Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of deforestation on water chemistry in the western Tatras and Beskid Slaski range in the Polish Carpathians

The article describes the research into the impact of deforestation on the values of physicochemical traits and chemical composition of waters which drain the catchments covered with forest, and those that have been deforested, in the western part of Polish Carpathians. The research was carried out in independent catchments in the Skrzyczne massif in the Beski Śląski (Silesian Beskidy Range) in 2013–2014, and in the Kościeliska Valley in the Western Tatras in 2015–2016. During field studies, water samples were collected monthly in catchments with various degrees of deforestation, and the physicochemical characteristics of water were measured (pH, PEW, T w ). In the laboratory, the chemical composition of water, specifically the content of 14 ions (Ca 2+ , Mg 2+ , Na + , K + , NH 4 + , Li + , HCO 3 – , SO 42– , Cl – , NO 2 – , NO 3 – , PO 43– , Br – , F – ), was determined by means of ion chromatography (DIONEX 2000). The conducted research has shown the impact of deforestation on the value of physical and chemical traits and chemical composition of water. Deforestation of the slopes caused changes in the structure of the chemical composition of water, especially in the area of correlations between anions. In the Tatras, in the basins deforested as a result of windfall, a significant increase in the proportion of NO 3 – (% mval ∙ dm –3 ) in the anionic element of the chemical composition of water was observed, and in the Beskid Śląski, in catchments deforested due to the tree stand decay associated with the imission of pollutants, a significant increase in SO 42– (% mval ∙ dm –3 ) and a significant decrease in HCO 3 – (% mval ∙ dm –3 ) in the an ionic structure of the chemical composition of waters was observed. These examples document the extremely important role played by forests, which cover mountain slopes, in the hydrochemical functioning of the catchment. In the spatial aspect, there is an unusual phenomenon of hydrochemical mosaicism consisting in the occurrence of different relationships between anions, especially hydrogen carbonates, sulphates and nitrates.
Improved DFT-Based Phasor Estimation Algorithm Using Down-Sampling

Fast and accurate fundamental phasor-estimation is a challenging task in digital relays. This letter proposes an improved discrete Fourier transform (DFT)-based phasor estimation algorithm. The accuracy of DFT-based methods is degraded owing to presence of decaying dc component (ddc) in fault current signals. To overcome the undesired effect of ddc, this letter introduces an auxiliary signal, which is generated by down-sampling of the fault current. The fundamental phasor is precisely estimated using sample-summation of the auxiliary signal besides DFT and sample-summation of the fault-current signal. Comprehensive simulations are performed for performance evaluation. The simulation results confirm that the proposed algorithm is fast and robust against ddc, harmonics, and noise. Moreover, the algorithm reduces the computational complexity, which makes it appropriate for digital relaying.
Aging, rather than Parkinson's disease, affects the responsiveness of PBMCs to the immunosuppression of bone marrow mesenchymal stem cells

Whether aging or Parkinson's disease (PD) affects the responses of peripheral blood mononuclear cells (PBMCs) to immunosuppression by bone marrow-derived mesenchymal stem cell (BM-MSCs) and which cytokines are more effective in inducing BM-MSCs to be immunosuppressive remains to be elucidated. PBMCs were isolated from healthy young (age 26–35), healthy middle-aged (age 56–60) and middle-aged PD-affected individuals. All the recruits were male. The mitogen-stimulated PBMCs and proinflammatory cytokine-pretreated BM-MSCs were co-cultured. The PBMC proliferation was measured using Cell Counting Kit-8, while the cytokine secretion was assayed by cytometric bead array technology. The immunosuppressive ability of BM-MSCs was confirmed in young healthy, middle-aged healthy and middle-aged PD-affected individuals. Among the three groups, the PBMC proliferation and cytokine secretion of the young healthy group were suppressed more significantly compared with those of the middle-aged healthy and middle-aged PD-affected group. No significant differences were identified in the PBMC proliferation and cytokine secretion between the patients with PD and the middle-aged healthy subjects. Interferon (IFN)-γ synergized with tumor necrosis factor (TNF)-α, interleukin (IL)-1α or IL-1β was more effective than either one alone, and the combinations of IFN-γ + IL-1α and IFN-γ + IL-1β were more effective than IFN-γ + TNF-α in inducing BM-MSCs to inhibit PBMC proliferation. The results of the present study suggested that aging, rather than PD, affects the response of PBMCs toward the suppression of BM-MSC, at least in middle-aged males. Patients with PD aged 56–60 remain eligible for anti-inflammatory BM-MSC-based therapy. Treatment of BM-MSCs with IFN-γ + IL-1α or IFN-γ + IL-1β prior to transplantation may result in improved immunosuppressive effects.
Class-Distinct and Class-Mutual Image Generation with GANs

Class-conditional extensions of generative adversarial networks (GANs), such as auxiliary classifier GAN (AC-GAN) and conditional GAN (cGAN), have garnered attention owing to their ability to decompose representations into class labels and other factors and to boost the training stability. However, a limitation is that they assume that each class is separable and ignore the relationship between classes even though class overlapping frequently occurs in a real-world scenario when data are collected on the basis of diverse or ambiguous criteria. To overcome this limitation, we address a novel problem called class-distinct and class-mutual image generation, in which the goal is to construct a generator that can capture between-class relationships and generate an image selectively conditioned on the class specificity. To solve this problem without additional supervision, we propose classifier's posterior GAN (CP-GAN), in which we redesign the generator input and the objective function of AC-GAN for class-overlapping data. Precisely, we incorporate the classifier's posterior into the generator input and optimize the generator so that the classifier's posterior of generated data corresponds with that of real data. We demonstrate the effectiveness of CP-GAN using both controlled and real-world class-overlapping data with a model configuration analysis and comparative study. Our code is available at this https URL.
Managers' perceptions of the effects of a national mandatory accreditation program in Danish hospitals. A cross-sectional survey.

OBJECTIVE This study aimed to examine and compare middle and senior hospital managers' perceptions of the effects of a mandatory accreditation program in Denmark, the Danish Healthcare Quality Program (Den Danske Kvalitetsmodel [DDKM]) after it was terminated in 2015. DESIGN A cross-sectional online questionnaire survey. SETTING All 26 somatic and psychiatric public hospitals in Denmark. PARTICIPANTS All senior and middle managers. METHODS A questionnaire with open and closed response (five-point Likert scale) questions. Quantitative data were analyzed descriptively and through ordered logistic regression by management level. Qualitative data were subjected to a software-assisted content analysis. RESULTS The response rate was 49% (533/1059). In both the qualitative and quantitative data sets, participants perceived the DDKM as having: led to an increased focus on registration, documentation and additional and unnecessary procedures. While the DDKM was perceived as increasing a focus on quality, the time required for accreditation was at the expense of patient care. There were significant differences by management level, with middle managers having more negative perceptions of the DDKM related to time spent on documentation and registration. CONCLUSION While the DDKM had some perceived benefits for quality improvement, it was ultimately considered time-consuming and outdated or having served its purpose. Including managers, particularly middle managers, in refinements to the new quality improvement model could capitalize on the benefits while redressing the problems with the terminated accreditation program.
The Role of Examples in the Teaching of Programming Languages

A common assumption made by teachers of programming languages, especially those teaching adults, is that their primary (core) teaching material consists of text, by which we mean rules, instructions, explanations, and discussions. Worked examples are seen as secondary, serving merely to illustrate the core material. We present evidence to demonstrate that young adult students do not share this assumption. Given instruction in the form of demonstrably clear text and worked examples, they do not treat the text as the primary source of information, but rather seem to focus on the worked examples. Worked examples may be seen as being the primary source of learning material even when they are not fully understood, but bear some superficial similarity to the task in hand.
Factors Associated With Cysticidal Treatment Response in Extraparenchymal Neurocysticercosis

Extraparenchymal neurocysticercosis is the most severe form of cysticercosis, and response to treatment is suboptimal. We sought to determine how demographic and clinical characteristics and albendazole sulfoxide concentrations were related to cysticidal treatment response. We conducted a longitudinal study of 31 participants with extraparenchymal vesicular parasites who received the same treatment, albendazole 30 mg/kg/day for 10 days with dexamethasone 0.4 mg/kg/day for 13 days, followed by a prednisone taper. Response to treatment was determined by parasite volumes before and 6 months after treatment. Eight participants (25.8%) had a complete treatment response, 16 (51.6%) had a treatment response > 50% but < 100%, and 7 (22.6%) had a treatment response < 50%. Complete treatment response was significantly associated with higher concentrations of albendazole sulfoxide (P = .032), younger age (P = .032), fewer cysts (P = .049) and lower pretreatment parasite volume (P = .037). Higher number of previous cysticidal treatment courses was associated with a noncomplete treatment response (P = .023). Although the large proportion of participants with less than a complete response emphasizes the need to develop more efficacious pharmacologic regimens, the association of albendazole sulfoxide concentrations with treatment response highlights the importance of optimizing existing therapeutic regimens. In addition, the association of treatment response with parasite volume emphasizes the importance of early diagnosis.
MicroRNA: A new player in response to therapy for colorectal cancer

Colorectal cancer (CRC) is one of the important malignancies that result in cancer‐related deaths worldwide. Multiple lines of evidence have indicated that different responses to therapy in CRC cells led to the failure of the current therapies. Hence, identification of the underlying cellular and molecular pathways involved in the emergence of different responses from CRC cells could contribute to finding and designing new therapeutic platforms to overcome the present limitations. Among the various targets involved in CRC pathogenesis, microRNAs (miRNAs) have key roles in many signaling pathways that are associated with the initiation and progression of CRC. Increasing evidence has confirmed that miRNAs as epigenetic regulators could play critical roles in the response (resistance or sensitivity) to therapy. Cancer stem cells are well‐known players in resistance to therapy in CRC. They have been shown to play significant roles via inhibition and activation of many miRNA networks. Hence, miRNAs could be involved in the resistance and sensitivity of therapy in CRC cells via affecting different mechanisms, such as activation of cancer stem cells. Here, we summarized the role of various miRNAs in response to therapy of CRC cells. Moreover, we highlighted the roles of these molecules in the function of cancer stem cells, which are known as important players in the resistance to therapy in CRC.
The contribution of social science to mental health services research and development: A SWOT analysis

It is government policy to integrate mental health and social care services. The two are radically different in terms of their respective research and development cultures and there is a risk that the social science contribution to the newly integrated agencies’ research and development agenda will be missing. This editorial is concerned with one crucial aspect – the lack of capacity to integrate social science insights into mental health services research. This is not due to a shortage of people being trained in social sciences, but rather the lack of opportunity for them to develop a career in mental health services research and development. Our capacity to integrate social science into mental health services research and development is examined below using a SWOT analysis, of strengths, weaknesses, opportunities and threats.
Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

Background Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs. Objective We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps—named entity recognition and relation extraction—our second objective was to improve the deep learning model using multi-task learning between the two steps. Methods We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively. Results Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance. Conclusions Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning.
Micro- and macro-vascular function in obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is common worldwide and is associated with cardiovascular disease. Endothelial dysfunction (ED) is regarded as a crucial step in disease development and has been demonstrated in peripheral vasculature in OSA population. However the effect of ventilatory dysfunction on myocardial perfusion is unknown. I performed comprehensive assessment of endothelial function in moderate to severe OSA subject compared with helthy and hypertensive controls, using flow mediated dilatation (FMD), laser Doppler flowmetry (LDF), flow cytometry in addition to studying vascular elastic properties. I also performed myocardial contrast echocardiography (MCE) and real time three-dimensional echocardiography (RT3DE). All these tests were repreated after 6 months of continuous positive airway pressure (CPAP) therapy. I demonstrated endothelial dysfunction in the peripheral circulation and coronary circulation as well as cardiac remodeling in OSA. However, I did not note a significant change in the indices of arterial stiffness and in the flow cytometric analysis. I noted marked improvement in the peripheral and coronary endothelial dysfunction after OSA subjects were treated with CPAP therapy for 6 months. In summary, micro- and macro-vascular dysfunction exists in otherwise healthy moderate to severe OSA subjects and improves with effective CPAP therapy. These findings may have strong clinical and prognostic implications.
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the coarse localization task by predicting pixel-level scores, requiring ad-hoc heuristics when mapping back to object-level scores. State-of-the-art object detectors on the other hand, allow for individual object scoring in an end-to-end fashion, while ironically trading in the ability to exploit the full pixel-wise supervision signal. This can be particularly disadvantageous in the setting of medical image analysis, where data sets are notoriously small. In this paper, we propose Retina U-Net, a simple architecture, which naturally fuses the Retina Net one-stage detector with the U-Net architecture widely used for semantic segmentation in medical images. The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors. We evaluate the importance of full segmentation supervision on two medical data sets, provide an in-depth analysis on a series of toy experiments and show how the corresponding performance gain grows in the limit of small data sets. Retina U-Net yields strong detection performance only reached by its more complex two-staged counterparts. Our framework including all methods implemented for operation on 2D and 3D images is available at github.com/pfjaeger/medicaldetectiontoolkit.
Modeling and Application of Hybrid Power Flow Controller in Distribution Systems

The smartening of distribution systems with distributed generation, storage devices, and intelligent loads have created challenges for the operation of distribution feeders, which can be addressed with power-electronics based controllers. However, the capital-intensive nature of these controllers is an obstacle for their wide use. The Hybrid Power Flow Controller (HPFC) is shown here to provide an adequate solution to the application of flexible ac transmission systems controllers to distribution systems, and hence the merits and the added value of this controller are discussed here through detailed modeling and simulations, examining its impact on a distribution system under different conditions. Thus, in this paper, a detailed dynamic model of the HPFC is developed and implemented in PSCAD, describing and proposing control strategies to properly operate this controller in distribution-system applications, such as an effective and simple procedure for starting up the device. The developed model is used to demonstrate the effectiveness of the controller for solving problems in distribution systems, such as voltage sags associated with feeder faults and power flow fluctuations due to intermittent renewable generation using a benchmark network as an illustrative example.
Bayesian adaptive group lasso with semiparametric hidden Markov models

This paper presents a Bayesian adaptive group least absolute shrinkage and selection operator method to conduct simultaneous model selection and estimation under semiparametric hidden Markov models. We specify the conditional regression model and the transition probability model in the hidden Markov model into additive nonparametric functions of covariates. A basis expansion is adopted to approximate the nonparametric functions. We introduce multivariate conditional Laplace priors to impose adaptive penalties on regression coefficients and different groups of basis expansions under the Bayesian framework. An efficient Markov chain Monte Carlo algorithm is then proposed to identify the nonexistent, constant, linear, and nonlinear forms of covariate effects in both conditional and transition models. The empirical performance of the proposed methodology is evaluated via simulation studies. We apply the proposed model to analyze a real data set that was collected from the Alzheimer's Disease Neuroimaging Initiative study. The analysis identifies important risk factors on cognitive decline and the transition from cognitive normal to Alzheimer's disease.
Preparation of the Blood-Enriched Agar with the Use of Red Cell Suspension

A culture medium is said to support the growth and development of different microorganisms. Certain bacteria like Staphylococcus aureus and Staphylococcus epidermidis entail hemoglobin found in the red blood cells. Because of its cost effectiveness and availability, expired human blood is being utilized in some developing countries. Despite the widely accepted disadvantages of using human blood as enrichment agent, many laboratories still opt to use it due to the unavailability of sheep blood or due to budgetary reasons. This study determined if the washed expired human blood can be used as an alternative enrichment agent in the preparation of Blood Agar Plate (BAP) culture medium in the isolation of Staphylococcus aureus ATCC 25923 and Staphylococcus epidermidis ATCC 12228. The cultural characteristics and hemolytic reactions of the selected microorganisms were recorded, assessed and compared with their growth in BAP. The stability of the washed expired human blood was evaluated in terms of temperature and storage period. Results reveal that expired human blood with washing improved the morphologic and hemolytic pattern of Staphylococcus aureus. The washing of blood had no effect on Staphylococcus epidermidis because it is a gamma hemolytic bacterium. Both unwashed expired and fresh human blood produced gamma hemolysis due to the interferences still present in them. Both washed expired human blood and washed fresh human blood produced beta hemolysis. Washed expired human blood could be stored for seven days and still could be used for microbial culture. Keywords - Blood, Blood Enriched Agar, Red Cell Suspension