follow up is needed to insure effective management. Risk factors for hematoma formation include age >45, gender (male sex), Graves’ disease, systolic blood pressure >150, and the use of Ketorolac or Aspirin. Identification of the RLN intra-operatively is an important step in order to decrease the risk of occurrence. While hypocalcemia is the most common post-operative complication, an increased risk of developing both hypocalcemia and hypoparathyroidism occurs in total thyroidectomy in comparison to subtotal thyroidectomy. In conclusion, complications from thyroidectomy may be life threatening indicating the necessity of post-operative care. The incidence and measures for decreasing the risk of post-operative complications may vary with the type of thyroidectomy performed.
Recognize and Alleviate a Resource Management Conundrum Facing Science Diaspora Networks

Increasingly, science diaspora networks are managed by formal organizations such as embassies or non-profit organizations. Researchers have studied these networks to understand how they influence international collaborations and science diplomacy, and to determine which network activities foster those outcomes and which do not. In this perspective, we suggest that many of these network organizations confront an underappreciated conundrum for managing resources: organizations with few resources must learn how to obtain more resources despite lacking means to do so. To substantiate our suggestion, we do the following. We review exploratory results from a study of network organizations that indicate that these organizations generally lack resources, learn too little from each other, and struggle to overcome the resource conundrum. We also show that this conundrum is expected from organizational theory based on bounded rationality. To help organizations confront the issue, we do the following. First we provide a new database of operating science diaspora networks. We encourage managers of network organizations to use it as a resource to identify peers with whom to regularly exchange knowledge about securing resources. We also suggest that other scientific organizations should infuse network organizations with fresh resources. Ultimately, we urge all relevant stakeholders to recognize that the conundrum results not from the shortcomings of individual managers, but rather is a legitimate organizational phenomena that must be addressed by organizational design.