bluuebunny/crossref_metadata_2025 huggingface dataset:
Transcriptomic differences between fibrotic and non-fibrotic testicular tissue reveal possible key players in Klinefelter syndrome-related testicular fibrosis
Authors: Willems Margo , Olsen Catharina , Caljon Ben , Vloeberghs Veerle , De Schepper Jean , Tournaye Herman , Saen Dorien Van , Goossens Ellen
Published: June 2022

Background: Klinefelter syndrome (KS; 47,XXY) affects 1-2 in 1000 males. Most KS men suffer from an early germ cell loss and testicular fibrosis from puberty onwards. Mechanisms responsible for these processes remain unknown. Previous genomics studies on KS tissue focused on germ cell loss, while a transcriptomic analysis focused on testicular fibrosis has not yet been performed. This study aimed to identify factors involved in the fibrotic remodelling of KS testes by analysing the transcriptome of (non-)fibrotic testicular tissue.Material and methods: RNA sequencing was performed to compare the genetic profile of testicular samples with (KS and testis atrophy) and without (Sertoli cell-only syndrome and fertile controls) fibrosis (n=5, each). Additionally, differentially expressed genes (DEGs) between KS and testis atrophy samples were studied to reveal KS-specific fibrotic genes. DEGs were considered significant when p<0.01 and log2FC>2. Next, downstream analyses (GO and KEGG) were performed. Lastly, RNA scope was performed to validate the results.Results: The first analysis (fibrotic vs non-fibrotic) resulted in 734 significant DEGs (167 up- and 567 down-regulated). Genes involved in the extracellular structure organization (e.g. VCAM1) were found upregulated. KEGG analysis showed an up-regulation of genes involved in the TGF-β pathway.The KS vs TA analysis resulted in 539 significant DEGs (59 up- and 480 down-regulated). Chronic inflammatory response genes were found upregulated. The overlap of X-linked DEGs from the two analyses revealed three genes: matrix-remodelling associated 5 (MXRA5), doublecortin (DCX) and variable charge X-Linked 3B (VCX3B). RNA in situ hybridization showed an overexpression of VCAM1, MXRA5 and DCX within the fibrotic group compared with the non-fibrotic group.Conclusion: In this study, DEGs between fibrotic and non-fibrotic tissue were first compared, revealing fibrotic genes, including VCAM1. Secondly, DEGs between KS and TA samples were studied and the analysis revealed KS-specific, fibrotic genes. When comparing X-linked genes of the first analysis (fibrotic versus non-fibrotic) with those of the second analysis (KS versus TA), KS-related X-linked fibrotic genes were revealed, i.e. MXRA5, DCX and VCX3B. Their potential role in KS-related testicular fibrosis needs further study.
Three-dimensional evaluation of postsurgical stability of Surgery-first approach in Skeletal Class III patients with two-jaw surgery and maxillary premolar extraction and study of the influencing factors.
Authors: Jiang Yi-ran , Yang Zhong-peng , Qi Yu-han , Peng Jia-le , Liu Xiao-jing , Li Zi-li , Fan Yi , Han Bing , Chen Gui , Xu Tian-min , Jiang Ruo-ping
Published: June 2022

Objectives to evaluate three-dimensional postsurgical stability of surgery-first approach with 2-jaw surgery and maxillary premolar extraction in skeletal class III patients and excavate factors influencing the relapse. Material and methods 24 patients were enrolled in this prospective cohort study. Serial cone-beam computer tomography images were superimposed and measured the surgical stability at the presurgical examination, 1 week, 3 month, 6 month and 12 month postoperatively. Patients were grouped by the amount of mandible sagittal relapse(stable group, n = 8; highly unstable group, n = 7). Skeletal and dental morphological variables, the amount of surgical changes, and orthodontic-related variables were compared between groups and analyzed for correlations with postsurgical stability. Results Considerable amount of pronation relapse on mandible were within three month postoperatively. The maxilla remained clinically stable. The coronoid process height, posterior maxillary height, the mandibular setback, distal segment intraoperative rotation and the amount maxillary second molar orthodontic intrusion showed significant difference between groups. The above variables, mandibular height, occlusal vertical dimension, and the mandibular and anterior maxillary intraoperative descent were significantly correlated with postoperative instability. Conclusions Mandibular pronation relapse mainly in the first three months after surgery.The maxilla postsurgical position is clinically stable. The factors for instability in the surgery-first approach include a longer coronoid process, longer facial height, a wider occlusal vertical dimension, greater surgical setback and descent, and more maxillary second molar intrusion. Clinical relevance Results can provide reference for patient selection, surgical planning and preoperative dental alignment stimulation for surgery-first approach. Trial Registration ChiCTR2200055787
Ethiopic Characters Recognition Model using Deep Learning
Authors: Malhotra Ruchika , Addis Maru Tesfaye
Published: June 2022

Purpose:-Many approaches have been developed to recognize different writing scripts, most of which focus on Latin scripts. However, in the Ethiopic script unavailability of readymade datasets and the attempts of a few studies show that the script requires further investigation by applying different recognition techniques. In addition, image quality degradation and numerous characters in the alphabet are other major problems with Ethiopic-script documents. In this study, a convolutional neural network-based character recognition model for Ethiopic character recognition is proposed. The contribution of this work is the development of a character recognition model using a convolutional neural network. Method:-The model is designed using seven convolutional layers, four max-pooling layers, and two fully connected layers. Automatic features are extracted from each cropped 28 by 28 images using convolutional and max-pooling layers to prepare for recognition. Results:-The proposed model is trained and tested with customized datasets and achievement of accuracy 97%, which is a promising recognition result of the script. Conclusion:-In future works, the dataset should include all the letters, punctuation marks, and symbols found in Ethiopic scripts for both printed and handwritten text and increase the dataset size for different deep learning algorithms to get better results.
Production of mouse offspring from zygotes fertilized with freeze-dried spermatids
Authors: Wakayama Sayaka , Ito Daiyu , Ooga Masatoshi , Wakayama Teruhiko
Published: June 2022

Recently, mice have been produced from Freeze-drying (FD) somatic cells by nuclear transfer, but the success rate was very low compared to FD spermatozoa. Since spermatozoa, unlike somatic cells, are haploid cell and their nuclei were hardened by protamine, it is unclear which is responsible for tolerance to FD treatment. Here, we attempt to produce offspring from FD spermatid, a haploid sperm progenitor cell, but nuclei were not yet replaced by protamine as same as somatic cells. We developed the method to collect FD spermatids from testicular suspension. When FD spermatids were injected into oocytes, healthy offspring were obtained, despite the significantly reduced success rate compared to FD spermatozoa. Offspring were also obtained from FD spermatids derived from immature male mice, which have not yet produced spermatozoa. These results indicate that nuclear protaminization, rather than haploid nuclei, is the key process responsible for tolerance to FD treatment.
Passive symmetry breaking of the space-time propagation in cavity dissipative solitons
Authors: Parshani Idan , Bello Leon , Meller Mallachi , Pe'er Avi
Published: June 2022

Dissipative solitons are fundamental wave-pulses that preserve their form in the presence of periodic loss and gain. The canonical realization of dissipative solitons is Kerr-lens mode locking (KLM) in lasers, which delicately balance nonlinear and linear propagation in both time and space to generate ultrashort optical pulses. This linear-nonlinear balance dictates a unique pulse energy, which cannot be increased (say by elevated pumping), indicating that excess energy is expected to be radiated in the form of dispersive or diffractive waves. Here we show that KLM lasers can overcome this expectation. Specifically, by breaking the spatial symmetry between the forward and backward halves of the round-trip in a linear cavity, the laser can modify the soliton in space to incorporate the excess energy. Increasing the pump power leads therefore to a different soliton solution, rather than to dispersive / diffractive loss. We predict the symmetry breaking by a complete numerical simulation of the spatio-temporal dynamics in the cavity, and confirm it experimentally in a KLM Ti:Sapphire laser with quantitative agreement to the simulation. The simulation opens a window to directly observe the nonlinear space-time dynamics that molds the soliton pulse, and possibly to optimize it.
Characterization of an Austenitic Stainless Steel Preform Deposited by Wire Arc Additive Manufacturing
Authors: de Souza Lídia Beatriz Oliveira , Santos Maria Ruth Neponucena , Garcia Regina Paula , Fernandes Diandro Bailoni , Vilarinho Louriel Oliveira
Published: June 2022

This work aimed to evaluate the chemical composition, microstructure and mechanical and electrochemical behavior of the 316L stainless steel manufactured by WAAM, comparing it with a sample of the same alloy in the annealed condition. The results indicate that the use of ER316LSi wire produces a component with chemical composition equivalent to the conventional 316L alloy. However, the microstructure of the deposited material is different with the presence of ferrite in an austenitic matrix. Two regions whose microstructure had different morphologies were also identified. In the region close to the fusion line between the deposited layers, the austenite grains are smaller, with a higher concentration of ferrite, causing an increase in microhardness in this region, when compared to the region more at the center of each layer. The WAAM process caused a decrease in the mechanical strength properties of the alloy, however it still meets the minimum requirements for most industrial applications required for the material studied. The electrochemical results in simulated seawater solution indicate that the corrosion resistance of the deposited sample is similar to that of the conventional specimen, with the potential for the passivating layer of the first to be superior to that of the second.
A novel bio-inspired strategy to prevent amyloidogenesis and synaptic damage in Alzheimer’s disease
Authors: Fede Giuseppe Di , Catania Marcella , Colombo Laura , Sorrentino Stefano , Cagnotto Alfredo , Lucchetti Jacopo , Barbagallo Maria , Vannetiello Ilaria , Vecchi Elena , Favagrossa Monica , Costanza Massimo , Giaccone Giorgio , Salmona Mario , Tagliavini Fabrizio
Published: June 2022

Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that affects millions of people worldwide. AD pathogenesis is intricate. It primarily involves two main molecular players – amyloid-β (Aβ) and tau - which actually have an intrinsic trend to generate molecular assemblies that are toxic to neurons. Incomplete knowledge of the molecular mechanisms inducing the onset and sustaining the progression of the disease, as well as the lack of valid models to fully recapitulate the pathogenesis of human disease, have until now hampered the development of a successful therapy for AD. The overall experience with clinical trials with a number of potential drugs – including the recent outcomes of studies with monoclonal antibodies against Aβ – seems to indicate that Aβ-targeting is not effective if it is not accompanied by an efficient challenge of Aβ neurotoxic properties. We took advantage from the discovery of a naturally-occurring variant of Aβ (AβA2V) that has anti-amyloidogenic properties, and designed a novel bio-inspired strategy for AD based on the intranasal delivery of a six-mer peptide (Aβ1-6A2V) retaining the anti-amyloidogenic abilities of the full-length AβA2V variant. This approach turned out to be effective in preventing the aggregation of wild type Aβ and averting the synaptic damage associated with amyloidogenesis in a mouse model of AD. The results of our preclinical studies inspired by a protective model already existing in nature, that is the human heterozygous AβA2V carriers which seem to be protected from AD, open the way to an unprecedented and promising approach for the prevention of the disease in humans.
Classification of breast lesions on DCE-MRI Data Using Fine-tuned MobileNet
Authors: Meng Ming-Zhu , Zhang Ming , He Guang-Yuan , Shen Dong , Wang Long
Published: June 2022

Background: Early and rapid diagnosis of breast cancer is very important. Traditional method for detecting and diagnosing breast cancer may lead to a false positive or negative result. Presently, most of the deep learning models used in breast cancer detection prevents their use on mobile phones or low-configuration devices. This study intends to evaluate the capability of MobileNetV1 and MobileNetV2 and their fine-tuned models to differentiate malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).Methods: The diagnostic efficiencies of MobileNetV1_False(V1_False), MobileNetV1_True (V1_True), MobileNetV2_False(V2_False) and MobileNetV2_True(V2_True) were tested and evaluated under the same program architecture and dataset (2124 images in benign group and 2226 images in malignant group). A relatively optimal model was selected (with the highest accuracy in the training set and test set), and two fine-tuning strategies (S0 and S1) were used to modify it. The accuracy (Ac) of different models in the training and test sets, as well as the precision (Pr), recall rate (Rc), f1 score(f1) and area under the receiver operating characteristic curve (AUC), were taken as the performance indicators. Results: The Ac of V1_True (0.9815) was higher than those of V1_False (0.9749), V2_False (0.9672) and V2_True (0.9699). Overfitting were observed in all models. Pr, Rc, f1 and AUC of V1_True were 0.79,0.73,0.74 and 0.74, respectively, they were higher than those of V1_False (0.77,0.73,0.73 and 0.73), V2_False (0.74,0.65,0.66 and 0.65) and V2_True (0.76,0.67,0.68 and 0.67).Conclusion: The MobileNetV1_True model can differentiate between benign and malignant breast lesions on breast DCE-MRI. Future work is necessary to improve the generalization capability of the proposed method.
New calculation of calorie content and determining nutritional level from day-to-day intake of food using Image Processing
Authors: .J Roopa Jayasingh , B Nagajayanthi , Neebha Mary , Sagayam Martin , Elngar Ahmed A.
Published: June 2022

One of the most urgent necessities of all individuals is food. Nowadays people mainly focus on diet because of diabetes, and obesity problems. When we have a healthy diet, it leads to a healthy life span. So, this paper gives a solution for this type of common problem. In this paper, we calculate nutritionally and calorie content utilizing image processing, display in the food segmentation and classification process, etc. This is very useful for dieticians despite the reality Ordinary people can benefit from this as well by manipulating their routine food patterns.
PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~200,000 patients
Authors: Giardiello Daniele , Hooning Maartje J , Hauptmann Michael , Keeman Renske , Heemskerk-Gerritsen Bernadette A. M , Becker Heiko , Blomqvist Carl , Bojesen Stig E , Bolla Manjeet K , Camp Nicola J , Czene Kamila , Devilee Peter , Eccles Diana M , Fasching Peter A , Figueroa Jonine D , Flyger Henrik , García-Closas Montserrat , Haiman Christopher A , Hamann Ute , Hopper John L , Jakubowska Anna , Leeuwen Floor E , Lindblom Annika , Lubiński Jan , Margolin Sara , Martinez Maria Elena , Nevanlinna Heli , Nevelsteen Ines , Pelders Saskia , Pharoah Paul D.P , Siesling Sabine , Southey Melissa C , Hout Annemieke H van der , Hest Liselotte P van , Chang-Claude Jenny , Hall Per , Easton Douglas F , Steyerberg Ewout W , Schmidt Marjanka K
Published: June 2022

Background:
Prediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.
Methods:
We included data from 207,510 invasive breast cancer patients participating in 23 studies. 8,225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent Area Under the Curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.
Results:
The discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI):0.56–0.74) versus 0.63 (95%PI:0.54–0.71). PredictCBC-2.0 was well-calibrated with an observed/expected (O/E) ratio at 10 years of 0.92 (95%PI:0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed potential clinical utility of PredictCBC-2.0 between thresholds of 4–12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.
Conclusions:
Additional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision making towards CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.
Evaluation of plasma levels of iron, copper, zinc, selenium and some transporter proteins in patients with IBD compared to IBS
Authors: shokri Behnosh , Kopi Tayebeh Azramezani , Alhooei Saman , parsian hadi , Kashifard Mehrdad , Mosapour Abbas , gholinia hemmat , Halalkhor Sohrab
Published: June 2022

Background and purpose: Inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) are both chronic gastrointestinal diseases with different pathophysiology but similar symptoms. In this study, we intend to investigate the pattern of changes in plasma levels of Fe, Zn, Se, Cu and ferritin, transferrin and ceruloplasmin, as well as oxidative and antioxidant capacity in IBD patients compared to IBS and the healthy group.Methods: This case-control study was performed on three groups of 30 patients with IBD and IBS and healthy individuals. ELISA method was used to measure the levels of ferritin and total oxidant, atomic absorption method for selenium and copper, and spectrophotometry for iron, zinc, transferrin and ceruloplasmin.Results: According to the results of this study, we showed that the levels of oxidative stress and transferrin, selenium, and zinc in men with IBD compared to IBS and control, showed a significant difference while in women no significant changes were observed in other factors except transferrin and Zn. There was also a significant increase in MDA, a decrease in TAC and a decrease in zinc and transferrin in the entire population with IBD. Also, lower levels of Fe and Se were observed in IBD patients compared to IBS and the healthy group. However, this difference was not statistically significant.Conclusion: The results of this study showed that the study of some trace elements may help us to find a more effective and easier method in diagnosing and differentiating people with IBD and IBS.
Prognostic Utility of SOX2, STAT3, and CD44high/CD24low Expression in Penile Cancer
Authors: Barros Felipe Dubourcq , Torres Leuridan Cavalcante , Araujo Carolline , Dubourcq Beatriz Cavalcanti , Dubourcq Luis Cavalcanti , Marinho Felipe da Silva , Guimaraes Gustavo
Published: June 2022

Purpose
Penile cancer has a high incidence in developing countries. The standard treatment is removal of the primary tumor and, when necessary, inguinal lymphadenectomy. Currently the most important prognostic factor is lymph node disease, however the available staging methods are inaccurate, and the high morbidity rate of lymphadenectomy has stimulated the study of predictive biomarkers of lymph node metastasis for selecting the patients who need lymphadenectomy. SOX2, STAT3 and CD44high/CD24low were chosen because they have provided good predictive results in other squamous cell carcinoma (SCC), although there are no studies for penile cancer. Thus, the expression of SOX2, STAT3, CD24+, and CD44 + in the penile cancer tumor microenvironment was investigated for correlation with tumor behavior in SCC.
Methods
This observational, prospective, translational study included 34 men and investigated the expression of SOX2, STAT3, CD24+, and CD44 + in tumor tissue by flow cytometry.
Results
The median age of the 38 evaluated patients with penile cancer was 61 (37–80) years. Most patients presented a tumor located on the glans penis (82.3%), with the usual histological type (79.4%) and 61.7% of patients presented stage pT2. No metastasis was found in 85.3% of patients. The expression of SOX2, STAT3 and CD44high/CD24low in the microenvironment of penile SCC treated with lymphadenectomy was significantly associated with aggressive tumor behavior (p < 0.05). STAT3 expression shows discrepant points when evaluated in context of angiolymphatic vascular invasion.
Conclusion
SOX2, STAT3 and CD44high/CD24low in penile SCC can be indicators of prognosis, allowing for selection of more aggressive treatment when necessary.
Divergent Directed Evolution of a TetR-type Repressor Towards Aromatic Molecules
Authors: Nasr Mohamed , Martin Vincent , Kwan David
Published: June 2022

Reprogramming cellular behaviour is one of the hallmarks of synthetic biology. To this end, prokaryotic allosteric transcription factors (aTF) have been repurposed as versatile tools for processing small molecule signals into cellular responses. Expanding the toolbox of aTFs that recognize new inducer molecules is of considerable interest in many applications. Here, we first establish a resorcinol responsive aTF-based biosensor in Escherichia coli using the TetR-family repressor RolR from Corynebacterium glutamicum. We then perform an iterative walk along the fitness landscape of RolR to identify new inducer specificities, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. Finally, we demonstrate the versatility of these engineered aTFs by transplanting them into the model eukaryote Saccharomyces cerevisiae. This work provides a framework for efficient aTF engineering to expand ligand specificity towards novel molecules on laboratory timescales, which, more broadly, is invaluable across a wide range of applications such as protein and metabolic engineering, as well as point-of-care diagnostics.
Analyzing and Predicting Success in Music
Authors: Kang Inwon , Manduluk Michael , Szymanski Boleslaw K.
Published: June 2022

The emergence of streaming services, e.g., Spotify, has changed the way people listen to music and the way musicians achieve fame and success. Classical music has been the backbone of Western media for a long time, but Spotify has introduced the public to a much wider variety of music, also opening a new venue for musicians to gain exposure. In this paper, we use open-source data from Spotify and Musicbrainz databases to construct collaboration-based and genre-based networks. Our goal is to find the correlation between various features of each artist, their current stage of a career, and the level of their success in the music field. We build regression models using XGBoost to first analyze correlation between features provided by Spotify. We then analyze the correlation between the digital music world of Spotify and the more traditional world of Billboard charts. We find that within certain bounds, machine learning techniques such as decision tree classifiers and Q-based models perform quite well on predicting success of musicians from the data on their early careers. We also find features that are highly predictive of the artist’s success. The most prominent among them are artists’ collaboration counts and the span of their career. Our findings also show that classical musicians are still very centrally placed in the general, genre agnostic network of musicians. Using these models and success metrics, we make suggestions that aspiring musicians can rely on considering which aspects of their career should be improved to increase their success measures in both Spotify and Billboard charts.
Optimization of simultaneous removal of phosphate and nitrate in synthetic wastewater by enhanced electrocoagulation process
Authors: Torabinejad-yazdi Shima , Delnavaz Mohammad
Published: June 2022

The presence of pollutants such as nitrate and phosphate compounds affects the quality of water resources. This study aims to investigate the simultaneous removal of nitrate and phosphate in a batch reactor using an enhanced electrocoagulation process. Initial concentration, current density, pH, and time were the chosen effective parameters considered in 5 levels. Their effect on the removal efficiency of phosphate and nitrate was evaluated. Experiments were designed by the response surface method (RSM) to save time and costs. For this purpose, first, 30 experiments were modeled by design expert software, and then optimal conditions were determined for variables. The optimal conditions were obtained by the following items: current density 36.5 A/m2, initial concentration 140 mg/L, pH 6, and time 65min. These conditions led to removing 77% of nitrate and 98% of phosphate. Finally, the effect of electrode coating with ZnO nanoparticles performed by the sol-gel method under optimal conditions was considered. Cyclic voltammetry (CV) was used to evaluate the impact of the coating layer. The modified electrode by nanoparticles provides a larger surface area which consumes less energy than a simple electrode.
Dynamical Model of Mild Atherosclerosis: Applied Mathematical Aspects
Authors: Mukherjee Debasmita , Muni Sishu Shankar , Fatoyinbo Hammed Olawale
Published: June 2022

Atherosclerosis is a chronic inflammatory disease occurs due to plaque accumulation in the inner artery wall. In atherosclerotic plaque formation monocytes and macrophages play a significant role in controlling the disease dynamics. In the present article, the entire biochemical process of atherosclerotic plaque formation is presented in terms of an autonomous system of nonlinear ordinary differential equations involving concentrations of oxidized low-density lipoprotein (LDL), monocytes, macrophages, and foam cells as the key dependent variables. To observe the capacity of monocytes and macrophages the model has been reduced to a two-dimensional temporal model using quasi steady state approximation theory. Linear stability analysis of the two-dimensional ordinary differential equations (ODEs) model has revealed the stability of the equilibrium points in the system. We have considered both one- and two- parameter bifurcation analysis with respect to parameters associated to the rate at which macrophages phagocytose oxidised LDL and the rate at which LDL enters into the intima. The bifurcation diagrams reveal the oscillating nature of the curves representing concentration of monocytes and macrophages with respect to significant model parameters. We are able to find the threshold values at which the plaque accumulation accelerates in an uncontrollable way. Further to observe the impact of diffusion, a spatiotemporal model has been developed. Numerical investigation of the partial differential equations (PDEs) model reveals the existence of travelling wave in the system which ensures the fact that the development of atherosclerotic plaque formation follows reaction-diffusion wave.
Role of nano selenium in alleviating the effects of salt stress in date palm trees (Phoenix dactylifera L.). A Fourier Transform Infrared (FTIR) Spectroscopy Study.
Authors: Mahdi Ali S. , Abd Abdulkareem M , Awad khairullah
Published: June 2022

The current study used the Fourier Transform Infrared (FTIR) spectroscopy to determine the corresponding changes caused by salt-induced biochemical changes at different NaCl concentrations (2.5 as control level, 5, 10, and 20 ds.m− 1). FTIR also determines the potential role of selenium nanoparticles (Se NPs) at two concentrations (80 and 160 ppm) in alleviating salinity stress on date palm (Phoenix dactylifera L.) trees. Notably, the study found the appearance of a new peak at 2850 cm− 1 in the lipid region (2800–3000 cm− 1) when date palm trees were exposed to salinity and tested at all levels. This peak was not seen in the control treatment or when salinity was combined with foliar spraying of Se NPs at the two tested concentrations (80 and 160 ppm). Furthermore, a clear and distinct peak at 1735 cm− 1 was only seen in plants exposed to salinity at 10 or 20 ds.m− 1. This peak was attributed to membrane lipid compounds that contain carbonyl-ester groups. In addition, the findings demonstrate that the treatments from various studies affected the secondary structure of proteins (1500–1800 cm− 1) and carbohydrates (1200–1500 cm− 1), and this was evident by the appearance and disappearance of some characteristic peaks in these regions.
Chronic ER stress promotes cGAS/mtDNA-induced autoimmunity via ATF6 in myotonic dystrophy type 2
Authors: Günther Claudia , Rösing Sarah , Ullrich Fabian , Meisterfeld Susann , Schmidt Franziska , Eberl Nadia , Wegner Julia , Schlee Martin , Wieland Anja , Hilbig Daniel , Reuner Ulrike , Mirtschink Peter , Drukewitz Stephan , Rapp Alexander , Zillinger Thomas , Beissert Stefan , Paeschke Katrin , Hartmann Gunther , Bartok Eva
Published: June 2022

Nucleic acid accumulation in repeat expansion disease poses multiple challenges to cellular integrity. Myotonic dystrophy type 2 (DM2) results from large CCTG repeats in the CNBP gene leading to myopathy and an increased prevalence of autoimmunity. Here, we observed that DM2 patients exhibited a type-I interferon signature in blood and cultured fibroblasts. RNA repeat accumulation was prevalent in the cytosol of DM2 patient fibroblasts, facilitating repeat-associated non-AUG translation. The ensuing chronic endoplasmic reticulum (ER) stress response led to an ATF6-controlled induction of type-I IFN dependent on the cGAS/STING pathway. Recapitulating chronic ER stress in the monocytic THP-1 cell line revealed its dependence on mitochondrial DNA (mtDNA). Correspondingly, mitochondrial stress and cytosolic leakage of mtDNA was observed in DM2 patient fibroblasts. Altogether, our study demonstrates a novel mechanism by which large repeat expansions cause chronic ER and mitochondrial stress and induce a type-I interferon response that predisposes to autoimmunity.
Keywords: ER stress, ATF6, Autoimmunity, type I IFN, repeat expansions
Nutritional profile and effects on fungal growth of yeast symbionts associated with North American spruce beetle (Dendroctonus rufipennis)
Authors: Davis Thomas Seth , Stewart Jane E. , Clark Caitlin , Buiten Charlene Van
Published: June 2022

We characterized yeasts isolated from the integument of North America spruce beetle (Dendroctous rufipennis Kirby) across four populations, using molecular methods to identify 59 representative isolates. Seven yeast species were detected; Wickerhamomyces canadensis (Wickerham) Kurtzman et al. (Sachharomycetales: Saccharomycetaceae) was the most common (74% of isolates) and found in all populations. Isolates of W. canadensis were subsequently tested for in vitro competitive interactions with symbiotic (Leptographium abietinum) and pathogenic (Beauvaria bassiana) fungi, and isolates were nutritionally profiled (N and P content). Three key findings emerged: (1) exposure to yeast headspace emissions had isolate-dependent effects on colony growth of symbiotic and pathogenic fungi; most isolates of W. canadensis slightly inhibited growth rates of symbiotic (L. abietinum) and entomopathogenic (B. bassiana) fungi. (2) A single volatile (2-phenylethanol) was produced by W. canadensis on growth media and was emitted at an average rate of 1.47 µg− 1·mg− 1 yeast·min− 1. Exposure to vapors of synthetic 2-phenylethanol reduced growth rates of both L. abietinum and B. bassiana by 36% on average. (3) W. canadensis cultures were high in both protein (0.8%) and phosphorous (7.2%) in comparison to spruce phloem, but isolates varied in nutritional content with the most nutrient-rich strains isolated from populations in outbreak or post-outbreak population phases. We conclude that W. canadensis is a primary yeast symbiont of D. rufipennis in the southern Rocky Mountains and may serve as a source of volatile emissions that can affect growth of associated microbes. Although W. canadensis has higher nutritional content than spruce tree phloem, the concentration of limiting nutrients (protein and phosphorous) is less than reported for the symbiotic fungus L. abietinum, and variation in nutritional content may be associated with beetle population density.
Prediction of Heart Disease using Data Mining Classifiers
Authors: K UMA
Published: June 2022

Fist size muscle acquires the name of an important part of the human body by pumping blood to all the parts of the body. Cardiovascular disease is the major cause of death in the nation. Though the data available in the health field is vast, still there is a need to develop decision supporting system to maintain, analyze, and knowledge evaluation. One such technique that can be used to address such a problem is data mining. Data mining techniques can help to classify the whether a patient having heart disease or not. This paper explores the different classification techniques for heart disease prediction. Logistic Regression, Support Vector Machine, Naïve Bayes, Nearest Neighbor, and Decision Tree methods are applied. To assess performance of the classifiers various measures are taken includes accuracy, recall, precision and F1-score. Methods: An abundant data generated by the healthcare industry leads to exploring the required information to make a decision-making system using data mining techniques. Classification methods such as SVM, Naive Bayes, KNN, Logistic Regression, and Decision Tree are applied to predict heart disease with a different dataset. Results: Five classification techniques are applied to predict the two different heart disease datasets. By inference, the different classifiers work differently on selected attributes of datasets in terms of accuracy, precision, recall, and F1-score measurements. Conclusion: Our research focuses on comparing the results of classification techniques applied to two different heart disease datasets. Various performance metrics were used to compare and contrast the two datasets of heart disease.
WITHDRAWN: Computational modeling of the combustion process in small-scale chemical reactors
Authors: Chen Junjie
Published: June 2022

A two-dimensional computational fluid dynamics model of a small-scale chemical reactor is solved to study the effects of different operating conditions on combustion characteristics and the steady-state, self-sustained flame stability of fuel-air mixtures. Computational fluid dynamics simulations are conducted to gain insights into small-scale chemical reactor performance such as temperatures, reaction rates, and flames. The factors affecting combustion characteristics are determined for small-scale chemical reactors. Particular focus is placed on determining essential factors that affect the performance of small-scale chemical reactors. The results indicate that flames can be stabilized in narrow channels but very careful design is necessary. Large transverse and axial gradients are observed even at these small scales under certain conditions. The cavity structure can induce recirculation of hot products, thereby improving flame stability. An optimum flow velocity for greatest flame stability exists. The flow rate has a strong effect on flame stability. Fast flows decrease the rate of heat transfer upstream and can cause blowout. Slow flows can also cause loss of flame stability by making conductive heat transfer so dominant that the reaction region is spread, and the external heat losses become too large compared to heat generation to sustain combustion. Small-scale combustion can offer advantages. Aside from the larger radial heat transfers, faster ignition can happen, and low temperatures could be envisioned. However, loss of flame stability due to blowout or extinction and high wall temperatures are main issues that require careful thermal management.
WITHDRAWN: Process characteristics of highly exothermic reactions in micro-structured systems
Authors: Chen Junjie
Published: June 2022

Micro-combustion is the sequence of exothermic chemical reaction between a fuel and an oxidant accompanied by the production of heat and conversion of chemical species at the micro level. The release of heat can result in the production of light in the form of a flame. This study relates to the process characteristics of highly exothermic reactions in micro-structured systems. Numerical simulations are conducted to gain insights into micro-structured system performance such as species concentrations, temperatures, and flames. The transport effects such as diffusion of heat and mass are investigated, including the exchange of mass, energy, and momentum within the micro-structured systems. The factors affecting the process characteristics of highly exothermic reactions are determined for the micro-structured systems. Transport phenomena are studied in reactor design. Particular focus is placed on determining essential factors that affect the performance of the micro-structured systems. The results indicate that highly exothermic reaction systems with large dimensions lead to a delay in flame ignition and may cause blowout. As the surface-to-volume ratio increases, heat loss to walls of the highly exothermic reaction system increases which leads to flame quenching. The cavity structure can induce recirculation of hot products, thereby improving flame stability. Fast flows can cause blowout and slow flows can cause extinction. There exists an optimum inlet velocity for greatest flame stability. A critical issue of fuel-rich cases is the loss of combustion efficiency. Combustion at the microscale can offer many advantages. Faster ignition and more efficient heat transfer can be achieved, but the design is challenging due to the loss of flame stability. Significant amount of heat is transferred through the walls which leads to flame quenching, but the increased rate of heat transfer through solid walls is advantageous in the case of steam reformers used for hydrogen production.
Bibliometric study of obtaining furfural and hydroxymethyl furfural from sugarcane bagasse
Authors: Murillo Sandra Peña , Nevárez Eddie Zambrano , Durango Eduardo Arango
Published: June 2022

This electronic document is a bibliometric observational study describing the production of furfural and hydroxymethylfurfural from sugarcane bagasse during 2015–2021 and was used as the primary measure, with the magazine, year of publication, first/last author, workplace, and autonomous community as the direct measures. Subsequently, articles were classified according to their content or research area. The impact factor was obtained from the Scopus and VoS Viewer bibliometric analysis. The results are presented in various figures. Through the search criteria used, 639 documents were retrieved in the 2015–2021 six-year term, excluding 307 because they did not correspond to affiliation with centers or institutions directly related to papers related to furfural and hydroxymethylfurfural. A total of 49 documents were selected, and an increase in interest in studying these compounds was verified in the research and the journals in which they were published. Most studies have been published in universities or research centers, and differences were observed in the volume of international and national scientific production, making it necessary to increase these investigations in Colombia.
Antibody levels poorly reflect on the frequency of memory B cells generated following SARS-CoV-2, seasonal influenza, or EBV infection
Authors: Wolf Carla , Köppert Sebastian , Becza Noémi , Küerten Stefanie , Kirchenbaum Greg A. , Lehmann Paul V.
Published: June 2022

Assessment of humoral immunity is commonly confined to measurements of serum antibody reactivity. These pre-formed immunoglobulin molecules can convey immunity by preventing a re-infection. In some instances, however, a re-infection does occur, and in such cases, it is the mobilization of an anamnestic B and T cell response that confers immune protection. Relying on fundamentally different mechanisms, therefore, passive immunity conveyed by pre-existing antibodies needs to be distinguished from active B cell memory. Here, we tested whether in healthy human individuals the antibody titers to SARS-CoV-2, seasonal influenza, or Epstein-Barr virus antigens correlated with the frequency of memory B cells reactive with the respective antigens. Weak correlations were found. The data suggest that assessment of humoral immunity by measurement of antibody levels does not reflect on memory B cell frequencies, and thus an individual’s potential to engage into an anamnestic antibody response against the same or an antigenically-related virus. Direct monitoring of the antigen-reactive memory B cell compartment is both required and feasible towards that goal. Word Count = 166
Multivariate Bayesian VARX model application to forecast BTC volatility, volatility indices and its node user’s countries
Authors: Ahmed wajid
Published: June 2022

The study assesses the forecasting accuracy based on deterministic and stochastic techniques of bitcoin and its node users emerging and developed countries with respective volatility indices. The study suggested Morgan Stanley Composite Index (MSCI) data base for the sample period from January 2009 till April 2020. The study extend the guideline of Catania, Grassi, and Ravazzolo (2019) after introducing the Bayesian VARX model with Multivariate VARX approach for forecasting analysis. The findings suggest a strong pairwise unconditional relationship among the crypto currency and stock market indices and Granger cause it too. Stochastic forecasting technique completely dominate and confirms that the stochastic trend existed through the sample time periods. Whereas historical decomposition analysis in response to bitcoin shocks support the Bayesian VARX approach for all cases.JEL Code: C11, C53, C58
Improving health behaviors in food insecure college students through a cooking and gardening nutrition intervention
Authors: Macchi Alison , Coccia Catherine
Published: June 2022

ObjectiveThis randomized control trial aimed to determine the effect of a Social Cognitive Theory (SCT) based, urban gardening, cooking and nutrition education intervention on health behavior mediators and fruit and vegetable intake in food insecure college students.ParticipantsParticipants included 89 food insecure college students (86% female, 59% Hispanic) randomized into a control (n=42) or intervention group (n=47).InterventionIntervention participants attended a 6-week hands-on, cooking and gardening nutrition education program. Surveys and anthropometric measurements for both groups were assessed at baseline and post-intervention.ResultsStudents who participated in the intervention experienced a significant improvement in self-efficacy (F=17.65, P<.001), reciprocal determination (F=4.16, P=.045), personal (F=5.65, P=.020) and family (F=5.36, P=.023) health views, and increased fruit and vegetable intake (F=19.19, P<.001), when compared to students in the control group.Conclusions and ImplicationsA SCT based cooking and gardening nutrition program tailored to food insecure college students may help improve health behavior mediators and fruit and vegetable intake.
Analysis of Bipolar Disorder Using fMRI
Authors: Jiji Wiselin , A Muthuraj , Vijayan Catherine Praiseye , A Rajesh
Published: June 2022

In this work, we have analysed bipolar disorder using fMRI based on brain regional activity measurements. In this research work, we have located the eights regions as per the literature review which are strongly affected brain regions because of BPD. For those regions, the below operations are carried out. Initially functional points are identified using independent component analysis and their connectivity has been established using correlation coefficients. Then located the activated points using Hierarchical Modular Analysis based on the strength of the interregional connectivity. Followed by constructing network between the activated points of each brain regions. Property of network parameters like centrality page rank and centrality degree, centrality closeness, assortativity and clustering coefficients are extracted. Finally, adaboost classifier consist of three weak classifiers, KNN, GDA and naïve bayes are experimented. It was found that this work had given 94.2% accuracy comparatively better than earlier research works.
ASSESSMENT OF THE PROBABILITY OF FRAUD IN THE PROCESS OF LENDING TO THE BANK'S CUSTOMERS
Authors: Yarovenko H. , Radko V.
Published: June 2022

The article is devoted to the current topic of assessing the likelihood of credit fraud in banks. This issue is related to the growth of economic processes digitalization and the transfer of payment transactions to the digital space. Its solution is carried out in eight scientific areas, confirmed by the construction and analysis of a map of scientometric bibliography of research on the problem of fraud in lending to bank customers. The article highlights clusters of scientific papers related to processes of protection of online transactions, machine, ensemble and incremental training to solve the problems of credit fraud, probabilistic approaches, techniques of detecting anomalies in operations related to money laundering in banks, the process of finding fraud in the financial sector, risk assessments, Data Mining. The data set from 122 variables and 307,511 records of the bank's customers were used to conduct a study to assess the likelihood of credit fraud in banks. The construction of the conceptual model made it possible to outline the stages of modelling, which was carried out using the modern Python programming language. The data was cleared of missing information and checked for compliance with the normal distribution law. As a result of the obtained data set, three models were built - logistic regression, decision tree and neural network. It turned out that the share of correct predictions in the training sample for logistic regression was 93.09%, for the decision tree and neural network - 100.00%, and in the test sample, respectively - 93.60%, 99.15%, 86, 67%. It indicates the adequacy of the data of both pieces and the high accuracy of forecasting. The constructed models were also tested for accuracy and quality. As a result, it turned out that all models are pretty accurate and high quality, but the decision tree is the most accurate, high quality and adequate model. Built-in models are universal tools for detecting fraudulent transactions, but they require constant monitoring and updating of information in connection with the emergence of new signs of criminal activity in the process of lending to customers.
Editorial
Authors: Nunes Muritiba Sérgio
Published: June 2022

Editorial da versão 8