Miracle-dz/UniFAR-FTUs huggingface dataset:
Purification of Pseudomonas sp. proteases through aqueous biphasic systems as an alternative source to obtain bioactive protein hydrolysates

Aqueous biphasic systems (ABSs) are an interesting alternative for separating industrial enzymes due to easy scale‐up and low operational cost. The proteases of Pseudomonas sp. M211 were purified through ABS platforms formed by polyethylene glycol (PEG) and citrate buffer salt. Two experimental designs 23 + 4 were performed to evaluate the following parameters: molar mass of PEG (MPEG), concentration of PEG (CPEG), concentration of citrate buffer (CCit), and pH. The partition coefficient (K), activity yield (Y), and purification factor (PF) were the responses analyzed. The best purification performance was obtained with the system composed of MPEG = 10,000 g/mol, CPEG = 22 wt%, CCit = 12 wt%, pH = 8.0; the responses obtained were K = 4.9, Y = 84.5%, PF = 15.1, and tie‐line length = 52.74%. The purified proteases of Pseudomonas sp. (PPP) were used to obtain hydrolysates of Lupinus mutabilis (Peruvian lupin cultivar) seed protein in comparison with the commercial protease Alcalase® 2.4L. A strong correlation between hydrolysis degree and radical scavenging activity was observed, and the highest antioxidant activity was obtained with Alcalase® (1.40 and 3.47 μmol Trolox equivalent/mg protein, for 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulphonic acid) and oxygen radical absorbance capacity, respectively) compared with PPP (0.55 and 1.03 μmol Trolox/mg protein). Nevertheless, the IC50 values were lower than those often observed for antioxidant hydrolysates from plant proteins. PEG/citrate buffer system is valuable to purify Pseudomonas proteases from the fermented broth, and the purified protease could be promising to produce antioxidant protein hydrolysates.
Unveiling Exception Handling Bug Hazards in Android Based on GitHub and Google Code Issues

This paper reports on a study mining the exception stack traces included in 159,048 issues reported on Android projects hosted in GitHub (482 projects) and Google Code (157 projects). The goal of this study is to investigate whether stack trace information can reveal bug hazards related to exception handling code that may lead to a decrease in application robustness. Overall 6,005 exception stack traces were extracted, and subjected to source code and byte code analysis. The out-comes of this study include the identification of the following bug hazards: (i) unexpected cross-type exception wrappings (for instance, trying to handle an instance of Out Of Memory Error "hidden" in a checked exception) which can make the exception-related code more complex and negatively impact the application robustness, (ii) undocumented runtime exceptions thrown by both the Android platform and third party libraries, and (iii) undocumented checked exceptions thrown by the Android Platform. Such undocumented exceptions make it difficult, and most of the times infeasible for the client code to protect against "unforeseen" situations that may happen while calling third-party code. This study provides further insights on such bug hazards and the robustness threats they impose to Android apps as well as to other systems based on the Java exception model.
Dynamic cubing for hierarchical multidimensional data space

Many modern applications such as sensor-based monitoring or certain business applications raise the need for real-time analysis. These applications very often operate in fast-evolving, dynamic environments. Therefore, traditional data warehouses, with their scheduled offline batch update strategy and mandatory ordered dimensions, are no longer suitable. In this paper, we present a multidimensional model for dynamic data warehousing in a hierarchical non-ordered multidimensional data space. We propose a dynamic partial cube materialisation and a tree storage structure that groups the multidimensional data in data partitions called minimum bounding spaces. Algorithms for building and maintaining the tree after a new fact is integrated and for querying the so-constructed cube are provided. We use Star Schema Benchmark and various synthetic and customisable data sets to compare the performance of our solution with the existing dynamic indexing technique. Experimental study shows performance improvement in both insertion time and queries over the data space.
Impact of Gender Differences and Glycated Haemoglobin on Atherosclerosis Risk in Type 2 Diabetic Patients from North Western Algeria

In type 2 diabetic patients, blood lipids and lipid ratios have been proven to be associated with atherosclerosis risk; however, results about the effect of gender differences had not been fully conclusive. In this transversal comparative study, a total of 129 adult type 2 diabetic patients (54 men and 75 women), from north western Algeria, was included to highlight the impact of gender differences on atherosclerosis risk (assessed by lipid ratios) according to their diabetic profile. Comparing between the two genders, no significant differences (p>0.05) were noted on diabetes duration, central obesity, body mass index and blood pressures. Moreover, the basic biochemical parameters of both glycaemic and lipid metabolism were not meaningfully divergent. However, higher significant values (p<0.05) of body weight, height, HbA1c levels, HDL and lipid ratios (TC/HDL, LDL/HDL and TG/HDL) were observed in male patients comparing to females. According to the HbA1c levels (HbA1c<7%vs. HbA1c≥ 7%); notable higher CT/HDL, LDL/HDL and TG/HDL in male patients comparing to women were found. The rates of these three lipid ratios were often higher in both classes of HbA1c. The consideration of gender differences is very important for prevention, diagnosis, treatment and management of atherosclerosis and CVD. The disparity in atherogenic risk, in both genders, estimated by lipid ratios need to be critically examined. Further, interventional studies are needed to determine how sex can modulate the impacts of diabetes control parameters on atherosclerosis risk.
LoRaWAN Networking in Mobile Scenarios Using a WiFi Mesh of UAV Gateways

Immediately after a disaster, such as a flood, wildfire or earthquake, networks might be congested or disrupted and not suitable for supporting the traffic generated by rescuers. In these situations, the use of a traditional fixed-gateway approach would not be effective due to the mobility of the rescuers. In the present work, a double-layer network system named LoRaUAV has been designed and evaluated with the purpose of finding a solution to the aforementioned issues. LoRaUAV is based on a WiFi ad hoc network of Unmanned Aerial Vehicle (UAV) gateways acting as relays for the traffic generated between mobile LoRaWAN nodes and a remote Base Station (BS). The core of the system is a completely distributed mobility algorithm based on virtual spring forces that periodically updates the UAV topology to adapt to the movement of ground nodes. LoRaUAV has been successfully implemented in ns-3 and its performance has been comparatively evaluated in wild area firefighting scenarios, using Packet Reception Ratio (PRR) and end-to-end delay as the main performance metrics. It is observed that the Connection Recovery and Maintenance (CRM) and Movement Prediction (MP) mechanisms implemented in LoRaUAV effectively help improve the PRR, with the only disadvantage of a higher delay affecting a small percentage of packets caused by buffer delays and disconnections.
A Survey of Methods in Variable Selection and Penalized Regression

Statistical learning often deals with the problem of ﬁnding a best predictive model from a set of possible models on the basis of the observed data. “Best” often means most parsimonious; thus a sparse model that is composed of a subset of variables is usually preferable to a full model that uses all input variables because of its better interpretability and higher prediction accuracy. To this extent, systematic approaches such as variable selection methods for choosing good interpretable and predictive models have been developed. This paper reviews variable selection methods in linear regression, grouped into two categories: sequential methods, such as forward selection, backward elimination, and stepwise regression; and penalized methods, also called shrinkage or regularization methods, including the LASSO, elastic net, and so on. In addition to covering mathematical properties of the methods, the paper presents practical examples using SAS/STAT ® software and SAS ® Viya ® .
Evaluation of heavy metals in selected fruits in Umuahia market, Nigeria: Associating toxicity to effect for improved metal risk assessment

The cases of metabolic disorder and diseases have been linked to the toxicity of heavy metals in foods crops. This study evaluates the degree of heavy metals in selected fruits sold at Umuahia market in Nigeria. Ten different fruits were randomly selected. The fresh fruits comprised Solanum lycopersicum (Tomatoes), Cucumis sativus (Cucumber), Solanum melongena (Nigerian Egggarden), Citrullus lanatus (Watermelon), Dacryodes edulis (African bush pear), Juglans regia (Walnut), Syzgium malaccense (Mountain apple), Annona muricata (Soursop), Pachycarpa kschum (Monkey kola), and Dennettia tripetala (Pepper fruit), respectively. These fruits were slices to pieces and air dried; they were ground and digested. The heavy metals were screened with atomic absorption spectrophotometer. Their health risk assessment was analyzed based on established models. Results showed that zinc and lead were undetected in fruits such as C. sativus, S. melongena, C. lanatus, D. edulis, S. malaccense, S. melongena, J. regia, A. muricata, P. kschum, and D. tripetala. Nickel was undetected in S. melongena, J. regia, A. muricata, P. kschum, and D. tripetala and the absence of cobalt in C. lanatus and J. regia. The concentration of copper, chromium (Cr), and cadmium (Cd) was higher compared to their respective permissible limits in fruits. Iron concentration was 2.914 ± 0.002 ppm in P. kschum, with the fruits hazard index less than one (0.3434). Cr and Cd concentration falls within tolerance level in all the fruits with acceptable cancer risk (<10−6). Total cancer risks of the fruits were >10−6, suggesting zero chance of developing cancer. This study suggests that most fruits are unsafe for human consumption and consumers should proffer a method of reducing the level of heavy metals in fruits.
A Framework to Quality Control Oceanographic Data

The ocean is an intrinsically challenging environment to collect data, which makes spurious measurements inevitable. Thus, the quality of oceanographic datasets is highly dependent on the ability to identify and remove bad samples. Quality control (QC) of oceanographic data has mostly relied on manual QC by experts, which, despite resulting in the best data quality, is not scalable and becomes impractical to handle large datasets or real-time data streams. To address this issue, automatic QC procedures have been proposed and widely used for decades (e.g., IOC/IODE, 1993; DATA–MEQ working group, 2010; GTSPP Real–Time Quality Control Manual, 2010; Morello et al., 2014; QARTOD group, 2016; Wong, Keeley, Carval, & Argo Data Management Team, 2015); however, these procedures are seldom organized and distributed as packages, so it is still common for new users to have to implement them from scratch. Additionally, different applications of the same dataset may require different QC procedures. For example, a particular user faced with the QC of a small dataset might be willing to apply a less conservative QC in order to preserve a larger number of data points, paying the price of having some false positives. CoTeDe is an Open Source Python package that provides a flexible way to automatic QC oceanographic data by combining multiple QC standards while allowing the users to fully control and tune the parameters according to their own needs.
Liver Fibrosis is Associated with NAFLD Activity Score in Chronic Hepatitis B Patients with Liver Steatosis

Background: Chronic hepatitis B (CHB) and non-alcoholic fatty liver disease (NAFLD) are two leading hepatic diseases that cause severe public health problems. The relationship between the severity of NAFLD and liver ﬁbrosis in CHB patients remains controversial. Objectives: This study aimed at further clarifying the association between them. Methods: A total of 272 CHB patients and 31 NAFLD patients without CHB, who underwent liver biopsies and routine blood tests wereincluded. TheNAFLDactivityscores(NAS)forNAFLDpatientsandtheMETAVIRscoringsystemforallthepatientswereusedto evaluate the liver pathology. The associations between steatosis or NAS score and liver ﬁbrosis were statistically analyzed. Results: METAVIR F0-4 was identiﬁed in 10.0%, 30.5%, 36.0%, 13.2%, and 10.3% of all the CHB patients, respectively. The prevalence of hepatic steatosis in CHB patients was 39.7% (n = 108). Among them, 26.9% (n = 29) of patients had NAS ≥ 5. The severity of ﬁbrosis was not related to the degree of steatosis nor NAS score (P > 0.05) in NAFLD patients without CHB. Similarly, the results suggested that there was no signiﬁcant diﬀerence in the severity of liver ﬁbrosis in CHB patients with varying degrees of steatosis (P > 0.05). However, the severity of liver ﬁbrosis increased with NAS score (P = 0.004) in CHB patients with NAFLD. Conclusions: The severity of liver ﬁbrosis in CHB patients was associated with NAS score, yet not with fat content.
DeepGestalt analysis of the SETD5-associated intellectual disability syndrome

Aim: Th is is the first computer-assisted study focused on the craniofacial features of the intellectual disability (ID)/ developmental delay (DD) syndrome related to haploinsufficiency of the SETD5 gene (SET domain-containing protein 5, MIM#615743), which is a chromatin regulator. The purpose of this novel research is to better delineate the facial phenotype of this condition and identify the associated dysmorphic features to consider for clinical diagnosis. Methods: A total of 18 2D frontal images of previously published pediatric individuals (aged 1-14 years, Caucasian ethnicity) with SETD5 mutations (SETD5, cohort 1) were uploaded to the RESEARCH application of the Face2Gene online platform (V.19.1.3) (FDNA Inc., Boston, MA, USA). Images from this group of patients were compared with 36 photos of individuals with two other known chromatin disorders, specifically KBG (KBGS, cohort 2, 18 images) and Koolen-de Vries syndromes (KdVS, cohort 3, 18 images), which share with the SETD5-related ID syndrome a very similar facial gestalt and peculiar dysmorphisms. An additional cohort of 18 unaffected controls that were matched for age and ethnicity (Ctrl., controls, cohort 4) was also included in the comparison experiment. Results: Results obtained from the binary comparison analysis were expressed in terms of Area Under the Curve and its Receiver Operating Characteristic curve for aggregated splits. A high facial overlap between the SETD5-related phenotype and KBGS was demonstrated. Other conditions considered for the study were well recognized by the system and differentiated using the unaffected controls. Conclusion: This study confirms the presence of distinctive dysmorphic features that characterize the SETD5-related facial phenotype, providing observations about its possible role in facial morphogenesis.
Characterization and Applications of Spatial Variation Models for Silicon Microring-Based Optical Transceivers

Photonic integrated circuits suffer from large process variations. Effective and accurate characterization of the variation patterns is a critical task for enabling the development of novel techniques to alleviate the variation challenges. In this study, we propose a hierarchical approach that effectively decomposes the spatial variations of silicon microring-based optical transceivers into wafer-level, intra-die, and inter-die components. We then demonstrate that the characterized variation models can be used to generate trustworthy synthetic data for architecture- and system-level solutions for variation alleviation. We further demonstrate the utility of our variation characterization method for accurate yield prediction based on partial measurement data.
State of the Art on: Conﬁgurable Markov Decision Process

Machine learning (ML) is a subfield of Artificial Intelligence that leverages statistical techniques to develop algorithms able to learn from data. Reinforcement learning (RL) is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. RL studies how software agents ought to take actions in an environment in order to maximize a cumulative reward. In recent years, the interest in Machine Learning has increased more and more both from the academic and industrial world. NeurIPS (Neural Information Processing Systems) and ICML (International Conference on Machine Learning) are the most relevant conferences related to the field of Machine Learning and the attendance in the last year has grown considerably. Other prestigious conferences with a wide spectrum on the AI field, are AAAI (AAAI Conference on Artificial Intelligence) and IJCAI (International Joint Conference on Artificial Intelligence). Relevant journals are the Journal of Machine Learning Research, Journal of Artificial Intelligence Research and Machine Learning (Springer).
Arsenic Toxicity in Plants: A Significant Environmental Problem

Received on 30 July and finalized on 22 August 2013. _____________________________________________________________________________ ABSTRACT Arsenic (As) contamination of soil and water is a major global problem that impacts on many areas of biological sciences. Widespread use of Arsenic as pesticides has significantly contributed to the elevation of arsenic concentration in soil. Arsenic contamination in groundwater is a sever global environmental problem. Arsenic is a widespread natural element, which is not a bioorganic element to plants. in terrestrial plants both organic and inorganic Arsenic species have been found, with the inorganic species As(V) and arsenite, As(III) being the most dominant. Arsenic availability to plants greatly influenced by its forms in the soil. The background levels of arsenic are around 5 mg per kg worldwide with substantial variation depending on the origin of the soil. The behavior of Arsenic is distinctly different under anaerobic or flooded and aerobic or non-flooded soil conditions with flooded or anaerobic conditions being likely the most hazardous in terms of uptake by plants and toxicity. Agricultural application of arsenicals has introduced many different kinds of arsenic compounds to the soil environment. Biomass production and yields of a variety of crops are reduced significantly at elevated arsenic concentrations. Arsenic concentrations are generally low in plants. In all plant species tested so far, it has been shown that arsenate is taken up via the phosphate transport systems. Arsenic toxicity in plants is now recognized as a serious threat to human health, as a consequence of consumption of contaminated plant material. This review paper attempted to summarize the incidents of arsenic contamination in the irrigation of water-soilplant system. It poses a significant risk to public health. Therefore, the first priority to remediate the crises should be early identification of the affected sources.
Python and C#: a comparative analysis fromsStudents' perspective

Python and C# are two programming languages of great educational value. Python has a simple and clear syntax, as well as a concise and readable source code, but is relatively slow, and its industrial applications are mostly web-based. Although the syntax and code readability of C# does not match the Python's high level, they are not very bad either; besides, the.NET language offers a fast JIT compiler, and can be found within a wide gamut of industrial applications. In this paper we present a comparison of these two languages, based on the data acquired from IT students who learnt both of them during programming courses. We use the Analytic Hierarchy Process (AHP) technique to show the dominance of one language over another in respective comparison criteria, and obtain an overall answer which of the two languages is better, according to the ratings given by students.
Textured Mesh vs Coloured Point Cloud: A Subjective Study for Volumetric Video Compression

Volumetric video (VV) pipelines reached a high level of maturity, creating interest to use such content in interactive visualisation scenarios. VV allows real world content to be captured and represented as 3D models, which can be viewed from any chosen viewpoint and direction. Thus, VV is ideal to be used in augmented reality (AR) or virtual reality (VR) applications. Both textured polygonal meshes and point clouds are popular methods to represent VV. Even though the signal and image processing community slightly favours the point cloud due to its simpler data structure and faster acquisition, textured polygonal meshes might have other benefits such as better visual quality and easier integration with computer graphics pipelines. To better understand the difference between them, in this study, we compare these two different representation formats for a VV compression scenario utilising state-of-the-art compression techniques. For this purpose, we build a database and collect user opinion scores for subjective quality assessment of the compressed VV. The results show that meshes provide the best quality at high bitrates, while point clouds perform better for low bitrate cases. The created VV quality database will be made available online to support further scientific studies on VV quality assessment.
Measurement of viscoelastic properties of treated and untreated cancer cells using passive microrheology

Experiments have shown that there is an increase in ultrasound backscatter from cells during cell death. Since cell scattering depends on the mechanical property variations, one step towards a better understanding of the phenomenon involves measuring the cells' viscoelastic properties. Two promising techniques used for such studies are particle tracking microrheology (1P) and two-point microrheology (2P). The main aim of this work is to develop and test the ability of both to measure changes in viscous and elastic moduli of breast cancer cells during chemotherapeutic treatments. First, the viscosities of glycerol-water mixtures measured using microrheology were found to be within 5% of rheometer values. The viscous and elastic moduli of 4% and 6% poly(ethylene oxide) solutions were successfully measured at 30°C and 37°C. For MCF-7 cells, a 10-fold increase in the elastic modulus was observed using 1P, without a corresponding increase in the viscous modulus. Thus, it was shown that MCF-7 cells undergo an increase in stiffness during apoptosis.
A Test-Suite Diagnosability Metric for Spectrum-Based Fault Localization Approaches

Current metrics for assessing the adequacy of a test-suite plainly focus on the number of components (be it lines, branches, paths) covered by the suite, but do not explicitly check how the tests actually exercise these components and whether they provide enough information so that spectrum-based fault localization techniques can perform accurate fault isolation. We propose a metric, called DDU, aimed at complementing adequacy measurements by quantifying a test-suite's diagnosability, i.e., the effectiveness of applying spectrum-based fault localization to pinpoint faults in the code in the event of test failures. Our aim is to increase the value generated by creating thorough test-suites, so they are not only regarded as error detection mechanisms but also as effective diagnostic aids that help widely-used fault-localization techniques to accurately pinpoint the location of bugs in the system. Our experiments show that optimizing a test suite with respect to DDU yields a 34% gain in spectrum-based fault localization report accuracy when compared to the standard branch-coverage metric.