Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic structure of the small yellow croaker (Larimichthys polyactis) across the Yellow Sea and the East China Sea by microsatellite DNA variation: implications for the division of management units

The small yellow croaker, Larimichthys polyactis, is a commercial fish of the order Perciformes that mainly inhabit estuaries and coastal waters.In recent years, the resources and catch of L. polyactis have undergone huge fluctuations. To detect genetic variations caused by the fluctuation of resources, genetic diversity of L. polyactis in the coastal waters of China were analyzed in this study using microsatellite DNA marker. The results revealed high genetic diversity of this species. The STRUCTURE, DAPC and FST results all indicated that there was no genetic structure consistent with the distribution pattern. Overall, our main findings are in agreement with previous studies, indicating that L. polyactis showed high genetic diversity and low genetic differentiation. Our results for high genetic connectivity among L. polyactis localities provide insights into the development of management strategies, that is, to manage this species as a single management unit.
Effect of forest fire prevention treatments on bacterial communities associated with productive Boletus edulis sites

Cistus ladanifer scrublands, traditionally considered as unproductive, have nonetheless been observed to produce large quantities of king bolete (Boletus edulis) fruitbodies. These pyrophytic scrublands are prone to wildfires, which severely affect fungi, hence the need for fire prevention in producing C. ladanifer scrublands. In addition, B. edulis productions have severely decreased in the last years. A deeper understanding of the B. edulis life cycle and of biotic and abiotic factors influencing sporocarp formation is needed to implement management practices that facilitate B. edulis production. For example, some bacteria likely are involved in sporocarp production, representing a key part in the triple symbiosis (plant–fungus–bacteria). In this study, we used soil DNA metabarcoding in C. ladanifer scrublands to (i) assess the effect of site history and fire prevention treatment on bacterial richness and community composition; (ii) test if there was any correlation between various taxonomic groups of bacteria and mycelial biomass and sporocarp production of B. edulis; and to (iii) identify indicator bacteria associated with the most productive B. edulis sites. Our results show that site history drives bacterial richness and community composition, while fire prevention treatments have a weaker, but still detectable effect, particularly in the senescent plots. Sporocarp production correlated positively with genera in Verrucomicrobia. Several genera, e.g. Azospirillum and Gemmatimonas, were identified as indicators of the most productive sites, suggesting a potential biological role in B. edulis fructification. This study provides a better understanding of the triple symbiosis (plant–fungus–bacteria) involved in C. ladanifer–B. edulis systems.
Stacked BiLSTM with ResNet50 for Medical Image Classification

The healthcare sector plays a significant role in the industry, where a client looks for the highest amount of care and services, no matter the cost. However, this sector has not satisfied society's presumption, even if this industry consumes a considerable percentage of the national budget. In the past, medical experts have been looking for smart medical solutions. This work focuses on accurate and early detection of illness from various medical images. Early detection not only aids in the development of better medications but can also save a life in the long run. Deep learning provides an excellent solution for early medical imaging in healthcare. This paper proposed a Stacked-based BiLSTM with Resnet50 Model using an AdaSwarm optimizer to classify and analyze the medical illnesses from the different medical image datasets. For this study, four medical datasets were used as benchmarks: Covid19, Pneumonia, Ma, and Lung Cancer. Accuracy, AUC, ROC, and F1 Score performance metrics are used to evaluate the prosed model from other models. The proposed model gives a mean ACCURACY, AUC, ROC, and F1 Score on these four datasets are 98%, 99%, 97%, and 98%, respectively.
Fitting Contralateral Neuroanatomical Asymmetry into the Amyloid Cascade Hypothesis

Alzheimer’s Disease (AD) is the most common cause of dementia. Due to the progressive nature of the neurodegeneration associated with the disease, it is of clinical interest to achieve an early diagnosis of AD. In this study, we analyzed the viability of asymmetry-related measures as potential biomarkers to facilitate the early diagnosis of AD. These measures were obtained from MAPER-segmented MP-RAGE MRI studies available at the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and by analyzing these studies at the level of individual segmented regions. The temporal evolution of these measures was obtained and then analyzed by generating spline regression models. Data imputation was performed where missing information prevented the temporal analysis of each measure from being realized, using additional information provided by ADNI for each patient. The temporal evolution of these measures was compared to the evolution of other commonly used markers for the diagnosis of AD, such as cognitive function, concentrations of Phosphorylated-Tau, Amyloid-β, and structural MRI volumetry. The results of the regression models showed that asymmetry measures, in particular regions such as the parahippocampal gyrus, differentiated themselves temporally before most of the other evaluated biomarkers. Further studies are suggested to corroborate these results.
Sargassum muticum and Osmundea pinnatifida Enzymatic Extracts: Chemical, Structural, and Cytotoxic Characterization

Seaweeds, which have been widely used for human consumption, are considered a potential source of biological compounds, where enzyme-assisted extraction can be an efficient method to obtain multifunctional extracts. Chemical characterization of Sargassum muticum and Osmundea pinnatifida extracts obtained by Alcalase and Viscozyme assisted extraction, respectively, showed an increment of macro/micro elements in comparison to the corresponding dry seaweeds, while the ratio of Na/K decreased in both extracts. Galactose, mannose, xylose, fucose, and glucuronic acid were the main monosaccharides (3.2–27.3 mg/glyophilized extract) present in variable molar ratios, whereas low free amino acids content and diversity (1.4–2.7 g/100 gprotein) characterized both extracts. FTIR-ATR and 1H NMR spectra confirmed the presence of important polysaccharide structures in the extracts, namely fucoidans from S. muticum or agarans as sulfated polysaccharides from O. pinnatifida. No cytotoxicity against normal mammalian cells was observed from 0 to 4 mglyophilized extract/mL for both extracts. The comprehensive characterization of the composition and safety of these two extracts fulfils an important step towards their authorized application for nutritional and/or nutraceutical purposes.
Report on the AAPS Immunogenicity Guidance Forum

In September 2018, the American Association of Pharmaceutical Scientists (AAPS) conducted an Annual Guidance Forum on the considerations related to immunogenicity testing for therapeutic protein products. In addition to a broad representation by the pharmaceutical industry, the event included strong representation by leading scientists from the US Food and Drug Administration (FDA). The agency and industry perspectives and updates to the guidance were presented. Specific topics that were discussed included the strategies of anti-drug antibody (ADA) assay cut-point assessments, the selection of ADA-positive controls (PCs), and the evaluation of PC performance. Assessment strategies and relevance of ADA assay attributes were also discussed, including assay drug tolerance and ADA assay sensitivity. The following is a summary of the discussion.
Privacy Enhanced Healthcare Data Management Using Associative Data Mining Approaches

Abstract Hospital medical records with health examination findings can be integrated to assist in uncovering the link between aberrant test results and illness. It is possible to establish a disease-preventive knowledge center using these integrated data by performing associated rule mining on the results. In order to integrate data, sensitive patient information must be shared. Patients’ privacy may be violated by the disclosure of sensitive information. Thus, privacy-preserving associated rule mining in physically partitioned healthcare data is addressed in this article. The suggested technique is further evaluated in terms of data protection, transmission, and computing costs.
Sleeve Gastrectomy for Youth With Cognitive Impairment or Developmental Disability

In this article, we examine the association of cognitive functioning and weight loss outcomes in adolescents with obesity undergoing LSG. BACKGROUND AND OBJECTIVES: Youth with cognitive impairment or developmental disability (CI/DD) face higher rates of obesity and secondary medical issues. Bariatric surgery may be a helpful tool for health improvement because it has been shown efficacious for adolescents. We aim to contribute to literature regarding bariatric surgery for adolescents with CI/DD and explore the association between cognitive functioning and weight loss outcomes. METHODS: Adolescents (N = 64) received a preoperative psychological evaluation, including cognitive assessment, and bariatric surgery at 1 weight loss program between 2010 and 2017. For these adolescents with measured cognitive performance, CI/DD was defined by an IQ <80 or previous diagnosis. In analyses, we compared adolescents with and without CI/DD. Structural equation modeling was used to assess the impact of cognitive functioning on weight loss 3 to 24 months postsurgery. RESULTS: There were no significant differences between adolescents with or without CI/DD in terms of preoperative BMI, age, and sex. Having CI/DD did not significantly impact weight loss or weight loss trajectory in the 2 years after surgery, although modeling revealed a trend toward individuals with CI/DD losing more weight over time. Similarly, intelligence scores did not predict weight loss after surgery. CONCLUSIONS: Bariatric surgery may be a helpful tool for adolescents with severe obesity and CI/DD. They could benefit from the surgery as much as those with typical development, and having CI/DD should not be used as a criterion to deny surgery. Continuing research with this population can be used to determine long-term outcomes in addition to defining best practices.
Generalized Cauchy matrix approach

The usual Cauchy matrix approach starts from a known plain wave factor vector $r$ and known dressed Cauchy matrix $M$. In this paper we start from a matrix equation set with undetermined $r$ and $M$. From the starting equation set we can build shift relations for some defined scalar functions and then derive lattice equations. The starting matrix equation set admits more choices for $r$ and $M$ and in the paper we give explicit formulae for all possible $r$ and $M$. As applications, we get more solutions than usual multi-soliton solutions for many lattice equations including the lattice potential KdV equation, the lattice potential modified KdV equation, the lattice Schwarzian KdV equation, NQC equation and some lattice equations in ABS list.
Subset selection for multi-Gabor and non-orthogonal wavelets expansions

Non-orthogonal wavelets and Gabor or multi-windows Gabor expansions involving well-localized synthesis/analysis functions are characterized by being redundant. This entails that the signal modeling is carried out through a rank deficient linear transformation and the expansion coefficients are not unique. In the finite dimensional case one solution for the coefficients (which provides the coefficients of minimum norm) is approached by the pseudo-inverse of the concomitant rank deficient transformation. In many applications this makes a great deal of sense. In other applications, however, the model-builder is not interested in a predictor that involves all the redundant factors. Instead, a predictor constructed out of the independent factors is sought. How to pick these factors is a problem of subset selection and we advance a new method for accomplishing such a goal.
A physically-based rainfall-runoff model and distributed dynamic hybrid control inverse technique

In this paper a physics-based rainfall-runoff model is developed utilizing an inverse technique based on hillslope hydrology, soil humidity and groundwater hydrodynamics, open-channel hydraulics theory and mathematical physical inverse problem theory. The model couples equations of two-dimensional transient subsurface flow, one-dimensional unsteady overland flow, one-dimensional groundwater flow, and one-dimensional channel flow. The inverse model allows for the spatio-temporal variation and parameters in hillslope, soil humidity, groundwater physical characteristics, and channel flow including vegetation interception, infiltration, evaporation, soil hydraulic conductivity, overland flow wave speed, subsurface flow wave speed, groundwater wave speed, stream wave speed and diffuse coefficient, etc. How to obtain the total unit-width discharge data from overground and underground flow to the channel becomes a key question in deciding whether or not to apply a physically-based mathematical model. A physically-based distributed hybrid control inverse model for rainfall-runoff is demonstrated by an application of a distributed and dynamic parameters inverse technique. The hybrid control technique (HCT) is proposed utilizing physics-based open-channel hydraulics and the mathematical physical inverse approach. The inverse solution provides a dynamic, distributed, nonlinear, and stable result. The model uses a finite difference method and a mathematical physical inverse method. The proposed HCT can represent the unit-width discharge process. The model and inverse technique have been applied to three subwatersheds (of 52 200, 108500 and 250000 ha) within the Feng Man Reservoir catchment in China. There was generally good agreement between observed and simulated responses in all parts of the catchment, through the use of inverse parameters, upstream flow and total unit-width discharge. The results indicate that the inverse technique works quite well in flood flow simulation using 33 years of flood season hydrological records at the mouth of the channel.
Seroprevalence and detection of H5N1 avian influenza virus in local chickens in Tabanan Regency, Bali, Indonesia

Aim: Avian Influenza (AI) is a zoonotic disease that causes death in poultry and humans. Monitoring the virus needs to be carried out continuously to prevent outbreaks of the disease. Seroprevalence and detection of H5N1 and H9N2 AI virus antigen were intended to monitor the presence of viruses in local chickens in Tabanan, a Regency of the Indonesian island Province of Bali. The research aims were to detect the presence of H5N1 AI virus, and to know the distribution of this virus in Tabanan Regency. Materials and Methods: Research located in six districts of Tabanan regency namely Baturiti, Penebel, Marga, Kediri, Tabanan, and Kerambitan. A total of 1,398 local chickens that never been vaccinated with AI were randomly sampled in this study. The samples collected were serum, cloacal and tracheal swabs. Serum samples were tested with hemagglutination inhibition (HI) assay. While samples of cloacal and tracheal swabs were isolated in 9-day-old germinated chicken eggs, followed by hemagglutination assay and RT-PCR test using H5N1 and H9N2 primers. Results: AI seroprevalence in local chickens in Tabanan Regency was 1% with the distribution in each district as follows; Penebel 1.6%, Kerambitan 1.2%, Marga 1%, while Tabanan, Kediri, and Baturiti 0.7% each. H5N1 AI virus was detected in 11 samples, i.e. five in Marga district and three in Penebel district, two in Kediri, and one in Tabanan, while the H9N2 AI virus was not detected. Conclusion: These results indicate that H5N1 AI virus may still circulate in local chickens in Tabanan Regency, Bali.
Detection of Cancer Cells Using G-Rich DNA Based Target Binding-Switching Calorimetric Biosensor

This paper reports the G-rich ssDNA for the colorimetric detection of cancer cells. The ssDNA-1 sequence has explored for the potential application of “Turn-On” colorimetric sensor for selective and sensitive detection of cancer cells. While complementary G-rich DNA strand form G-quadruplex with hemin molecule, which is more effective to catalyze the peroxidase mimicking activity towards TMB chromogenic substrate. The ssDNA-1 exhibits good selectivity for cancer cells. The colorimetric intensity of TMB was enhanced upon interaction of leukemic lymphoblasts cancer cells. The effect of pH has turned the selective sensing performances of the biosensor for detecting cancer cells with a lower detection limit of 0.54 nM, 0.18 nM, and 0.2 nM respectively.
Treatment Outcome and Prognostic Factors of Orthograde Retreatment: A Retrospective Study

Aims: This study aims to evaluate the outcome and prognostic factors related to post-treatment disease in orthograde retreatment after a minimum of 1-year follow-up period. Materials and Methods: A retrospective cohort study was performed. The data of all orthograde retreatment charts between January 2006 and March 2019 from two endodontic centers in Thailand were collected. Retreatment outcomes were evaluated and prognostic factors were analyzed by multivariable binary logistic regression. Results: The average recall period was 4.3 years, ranging from 1 to 12 years. The sample in the present study consisted of 245 teeth in 220 patients, of which 161 teeth (65.70%) were classified as “healed,” 28 teeth (11.40%) were classified as “healing,” and 56 teeth (22.90%) as “diseased. ” The bivariate analysis showed that the statistically significant factors included the presence of pre-operative lesions, the pre-operative lesion size, access opening through the crown, chlorhexidine adjunctive irrigation, and solvent usage. Multivariable binary logistic regression identified pre-operative lesions and solvent usage as significant prognostic factors with adjusted odds ratios of 6.30 (confidence interval [CI], 2.72–14.63; P < 0.01) and 2.54 (CI, 1.24–5.23; P = 0.01), respectively. The healed rate was higher when the pre-operative lesions were absent and when the solvent was used. Conclusion: The healed rate of orthograde retreatment after 1 to 12 years of follow-up was 65.70% and 77.10% for a lenient success rate. Pre-operative lesion and solvent usage were significant prognostic factors. The healed rate was higher with the absence of pre-operative lesions and usage of solvent.
Logistic regression methods for classification of imbalanced data sets

Classification of imbalanced data sets is one of the important researches in Data Mining community, since the data sets in many real-world problems mostly are imbalanced class distribution. This thesis aims to develop the simple and effective imbalanced classification algorithms by previously improving the algorithms performance of general classifiers i.e. Kernel Logistic Regression Newton-Raphson (KLR-NR) and Regularized Logistic Regression NR (RLR-NR) which are Logistic Regression (LR)based methods. Both LR-based methods have strong statistical foundation and well known classifiers which have simple solution of unconstrained optimization problem in performing the good performance as well as Support Vector Machine (SVM) which is determined as state-of-the art classifier in Kernel methodology and Data Mining community. However, the imbalanced LR-based methods are not extensively developed such as imbalanced SVM-based methods. Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. Numerical results have showed that the use of Truncated Newton method for KLR-NR and RLR-NR which respectively resulted in Newton Truncated Regularized KLR (NTR-KLR) and NTR RLR (NTR-LR), is effective in handling the numerical problems on the huge matrix of linear system of Newton-Raphson update rule i.e. the training time and the singularity problem. These results can be seen as further explanation on the success of Truncated Newton method in TR-KLR and TR Iteratively Re-weighted Least Square (TR-IRLS) algorithm respectively, because of the equivalence of iterative method used by these algorithms. Moreover, only with the use of simple solution of unconstrained optimization problem, numerical results have demonstrated that proposed NTR-KLR and proposed NTR-LR respectively have comparable classification performance with RBFSVM (SVM with Radial Basis Function Kernel). The imbalanced problem of both proposed general classification algorithms which is the limitation of accuracy performance specifically in classifying on the minority class has motivated this research to improve their classification performance on imbalanced data sets. In general, numerical results have showed that the use of adapted Modified AdaBoost methods for NTR-KLR and NTR-LR which respectively resulted in AdaBoost NTR Weighted KLR (AB-WKLR) and AB NTR Weighted RLR (AB-WLR) is significantly successful in improving the accuracy and stability performance of general classifiers i.e. NTR-KLR and NTR-LR respectively. The improvements on both error by g-means and standard deviation of g-means with 5-Fold SCV could be achieved as high as more than 60. Furthermore, numerical results have demonstrated that proposed AB-WKLR and proposed AB-WLR respectively have comparable performances with AdaBoostSVM in classifying imbalanced data sets, only with the use of simple solution of unconstrained weighted optimization problem. Thus, both proposed imbalanced LR-based methods is simple and effective for classification of imbalanced data sets and have promising results.
Electrical Treeing of Epoxy Resin under Tensile and Compressive Stresses

In this paper, the morphological characteristics of electrical tree in epoxy resin under tensile and compressive stresses were investigated when the applied mechanical stress was parallel to the direction of the electric field. The applied tensile and compressive stress varied between 0 and ± 30 MPa with a direction parallel to the needle-to-needle electrodes. The results show that tensile stress promotes the growth of trees in perpendicular direction to the needle axis. The greater the tensile stress, the more divergent the electrical trees are, and the greater the accumulated damage is. The compressive stress inhibits the growth of trees in perpendicular direction to the needle axis. The greater the compressive stress, the less divergent the electrical trees are, and the greater the accumulated damage is. Moreover, the applied stress has little effect on the growth rate of the electrical tree in the direction parallel to the stress. Results of this paper are helpful to understand the bursting breakdown phenomenon of tri-post insulators in GIL.
Copper Extraction from Complex Waste Dumps by Biochemical Leaching Method

The article presents the research results for the effectiveness of copper extraction from ores of the Satpayev deposit with complex, mixed composition bacterial leaching. The experiments with pre-acidification of copper samples were performed using biological oxidation by A. Ferrooxidans bacterial culture, adapted strain. Biochemical open-ing moes of mineral raw materials using A. Ferrooxidans bacteria of a particular strain adapted to the copper waste composition and capable of oxidizing sulfide and iron-bearing minerals were worked out. The optimal growth conditions for microorganisms in copper mineral pulp medium were established (temperature, pH, S: L.) The highest leaching efficiency, up to 88.65%, was observed in the experiments with preliminary bio-oxidation of samples by A. Ferrooxidans bacterial culture.