ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Preferences for anticoagulation therapy in atrial fibrillation: the patients' view

Since the introduction of new oral anticoagulants (NOACs), besides vitamin-K antagonists, an additional option for stroke prevention of patients with atrial fibrillation (AF) is available. The objective of this study was to assess AF patients' preferences with regard to the attributes of these different treatment options. We conducted a multicenter study among randomly selected physicians. Preferences were assessed by computer-assisted telephone interviews. We used a discrete-choice-experiment (DCE) with four convenience-related treatment dependent attributes (need of bridging: yes/no, interactions with food/nutrition: yes/no, need of INR controls/dose adjustment: yes/no; frequency of intake: once/twice daily) and one comparator attribute (distance to practitioner: <1 km/>15 km). Preferences measured in the interviews were analyzed descriptively and based on a conditional logit regression model. A total of 486 AF patients (age: 73.9 ± 8.2 years; 43.2 % female; mean CHA2DS2-VASc: 3.7 ± 1.6; current medication: 48.1 % rivaroxaban, 51.9 % VKA) could be interviewed. Regardless of type of medication, patients significantly preferred the attribute levels (in order of patients' importance) "once daily intake" (Level: once = 1 vs. twice = 0; Coefficient = 0.615; p < 0.001), "bridging necessary" (yes = 1 vs. no = 0; -0.558; p < 0.001), "distance to practitioner of ≤1 km (>15 km = 0 vs. ≤1 km = 1; 0.494; p < 0.001), "interactions with food/nutrition" (yes = 1 vs. no = 0; -0.332; p < 0.001) and "need of INR controls/dose adjustment" (yes = 1 vs. no = 0; -0.127; p < 0.001). In our analyses, "once daily frequency of intake" was the most important OAC-attribute for patients' choice followed by "no bridging necessary" and "no interactions with food/nutrition". Thus, patients with AF seem to prefer treatment options which are easier to administer.
[Temporality of Counter-Knowledge in the West German Organic Farming Scene (1970-1999): From Old to New!]

The Stiftung Ökologischer Landbau (SÖL), founded in the mid-1970s, set out to promote organic farming in the Federal Republic of Germany (FRG). To this end, it brought together protagonists from the scientific community and the environmental movement to build a knowledge base for organic agriculture by drawing on the science-based concepts of natural and organic farming of the 1920s and 1930s. Based on the history of its founding, its structure, and work, this article demonstrates that temporality played an essential role in the establishment of alternative bodies of knowledge. Contrary to the established model of linear scientific-technological progress, the aim was to return to bodies of knowledge and practices that had largely disappeared from the scientific canon of knowledge, but also from agricultural practice, in previous processes of forgetting and marginalization. This is exemplified by the so-called "spade diagnosis," a method developed in the 1930s by soil biologists to assess arable soil. Concepts and practice of counter-knowledge amounted to a model of conservative modernization in organic farming.
Endothelial dysfunction in COVID-19: an overview of evidence, biomarkers, mechanisms and potential therapies

The fight against coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection is still raging. However, the pathophysiology of acute and post-acute manifestations of COVID-19 (long COVID-19) is understudied. Endothelial cells are sentinels lining the innermost layer of blood vessel that gatekeep micro- and macro-vascular health by sensing pathogen/danger signals and secreting vasoactive molecules. SARS-CoV-2 infection primarily affects the pulmonary system, but accumulating evidence suggests that it also affects the pan-vasculature in the extrapulmonary systems by directly (via virus infection) or indirectly (via cytokine storm), causing endothelial dysfunction (endotheliitis, endothelialitis and endotheliopathy) and multi-organ injury. Mounting evidence suggests that SARS-CoV-2 infection leads to multiple instances of endothelial dysfunction, including reduced nitric oxide (NO) bioavailability, oxidative stress, endothelial injury, glycocalyx/barrier disruption, hyperpermeability, inflammation/leukocyte adhesion, senescence, endothelial-to-mesenchymal transition (EndoMT), hypercoagulability, thrombosis and many others. Thus, COVID-19 is deemed as a (micro)vascular and endothelial disease. Of translational relevance, several candidate drugs which are endothelial protective have been shown to improve clinical manifestations of COVID-19 patients. The purpose of this review is to provide a latest summary of biomarkers associated with endothelial cell activation in COVID-19 and offer mechanistic insights into the molecular basis of endothelial activation/dysfunction in macro- and micro-vasculature of COVID-19 patients. We envisage further development of cellular models and suitable animal models mimicking endothelial dysfunction aspect of COVID-19 being able to accelerate the discovery of new drugs targeting endothelial dysfunction in pan-vasculature from COVID-19 patients.
Physical Unclonable Functions in the Internet of Things: State of the Art and Open Challenges

Attacks on Internet of Things (IoT) devices are on the rise. Physical Unclonable Functions (PUFs) are proposed as a robust and lightweight solution to secure IoT devices. The main advantage of a PUF compared to the current classical cryptographic solutions is its compatibility with IoT devices with limited computational resources. In this paper, we investigate the maturity of this technology and the challenges toward PUF utilization in IoT that still need to be addressed.
Anesthesia in a patient with dyskeratosis congenita presenting for urgent subtotal gastrectomy

Dyskeratosis congenita is a rare and complex congenital disease that may complicate surgical treatment and impact anesthetic care. We present the perioperative management of a patient with severe pancytopenia, respiratory dysfunction, and oral leukoplakia who presented for urgent surgery for removal of a gastric hemorrhagic malignant tumor. Important issues in the management of this patient include choice of anesthetic technique, correction of pancytopenia (thrombocytopenia in particular), judicious perioperative fluid management to avoid dilutional coagulopathy, antibiotic prophylaxis, and strict aseptic technique. Careful management of a potentially difficult airway and a higher likelihood of respiratory insufficiency further complicate patient care. Knowledge of this rare disease process and its potential impact on anesthetic management is paramount for safe perioperative patient care.
Recommendations for the integral diagnosis of chronic viral hepatitis in a single analytical extraction

The Spanish Society of Digestive Pathology (SEPD), the Spanish Association for the Study of the Liver (AEEH), the Spanish Society of Infections and Clinical Microbiology (SEIMC) and its Viral Hepatitis Study Group (GEHEP), and with the endorsement of the Alliance for the Elimination of Viral Hepatitis in Spain (AEHVE), have agreed on a document to carry out a comprehensive diagnosis of viral hepatitis (B, C and D), from a single blood sample; that is, a comprehensive diagnosis, in the hospital and/or at the point of care of the patient. We propose an algorithm, so that the positive result in a viral hepatitis serology (B, C and D), as well as human immunodeficiency virus (HIV), would trigger the analysis of the rest of the virus, including the viral load when necessary, in the same blood draw. In addition, we make two additional recommendations. First, the need to rule out a previous hepatitis A virus (VHA) infection, to proceed with its vaccination in cases where IgG-type studies against this virus are negative and the vaccine is indicated. Second, the determination of the HIV serology. Finally, in case of a positive result for any of the viruses analyzed, there must be an automated alerts and initiate epidemiological monitoring.
MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks

Neural network-based recommendation algorithms have become the state-of-the-art in recommender systems and can achieve very high predictive accuracy. However, these models are usually considered as black boxes in terms of their interpretability due to the complex structure of their hidden layers. In this research work, we propose MP4Rec, a recommender system using heterogeneous information networks to provide both accurate and explainable recommendations. MP4Rec uses of user-user and item-item similarity matrices and applies a newly proposed pair-wise objective function to make top-N recommendations which are transparent and explainable. The similarity matrices are created from metapaths constructed with the PathSim algorithm, node embeddings with cosine similarity or their combinations. The proposed pair-wise objective function incorporates an additional soft constraint for pushing more explainable items into the top-N recommendations. We have performed several experiments that show the effectiveness of our model by outperforming the state-of-the-art and providing both accurate and explainable recommendations in three well-known datasets.
New calculation method for exact length weighting factor in cone-beam computed tomography

In iterative reconstruction algorithms, it is very important to calculate the weighting factor quickly and accurately, which directly affects the reconstruction efficiency and accuracy of computed tomography images. In this paper, a new calculation method for accurate length weighting factor is developed. Firstly, the ray equation is determined according to the coordinates of the source and the ray's projection. Second, the intersections of the ray and the equidistant parallel planes along three coordinate axes are calculated, and the points intersecting with the reconstructed object are put into a set. Finally, when the X-axis coordinates of all points in the set are different, the points are arranged according to their X-axis coordinates; when the X-axis coordinates of all points are same, the points are arranged according to their Y-axis coordinates. The sorted point set is the intersections that the ray traverses the reconstructed object in sequence. The index and weighting factor of all voxels that the ray passes through can be determined by the sorted point set. Compared with the classical Siddon's method, it avoids the step of describing the intersections with parameters, and its calculation speed is increased by nearly 20%. The numerical simulations and real data reconstruction experiments are conducted to validate the proposed calculation method of exact length weighting factor.
Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step

Nowcasting is an important technique for weather forecasting because sudden weather changes significantly affect human life. The encoding-forecasting model, which is a state-of-the-art architecture in the field of data-driven radar extrapolation, does not particularly focus on the latest data when forecasting natural phenomena. This paper proposes a weighted broadcasting method that emphasizes the latest data of the time step to improve the nowcasting performance. This weighted broadcasting method allows the most recent rainfall patterns to have a greater impact on the forecasting network by extending the architecture of the existing encoding-forecasting model. Experimental results show that the proposed model is 1.74% and 2.20% better than the existing encoding-forecasting model in terms of mean absolute error and critical success index, respectively. In the case of heavy rainfall with an intensity of 30 mm/h or higher, the proposed model was more than 30% superior to the existing encoding-forecasting model. Therefore, applying the weighted broadcasting method, which explicitly places a high emphasis on the latest information, to the encoding-forecasting model is considered as an improvement that is applicable to the state-of-the-art implementation of data-driven radar-based precipitation nowcasting.
Traffic sign detection based on improved faster R-CNN for autonomous driving

The timely and accurate identification of traffic signs plays a significant role in realizing the autonomous driving of vehicles. However, the size of traffic signs accounts for a low proportion of the input picture, which increases the difficulty of detection. This paper proposes an improved faster R-CNN traffic sign detection method. ResNet50-D feature extractor, attention-guided context feature pyramid network (ACFPN), and AutoAugment technology are designed for the faster R-CNN model. ResNet50-D is selected as the backbone network to obtain more characteristic information. ACFPN is performed to decrease the loss of contextual information. And data augmentation and transfer learning are adopted to make model training more convenient and time-saving. To prove the availability of the proposed method, we compare it with mainstream approaches (SSD, YOLOv3, RetinaNet, cascade R-CNN, FCOS, and CornerNet-Squeeze) and state-of-the-art methods. Experimental results on the CCTSDB dataset show that the improved faster R-CNN achieves the frames per second of 29.8 and the mean average precision of 99.5%, which is superior to the state-of-the-art methods and more suitable for traffic sign detection. Moreover, the proposed model is extended to the Tsinghua-Tencent 100 K (TT100K) dataset and also achieves a competitive detection result.
The development and application of a simple methodology for recording rock art using consumer-grade digital cameras

A simple methodology for recording rock art has been recently developed in Australia and tested on Aboriginal rock art, including both petroglyphs and pictographs (engraved and painted images, respectively). The approach was based on commercial photogrammetric software and consumer-grade digital cameras, because it was believed that archaeologists, conservators and site managers need simple and cost-effective methods to record and document rock art. This methodology has been adopted subsequently by the Northumberland and Durham Rock Art Project working in conjunction with English Heritage, to assist in recording 1500 prehistoric engraved panels located across the north-east of England. Significantly, the fieldwork was carried out by enthusiastic volunteers, willing to sacrifice their weekends to capture imagery suitable for digital photogrammetry. This paper explains briefly how the recording technique was developed in Australia before indicating how expertise and equipment were integrated to allow UK-based volunteers to carry out data acquisition and, perhaps surprisingly, also the photogrammetry. This will demonstrate the value of mobilising the voluntary sector for heritage recording, which is feasible only if recording methodologies are based on cheap and simple instrumentation.
UBR2 targets myosin heavy chain IIb and IIx for degradation: Molecular mechanism essential for cancer-induced muscle wasting

Cancer cachexia is a lethal metabolic syndrome featuring muscle wasting with preferential loss of fast-twitching muscle mass through an undefined mechanism. Here, we show that cancer induces muscle wasting by selectively degrading myosin heavy chain (MHC) subtypes IIb and IIx through E3 ligase UBR2-mediated ubiquitylation. Induction of MHC loss and atrophy in C2C12 myotubes and mouse tibialis anterior (TA) by murine cancer cells required UBR2 up-regulation by cancer. Genetic gain or loss of UBR2 function inversely altered MHC level and muscle mass in TA of tumor-free mice. UBR2 selectively interacted with and ubiquitylated MHC-IIb and MHC-IIx through its substrate recognition and catalytic domain, respectively, in C2C12 myotubes. Elevation of UBR2 in muscle of tumor-bearing or free mice caused loss of MHC-IIb and MHC-IIx but not MHC-I and MHC-IIa or other myofibrillar proteins, including α-actin, troponin, tropomyosin, and tropomodulin. Muscle-specific knockout of UBR2 spared KPC tumor-bearing mice from losing MHC-IIb and MHC-IIx, fast-twitching muscle mass, cross-sectional area, and contractile force. The rectus abdominis (RA) muscle of patients with cachexia-prone cancers displayed a selective reduction of MHC-IIx in correlation with higher UBR2 levels. These data suggest that UBR2 is a regulator of MHC-IIb/IIx essential for cancer-induced muscle wasting, and that therapeutic interventions can be designed by blocking UBR2 up-regulation by cancer.
Estimation of the instantaneous pitch of speech

An accurate estimation of the pitch is essential for many speech processing applications, such as speech synthesis, speech coding, and speech enhancement. A widely used assumption in most common pitch estimation methods is that pitch is constant over a segment of short duration. This assumption does not apply in reality and leads to inaccurate pitch estimates. In this paper, we present a method for continuous pitch estimation that is able to track fast changes. In the presented framework, the pitch is modeled by a B-spline expansion and optimized in a multistage procedure for increased robustness. The performance of the continuous optimization procedure is compared to state-of-the-art pitch estimation methods and is evaluated both for artificial speech-like signals with known pitch, and for real speech signals. The results of the experiments show that our method leads to a higher accuracy of the estimate of the pitch than state-of-the-art methods.
Facile Synthesis of Transition-Metal-Doped (Fe, Co, and Ni) CuS/CuO/CS Nanorod Arrays for Superior Electrocatalytic Oxygen Evolution Reaction

Electrochemical water splitting is a promising way to produce sustainable, renewable, and clean H-2 fuel. The anodic half-reaction of electrochemical water splitting, oxygen evolution reaction (OER) lowers the overall efficiency of the system due to this four-electron process with sluggish reaction kinetics. The state-of-the-art catalysts for OER are based on precious metals (Ru and Ir). In this article, transition-metal-doped (Fe, Co, and Ni) CuS/CuO nanorod arrays on copper sheet (CS) substrates were synthesized for the first time via the facile solvothermal method, and the electrocatalytic activity of the synthesized material toward OER in alkaline media was investigated. Fe-doped CuS/CuO/CS showed superior electrochemical performance with an overpotential of only 340 mV at 10 mA/cm(2) current density. The OER performance of the material was compared with the state-of-the-art catalyst for RuO2/CS. The overpotential of RuO2/CS was 320 mV at 10 mA/cm(2) current density which is only 20 mV lower than the state-of-the-art catalyst. The enhancement of the OER activity was obtained by valence regulation upon doping Fe3+ and Fe2+ into CuS/CuO/CS nanorod arrays (NAs). The charge transfer resistance was lowered from 10.2 Omega/cm(2) for pristine CuS/CuO/CS NA to 1.2 Omega/cm(2) upon Fe doping. The electrochemically active surface area was increased from 36 to 51 cm(EcsA)(2) upon Fe doping. The Fe-doped CuS/CuO/CS shows an exceptionally high turnover frequency (TOF) of 0.68 s(-1). The catalyst was stable up to 1000 cycles of OER over 10 h. The increase in electrical conductivity, increase in electrochemically active surface area (ECSA), and creation of defect sites leading to preferential absorption of OH- upon Fe doping led to enhanced OER activity of Fe-CuS/CuO/CS. This is the first report of an Fe-doped CuS/CuO/CS nanoarray that shows superior OER activity with high TOF and excellent stability.
Links between the concentrations of gaseous pollutants measured in different regions of Estonia

The factors that determine the concentrations of air pollutants (NO, NO2, SO2, O3), measured in 8 monitoring stations (4 rural background, 3 urban, and 1 industrial) in Estonia, are studied applying the factor analysis. The factor analysis reveals remarkable impact of COVID-19 lockdown, effects caused by dramatic decrease in oil-shale based energy production in Estonia provoked by new socio-economic conditions such as elevated price for CO2 emission quota, differences between rural and urban stations, maritime-continental difference for NO2 and ozone, and specific industrial impact in case of SO2. The multiple regression analysis to predict the ozone concentration in one rural background station at Tahkuse was performed, based on the ozone concentrations measured in other stations and the concentrations of NO, NO2, and CO2, recorded in the same station. It was found that the ozone concentration at Tahkuse is rather well predictable (determination coefficient, i.e., correlation coefficient squared, R 2 = 0.714), using only the concentrations from another rural station at Saarejärve that is about 110 km away from Tahkuse. Adding all the available data into the list of regression analysis arguments, the model predictability is improved moderately (determination coefficient R 2 = 0.795). Large model residuals above all tend to occur with the values measured and predicted at summer nights. Surprisingly, neither NO nor NO2 concentration measured in the Tahkuse station did appear a good predictor for ozone (R 2 = 0.02 and 0.05, respectively), possibly long-range transport of ozone (that has also experienced NO and/or NO2 influence during transport) overrides the local effects of NO and/or NO2.
Current trends in deep learning for Earth Observation: An open-source benchmark arena for image classification

We present AiTLAS: Benchmark Arena - an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena.