Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential value and impact of data mining and machine learning in clinical diagnostics

Abstract Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.
Chemical methods for measuring RNA expression with metabolic labeling

Tracking the expression of RNA in a cell‐specific manner is a major challenge in basic and disease research. Herein we outline the current state of employing chemical approaches for cell‐specific RNA expression studies. We define the utility of metabolic labels for tracking RNA synthesis, the approaches for characterizing metabolic incorporation and enrichment of labeled RNAs, and finally outline how these approaches have been used to study biological systems by providing mechanistic insights into transcriptional dynamics. Further efforts on this front will be the continued development of novel chemical handles for RNA enrichment and profiling as well as innovative approaches to control cell‐specific incorporation of chemically modified metabolic probes. These advancements in RNA metabolic labeling techniques permit sensitive detection of RNA expression dynamics within relatively small subsets of cells in living tissues and organisms that are critical to performing complex developmental and pathological processes.
Contrasting drivers of diversity in hosts and parasites across the tropical Andes

Significance Interactions between hosts and parasites can reciprocally limit the ranges of individual species. But to what extent do they affect composition of communities? In the Andean biodiversity hot spot, we tested why host and parasite communities vary over spatial and temporal scales. Variation in rainfall was the predominant predictor of turnover for both hosts and parasites. However, the effects of species interactions were asymmetric: Parasite communities were strongly predicted by host communities, but not vice versa. Against conventional wisdom, climatic effects on parasite turnover were primarily local while biotic effects predominated regionally. Using our predictive models, we mapped faunal dissimilarity, illustrating hot spots of turnover that can guide conservation of host and parasite biodiversity. Geographic turnover in community composition is created and maintained by eco-evolutionary forces that limit the ranges of species. One such force may be antagonistic interactions among hosts and parasites, but its general importance is unknown. Understanding the processes that underpin turnover requires distinguishing the contributions of key abiotic and biotic drivers over a range of spatial and temporal scales. Here, we address these challenges using flexible, nonlinear models to identify the factors that underlie richness (alpha diversity) and turnover (beta diversity) patterns of interacting host and parasite communities in a global biodiversity hot spot. We sampled 18 communities in the Peruvian Andes, encompassing ∼1,350 bird species and ∼400 hemosporidian parasite lineages, and spanning broad ranges of elevation, climate, primary productivity, and species richness. Turnover in both parasite and host communities was most strongly predicted by variation in precipitation, but secondary predictors differed between parasites and hosts, and between contemporary and phylogenetic timescales. Host communities shaped parasite diversity patterns, but there was little evidence for reciprocal effects. The results for parasite communities contradicted the prevailing view that biotic interactions filter communities at local scales while environmental filtering and dispersal barriers shape regional communities. Rather, subtle differences in precipitation had strong, fine-scale effects on parasite turnover while host–community effects only manifested at broad scales. We used these models to map bird and parasite turnover onto the ecological gradients of the Andean landscape, illustrating beta-diversity hot spots and their mechanistic underpinnings.
Giant caseous mitral annular calcification mimicking ventricular pseudoaneurysm

An 82‐year‐old woman with precordial pain at rest was admitted to the Emergency Department for possible cardiac heart disease; electrocardiogram excluded ischemia and high‐sensitive troponin was normal. Echocardiogram revealed a hyperechoic mass adjacent to the mitral annulus. Electrocardiography‐gated computed tomography (CT) angiography exam confirmed the presence of the mass protruding into the atrioventricular groove, adjacent to the posterior mitral. On the precontrast images the lesion was hyperdense with some scattered central calcific spots. CT findings are typical of a giant caseous calcification of the mitral annulus and excluded the diagnoses of pseudoaneurysm (it does not show any communication with the left ventricular cavity), neoplasm/abscess (complete caseous/calcified content) or infected/abscessified mitral calcification (absence of internal hypodense core). This is a benign condition that can be easily misdiagnosed as ventricular aneurysm or pseudoaneurysm on the contrast‐enhanced images, when the caseous content is isodense to the iodinated blood pool.
Quantifying false positional corrections due to facial motion using SGRT with open‐face Masks

Abstract Purpose Studies have evaluated the viability of using open‐face masks as an immobilization technique to treat intracranial and head and neck cancers. This method offers less stress to the patient with comparable accuracy to closed‐face masks. Open‐face masks permit implementation of surface guided radiation therapy (SGRT) to assist in positioning and motion management. Research suggests that changes in patient facial expressions may influence the SGRT system to generate false positional corrections. This study aims to quantify these errors produced by the SGRT system due to face motion. Methods Ten human subjects were immobilized using open‐face masks. Four discrete SGRT regions of interest (ROIs) were analyzed based on anatomical features to simulate different mask openings. The largest ROI was lateral to the cheeks, superior to the eyebrows, and inferior to the mouth. The smallest ROI included only the eyes and bridge of the nose. Subjects were asked to open and close their eyes and simulate fear and annoyance and peak isocenter shifts were recorded. This was performed in both standard and SRS specific resolutions with the C‐RAD Catalyst HD system. Results All four ROIs analyzed in SRS and Standard resolutions demonstrated an average deviation of 0.3 ± 0.3 mm for eyes closed and 0.4 ± 0.4 mm shift for eyes open, and 0.3 ± 0.3 mm for eyes closed and 0.8 ± 0.9 mm shift for eyes open. The average deviation observed due to changing facial expressions was 1.4 ± 0.9 mm for SRS specific and 1.6 ± 1.6 mm for standard resolution. Conclusion The SGRT system can generate false positional corrections for face motion and this is amplified at lower resolutions and smaller ROIs. These errors should be considered in the overall tolerances and treatment plan when using open‐face masks with SGRT and may warrant additional radiographic imaging.
Improved Bacterial and Viral Recoveries from 'Complex' Samples using Electrophoretically Assisted Acoustic Focusing

We describe an electrophoretically assisted acoustic focusing technique to rapidly extract and enrich viral and bacterial loads from ‘complex samples,’ applied in this case to human nasopharyngeal samples as well as simplified surrogates. The acoustic forces capture and remove large particles (> 2 µm) such as host cells, debris, dust, and pollen from the sample. We simultaneously apply an electric field transverse to the flow direction to transport small (≤ 2µm), negatively-charged analytes into a separate purified recovery fluid using a modified H-filter configuration.
Malicious DNS Tunneling Detection in Real-Traffic DNS Data

While originally not intended for data transfer, the Domain Name System (DNS) is currently used to this end anyway, in a process called DNS tunneling (DNST). Malicious users exploit DNST for data exfiltration from infected machines, posing a critical security threat. We train and evaluate state-of-the-art convolutional neural network, random forest, and ensemble classifiers to detect tunneling in DNS traffic. Finally, we assess the classifiers’ performance and robustness by exposing them to one day of real-traffic data.
Boundary Aware U-Net for Retinal Layers Segmentation in Optical Coherence Tomography Images

Retinal layers segmentation in optical coherence tomography (OCT) images is a critical step in the diagnosis of numerous ocular diseases. Automatic layers segmentation requires separating each individual layer instance with accurate boundary detection, but remains a challenging task since it suffers from speckle noise, intensity inhomogeneity, and the low contrast around boundary. In this work, we proposed a boundary aware U-Net (BAU-Net) for retinal layers segmentation by detecting accurate boundary. Based on encoder-decoder architecture, we design a dual tasks framework with low-level outputs for boundary detection and high-level outputs for layers segmentation. Specifically, we first use the multi-scale input strategy to enrich the spatial information in the deep features of encoder. For low-level features from encoder, we design an edge aware (EA) module in skip connection to extract the pure edge features. Then, a U-structure feature enhanced (UFE) module is designed in all skip connections to enlarge the features receptive fields from the encoder. Besides, a canny edge fusion (CEF) module is introduced to aforementioned architecture, which can fuse the priory edge information from segmentation task to boundary detection branch for a better predication. Furthermore, we model each boundary as a vertical coordinates distribution for boundary detection. Based on this distribution, a topology guarantee loss with combined A-scan regression loss and structure loss is proposed to make an accurate and guaranteed topological boundary set. The method is evaluated on two public datasets and the results demonstrate that the BAU-Net achieves promising performance than other state-of-the-art methods.
Optimization of Linoleic Acid from Idesia polycarpa Maxim. var. vestita Diels by Response Surface Methodology

Linoleic acid (LA) was extracted and purified from Idesia polycarpa Maxim.var. vestita Diels (IPMVVD) oil by urea adduction fractionation, and the response surface methodology (RSM) was successfully employed to optimize the process. The optimal conditions for puriﬁcation of LA from mixed fatty acids by urea adduction fractionation were as follows: a weight ratio of 4.4:1.0:1.0 (w/w/w) of methanol/urea/mixed fatty acids, a temperature of -5°C and a time of 24.6 h, respectively. Under these conditions the experimental LA yield (48.65%) agreed with the predicted value and the purity of LA was 98.74%.
Beauty or beast? Farmers’ dualistic views and the influence of aesthetic appreciation on tolerance towards black-backed jackal and caracal

Various species of wild, adaptable, medium-sized carnivores occur outside of protected areas, often coming into contact with people and their domestic animals. Negative human-carnivore interactions can lead to antagonistic attitudes and behavior directed at such species. In the South African Karoo, a semi-arid rangeland, the predation of small-livestock by mesopredators is common and farmers typically use a combination of non-lethal and lethal methods to try and prevent livestock losses. We used ethnographic field observations and semi-structured interviews as part of a mixed methods approach, including the quantitative and qualitative analysis of farmers’ narratives to illustrate the nuanced ways in which sheep farmers relate to the two mesopredators that consume the most livestock on their farms; black-backed jackal and caracal. Overall, farmers attributed negative characteristics to jackal and caracal but farmers’ narratives provided evidence of complex perceptions in that the animals were admired as well as disliked. Both species were seen as charismatic due to traits such as their physical appearance, their “cunning” nature and their remarkable adaptability to human activities, including lethal control. Aesthetic appreciation was an important predictor of tolerance towards both species whereas negative attitudes were associated with the perception that mesopredators should only occur within protected areas. Attitudes towards jackals also appeared to have been affected by cultural representations of them as “thieves”. We showed that perceiving mesopredators as beautiful increased the average marginal probability of a farmer tolerating them, and that this strong relationship held when controlling for other covariates such as livestock predation. We advocate the importance of understanding the cultural and aesthetic aspects of predators and considering existing positive dimensions of human-wildlife relationships that may encourage increased farmers’ tolerance, which might promote coexistence.
Food insecurity: Its prevalence and relationship to fruit and vegetable consumption.

BACKGROUND Food insecurity in UK households is a substantial and growing concern. The present study identified those at risk of food insecurity and explored the relationship between food security and fruit and vegetable consumption. METHODS Data were examined from the Food and You survey (2016) for a large representative sample (n = 3118) living in England, Wales and Northern Ireland. A 'Food Security Score' and a 'Food Changes Score' (relating to financially driven changes to food habits) were compiled and relationships with fruit and vegetable consumption were examined. RESULTS The prevalence of marginal, low and very low food security was 12.6%, 5.4% and 2.8%, respectively. Significant correlations were observed between food security and fruit and vegetable consumption. Food security and food changes, independently, were significant predictors for fruit and vegetable consumption. With every unit increment in the Food Security Score (i.e., more food insecure), an 11% decrease in the odds of being a high fruit and vegetable consumer was evident. Likewise, the odds of being a high fruit and vegetable consumer decreases by 5% with every increment in the financially driven Food Changes Score. CONCLUSIONS A notable proportion (more than one-fifth) experienced marginal, low or very low food security. Food insecurity and financially driven food changes were accompanied by decreases in the odds of being a high fruit and vegetable consumer. Findings underline the potential consequences of food insecurity, and point to further work aiming to examine other dietary implications, as well as strategies to mitigate against food insecurity and its detriment.
Psychological, Structural, Social and Economic Determinants of Suicide Attempt: Risk Assessment and Decision Making Strategies

The aim of this research was to conduct a risk assessment and management of psychological, structural, social and economic determinants (PSSED) in a suicide attempt. The sample consisted of 353 individuals who had a recorded history of suicidal attempt; and 20 professional individuals by purposive sampling method within a descriptive cross-sectional design. Worksheets for RAM and AHP were used for data collection in this study. The rate of suicide attempt was 7.21 per 100,000 population in this study. Analysis showed that depression and mental disorders; personality disorders; family problems; socio-cultural and economic problems; lack of awareness; and low level of education have a high level of risk for suicide attempts. Psychiatric and psychological services; awareness and knowledge of life skills; medical services to dysfunctional families; development of community-based planning for PSSED of suicide; and employment and entrepreneurship services may lower suicide attempt risk.
Magnetic and Electric Effects on Transport Properties

with an introdu ctory section in which the recent history of the subject is presented along with a short outline of the underlying physics. A survey of the principal developments will then be given. In order to facilitate the review procedure, the subject matter will be sub divided in the following manner: the influence of a static magnetic field on the transport properties of dilute gases is split into two parts, one treat­ ing theory and the other experiment; this is followed by a section dealing with the analogous situation in a rarefied gas; then, related work concerning the influence of a static electric field on the transport properties of polar gases is described; and, finally, the influence of alternating fields on the trans­ port properties is covered briefly.
New Admissibility and Admissibilization Criteria for Nonlinear Discrete-Time Singular Systems by Switched Fuzzy Models

Admissibility analysis and control synthesis for nonlinear discrete-time singular systems are considered in this article. With regard to the type-1 and interval type-2 fuzzy singular systems, the partition of membership functions and scale transform is imposed, and new switched fuzzy systems, which are equivalent to the original systems, are established. A relaxed stability criterion is derived to ensure the admissibility of the system by using the piecewise Lyapunov function and singular value decomposition. Moreover, two classes of switched controllers are designed for the systems. One is for type 1 systems and the membership functions are consistent with those of the systems. The other can be applied to both of the fuzzy systems by introducing linear membership functions in each subregion. Two criteria are obtained to guarantee that the closed-loop systems are admissible. Several illustrative examples are provided to show the effectiveness of the developed methods.
Optimization of Procedurally Generated Landscapes

This paper discusses the optimization of procedurally generated polygonal landscapes. The Level of Detail method is considered and its shortcomings are listed. The proposed approach to solving the optimization problem based on the Ramer-Douglas-Pecker algorithms for the three-dimensional case, De-launay triangulation, and the Hausdorff metric is presented. To achieve bet-ter results in number of triangles of the optimized mesh and maintaining landscape detail than some LOD implementations the follows is proposed: from a height map, in a certain way, points are selected that most accurately convey the curvature and terrain features. Other points are deleted. The basis of the obtained points, an irregular triangulated grid is constructed. The pro-posed method for analyzing the similarity of polygonal models of an arbi-trary topological type can serve as a basis for the implementation of the cor-responding algorithms. The use of the weighted average when calculating the normal vectors, according to the authors, increases the accuracy of the sub-sequent calculation of the Hausdoff metric. Issues of assessing the quality of optimization are considered. A mathematical model is proposed. A proto-type of the optimizer for the polygonal mesh was developed and tested.
Delegated Key-Policy Attribute-Based Set Intersection over Outsourced Encrypted Data Sets for CloudIoT

Private set intersection (PSI) is a fundamental cryptographic primitive, allowing two parties to calculate the intersection of their data sets without exposing additional private information. In cloud-based IoTsystem, IoT-enabled devices would like to outsource their data sets in their encrypted form to the cloud. In this scenario, how to delegate the set intersection computation over outsourced encrypted data sets to the cloud and how to achieve the fine-grained access control for PSI without divulging any additional information to the cloud are still open problems. With that in mind, in this work, we combine key-policy attributebased encryption (KP-ABE) and PSI to introduce such a novel concept, called delegated key-policy attribute-based set intersection over outsourced encrypted data sets (KP-ABSI), to solve this problem. ,en we propose a first concrete KP-ABSI scheme and analyze its efficiency.