Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunogenic epitope scanning in bacteriolytic enzymes Pal and Cpl-1 and engineering Pal to escape antibody responses

Bacteriolytic enzymes are promising antibacterial agents, but they can cause a typical immune response in vivo. In this study, we used a targeted modification method for two antibacterial endolysins, Pal and Cpl-1. We identified the key immunogenic amino acids, and designed and tested new, bacteriolytic variants with altered immunogenicity. One new variant of Pal (257-259 MKS → TFG) demonstrated decreased immunogenicity while a similar mutant (257-259 MKS → TFK) demonstrated increased immunogenicity. A third variant (280-282 DKP → GGA) demonstrated significantly increased antibacterial activity and it was not cross-neutralized by antibodies induced by the wild-type enzyme. We propose this variant as a new engineered endolysin with increased antibacterial activity that is capable of escaping cross-neutralization by antibodies induced by wild-type Pal. We show that efficient antibacterial enzymes that avoid cross-neutralization by IgG can be developed by epitope scanning, in silico design, and substitutions of identified key amino acids with a high rate of success. Importantly, this universal approach can be applied to many proteins beyond endolysins and has the potential for design of numerous biological drugs.
Estimation of Some Genetic Parameters and Correlation in the Varieties of Bread Wheat under the Conditions of AL-Diwaniyah

A field experiment was carried out in the fields of one of the farmers in AL Diwaniyah Governorate for the winter season 2015/2016 using nine varieties of wheat bread (Tamoze 1, Tamoze 2, , Maksebak, Sabah, Eba 95, Latifia, Eba 99, Abu Ghraib and Sham 6). RCBD Used design with three replicate in order to estimation of some genetic parameters and correlation of bread wheat varieties under the experiment Diwaniyah to determine the most effect characters on yield that can be used as guide to select and improve the yield . an account Genetic and phenotypic variation , correlation coefficient, inheritance ratio in broad and narrow Expected genetic improvement ratio for character hight plant , number of Branches , spike length , number of grains spike ,weight of 1000 and yield . The results showed that there were significant differences between the cultivars, with the superiority of the Eba 99 by giving the highest rate of 1043.3 kg / dunum between the varieties. There were significant differences between the studied characters Abu99 the superiority in number of branches , length of spick , number of the seed spick and ,weight of 1000 grin their average ( 11.33, 12.0, 66.3 and 47.3 gram, respectively), which were positively reflected in grain yield.The genetic, environmental and morphological differences of the studied character were significant and the values of the genetic and phenotypic factors were correlated to most traits and inheritance in the broad sense higher (0.592 - 0.728)As an average percentage medal of most traits, but genetic and phenotypic correlation were significant in the desired direction of most yield and therefore yield can be considered as an electoral tool in breeding and plant improvement programs.
Distributed Dynamic Self-Triggered Control for Uncertain Complex Networks With Markov Switching Topologies and Random Time-Varying Delay

This paper studies the synchronization of complex networks with probabilistic interval delay and Markov switching topologies by using a novel dynamic self-triggered control (DSTC) scheme. Employing the probability distribution information of the input time-delay, the control protocol is transformed into a new rule with stochastic parameters. Also, a more general case of switching topologies, Markov switching topologies with partial information on transition rate, is discussed. By introducing a free-connection weighting matrix scheme and using the stability theory, a less conservative synchronization result is derived. It is shown by a numerical example that the DSTC method can reduce the sampling frequency apparently, and the tolerable delay upper bound can also be relaxed. In addition, continuous communication and the Zeno-behavior can be avoided.
Non-local Dense RepPoints for Instance Segmentation in Remote Sensing

Recent advances in deep convolutional neural networks have significantly improved instance segmentation. In large-scale remote sensing images, however, the high density, arbitrary shapes and orientation, large aspect ratios, and huge scale variation of the objects pose significant challenges to general instance segmentation algorithms. In this paper, we propose a new framework, called non-local dense RepPoints, to improve the performance of instance segmentation in remote sensing images. First, we propose a hierarchical non-local block that iteratively integrates global information, and our method enables the model to accurately represent the relationship between two locations. Second, we enhance Dense RepPoints by designing an efficient dynamic vector to more efficiently model the objects by a large number of adaptive points. We conduct experiments on the iSAID dataset and compare our method with several commonly-used state-of-the-art networks. The experimental results demonstrate that our proposed approach can achieve promising results.
Assessing the impact of ionising radiation in temperate coastal sand dune ecosystems: measurement and modelling

This thesis presents the results of a 6-year research project to investigate the radioecology of temperate coastal sand dunes. Samples (n = 617) of soil, water and biota were collected from the Drigg coastal sand dunes (West Cumbria, UK) between February 2005 and October 2007. Biota groups sampled included amphibians, birds, invertebrates, mammals, reptiles, plants (including lichens and mosses) and fungi. All samples were analysed for 40K, 137Cs and 241Am. A sub-set of samples (n = 26) was analysed for 90Sr, 99Tc, 238Pu and 239+240Pu. Additional soil analyses included soil moisture, bulk density, pH, organic matter content, carbonate content and cation concentrations (Ca2+, K+, Na+ & Mg2+). The application of three publicly-available environmental radiation protection models (ERICA, R&D128/Sp1a & RESRAD-BIOTA) to an assessment of ionising radiation impacts at the Drigg coastal sand dunes site was evaluated. Soil activity concentration data were used as input data and model results compared with measured activity concentrations in sand dune biota. Radionuclide concentration ratios (CRs) were identified as an important source of variation in model predictions. For sand dune small mammals, Am, Cs and Pu CRs were found to be 1 – 2 orders of magnitude lower than those for small mammals in other terrestrial ecosystems. For reptiles, the variability could be attributed to the paucity of data on transfer to this vertebrate group. Through literature review, mining of unpublished data sets and analysis of samples collected from the Drigg coastal sand dunes, CR databases were developed for reptiles (across a range of ecosystem types) and for sand dune biota. Analysis of sand dune soil data suggested that both sea-to-land transfer and the transport of sand grains in saltation influence the soil activity concentrations in coastal sand dunes. The low CRs for sand dune biota may be due to low bioavailability of particulate-bound radionuclides.
A Learned Query Rewrite System

Query rewriting is a challenging task that transforms a SQL query to improve its performance while maintaining its result set. However, it is difficult to rewrite SQL queries, which often involve complex logical structures, and there are numerous candidate rewrite strategies for such queries, making it an NP-hard problem. Existing databases or query optimization engines adopt heuristics to rewrite queries, but these approaches may not be able to judiciously and adaptively apply the rewrite rules and may cause significant performance regression in some cases (e.g., correlated subqueries may not be eliminated). To address these limitations, we introduce LearnedRewrite, a query rewrite system that combines traditional and learned algorithms (i.e., Monte Carlo tree search + hybrid estimator) to rewrite queries. We have implemented the system in Calcite, and experimental results demonstrate LearnedRewrite achieves superior performance on three real datasets.
Comparison of combination of dimensionality reduction and classification techniques for identifying tree species using integrated QuickBird imagery and Lidar data

Abstract. Combining data of different types and from different sources for the classification of tree species has gained popularity recently, but training models on such datasets often requires more computational demands and does not always result in higher accuracy due to feature redundancy and irrelevance. Thus preprocessing data using dimensionality reduction (DR) methods can be employed to improve the classification accuracy and reduce computations. The objective of this research is to investigate and compare tree species classification performance for different classification algorithms [naive Bayes (NB), logistic regression (LR), random forest (RF), and support vector machine (SVM)], combined with various DR methods (correlation-based feature selection filter, information gain, wrapper methods, and principal component analysis). Two primary datasets are used—QuickBird and LiDAR, as well as derived topography data. When DR is used prior to classification, the NB classifier had a significant improvement in accuracy. SVM and RF had the best classification accuracy without DR. The overall accuracies (OA) of SVM and RF are 88.2% and 87.2% (kappa 0.84 and 0.83), respectively, followed closely by LR (OA: 84.8%, kappa: 0.79) and more distantly by NB (OA: 79%, kappa: 0.72). It is recommended to use SVM and RF without DR or NB with DR for tree species classification.
Research on Human-Redundant Robot Interaction and Configuration Optimization

Redundant robots have obvious advantages such as good motion performance, avoiding singularity and joint limit. Human-robot interaction is an important content in robot field. Currently, there are relatively few research on cooperative compliance of redundant robot. Therefore, taking 4-DOF redundant robot as the research object, the forward and inverse kinematics model is obtained. In order to make full use of redundant characteristics, the inverse kinematics algorithm based on augmented Jacobian matrix with the optimization goal of avoiding joint limit is adopted, which greatly shortens the solution time and can meet real-time control requirement. Then, admittance control system is established for human-robot interaction, and force error is converted into motion error. Through the expansion of Jacobian matrix and admittance control system, human-robot interaction is achieved, while configuration optimization control is also realized. The research provides an important reference for compliance interaction of human-redundant robot cooperation.
Narrow and wide replication of Chalfin and McCrary (REStat, 2018)

We undertake a narrow and wide replication of "Are US Cities Underpoliced? Theory and Evidence" by Chalfin and McCrary. Using data from medium to large cities in the United States from 1960 to 2010, the authors estimate the effect of police on crime. To correct for the presence of measurement error, they propose to combine the information from two proxies of the police variable within the generalized method of moments framework. Throughout our replication exercise, we find that, in general, the original results are robust to computation in R compared to STATA and to the inclusion of more recent data (until 2019).
Wheat Stubble To Burn or Not to Burn: An Economic Analysis

Wheat producers‟ options for managing wheat stubble in central Kansas after harvest include using a no-till system that leaves the residue in place, tilling the ground to incorporate some or all residue into the soil, and/or burning the stubble. According to Shroyer, Hargrove and AlKhatib (2006), wheat producers burn stubble fields before fall planting in order to remove residue for easier planting, while at the same time providing control of some disease organisms and weed species. Burning of wheat stubble also has some disadvantages. These include long-run reduction in soil organic matter, loss of nutrients, hardening of the seedbed and reduced water infiltration capacity. These disadvantages are issues that the farm manager must weigh in the decision to burn or not.
Medically Unexplained Symptoms among Adults from Russia: An Assessment using the Patient Health Questionnaire-15

Background The Patient Health Questionnaire-15 (PHQ-15) is one of the most frequently used instruments to measure medically unexplained symptoms in the general population, as well as in groups of patients with mental and physical health problems. Objective This study aimed to examine the psychometric properties of the PHQ-15 in assessing a Russian community sample. Design A total of 1153 Russian adults age 18 or older participated in this cross-sectional study. They completed the Russian versions of the PHQ-15 and Symptom Check List-90-Revised, SCL-90-R (SCL-90-R). Exploratory and confirmatory factor analyses were used to examine the factor structure of the Russian PHQ-15, and multi-group confirmatory factor analyses were used to test measurement invariance across sex and age. Cronbach’s alpha coefficients and Pearsons Correlation Coefficients were used to evaluate the internal reliability and convergent validity of the Russian PHQ-15. Results Exploratory factor analysis revealed a three-factor solution highlighting pain-fatigue, gastrointestinal, and cardiopulmonary symptoms. Confirmatory factor analysis confirmed a bifactor structure for the Russian PHQ-15 merging general and specific somatic symptoms. A multi-group confirmatory factor analysis showed partial invariance across sex and age. The Russian PHQ-15 demonstrated acceptable Cronbach’s alpha coefficients ranging from 0.72 to 0.75 for specific factors and a good Cronbach’s alpha for the total score (a = 0.85), proving the questionnaire’s internal reliability. Finally, positive correlations between the PHQ-15 and SCL-90-R dimensions, and positive intercorrelations between PHQ-15 specific factors, suggested convergent validity. Conclusion The Russian PHQ-15 is a reliable and valid instrument for assessing medically unexplained symptoms in the general population. This instrument can be used in diagnostic and counseling settings.
FREE DANCE RANDOMIZED CLINICAL TRIAL FOR WOMEN UNDERGOING BREAST CANCER SURGERY

Objective: to analyze the effect of a free dance protocol (pre and post 12-week intervention) on depressive symptoms, body image and sexual function in women undergoing breast cancer surgery. Methods: a randomized clinical trial, composed of 24 women undergoing surgery after the diagnosis of breast cancer, who were randomized into two groups: Free dance Intervention Group and Control Group. The free dance group received the intervention twice a week, 60 minutes per session, for 12 weeks. The control group maintained their routine activities during the 12 weeks. All participants were evaluated before and after the 12 weeks of intervention. Outcomes evaluated were depressive symptoms (Beck Depression Questionnaire - BDI), body image (Body Image after Breast Cancer questionnaire) and sexual function (Female Sexual Function Index questionnaire). Results: Body image showed a significant intragroup difference in free dance in the vulnerability domain (p=0.031), significant intergroup difference in the limitation domain (p=0.045) and concern with the body (p=0.035). Sexual function showed a significant intragroup difference in free dance in the desired domain (p=0.031) and a significant intragroup difference in the control group in the lubrication domain (p=0.021), however in a negative way. No differences were found for depressive symptoms. Conclusions: The practice of free dancing showed a positive effect, even if small, on body image and a negative effect on sexual function in women undergoing breast cancer surgery.
Wastewater-based epidemiology and quantitative microbial risk assessment associated with wastewater contact of norovirus in Bangkok, Thailand

Norovirus is one of the leading causes of acute gastroenteritis worldwide. Wastewater surveillance offer significant benefits such as enhancing our understanding of the disease occurrence in the population, evaluating the effectiveness of pathogen reduction in the treatment process, and tracking the spread of pathogens in the environment which pose a health risk to humans. Hence, wastewater surveillance is crucial. In this study, the influent and effluent samples were collected bi-monthly from March 2020 to March 2021 from three distinct wastewater treatment plants in Bangkok. Wastewater samples were concentrated and subjected to quantitative polymerase chain reaction (qPCR) to assess norovirus concentrations. Additionally, microbial indicators, coliform bacteria and coliphage, were evaluated using cell culture methods. Concentration of�E. coli�and total coliform in the influent and effluent ranged from 4.04  5.03 log CFU/mL and 2.64  3.76 log CFU/ml, respectively. Concentration of coliphage in the influent and effluent ranged from 1.76 log PFU/ml and 0.42 log PFU/ml, respectively. Average concentrations of norovirus GI and GII were (1.90 and 1.35 log copies/ml) and (0.91 and 0.37 log copies/ml) in the influent and effluent, respectively. Removal efficiency for microbial indicators was 1.26  1.40 log CFU, PFU/ml while the removal of norovirus GI and GII were 0.75 and 0.54 log copies/ml. Seasonal variations were observed, with the highest concentrations of microorganisms during the rainy season and the highest norovirus concentration in winter. An epidemiological comparison between norovirus concentrations from wastewater treatment plants and reported norovirus cases showed similar patterns, suggesting that wastewater could serve as a useful tool for detecting infections within a population, particularly when clinical data are limited. Effluent samples were used to determine the Spearman's correlation between the microbial indicators and both norovirus strains. Our analysis found that these microbial indicators could not represent norovirus due to low correlation (rs�< 0.3). Additionally, the effluent was also used to assess the probability of illness with acute gastroenteritis from exposure to wastewater by quantitative microbial risk assessment from fishing activities and small craft boating. Exposure to effluent from the system, the probability of both activities had a median probability of illness below 0.036. Despite the ability of wastewater treatment plants to reduce the number of microorganisms, the treated wastewater still harbored these microorganisms presenting a potential health risk if discharged into public water sources or used in activities. ���Efficient wastewater treatment systems are recommended to be developed, including disinfection methods, to mitigate the risks from waterborne pathogens. � � �
Foam-mat-freeze dried clove essential oil nanoemulsion for inhibition of spoilage microorganisms in whiteleg shrimp Litopenaeus vannamei

This research developed foam-mat freeze-dried clove essential oil nanoemulsion (FFD-CN) for inhibiting food spoilage microorganisms. This research comprised three parts. Firstly, the fabrication of clove essential oil nanoemulsion (CN) was optimized. � The characteristics including particle size, polydispersity index (PDI), Zeta-potential, and viscosity of the fabricated nanoemulsions were observed. �� The optimal CN having a droplet size of 30.76 nm, PDI of 0.179, Zeta-potential of -50.01 mV, and viscosity of 7.81 cP was achieved at 1% v/v clove oil and 3% v/v Tween? 80 concentration and 10,000 psi operating pressure. The CN exhibited stability against particle aggregation and phase separation throughout storage. Moreover, the CN exhibited better antimicrobial activity against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa when compared to clove oil. Secondary, the foam-mat freeze-drying (FFD) technique was used to maintain stability and antimicrobial activity of CN. � The optimal condition was found at 3% (v/v) of foaming agent (MethocelTM), 60 ? C drying temperature, and 72 h drying time. The obtained FFD-CN had the droplet size of 26.14 nm, PDI of 0.193, water activity (aw) of 0.273, solubility of 88.71%, and viscosity of 12.85 cP. � The FFD-CN exhibited good stability and maintained antimicrobial activity, as analyzed using minimum inhibitory concentration (MIC), against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus better than CN. Lastly, the FFD-CN was investigated for its efficiency to maintain the quality of whiteleg shrimp during chilled storage (4 ? 2 ? C) in comparison to CN and distilled water (control). The CN and FFD-CN were found to be effective in inhibiting the growth of bacteria in whiteleg shrimp. FFD-CN was more effective than CN in delaying the changes in trimethylamine (TMA), total volatile basic nitrogen (TVB-N), total viable count (TVC), texture, color, and pH. The analysis of volatile compounds of all samples showed that FFD-CN could maintain a higher concentration of eugenol than CN and clove oil. The results also suggested that Control, CN, and FFD-CN treated shrimp had the shelf life of 6, 8, and 10 days, respectively. The FFD-CN can effectively be used as a natural preservative for maintaining the quality and shelf life of whiteleg shrimp.
ChatGPT’s Skills in Statistical Analysis Using the Example of Allergology: Do We Have Reason for Concern?

Background: Content generated by artificial intelligence is sometimes not truthful. To date, there have been a number of medical studies related to the validity of ChatGPT’s responses; however, there is a lack of studies addressing various aspects of statistical analysis. The aim of this study was to assess the validity of the answers provided by ChatGPT in relation to statistical analysis, as well as to identify recommendations to be implemented in the future in connection with the results obtained. Methods: The study was divided into four parts and was based on the exemplary medical field of allergology. The first part consisted of asking ChatGPT 30 different questions related to statistical analysis. The next five questions included a request for ChatGPT to perform the relevant statistical analyses, and another five requested ChatGPT to indicate which statistical test should be applied to articles accepted for publication in Allergy. The final part of the survey involved asking ChatGPT the same statistical question three times. Results: Out of the 40 general questions asked that related to broad statistical analysis, ChatGPT did not fully answer half of them. Assumptions necessary for the application of specific statistical tests were not included. ChatGPT also gave completely divergent answers to one question about which test should be used. Conclusion: The answers provided by ChatGPT to various statistical questions may give rise to the use of inappropriate statistical tests and, consequently, the subsequent misinterpretation of the research results obtained. Questions asked in this regard need to be framed more precisely.