Observations of a black hole x-ray binary indicate formation of a magnetically arrested disk.

Accretion of material onto a black hole drags any magnetic fields present inwards, increasing their strength. Theory predicts that sufficiently strong magnetic fields can halt the accretion flow, producing a magnetically arrested disk (MAD). We analyzed archival multiwavelength observations of an outburst from the black hole x-ray binary MAXI J1820+070 in 2018. The radio and optical fluxes were delayed compared with the x-ray flux by about 8 and 17 days, respectively. We interpret this as evidence for the formation of a MAD. In this scenario, the magnetic field is amplified by an expanding corona, forming a MAD around the time of the radio peak. We propose that the optical delay is due to thermal viscous instability in the outer disk.

Anomaly Detection Module for Network Traffic Monitoring in Public Institutions.

It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be achieved with many different approaches. In this paper, we put our attention on the increase in network traffic safety based on the continuous monitoring of network traffic statistics and detecting possible anomalies in the network traffic description. The developed solution, called the anomaly detection module, is mostly dedicated to public institutions as the additional component of the network security services. Despite the use of well-known anomaly detection methods, the novelty of the module is based on providing an exhaustive strategy of selecting the best combination of models as well as tuning the models in a much faster offline mode. It is worth emphasizing that combined models were able to achieve 100% balanced accuracy level of specific attack detection.

Antioxidative Properties and Acrylamide Content of Functional Wheat-Flour Cookies Enriched with Wild-Grown Fruits.

This study aimed to evaluate the impact of widely grown fruits (wild roses, elderberries, sea buckthorns, rowans, chokeberries, and hawthorns) as a functional ingredient in wheat-flour cookie formulation on antioxidative properties with a simultaneous reduction of the carcinogen-like compound acrylamide. The organoleptic features of the cookies were assessed by a panel of consumers. The following parameters were measured: chemical composition, total polyphenols, polyphenolic profile, antioxidant activity, and acrylamide content. The overall ratings of the tested cookies with the addition of chokeberries, hawthorns, sea buckthorns, and elderberries were more than satisfactory, while wild rose and rowan cookies were the most widely accepted and best rated by the panelists. The antioxidant activity of the tested cookies was 1.1−15.22 µmol trolox·g−1 dm and 2.46−26.12 µmol Fe (II)·g−1 dm as measured by the ABTS and FRAP methods, respectively. All the fruit-enriched cookies had significantly higher antioxidative properties (p < 0.05) in comparison to the control cookies, but among the fruit-enriched cookies, there were differences in the quality and quantity of particular polyphenols. The acrylamide content was significantly decreased by 59% (hawthorn), 71% (rowan), 87% (wild rose), 89% (sea buckthorn), 91% (elderberry), and 94% (chokeberry) compared with the control cookies (p < 0.05). Cookies enriched with wild-grown fruits could constitute a promising novel snack food.

Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia.

Flow cytometry technique (FC) is a standard diagnostic tool for diagnostics of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) assessing the immunophenotype of blast cells. BCP-ALL is often associated with underlying genetic aberrations, that have evidenced prognostic significance and can impact the disease outcome. Since the determination of patient prognosis is already important at the initial phase of BCP-ALL diagnostics, we aimed to reveal specific genetic aberrations by finding specific multiple antigen expression patterns with FC immunophenotyping. The FC immunophenotype data were analysed using machine learning methods (gradient boosting, decision trees, classification rules). The obtained results were verified with the use of repeated cross-validation. The t(12;21)/ETV6-RUNX1 aberration occurs more often when blasts present high expression of CD10, CD38, low CD34, CD45 and specific low expression of CD81. The t(v;11q23)/KMT2A is associated with positive NG2 expression and low CD10, CD34, TdT and CD24. Hyperdiploidy is associated with CD123, CD66c and CD34 expression on blast cells. In turn, high expression of CD81, low expression of CD45, CD22 and lack of CD123 and NG2 indicates that none of the studied aberrations is present. Detecting aberrations in pediatric BCP-ALL, based on the expression of multiple markers, can be done with decent efficiency.

Influence of Xanthan Gum Addition on the Short- and Long-Term Retrogradation of Corn Starches of Various Amylose Content.

Starch retrogradation is a complex process and in most food products is undesirable. Knowing and understanding the mechanisms and factors that influence this process may become the key to a better and innovative approach to food design. In this paper, we investigated the effect of 0%, 0.05% and 0.20% (w/w) xanthan gum (XG) addition on the short- and long-term retrogradation of 4%, 5% and 6% corn starch gels, depending on the amylose/amylopectin ratio in the starch. The changes were monitored throughout 90 days. The pasting characteristics of blends, rheological and texture analyses, as well as syneresis, revealed that XG stabilizes the starch in the short term, but it does not inhibit retrogradation caused by amylopectin. After 30 days of storage, the destabilization of the starch-hydrocolloid mixture was observed. Based on the obtained results, a probable mechanism for the retrogradation of corn starch process in the presence of xanthan gum was proposed.

MAINE: a web tool for multi-omics feature selection and rule-based data exploration.

SUMMARY: Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE-Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. AVAILABILITY AND IMPLEMENTATION: MAINE is freely available at maine.ibemag.pl as an online web application. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data on methane concentration collected by underground coal mine sensors.

Coal mining requires working in hazardous conditions. Miners in an underground coal mine can face several threats, such as, e.g. methane explosions. To provide protection for people working underground, systems for active monitoring of production processes are typically used. One of their fundamental applications is screening dangerous gas concentrations (methane in particular) to prevent spontaneous explosions. Such a system is the source of the data set containing raw data collected at an underground coal mine. The data is collected from 28 different sensors placed at various locations around the coal mine. All the attributes except one are numeric, and the examples collected form a time series. This data set can be used in a variety of analytical tasks, including classification, regression, time series and stream data analysis.

Generator of Slow Denial-of-Service Cyber Attacks.

In today's world, the volume of cyber attacks grows every year. These attacks can cause many people or companies high financial losses or loss of private data. One of the most common types of attack on the Internet is a DoS (denial-of-service) attack, which, despite its simplicity, can cause catastrophic consequences. A slow DoS attack attempts to make the Internet service unavailable to users. Due to the small data flows, these attacks are very similar to legitimate users with a slow Internet connection. Accurate detection of these attacks is one of the biggest challenges in cybersecurity. In this paper, we implemented our proposal of eleven major and most dangerous slow DoS attacks and introduced an advanced attack generator for testing vulnerabilities of protocols, servers, and services. The main motivation for this research was the absence of a similarly comprehensive generator for testing slow DoS vulnerabilities in network systems. We built an experimental environment for testing our generator, and then we performed a security analysis of the five most used web servers. Based on the discovered vulnerabilities, we also discuss preventive and detection techniques to mitigate the attacks. In future research, our generator can be used for testing slow DoS security vulnerabilities and increasing the level of cyber security of various network systems.

Symptom-based early-stage differentiation between SARS-CoV-2 versus other respiratory tract infections-Upper Silesia pilot study.

In the DECODE project, data were collected from 3,114 surveys filled by symptomatic patients RT-qPCR tested for SARS-CoV-2 in a single university centre in March-September 2020. The population demonstrated balanced sex and age with 759 SARS-CoV-2( +) patients. The most discriminative symptoms in SARS-CoV-2( +) patients at early infection stage were loss of taste/smell (OR = 3.33, p < 0.0001), body temperature above 38℃ (OR = 1.67, p < 0.0001), muscle aches (OR = 1.30, p = 0.0242), headache (OR = 1.27, p = 0.0405), cough (OR = 1.26, p = 0.0477). Dyspnea was more often reported among SARS-CoV-2(-) (OR = 0.55, p < 0.0001). Cough and dyspnea were 3.5 times more frequent among SARS-CoV-2(-) (OR = 0.28, p < 0.0001). Co-occurrence of cough, muscle aches, headache, loss of taste/smell (OR = 4.72, p = 0.0015) appeared significant, although co-occurrence of two symptoms only, cough and loss of smell or taste, means OR = 2.49 (p < 0.0001). Temperature > 38℃ with cough was most frequent in men (20%), while loss of taste/smell with cough in women (17%). For younger people, taste/smell impairment is sufficient to characterise infection, whereas in older patients co-occurrence of fever and cough is necessary. The presented study objectifies the single symptoms and interactions significance in COVID-19 diagnoses and demonstrates diverse symptomatology in patient groups.

Sensor-Based Predictive Maintenance with Reduction of False Alarms-A Case Study in Heavy Industry.

In this paper, the problem of the identification of undesirable events is discussed. Such events can be poorly represented in the historical data, and it is predominantly impossible to learn from past examples. The discussed issue is considered in the work in the context of two use cases in which vibration and temperature measurements collected by wireless sensors are analysed. These use cases include crushers at a coal-fired power plant and gantries in a steelworks converter. The awareness, resulting from the cooperation with industry, of the need for a system that works in cold start conditions and does not flood the machine operator with alarms was the motivation for proposing a new predictive maintenance method. The proposed solution is based on the methods of outlier identification. These methods are applied to the collected data that was transformed into a multidimensional feature vector. The novelty of the proposed solution stems from the creation of a methodology for the reduction of false positive alarms, which was applied to a system identifying undesirable events. This methodology is based on the adaptation of the system to the analysed data, the interaction with the dispatcher, and the use of the XAI (eXplainable Artificial Intelligence) method. The experiments performed on several data sets showed that the proposed method reduced false alarms by 90.25% on average in relation to the performance of the stand-alone outlier detection method. The obtained results allowed for the implementation of the developed method to a system operating in a real industrial facility. The conducted research may be valuable for systems with a cold start problem where frequent alarms can lead to discouragement and disregard for the system by the user.

Molecular Analysis of Retrogradation of Corn Starches.

Changes of the molecular dynamics of water in 5% corn starch pastes and 5% systems composed of starch and non-starchy hydrocolloid were studied during short and long term retrogradation. Low Field NMR was used to record mean correlation times (τc) of water molecules. This molecular parameter reflects the rotation of water molecules within the network of paste. Starches of different amylose and amylopectin content were selected for this study. Comparison of the changes of τc shows how particular polymers bind water molecules. During 90 days of storage, over 50% increase in mean correlation time was recorded in pastes of starches with high amylose content. This suggests that the formation of polymeric network is controlled by amylose to which water is binding. Amylopectin was found to influence the mobility of water in the pastes to a lesser extent with changes in mean correlation times of approximately 10-15% over 90 days. On retrogradation, amylopectin, Arabic and xanthan gums hindered the formation of solid phase structures. Guar gum evoked an increase in mean correlation times of approximately 40-50% during the prolonged process of changes of the molecular dynamics of water. This indicates continued expansion of the polymeric network. Mean correlation time available from spin-lattice and spin-spin relaxation times can be useful in the analysis of the rotational vibrations of the water molecules in biopolymeric structures.

Short- and long-term retrogradation of potato starches with varying amylose content.

BACKGROUND: Gels of potato starches with varying amylose content were prepared and the degree of pasting and the course of retrogradation were studied. The average molar masses of granular and pasted starches were estimated. Determination of the degree of pasting involved use of optical microscopy and the study of pasting characteristics. The studies of susceptibility to retrogradation considered mechanical spectra, hardness, syneresis, resistant starch content, and X-ray measurements. RESULTS: Heating of the starch suspensions at 95 °C resulted in almost complete deterioration of granules. Changes in storage modulus (G') exceeded these of loss modulus (G"). Values of G' and G", hardness and syneresis increased with the period of the sample storage and, simultaneously, the relevant tangent of the phase shift angle (tg (G"/G')) decreased. A tendency was observed for the resistant starch (RS) content to increase on prolonged storage of gels. The patterns of diffractograms for the pasted and lyophilized samples only differed slightly. CONCLUSION: The pastes of all the studied potato starches were characterized by a similar degree of pasting. The most essential changes in the physicochemical properties of the gels were observed between the 30th and 90th days of storage. The susceptibility of potato starch gels to retrogradation, especially within the first 2 h, was controlled, mainly by the amylose content. © 2018 Society of Chemical Industry.

Learning rule sets from survival data.

BACKGROUND: Survival analysis is an important element of reasoning from data. Applied in a number of fields, it has become particularly useful in medicine to estimate the survival rate of patients on the basis of their condition, examination results, and undergoing treatment. The recent developments in the next generation sequencing open new opportunities in survival study as they allow vast amount of genome-, transcriptome-, and proteome-related features to be investigated. These include single nucleotide and structural variants, expressions of genes and microRNAs, DNA methylation, and many others. RESULTS: We present LR-Rules, a new algorithm for rule induction from survival data. It works according to the separate-and-conquer heuristics with a use of log-rank test for establishing rule body. Extensive experiments show LR-Rules to generate models of superior accuracy and comprehensibility. The detailed analysis of rules rendered by the presented algorithm on four medical datasets concerning leukemia as well as breast, lung, and thyroid cancers, reveals the ability to discover true relations between attributes and patients' survival rate. Two of the case studies incorporate features obtained with a use of high throughput technologies showing the usability of the algorithm in the analysis of bioinformatics data. CONCLUSIONS: LR-Rules is a viable alternative to existing approaches to survival analysis, particularly when the interpretability of a resulting model is crucial. Presented algorithm may be especially useful when applied on the genomic and proteomic data as it may contribute to the better understanding of the background of diseases and support their treatments.

Data- and expert-driven rule induction and filtering framework for functional interpretation and description of gene sets.

BACKGROUND: High-throughput methods in molecular biology provided researchers with abundance of experimental data that need to be interpreted in order to understand the experimental results. Manual methods of functional gene/protein group interpretation are expensive and time-consuming; therefore, there is a need to develop new efficient data mining methods and bioinformatics tools that could support the expert in the process of functional analysis of experimental results. RESULTS: In this study, we propose a comprehensive framework for the induction of logical rules in the form of combinations of Gene Ontology (GO) terms for functional interpretation of gene sets. Within the framework, we present four approaches: the fully automated method of rule induction without filtering, rule induction method with filtering, expert-driven rule filtering method based on additive utility functions, and expert-driven rule induction method based on the so-called seed or expert terms - the GO terms of special interest which should be included into the description. These GO terms usually describe some processes or pathways of particular interest, which are related to the experiment that is being performed. During the rule induction and filtering processes such seed terms are used as a base on which the description is build. CONCLUSION: We compare the descriptions obtained with different algorithms of rule induction and filtering and show that a filtering step is required to reduce the number of rules in the output set so that they could be analyzed by a human expert. However, filtering may remove information from the output rule set which is potentially interesting for the expert. Therefore, in the study, we present two methods that involve interaction with the expert during the process of rule induction. Both of them are able to reduce the number of rules, but only in the case of the method based on seed terms, each of the created rule includes expert terms in combination with the other terms. Further analysis of such combinations may provide new knowledge about biological processes and their combination with other pathways related to genes described by the rules. A suite of Matlab scripts that provide the functionality of a comprehensive framework for the rule induction and filtering presented in this study is available free of charge at: http://rulego.polsl.pl/framework .

Thixotropic properties of waxy potato starch depending on the degree of the granules pasting.

This paper presents the rheological instability (thixotropy/antithixotropy) of waxy potato starch (WPS) pastes depending on their concentration (1-5% w/w) and pasting temperature (80, 95 and autoclaved: 121°C, at 0.1MPa). The hysteresis loop, apparent viscosity at constant shear rate as well as the in-shear structural recovery tests with and without pre-shearing were applied. The pastes were also characterized by the granularity profile, molecular weight, polydispersity and optical transmittance. Differences in rheological properties of the pastes prepared at 80 and 95°C as well as autoclaved resulted from degree of granules pasting. At 121 °C dissolution of the granules occurred, while at the lower temperatures only the partial pasting of the granules took place. Pasting temperature of WPS significantly influenced rheological parameters of the resulted pastes which had thixotropic, antithixotropic or mixed thixotropic/antithixotropic behavior. Autoclaved pastes, regardless their concentration were antithixotropic as demonstrated by the areas of hysteresis loops derived from the flow curves signalized by the degree of structure recovery (DSR) which exceeded unity. The apparent viscosity of WPS pasted at 121°C strongly decreased as compared to the samples pasted at lower temperatures. Samples pasted at 80 and 95°C showed both thixotropic and antithixotropic behavior, with a predominance of the latter. The starch concentration played an important role in the formation of the rheological properties of the resulted pastes. Its influence was strongly connected with the degree of the granules pasting, therefore with the temperature of pastes preparation. For the pastes prepared at 80 and 95°C the values of thixotropy and apparent viscosity increased, while the values of DSR decreased with an increase of concentration. In the autoclaved pastes the antithixotropy, DSR and apparent viscosity increased with increasing starch concentration. It was also found that apart from the concentration and temperature also the shear rate influence the thixotropic behavior.

Thixotropic properties of normal potato starch depending on the degree of the granules pasting.

The aim of this paper was the study of the rheological instability (thixotropy and/or antithixotropy) of normal potato starch (NPS) pastes depending on their concentration (2-5%) and degree of pasting. Flow curves with hysteresis loops, apparent viscosity at constant shear rate and in-shear structural recovery tests were carried out. Granule size profiles, the pasting characteristic of corresponding starch suspensions and the transmittance of the pastes, the molecular weights and polydispersity of granular starch and its pastes prepared at 80, 95 and 121°C were also studied. The degree of pasting was dependent on the temperature and the concentration and influenced strongly the rheological behavior of the pastes. All pastes belonged to the non-Newtonian liquids thinned by shear and were rheologically unstable to the various extent. Thixotropic properties were connected to the size and the number of the starch granules in the pastes as well as depended on the measuring method used. In the 2 and 3% samples pasted at 80°C the swelling of the granules prevailed their destruction (thixotropy was observed). In the other samples the destruction predominated the swelling (antithixotropy observed).

Cocoa agronomy, quality, nutritional, and health aspects.

The history of cocoa and chocolate including the birth and the expansion of the chocolate industry was described. Recent developments in the industry and cocoa economy were briefly depicted. An overview of the classification of cacao as well as studies on phenotypic and genetic diversity was presented. Cocoa agronomic practices including traditional and modern propagation techniques were reviewed. Nutrition-related health benefits derived from cocoa consumption were listed and widely reviewed. The specific action of cocoa antioxidants was compared to those of teas and wines. Effects of adding milk to chocolate and chocolate drinks versus bioavailability of cocoa polyphenols were discussed. Finally, flavor, sensory, microbiological, and toxicological aspects of cocoa consumption were presented.

An addition of sourdough and whey proteins affects the nutritional quality of wholemeal wheat bread.

BACKGROUND: Bread can be a good source of nutrients as well as non-nutrient compounds. This study was designed to assess the effect of adding of sourdough and whey proteins to wholemeal (WM) bread produced by bake-off technology on chemical composition and bioavailability of proteins, calcium, phosphorus, magnesium and iron content in Wistar rats. MATERIAL AND METHODS: Wholemeal breads were baked with using conventional or bake off technology. In breads chemical composition, selected minerals content, amino acid composition were measured. Five week-old Wistar rats (n = 30, male), were randomly divided into fi ve groups and fed with modified AIN-93G diets containing experimental breads. In animal study the nutritional value of breads' proteins and concentration of selected minerals in serum, liver and femoral bone, were measured. RESULTS: The body weight gain, biological value (BV) and net protein utilization (NPU) were significantly higher in rats fed with partially baked frozen wholemeal (PBF WM) bread with sourdough and whey proteins. The level of magnesium was significantly lower in serum of animals fed with the diet containing PBF WM bread with sourdough and whey proteins in comparison to rodents fed with conventional WM bread with sourdough. The content of iron was significantly higher in liver of rats fed with PBF WM with sourdough bread in comparison to the groups fed with conventional WM and conventional WM with sourdough breads. CONCLUSIONS: Sourdough addition can be recommended in a production of whole wheat partially baked frozen bread but its use is further more beneficial if it is fermented with whey proteins.