Miracle-dz/UniFAR-FTUs huggingface dataset:
A biomimicking approach for hemicellulose processing

Hemicelluloses with high molecular mass are needed for the manufacture of value added products such as food packaging barrier films. In this work such molecules were recovered from chemithermomechanical pulp (CTMP) process water using an innovative three-stage process comprising membrane separation and enzymatic treatment with laccase. Microfiltration followed by ultrafiltration was found to be a suitable combination in the first stage, providing a concentrated and purified hemicellulose fraction suitable for enzymatic treatment. In both membrane processes a high average flux (260 and 115 l/m(2) h) and a low fouling tendency were observed. A marked increase in the average molecular mass of hemicelluloses with bound lignin moieties was achieved by laccase treatment in the second stage. The enzymatically crosslinked hemicelluloses were finally recovered in the third stage using ultrafiltration. In the final high molecular mass solution the hemicellulose concentration was 54 g/l, the contribution of hemicelluloses to the total solids content 43%, and the viscosity of the solution 27 mPa s. The results demonstrate that a hemicellulose fraction of high quality can be produced from CTMP process water, and that this could constitute a suitable feedstock for the production of, for example, barrier films for renewable packaging.
A Competition Period Evaluation Concerning Seasonal Variables of Elite Track and Field Athletes in Vertical Jumping Events: A Different Insight for Coaching Education

Aim of this study was to determine variables of elite athletes’ competition seasons in vertical jumping events and to determine relationships between these variables. Also, to constitute prediction models of season best (SB) and season performance average based on season first performance to offer a new insight for coaching education. Research group consisted of male and female elite athletes who ranked in the top 100 in high jump (HJ) and pole vault (PV), during 2018 season. Athletes’ competition information was reached from 2018 world rankings. Ages, total number of days in season, days between competitions, total number of competitions, number of competitions that season’s best score was performed, ratio of SB to total number of competitions, percentages of first, end, average scores were calculated. Statistical comparison of gender groups was analyzed using Independent Samples t-Test. Pearson correlation coefficients were used to express relationships. Polynomial regression analysis was used to find coefficients of determination for relationships. Quadratic equations were calculated to predictive SB performance and season average performances according average of first two performances by gender/events. In PV, there were differences between genders for season first, end, and average percentages calculated according to SB (p < 0.05). Strong relationships were determined between season average and season first performances in female athletes. Predicted models created according to season first performance may be considered as early evaluations for coaches. Coach can use these prediction models as a new and different education material for their training plans. By doing so, in case of calculating a prediction far away from the coach’s aim it is possible that coach can take necessary measures at the beginning of season.
Coordinated Energy Scheduling of a Distributed Multi-Microgrid System Based on Multi-Agent Decisions

Regarding the different ownerships and autonomy of microgrids (MGs) in the distributed multi-microgrid (MMG) system, this paper establishes a multi-stage energy scheduling model based on a multi-agent system (MAS). The proposed mechanism enables a microgrid agent (MGA), a central energy management agent (CEMA), and a coordination control agent (CCA) to cooperate efficiently during various stages including prescheduling, coordinated optimization, rescheduling and participation willingness analysis. Based on the limited information sharing between agents, energy scheduling models of agents and coordinated diagrams are constructed to demonstrate the different roles of agents and their interactions within the MMG system. Distributed schemes are introduced for MG internal operations considering demand response, while centralized schemes under the control of the CCA are proposed to coordinate MGAs. Participation willingness is defined to analyze the MGA’s satisfaction degree of the matchmaking. A hierarchical optimization algorithm is applied to solve the above nonlinear problem. The upper layer establishes a mixed-integer linear programming (MILP) model to optimize the internal operation problem of each MG, and the lower layer applies the particle swarm optimization (PSO) algorithm for coordination. The simulation with a three-MG system verifies the rationality and effectiveness of the proposed model and method.
Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
SMS Spam Identification and Risk Assessment Evaluations

Short Message Service (SMS) constitutes one of the most used communication medium. It has become an integral part of people’s lives and like other communication media, SMS texts have been used for propagating spam messages. Despite the fact that a broad range of spam techniques have been proposed to reduce the frequency of such incidents, many difficulties are still present due to text ambiguity; there, the same words can be used in seemingly similar texts which makes it more difficult to identify spam messages. In this paper, we propose an approach for identifying and classifying spam SMS based on the Syntactical features and patterns of the message. The proposed approach consists of four main parts, namely, SMS Pre-processing, Syntactical Features Extraction and Pattern Formulation, Classification and, Risk Analysis. Experimental results show that the proposed approach achieves a good level of accuracy. In addition, to show the effectiveness of handling class imbalance on the classification performance, two additional experiments were conducted using the implementation of the SMOTE algorithm. There, the results depicted that handling class imbalance help in improving identification and classification accuracy. Furthermore, based on the above, a risk model has been proposed that addresses the risk probability and the impact of spam SMS.
AVIRIS-NG hyperspectral data analysis for pre- and post-MNF transformation using per-pixel classification algorithms

Abstract The hyperspectral image such as AVIRIS-NG provides a lot of spectral information and fine resolution that identify and discriminate similar objects based on their spectral reflectance. This study reveals a comparative analysis of multiple classification algorithms for AVIRIS-NG data, before and after minimum noise fraction (MNF) transformation for land use land cover classification. Applied distance methods are entirely based upon the availability of a complete set of endmembers for the data and used to classify the pixels in terms of endmembers. Applied classifiers yield more accurate results especially in terms of the overall accuracy after dimensionality reduction. As a result, minimum distance achieved highest 97.76% overall accuracy with a Kappa coefficient of 0.97, whereas Mahalanobis distance yields more precise results before MNF with 95.34% overall accuracy and a Kappa coefficient of 0.94. This study also indicates the importance of data dimensionality reduction for hyperspectral imagery.
Rural Woman and Food Security: Diversification of Cassava-Based Foods in Gunungkidul District, Yogyakarta

ABSTRACT This article outlines the role of rural women in diversification of local foods in Indonesia, specifically cassava. Cassava is the third main staple food in Indonesia, after rice and corn. By referring to the concept of food security and food diversification, and using the case study method, this article describes and discusses rural women’s innovations and business initiatives in developing nutritious processed cassava products in Gunungkidul Regency. These products are available at various outlets and are accessible and affordable to the public. Rural women have become the main actors in the diversification of local foods while also playing an important role in creating food security that has become a strategic agenda in Indonesia.
TDZ Plays Key Role in Shoot Regeneration from Different Explants of Picrorhiza kurroa: An Endangered Medicinal Herb of Western Himalayas

Picrorhiza kurroa plants were collected from its natural habitat. In vitro plants were raised from the leaves of high yielding collection screened in an earlier study. Leaves, roots and internodal segments were cultured for 15 days. The effect of thidiazuron (1-phenyl-3-(1, 2, 3- thiadiazol-5-yl) urea; TDZ) pretreatment for 15 days on regeneration potential of different explants viz. leaves, roots and internodes of Picrorhiza kurroa was studied. Regeneration potential varied significantly with the type of explant. Regeneration response of 100% with 46.25 shoots per explant was obtained in leaf segments of 2.0 cm length pretreated with 0.5 µM TDZ for 15 days and then transferred to 2.32 µM kinetin (Kn) containing MS basal medium. In case of root explants maximum shoot number (17.12) was obtained on 0.5 µM TDZ pretreated for 15 days and then to 3.64 µM Kn. Maximum shoots per explants (12.33) were obtained in case of internodes pretreated with 0.5 µM TDZ for 15 days and transferred to 1.16 µM Kn. Regenerated shoots from different explants developed in vitro rooting on MS basal medium within 7-8 days. Conclusively, an efficient and repeatable protocol for rapid regeneration from different explants and in vitro rooting has been developed in P. kurroa which can be effectively used for its conservation.
A first assessment of cyanobacterial blooms in oligotrophic Lake Superior

Lake Superior is often described as the most pristine of the Laurentian Great Lakes, but in the past decade Dolichospermum blooms have been observed. Land use in the adjacent watershed has not changed appreciably during this time, but the lake is warming and climatological variables correspond with presence of blooms. Blooms occurred only in relatively warm years as measured by degree days. Furthermore, the two largest blooms, in 2012 and 2018, occurred during years of especially extreme rainfall, providing coincidental evidence that intense storms provide nutrients or living propagules to the blooms from the watershed. Nearshore lake water in the narrow zone where blooms appear shows some riverine influence compared to water further offshore even in the absence of blooms. Nevertheless, water chemistry associated with the largest bloom in 2018 more closely resembled nonbloom nearshore lake water than river water, suggesting that blooms develop or at least persist outside of distinct river plumes. Concentrations of P and N during peak bloom density greatly exceeded any nonbloom lake or river waters, indicating that a buildup of phytoplankton biomass perhaps by floating and drifting to shore also is a significant factor in bloom occurrence. One potentially toxic substance (Anabaenopeptin A) was observed but at low concentration. At peak phytoplankton concentration, high seston C : P indicated severe P limitation while low C : N pointed against N limitation. If these newly observed blooms are indeed driven by temperature and rainfall as this evidence suggests, blooms may continue.
Evaluation of efficacy of collection techniques for human genomic DNA MAOA-uVNTR polymorphism

For large scale DNA related studies it is necessary for non-invasive and cost-effective method for obtaining sufficient quantity and quality of DNA. In this study we collected buccal cells, dried blood and saliva from 10 participants by commercially available two types of foam swabs (Puritan and HiMedia), blood collection card (NucleoSave) and saliva collection kit (Oragene OG-500) respectively. Genomic DNA was extracted from the seven days old buccal cells and dried blood using QIAamp DNA Mini Kit (Qiagen) with a modification in the final elution step which resulted in high yield of DNA. Oragene DNA extraction protocol was followed to extract DNA from saliva. Integrity, yield and purity of extracted DNA were compared by agarose gel electrophoresis, spectrophotometry and PCR amplification success for specific gene. Though, the DNA yield was significantly greater from samples of saliva than buccal cells and dried blood, contamination was more in DNA from saliva. Purity of DNA obtained from buccal cells collected by Puritan foam swabs was better for the PCR reaction as evidenced by the successful amplification of 30-bp repeat polymorphism in the promoter of the MAOA gene. Hence, we conclude that Puritan foam swab is an ample and cost-effective method for collecting buccal cells for extracting genomic DNA for epidemiological and forensic studies. Keywords— Saliva, buccal cell, dried blood, genomic DNA, PCR
CoCo: RNA-seq read assignment correction for nested genes and multimapped reads

Motivation Next generation sequencing techniques revolutionized the study of RNA expression by permitting whole transcriptome analysis. However, sequencing reads generated from nested and multi-copy genes are often either misassigned or discarded, which greatly reduces both quantification accuracy and gene coverage. Results Here we present CoCo, a read assignment pipeline that takes into account the multitude of overlapping and repetitive genes in the transcriptome of higher eukaryotes. CoCo uses a modified annotation file that highlights nested genes and proportionally distributes multimapped reads between repeated sequences. CoCo salvages over 15% of discarded aligned RNA-seq reads and significantly changes the abundance estimates for both coding and non-coding RNA as validated by PCR and bed-graph comparisons. Availability The CoCo software is an open source package written in Python and available from http://gitlabscottgroup.med.usherbrooke.ca/scott-group/coco. Contact michelle.scott@usherbrooke.ca
A Formula for the Maximum Vertical Velocity in Supercell Updrafts

In supercell environments, previous authors have shown strong connections between the vertical wind shear magnitude, updraft width, and entrainment. Based on these results, it is hypothesized that the influences of entrainment-driven dilution on buoyancy and maximum updraft vertical velocity w in supercell environments are a predictable function of the vertical wind shear profile. It is also hypothesized that the influences of pressure perturbation forces on maximum updraft w are small because of a nearly complete offset between upward dynamic pressure forces and downward buoyant pressure forces. To address these hypotheses, we derive a formula for the maximum updraft w that incorporates the effects of entrainment-driven dilution on buoyancy but neglects pressure gradient forces. Solutions to this formula are compared with output from previous numerical simulations. This formula substantially improves predictions of maximum updraft w over past CAPE-derived formulas for maximum updraft w, which supports the first hypothesis. Furthermore, integrated vertical accelerations along trajectories show substantial offsets between dynamic and buoyant pressure forces, supporting the second hypothesis. It is argued that the new formula should be used in addition to CAPE-derived measures for w in forecast and research applications when accurate diagnosis of updraft speed is required.
The Group B Streptococcal surface antigen I/II protein, BspC, interacts with host vimentin to promote adherence to brain endothelium and inflammation during the pathogenesis of meningitis

Streptococcus agalactiae (Group B Streptococcus, GBS) normally colonizes healthy adults but can cause invasive disease, such as meningitis, in the newborn. To gain access to the central nervous system, GBS must interact with and penetrate brain or meningeal blood vessels; however, the exact mechanisms are still being elucidated. Here, we investigate the contribution of BspC, an antigen I/II family adhesin, to the pathogenesis of GBS meningitis. Disruption of the bspC gene reduced GBS adherence to human cerebral microvascular endothelial cells (hCMEC), while heterologous expression of BspC in non-adherent Lactococcus lactis conferred bacterial attachment. In a murine model of hematogenous meningitis, mice infected with ΔbspC mutants exhibited lower mortality as well as decreased brain bacterial counts and inflammatory infiltrate compared with mice infected with WT GBS strains. Further, BspC was both necessary and sufficient to induce neutrophil chemokine expression. We determined that BspC interacts with the host cytoskeleton component vimentin, and confirmed this interaction using a bacterial two-hybrid assay, immunofluorescent staining, and imaging flow cytometry. Vimentin null mice were protected from WT GBS infection and also exhibited less inflammatory cytokine production in brain tissue. These results suggest that BspC and the vimentin interaction is critical for the pathogenesis of GBS meningitis. AUTHOR SUMMARY Group B Streptococcus (GBS) typically colonizes healthy adults but can cause severe disease in immune compromised individuals, including newborns. Despite wide-spread intrapartum antibiotic prophylaxis given to pregnant women, GBS remains a leading cause of neonatal meningitis. To cause meningitis, GBS must interact with and penetrate the blood-brain barrier (BBB), which separates bacteria and immune cells in the blood from the brain. In order to develop targeted therapies to treat GBS meningitis, it is important to understand the mechanisms of BBB crossing. Here, we describe the role of the GBS surface factor, BspC, in promoting meningitis and discover the host ligand for BspC, vimentin, which is an intermediate filament protein that is constitutively expressed by endothelial cells. We determined that BspC interacts with the C-terminal domain of cell-surface vimentin to promote bacterial attachment to brain endothelial cells and that purified BspC protein can induce immune signaling pathways. In a mouse model of hematogenous meningitis, we observed that a GBS mutant lacking BspC was less virulent compared to WT GBS and resulted in less inflammatory disease. We also observed that mice lacking vimentin were protected from GBS infection. These results reveal the importance of the BspC-vimentin interaction in the progression of GBS meningitis disease.
Induction of Callus in Leaf Explants of Crinum americanum L. (Amaryllidaceae)

Amaryllidaceae include plant species that present alkaloids with analgesic, anti-cancer, antibacterial, anti-viral, anti-fungal and anti-malarial activities. Due to this pharmacological value, several species of this family have been widely studied and among them is White lilly, Crinum Original Research Article Gomes-Copeland et al.; EJMP, 31(11): 49-56, 2020; Article no.EJMP. 59346 50 americanum. The objective of this work was to induce callogenesis on leaf explants of C. americanum cultivated in vitro for future production of alkaloids. Leaf explants were grown on a culture medium (solid) Murashige and Skoog (1962) supplemented with different concentrations and combinations of plant growth regulators, auxin 2,4-dichlorophenoxyacetic acid and cytokinin 6benzylaminopurine and their effect on callogenesis assessed for percentage oxidation and explants responsive to callus induction. Callus formation started 10 days after hormone inoculation, and within 30 days after inoculation the best callogenesis and callus biomass growth were observed in medium containing 2.5 mg L of 2,4-dichlorophenoxyacetic acid and 10 mg L of 6benzylaminopurine. The lowest percentage of oxidation was observed on explants cultivated on medium containing 5 mg L -1 of 6-benzylaminopurine and 2.5 mg L -1 of 2,4-dichlorophenoxyacetic acid. The calli obtained were compact and embryogenic. This work contributes not only to future studies on in vitro callogenesis of this species, but also to a possible protocol for the production of alkaloids of interest from cell suspension cultures produced in vitro. This is the first report of callus formation in Crinum americanum explants.
CNN SEMANTIC SEGMENTATION TO RETRIEVE PAST LAND COVER OUT OF HISTORICAL ORTHOIMAGES AND DSM: FIRST EXPERIMENTS

Abstract. Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets.