Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Contraindications for Thrombolysis: Risk of Hemorrhagic Transformation and Neurological Deterioration after Thrombolysis in Mice with Recent Ischemic Stroke and Hyperglycemia

(1) Intravenous thrombolysis with recombinant tissue plasminogen activator (rt-PA) in patients with acute ischemic stroke is limited because of several contraindications. In routine clinical practice, patients with a recent stroke are typically not treated with rt-PA in case of a recurrent ischemic event. The same applies to its use in the context of pulmonary artery embolism and myocardial infarction with a recent stroke. In this translational study, we evaluated whether rt-PA treatment after experimental ischemic stroke with or without additional hyperglycemia increases the risk for hemorrhagic transformation (HT) and worsens functional outcome regarding the old infarct area. (2) In total, 72 male C57BL/6N mice were used. Ischemic stroke (index stroke) was induced by transient middle cerebral artery occlusion (tMCAO). Mice received either rt-PA or saline 24 h or 14 days after index stroke to determine whether a recent ischemic stroke predisposes to HT. In addition to otherwise healthy mice, hyperglycemic mice were analyzed to evaluate diabetes as a second risk factor for HT. Mice designated to develop hyperglycemia were pre-treated with streptozotocin. (3) The neurological outcome in rt-PA and saline-treated normoglycemic mice did not differ significantly, either at 24 h or at 14 days. In contrast, hyperglycemic mice treated with rt-PA had a significantly worse neurological outcome (at 24 h, p = 0.02; at 14 days, p = 0.03). At 24 h after rt-PA or saline treatment, HT scores differed significantly (p = 0.02) with the highest scores within hyperglycemic mice treated with rt-PA, where notably only small petechial hemorrhages could be detected. (4) Thrombolysis after recent ischemic stroke does not increase the risk for HT or worsen the functional outcome in otherwise healthy mice. However, hyperglycemia as a second risk factor leads to neurological deterioration after rt-PA treatment, which cannot be explained by an increase of HT alone. Direct neurotoxic effects of rt-PA may play a role.
Bibliometric Analysis of Literature on Physical Activity and COVID-19

The coronavirus disease 2019 (COVID-19) pandemic induced a sudden surge in COVID-19 related publications. This bibliometric analysis aimed to analyze literature on physical activity and COVID-19 published in the PubMed database. The search terms ((physical activity [MeSH Terms] OR physical inactivity [MeSH Terms]) AND COVID-19 [MeSH Terms]) were applied to obtain publications from the inception of PubMed to February 2022. The analyses included the year of publication, type of publication, and origin of publication by country, region, and country income. The research areas were analyzed for research articles and systematic reviews. Of 1268 articles, 143 articles were excluded, and 1125 articles were analyzed. A total of 709 articles (63.02%) were published in 2021. A majority of publications were research articles (n = 678, 60.27%). The USA (n = 176, 15.64%), countries in the European Region (n = 496, 44.09%), and high-income countries (n = 861, 76.53%) were dominant publishing countries. Of 699 research articles and systematic reviews, surveillance and trends of physical activity were the main research area, followed by health outcomes, and correlates and determinants of physical activity. There is a wide gap in publication productivity in the field of physical activity and health during the pandemic among different countries’ economic statuses.
Comparison of CNN-based segmentation models for forest type classification

Abstract. We present the results from evaluating various Convolutional Neural Network (CNN) models to compare their usefulness for forest type classification. Machine Learning based on CNNs is known to be suitable to identify relevant patterns in remote sensing imagery. With the availability of free data sets (e.g. the Copernicus Sentinel-2 data), Machine Learning can be utilized for forest monitoring, which provides useful and timely information helping to measure and counteract the effects of climate change. To this end, we performed a case study with publicly available data from the federal state of North Rhine-Westphalia in Germany. We created an automated pipeline to preprocess and filter this data and trained the CNN models UNet, PSPNet, SegNet, and FCN-8. Since the data contained large rural areas, we augmented the imagery to improve classification results. We reapplied the trained models to the data, compared the results for each model, and evaluated the effect of augmentation. Our results show that UNet performs best with a categorical accuracy of 73% when trained with augmented imagery.
Hypertension and Type 2 Diabetes—The Novel Treatment Possibilities

Elevated blood pressure and hyperglycaemia frequently coexist and are both components of metabolic syndrome. Enhanced cardiovascular risk is strongly associated with diabetes and the occurrence of hypertension. Both hypertension and type 2 diabetes, if treated inappropriately, lead to serious complications, increasing the mortality of patients and generating much higher costs of health systems. This is why it is of great importance to find the missing link between hypertension and diabetes development and to simultaneously search for drugs influencing these two disorders or even drugs aimed at their pathological bases. Standard antihypertensive therapy mainly focuses on blood pressure reduction, while novel drugs also possess a wide range of pleiotropic modes of actions, such as cardio- and nephroprotective properties or body weight reduction. These properties are especially desirable in a situation when type 2 diabetes coexists with hypertension. This review describes the connections between diabetes and hypertension development and briefly summarises the current knowledge regarding attempts to define targets for the treatment of high blood pressure in diabetic patients. It also describes the standard hypotensive drugs preferred in patients with type 2 diabetes, as well as novel drugs, such as finerenone, esaxerenone, sodium–glucose co-transporter-2 inhibitors, glucagon-like peptide-1 analogues and sacubitril/valsartan.
Endovascular Obliteration of the Greater Saphenous Vein: The Closure Procedure

●Abstract:The Closure procedure is an endovascular method to obliterate the greater saphenous vein as an alternative to surgical ligation and stripping. It is based upon the effect that controlled heat exerts upon the collagen in the vein wall, resulting in shrinkage of the wall and its lumen. The development of the technique and its early applications are reviewed. Technical details of the procedure are described as they have evolved in the Straub Clinic experience. Key points in technique relate to use of positioning of the table to facilitate different stages in the procedure, the use of tumescent infiltration in the subcutaneous tissue to provide a ‘dry’ vein, careful monitoring with the duplex scan during the procedure, and utilization of guidewires to assist in catheter placement are discussed. Two series of cases are presented to examine the results obtained to date with this treatment and to highlight both the advantages and the areas of potential concern for the future. The infrequent (1%) but real complication of DVT in the femoral vein is placed in perspective and recommendations are made to minimize this problem. The potential for future recurrences due to thigh branch persistence exists but has not materialized in the first two years of experience. Other perceived complications of Closure, such as neuritis and thermal injury to the skin, have not materialized as serious problems. The advantages of this endovascular procedure have been rapid recovery and minimal pain, with early return to normal activity and to work. It is a highly desirable method to patients who are attracted by the minimally invasive, non-surgical nature of the procedure, and the absence of surgical scarring in the post-operative recovery.
Parametric Inference using Structural Break Tests

We present methods for testing hypotheses and estimating confidence sets for structural parameters of economic models in the presence of instabilities and breaks of unknown form. These methods constructively explore information generated by changes in the data-generating process to improve the inference of parameters that remain stable over time. The proposed methods are suitable for models cast in the generalized method of moments framework, which makes their application wide. Moreover, they are robust to the presence of weak instruments. The genstest command in Stata implements these methods to conduct hypothesis tests and to estimate confidence sets.
DAISY: An Implementation of Five Core Principles for Transparent and Accountable Conversational AI

Abstract We present a detailed implementation of five core principles for transparent and acccountable conversational AI, namely interpretability, inherent capability to explain, independent data, interactive learning, and inquisitiveness. This implementation is a dialogue manager called DAISY that serves as the core part of a conversational agent. We show how DAISY-based agents are trained with human-machine interaction, a process that also involves suggestions for generalization from the agent itself. Moreover, these agents are capable to provide a concise and clear explanation of the actions required to reach a conclusion. Deep neural networks (DNNs) are currently the de facto standard in conversational AI. We therefore formulate a comparison between DAISY-based agents and two methods that use DNNs, on two popular data sets involving multi-domain task-oriented dialogue. Specifically, we provide quantitative results related to entity retrieval and qualitative results in terms of the type of errors that may occur. The results show that DAISY-based agents achieve superior precision at the price of lower recall, an outcome that might be preferable in task-oriented settings. Ultimately, and especially in view of their high degree of interpretability, DAISY-based agents are a fundamentally different alternative to the currently popular DNN-based methods.
Effect of storage conditions on seed quality of soybean (Glycine max L.) germplasm

Soybean is one of the most important oil seed crops. However, soybean seed is structurally weak and inherently short-lived, making the crop vulnerable to long period storage. Thus, it is crucial to study the effect of storage conditions on the quality of soybean seeds (Glycine max L.). The genetic material consisted of 10 soybean varieties, whose seeds were stored under both cooling (refrigerator) and room temperature conditions and were subsequently subjected to germination test, electric conductivity test and estimation of free fatty acids percentage. In order to determinethe long-term effects of storage on seed quality, all genotypes were subjected to accelerated ageing at 40℃ up to 48 days and viability equation was used to determine the Ki value. Overall findings revealed the significant effect of storage conditions on seed quality and, at the same time, underlined the beneficial effect of storage under cooling conditions, which is expressed as higher germination percentage, reduced electric conductivity and free fatty acids percentage and improved storage longevity. Further, our data provide conclusive evidence for the qualitative superiority of the varieties Adonai, Celina and Neoplanta, especially upon storage under cooling conditions, as they were characterized by higher germination percentage and improved tolerance to storage (storage potential).
An in-Depth Analysis of the Software Features’ Impact on the Performance of Deep Learning-Based Software Defect Predictors

Software Defects Prediction represents an essential activity during software development that contributes to continuously improving software quality and software maintenance and evolution by detecting defect-prone modules in new versions of a software system. In this paper, we are conducting an in-depth analysis on the software features’ impact on the performance of deep learning-based software defect predictors. We further extend a large-scale feature set proposed in the literature for detecting defect-proneness, by adding conceptual software features that capture the semantics of the source code, including comments. The conceptual features are automatically engineered using Doc2Vec, an artificial neural network based prediction model. A broad evaluation performed on the Calcite software system highlights a statistically significant improvement obtained by applying deep learning-based classifiers for detecting software defects when using conceptual features extracted from the source code for characterizing the software entities.
The role of dispositional reinforcement sensitivity and self-esteem in social interaction anxiety and social phobia

Social anxiety is related to normal variation in personality and manifests as anxiety concerning interactions with others (social interaction anxiety), and/or as a fear of social scrutiny whilst performing tasks when under observation from others (social phobia). In revised Reinforcement Sensitivity Theory (rRST) a behavioral inhibition system (BIS) facilitates defensive approach behaviors and anxiety in situations of uncertainty. A fight-flight-freeze system (FFFS) facilitates fear and avoidance behaviors, and a behavioral approach system (BAS) facilitates anticipated reward and/or approach-based behaviors. rRST suggests that a socially anxious phenotype would experience elevated BIS sensitivity, elevated FFFS sensitivity, and dampened BAS sensitivity. We used self-report measures to test if the effects of social interaction anxiety and social phobia (which reflects the fear of social scrutiny) are separable within rRST, as in rRST anxiety and fear are separate constructs. Low levels of self-esteem are a risk factor for social anxiety, thus we tested how two sub-components of self-esteem referred to as self-acceptance and self-assessment predict social interaction anxiety and social phobia. 405 participants (mean age = 30.6; 86% female) completed the online study. Social interaction anxiety and social phobia were positively correlated with BIS and FFFS-flight sensitivity, and were negatively correlated with BAS, and FFFS-fight sensitivity in males and females. Social interaction anxiety and social phobia were negatively correlated with self-acceptance in males and females. Multiple regression showed that for females BIS and FFFS-flight scores were prominent positive predictors of social interaction anxiety whereas BIS was a prominent positive predictor of social phobia. For males the FFFS-fight subscale was a prominent negative predictor of social interaction anxiety. Overall, a synthesis of the present study and previous studies suggests that there may be subtle differences in how trait social interaction anxiety and trait social phobia relate to reinforcement sensitivity.
AoI-Minimal Task Assignment and Trajectory Optimization in Multi-UAV-Assisted IoT Networks

This article investigates an unmanned-aerial-vehicle (UAV)-assisted Internet of Things system, where multiple UAVs take off from a data center (DC), collect data from multiple ground sensor nodes (SNs), distribute data for multiple users, and finally return to DC. A centralized information-sharing mechanism is designed among UAVs before data distribution, such that UAVs’ repeatedly visiting the same user can be avoided to save energy and time. A nonconvex Age-of-Information (AoI) minimization problem is formulated, where the task assignment, selection of interaction point (IPT), and UAVs’ trajectories are optimized jointly. It is decomposed into two subproblems: one is a joint task assignment and UAV trajectory optimization problem, which is solved by a multipopulation-based genetic algorithm (MPGA), as well as a hybrid algorithm combining dynamic programming (DP) with an improved $K$ -means clustering; and the other is an IPT selection problem, which is modeled as a Fermat–Weber location problem and is solved by the convex optimization technique. Simulation results show that the MPGA performs better than the hybrid algorithm as well as several benchmark algorithms.
Optimal design of a pelton turbine nozzle via 3D numerical simulation

Currently, untapped water resources in China are mostly located in the southwestern region notable for high ranges of Qingha-Tibet Plateau, Hengduan Mountains and Yunnan-Kweichow Plateau. These water resources have the remarkable property of being high in water head within a short distance. The Pelton turbine counts as the most adequate type of water turbine to harness these water resources. An internal-controlled Pelton nozzle design is elucidated in this paper in line with the formula and an existing model in the case of a1100m water head [1]. The impact exerted by nozzle and spear angle on the nozzle hydraulic efficiency under a set of needle strokes was ascertained from the numerical perspective. Elucidated hydraulic performance of different nozzle design was anatomized. An energy criterion was proposed through acquiring the jet efficiency to select the appropriate hydraulic design of Pelton nozzle [2]. The numerical results were indicative of the design with 100°/70° combination of nozzle and spear angle that was superior in overall hydraulic efficiency. For this reason, this design can be referenced in engineering applications.
Lookup tables for transionospheric effects on signals

Lookup tables have been constructed that address group delay and RF carrier phase advance for transionospheric signal propagation referenced to a ground‐based transmitter. The tables are based on parameterizations of ray‐tracing results using the Reilly ray trace/ICED, Bent, Gallagher (RIBG) model with the magnetic field turned off. The quantities of interest are incremental group and phase path lengths referenced to straight‐line distance between transmitter and receiver and are designated by Δlg and Δlp (as defined, both quantities are positive). Equations are presented that relate these quantities to group delay and ionospheric Doppler shifts. Tables currently in use are three‐dimensional, expressed in terms of frequency (f), elevation angle (θ), and straight‐line total electron content (TECSL). At high frequencies, Δlg and Δlp can be accurately expressed by a straight‐line propagation formula that is proportional to TECSL. With decreasing frequency, these quantities become greater than their straight‐line counterparts due to additional refraction effects, including ray bending. Extensive ray‐tracing runs have been performed under a variety of ionospheric conditions to produce scatterplots of Δlg and Δlp versus TECSL for given pairs of [f, θ] values. The current ranges in these variables are from 20 to 100 MHz and 5° to 90°, respectively. The tables contain coefficients from fourth‐degree polynomial fits to the distributions of points within the scatterplots. Where scatter is problematic, the introduction of a fourth parameter, slab thickness, is shown to significantly reduce this scatter. Magnetic field effects are discussed and are shown to have a small effect on table coefficients. Discussion is also included on the use of the tables with a model ionosphere.
Optimization of the Structural Performance of Buried Reinforced Concrete Pipelines in Cohesionless Soils

Pipelines are widely used to transport water, wastewater, and energy products. However, the recently published American Society of Civil Engineers report revealed that the USA drinking water infrastructure is deficient, where 12,000 miles of pipelines have deteriorated. This would require substantial financial investment to rebuild. Furthermore, the current pipeline design practice lacks the guideline to obtain the optimum steel reinforcement and pipeline geometry. Therefore, the current study aimed to fill this gap and help the pipeline designers and practitioners select the most economical reinforced concrete pipelines with optimum steel reinforcement while satisfying the shear stresses demand and serviceability limitations. Experimental testing is considered uneconomical and impractical for measuring the performance of pipelines under a high soil fill depth. Therefore, a parametric study was carried out for reinforced concrete pipes with various diameters buried under soil fill depths using a reliable finite element analysis to execute this investigation. The deflection range of the investigated reinforced concrete pipelines was between 0.5 to 13 mm. This indicates that the finite element analysis carefully selected the pipeline thickness, required flexural steel reinforcement, and concrete crack width while the pipeline does not undergo excessive deformation. This study revealed that the recommended optimum reinforced concrete pipeline diameter-to-thickness ratio, which is highly sensitive to the soil fill depth, is 6.0, 4.6, 4.2, and 3.8 for soil fill depths of 9.1, 12.2, 15.2, and 18.3 m, respectively. Moreover, the parametric study results offered an equation to estimate the optimum pipeline diameter-to-thickness ratio via a design example. The current research outcomes are imperative for decision-makers to accurately evaluate the structural performance of buried reinforced concrete pipelines.
Long Term Cell Immune Response to COVID-19 Vaccines Assessment Using a Delayed-Type Hypersensitivity (DTH) Cutaneous Test

Objective: As we progress with mass vaccination against SARS-CoV-2, there are key questions about the immunogenicity of COVID-19 vaccines that still are not answered. Conventional methods to measure cellular immune responses are complex and expensive in a pandemic situation. Patients and methods: Forty healthy healthcare workers accepted to participate during the vaccination schedule with a mRNA vaccine against SARS-CoV-2. Measurement of Delayed-Type Hypersensitivity (DTH) cutaneous response after intradermal test of protein S of SARS-CoV-2 at day 35 and day 200 was performed. At the same time, a specific anti-RBD IgG using a classic ELISA before vaccination, and on days 0, 35, and 200 was performed. Results: All 40 individuals had a positive DTH skin response at day 35, whereas 39 participants had a positive skin test at day 200. Moreover, although all 40 individuals showed a positive humoral response of specific IgG against spike protein at day 35, with most of them having significantly lower levels at day 200. Conclusion: DTH could be proposed as an ideal and easy method to predict cellular immunity response to mRNA vaccines 200 days after starting an immunization schedule with mRNA vaccine for COVID-19.
Penerapan Model PBL dalam Matakuliah Teori Pengambilan Keputusan untuk Meningkatkan Kemampuan Berpikir Kritis Mahasiswa

Penelitian ini bertujuan untuk meningkatkan keterampilan berpikir kritis dan hasil belajar mata kuliah teori pengambilan keputusan melalui penerapan model Problem-Based Learning (PBL) pada mahasiswa IAIN Pekalongan. Penelitian ini adalah penelitian tindakan kelas. Subjek penelitian adalah mahasiswa yang menempuh mata kuliah teori pengambilan keputusan kelas IIA Prodi Ekonomi Syariah IAIN Pekalongan yang berjumlah 29 mahasiswa. Pengumpulan data menggunakan metode observasi dengan instrumen checklist dan tes unjuk kerja. Data yang diperoleh dianalisis secara deskriptif. Hasil penelitian menunjukkan sebagai berikut: (a) penerapan model PBL dalam pembelajaran mata kuliah teori pengambilan keputusan dapat meningkatkan keterampilan berpikir kritis mahasiswa dalam pembelajaran yaitu sebesar 24,2%, (b) Keterampilan berpikir kritis mahasiswa setelah penerapan PBL yaitu mahasiswa dengan kategori keterampilan berpikir kritis sangat tinggi sebanyak 20 mahasiswa (69%), kategori tinggi sebanyak 7 mahasiswa (24,2%), kategori rendah sebanyak 2 mahasiswa (6,9%) dan kategori sangat rendah yaitu sebanyak 0 mahasiswa (0%), (c) penerapan PBL dapat meningkatkan hasil belajar mahasiswa sebesar 31,03%, dan (d) Hasil belajar mahasiswa setelah penerapan PBL yakni jumlah mahasiswa yang mencapai KKM sebanyak 29 mahasiswa (100%).