Miracle-dz/UniFAR-FTUs huggingface dataset:
Torture-survivors’ experiences of healthcare services for pain: a qualitative study

Background: Increasing numbers of torture-survivors are presenting to UK healthcare services with persistent pain. However, there is a paucity of evidence surrounding the management of persistent pain among torture-survivors and their experience of healthcare services for pain is currently unknown. This qualitative study explores their experiences of services for managing pain, to inform clinical practice and service provision. Methods: Thirteen participants were recruited from a specialist pain clinic for torture-survivors in the United Kingdom. Utilising an ethnographic approach, data were collected via clinic appointment observations, interviews and medical records and analysed using inductive thematic analysis. Results: Three themes emerged in relation to torture-survivors’ experiences of healthcare services for pain: the patient–clinician relationship; multiplicity of diagnoses and treatments; lack of service integration. Participants described limited engagement in decision-making processes regarding their care. Lack of recognition of torture experience when diagnosing and treating pain, alongside multiple unsuccessful treatments, led to confusion, frustration and hopelessness. These issues were exacerbated by the disconnect between physical and mental health services. Conclusion: This study provides new insight into the challenges faced by torture-survivors when accessing healthcare services for pain. Our findings suggest current service provision is not meeting their complex needs. Clinical implications include the need for integrated care systems and better recognition of the influence of torture experience on persistent pain. Strategies to engage and empower torture-survivors in the management of their pain are suggested.
INTELLECTUAL CAPITAL AND ISLAMIC SOSIAL REPORTING INDEX: THE CASE OF INDONESIAN ISLAMIC BANKING

Purpose of the study: This study aims to explain the effect of Islamic Banking Intellectual Capital and Islamic Social Reporting on the Islamic Financial Performance Index of Sharia Bank Indonesia. Methodology: The data obtained in this study consisted of a total of 368 from annual financial reports and sustainability reports that were officially published by each Islamic bank in Indonesia. The analytical method used in this study is the Partial Least Square (PLS) method which is processed with SmartPLS 3 software. Main Findings: There is an essential influence on the implementation of Islamic Banking Intellectual Capital and Islamic Social Reporting on Islamic Financial Performance in Islamic Banking in Indonesia. This finding also shows that intellectual capital and social reporting by Islamic banks have a significant positive effect on the financial performance of Islamic banks in Indonesia. Applications of this study: This research will help next researchers to develop research in the banking world and can be used by banking institutions to become scientific input. Besides, mobilizing and using intangible resources properly will improve the financial performance of an organization. Novelty / Originality of this study: The existence of Islamic Banking Intellectual Capital combined with Islamic Social Reporting can affect and improve the competitiveness of Islamic Banking in Indonesia. This study will be a complete research and provide clear information for practitioners and academics.
Current genetic status of honey bees in Anatolia in terms of thirty polymorphic microsatellite markers

Uc fitocografik floristik bolgeye sahip olan Turkiye, uc kita arasinda dogal bir koprudur. Bugune kadar Turkiye sinirlari icerisinde bircok bal arisi alt turu ve ekotipi bildirilmistir. Ancak, kayit disi yetistiricilik ve kontrolsuz gocer aricilik uygulamalarina bagli melezlesmenin yerel bal arisi populasyonlarinin genetik cesitliligini etkiledigi dusunulmektedir ve bu, uzun evrimsel sureclerden kaynaklanan allel kombinasyonlarinin kaybiyla sonuclanabilir. Bal arisi altturleri uzerinde genetik degiskenligin kaybini onlemek amaciyla bircok ulkede tanimlama ve koruma calismalari yapilmistir. Bu temelde, 2018’de aricilik faaliyetlerindeki oneminden dolayi secilen bes ilden (Artvin, Duzce, Hatay, Kirklareli ve Mugla) dort yaygin bal arisi irkinin [ Apis mellifera anatoliaca Maa, 1953, Apis mellifera carnica Pollmann,1879, Apis mellifera caucasica Pollmann, 1889, Apis mellifera syriaca Skorikov, 1829 (Hymenoptera: Apidae)] genetik acidan durumlari ve filogenetik iliskileri otuz mikrosatellit lokusu kullanilarak guncellenmeye calisilmistir. Populasyonlar arasi genetik mesafe 0.30 ile 0.70 arasinda degismistir. Genetik varyasyonlar, populasyonlar arasinda %8.96, populasyonlardaki bireyler arasinda %44.9 ve tum bireyler arasinda %46.1 olarak hesaplanmistir. Bal arisi ile ilgili daha fazla genetik arastirma, gelecekteki potansiyel sorunlardan kacinmak icin avantajli olacaktir.
A Comparison of Human against Machine-Classification of Spatial Audio Scenes in Binaural Recordings of Music

The purpose of this paper is to compare the performance of human listeners against the selected machine learning algorithms in the task of the classification of spatial audio scenes in binaural recordings of music under practical conditions. The three scenes were subject to classification: (1) music ensemble (a group of musical sources) located in the front, (2) music ensemble located at the back, and (3) music ensemble distributed around a listener. In the listening test, undertaken remotely over the Internet, human listeners reached the classification accuracy of 42.5%. For the listeners who passed the post-screening test, the accuracy was greater, approaching 60%. The above classification task was also undertaken automatically using four machine learning algorithms: convolutional neural network, support vector machines, extreme gradient boosting framework, and logistic regression. The machine learning algorithms substantially outperformed human listeners, with the classification accuracy reaching 84%, when tested under the binaural-room-impulse-response (BRIR) matched conditions. However, when the algorithms were tested under the BRIR mismatched scenario, the accuracy obtained by the algorithms was comparable to that exhibited by the listeners who passed the post-screening test, implying that the machine learning algorithms capability to perform in unknown electro-acoustic conditions needs to be further improved.
The antibacterial effect of infantile fecal Lactobacillus against Escherichia coli causing healthcare-associated infection

Background: Healthcare-associated infections (HAIs) are a major public health problem worldwide. Increasing resistance of HAIs agents to conventional antibiotics also becomes a major therapeutic challenge for physicians. The aim of this research was to determine the antibacterial properties of Lactobacillus plantarum and Lactobacillus fermentum isolated from the stool of healthy infants against Escherichia coli causing HAI. Methods: Healthy infantile fecal samples and HAIs-origin samples (urine, blood, and etc.) were collected. Phenotypic isolation and characterization were done. Genotypic identification of lactic acid bacteria was performed by 16S rRNA gene sequencing. The antibiotic susceptibility test was performed using disc diffusion method. Antagonistic effect of Lactobacillus strains by well diffusion method. Results: Of 105 HAI-origin isolates, 25 were identified as E. coli. , L. plantarum, and L. fermentum had more probiotic potential. E. coli isolates showed high resistance to Ceftriaxone, Cotrimoxazole, and Ciprofloxacin. Examined Lactobacillus species showed a significant inhibitory effect against E. coli isolates. Conclusion: L. plantarum had considerable inhibitory activity against E. coli isolates with HAIs-origin that were resistant to the most conventional antibiotics.
Quality Enhancement and Shelf-Life Extension of Raw Beef Patties Formulated with Lactate/Thyme Essential Oil during Refrigerated Storage

Consumers are increasingly aware of diet related health problems and therefore demanding natural ingredients which are expected to be safe and health promoting. The effects of the classical and widely applied food preservative sodium lactate (NaL, 2 %) and thyme essential oil (TEO, 0.05 %), added individually or in combination, on the microbiological status, chemical criteria, sensorial qualities and shelf-life of raw ground beef patty during refrigerated storage at 4±1°C for 15 days were investigated. The results indicated that significant (p<0.05) incremental pattern was observed in TVBN, TBARS and pH values in all beef patty samples during subsequent cold storage by different rates. Thyme essential oil was significantly (p<0.05) more effective in retarding lipid oxidation than sodium lactate. However, the lowest significant (p<0.05) incremental rate was recorded in samples treated by NaL/TEO in mixyure. The results also revealed that both treatments (NaL aand TEO) significantly (p<0.05) reduced the microbial load of aerobic plate counts (APC), Psychrotrophic Counts (PTC) and Enterobacteriaceae Counts (EBC) throughout the storage period in comparison with the control. Moreover, NaL-treated patties exhibited lower (p<0.05) bacterial counts as compared to TEO-treated samples. In addition, samples formulated with NaL/TEO in mixture showed the lowest incremental pattern in all bacterial counts at any time of the refrigerated storage. The results also revealed that a small but statistically significant (P<0.05) reduction in the color parameters L*, a* and b* values was recorded throughout the storage, which indicated an increasing graying in patties samples over storage days particularly in control ones. However, natural preservatives, either alone or in combination significantly (p<0.05) reduced color deterioration in beef patty samples. Finally, there was also a significant (p<0.05) enhancement in sensory attributes and shelf-life stability of patty samples due lactate and/or TEO, the untreated patty samples would have a refrigerated shelf- life of 8 days while after natural active addition, the shelf- life extended to 12 days in lactate treated patties, 11 days in TEO treated samples and 15 days in patty samples formulated with NaL and TEO in combination. The obtained data support each other and was confirmed by the sensorial evaluation. Moreover, a synergetic effect between lactate and thyme essential oil was noticed. Consequently, combination of lactate and thyme essential oil could have a benifit for commercial use to improve microbiological safety, maintain the chemical quality, and extend the shelf life of ground beef during refrigerated storage.
Numerical Simulation and Multi-Objective Optimization of Partition Cooling in Hot Stamping of the Automotive B-Pillar Based on RSM and NSGA-II

In this study, the simulation and optimization of the partition cooling in the hot stamping process was carried out for an automotive B-pillar through minimizing the maximum thickening rate and the maximum thinning rate located in the rapid and slow cooling zones. The optimization was implemented by investigating the process parameters such as friction coefficient, sheet austenitizing temperature, holding time, heating zone temperature, the upper binder force and the lower binder force. The optimal Latin hypercube design (OLHD), the response surface methodology (RSM) and the non-dominated sorting genetic algorithm (NSGA-II) were combined to establish the relationship between process parameters and form quality objectives. After multi-objective optimization, the maximum thickening rate and the maximum thinning rate of the slow cooling zone and rapid cooling zone were 11.1% and 12.4%, 4.7% and 7.1%, respectively. Afterwards, the simulation was performed according to the optimized parameter combinations to analyze the temperature field, microstructure, tensile strength, hardness, thickening rate and thinning rate, and forming quality. Moreover, the hot stamping test and experimental results showed that the microstructure of the ferrite and pearlite structure was uniformly distributed in the slow cooling zone, and its tensile strength reached 680 MPa, the elongation was 11.4% and the hardness was 230.56 HV, while the lath martensite structure was obtained in the rapid cooling zone, with tensile strength of up to 1390 MPa, elongation of about 7.0% and hardness reaching 478.78 HV. The results of thickness, microstructure, tensile strength and the hardness test correspond well with the simulation results.
Avaliação de equações para estimar o consumo de vacas leiteiras

O presente estudo teve por objetivo realizar uma comparacao do poder de predicao de diferentes equacoes de consumo de vacas leiteiras. O trabalho foi conduzido atraves de pesquisas na literatura coletando informacoes sobre massa corporal, producao de leite, dias de lactacao, consumo de materia seca e teor de gordura no leite de vacas leiteiras criadas no Brasil. Todas estas informacoes, menos o consumo de materia seca observado, foram utilizadas para calcular o consumo de materia seca predito com as equacoes dos modelos: National Research Council (NRC) 2001, Cornell Net Carbohydrate and Protein System (CNCPS), Agricultural and Food Research Council (AFRC) 1993 e De Freitas et al., (2006). Posteriormente, estes dados calculados com as equacoes foram usados para avaliar o poder de predicao por meio da comparacao grafica dos seus residuos padronizados conforme Draper e Smith (1966) e Montgomery (2005) e por meio do criterio de Akaike (AKAIKE, 1974). Os modelos 2, 3 e 4 falharam em representar o comportamento dos dados observados, pois apresentaram valores de Δr > 10. O modelo do NRC, 2001 (modelo 1) foi considerado como melhor escolha por apresentar o ERr = 1, todos os outros apresentaram ERr muito maior do que 20 e portanto, devem ser considerados piores escolhas. O modelo 3 apresentou tendencia a subestimar os valores preditos com 76% dos pontos acima da linha de nulidade. O modelo 1 e o que melhor prediz o consumo de materia seca de vacas leiteiras nas condicoes estudadas.
Morphological and molecular characterization of the toxic dinoflagellate Ostreopsis cf. ovata (Dinophyta) from Brazil (South Atlantic Ocean)

Ostreopsis cf. ovata is a toxic epiphytic dinoflagellate widely distributed in warm waters that often co-occur with species of the genera Coolia , Fukuyoa , Gambierdiscus and Prorocentrum . We investigated a strain isolated from the coast of Ubatuba, Brazil (South-West Atlantic Ocean) by light and epifluorescence microscopies; we also report molecular data based on the LSU rDNA and ITS markers. Cells were 35-65 µm in the dorso-ventral diameter and 20-40 µm wide. We obtained the sequence of a ~1900 base pair region of the rRNA gene cistron. In the LSU rDNA phylogeny, the sequences under the names O. ovata and O. cf. ovata branched into three clades. The ITS marker showed greater resolving power and the sequences of O. ovata / O. cf. ovata split into five clades. Our ITS sequence branched in a clade with sequences of strains from the Mediterranean Sea, European Atlantic coasts, subtropical NE Atlantic, other sequences from Brazil at Rio de Janeiro, and a few sequences from Japan. The cell dimensions and thecal plate arrangement were under the variability range reported in other ocean regions. Our observations confirm O. cf. ovata as the most commonly recorded species of Ostreopsis in the SW Atlantic Ocean. Ostreopsis cf . ovata co-occurred with Coolia malayensis in Brazil and Asia, but it has been commonly reported from the Mediterranean Sea, where C. malayensis has not yet been recorded; while Coolia malayensis has been reported from the Caribbean Sea, but not O. ovata . With the current knowledge, it is difficult to understand the factors that determine the biogeography of the tropical epiphytic dinoflagellates.
FAU-Net: Fixup Initialization Channel Attention Neural Network for Complex Blood Vessel Segmentation

Medical image segmentation based on deep learning is a central research issue in the field of computer vision. Many existing segmentation networks can achieve accurate segmentation using fewer data sets. However, they have disadvantages such as poor network flexibility and do not adequately consider the interdependence between feature channels. In response to these problems, this paper proposes a new de-normalized channel attention network, which uses an improved de-normalized residual block structure and a new channel attention module in the network for the segmentation of sophisticated vessels. The de-normalized network sends the extracted rough features to the channel attention network. The channel attention module can explicitly model the interdependence between channels and pay attention to the correlation with crucial information in multiple feature channels. It can focus on the channels with the most association with vital information among multiple feature channels, and get more detailed feature results. Experimental results show that the network proposed in this paper is feasible, is robust, can accurately segment blood vessels, and is particularly suitable for complex blood vessel structures. Finally, we compared and verified the network proposed in this paper with the state-of-the-art network and obtained better experimental results.
Recurrent neural network reveals overwhelming sentiment against 2017 review of US monuments from humans and bots

In the United States, the conservation of federal lands reflects a social history of public advocacy, public policy, and public comments. US federal agencies solicit public comments to scope for ideas, solve problems, and use the best available science for policy‐making, legislation, and management. Online comment submission has led to staggering numbers of comments that are challenging to summarize. Here, we analyze comments received by the Department of the Interior in response to the proposed executive review of 27 national monuments designated and expanded between 1996 and 2016. We used a deep recurrent neural network (AWD‐LSTM) to classify sentiment of 754,707 comments with higher precision and recall (F1‐score = 0.98) than support vector machine and Naïve Bayes approaches. Over 97% of unique comments opposed the executive review, suggesting overwhelming support for maintaining national monument designations. Using cosine similarity, we also found that duplicates or potential automated software bots comprised over two‐thirds of comments. We offer recommendations for comment submission, collection, and analysis in the current techno‐political climate.
Effects of the Subjective Family Class on the Suicide Ideation of Adolescents: Double Mediating Effect of Social Support and Sense of Coherence

Purpose: The study was conducted to identify the relationship between subjective family class and suicidal ideation and to determine the double mediating effect of social support and sense of coherence on their relationship. Methods: Secondary analysis of data from 2015 Korean Psycho-social Anxiety which was a population-based representative was performed. Double mediating effects of social support and sense of coherence on suicidal ideation were analyzed using the PROCESS macro (model 6) with bootstrapping methods. Results: Subjective family class directly influenced social support ( β =.20, p < .001) and sense of coherence ( β =.10, p < .001). Subjective family class directly influenced suicide ideation ( β =-.08, p < .001). Sense of coherence was directly influenced by social support ( β =.28, p < .001). Suicide ideation was indirectly influenced by subjective family class through social support ( β =-.22, p < .001) and sense of coherence ( β =-.36, p =.001). The size of direct effect of subjective family class on suicide ideation was 0.24 and the total size of indirect effect was 0.12. Conclusion: Intervention to improve social support and sense of coherence is needed to prevent the suicide among adolescents.
Process Validation of Oral Solid Dosage Forms - A Review

Aim of the present review is to summarize various steps involved in process validation of the oral solid dosage forms in pharmaceutical industries. Oral solid dosage form such as tablets, capsules etc are widely used due to its easy administration and easily available to the market at affordable price. Validation it is an important part of current good manufacturing practice. Process validation is an integral part of GMP as it provides evidence for the quality of drug product. Performing process validation in pharmaceutical industries, will help to design, control and maintain the particular process. The critical process steps such as sifting, mixing, compression etc and critical process parameters such as sieve integrity before and after passing the raw materials, sieve size, mixing time, mixing speed, compression force etc., it is well stated. Process validation establishes evidence and provides high degree of assurance that a particular process will consistently produce a product meeting its predefined specifications.
Present status of in silico approach for micro-RNA identification

MicroRNAs (miRNAs) are endogenous, small, noncoding RNAs of 18-25 nucleotides in length that negatively regulate their complementary messenger-RNAs at the post transcriptional level. Three methods are principally deployed to identify miRNA such as classic cloning, sequencing and computational approach. Computational approach is carried out mainly based on bioinformatics which is using small RNA library construction and sequencing to find miRNAs in the known genotypes (sequences). Another technique called the classical approach that is an example of forward genetics in which researchers determine the unknown genotype (miRNA sequence) of a known phenotype. The miRNAs have shown both evolutionarily converged nature (conserved miRNAs) from species to species within the same kingdom and species specific expression (species specific miRNAs). Majority of the miRNA genes are observed as conserved miRNAs as orthologs. This conserved nature of majority of miRNAs becomes an important logical tool for bioinformatics discovery of miRNAs in other species. Identification of miRNAs by using bioinformatics tools is now a commonplace and of the most widely used methods and it has facilitated the prediction of new miRNAs in both plant and animal systems. This is largely used due to its low cost and high efficiency.
An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data

Many process-based models for carbon flux predictions have faced a wide range of uncertainty issues. The complex interactions between the atmosphere and the forest ecosystems can lead to uncertainties in the model result. On the other hand, artificial intelligence (AI) techniques, which are novel methods to resolve complex and nonlinear problems, have shown a possibility for forest ecological applications. This study is the first step to present an objective comparison between multiple AI models for the daily forest gross primary productivity (GPP) prediction using satellite remote sensing data. We built the AI models such as support vector machine (SVM), random forest (RF), artificial neural network (ANN), and deep neural network (DNN) using in-situ observations from an eddy covariance (EC) flux tower and satellite remote sensing data such as albedo, aerosol, temperature, and vegetation index. We focused on the Gwangneung site from the Korea Regional Flux Network (KoFlux) in South Korea, 2006–2015. As a result, the DNN model outperformed the other three models through an intensive hyperparameter optimization, with the correlation coefficient (CC) of 0.93 and the mean absolute error (MAE) of 0.68 g m−2 d−1 in a 10-fold blind test. We showed that the DNN model also performed well under conditions of cold waves, heavy rain, and an autumnal heatwave. As future work, a comprehensive comparison with the result of process-based models will be necessary using a more extensive EC database from various forest ecosystems.