bluuebunny/crossref_metadata_2025_split huggingface dataset:
Precancerous microcalcification detection of breast cancer mammogram images using linear time-invariant filtering Wiener method with Tophat transformation
Authors: Jamil Razia , Dong Min , Bano Shahzadi , Javed Arifa , Abdullah Muhammad 
Published: October 2023

Abstract
Breast microcalcifications, tiny calcium salt deposits, can develop anywhere in the breast tissue. Breast microcalcifications are a frequent mammographic finding. For a proper diagnosis, it is essential to spot microcalcifications in mammograms as soon as possible because they are a typical early indicator of breast cancer. Computer-based detection output can help the radiologist improve diagnosis accuracy because of their tiny size and tendency to be unseen to the radiologist doing the examination. Because of its small size, it is difficult to notice with the naked eye. The identification of microcalcification is essential for cancer prevention. In this paper, I proposed a linear time-invariant filtering Wiener method with Tophat transformation (LFWT) breast microcalcification detection method, applied mammographic image corrections using a Wiener filter to remove noise, and used Contrast Limited Adaptive Histogram Equitation (CLAHE) to improve mammographic image quality. The Wiener and CLAHE filter makes visible the cancer part. After image enhancement, Tophat morphological operators such as opening and closing are applied and the mask is detected. After that, the edges are extracted and selected according to the actual image. The diagnostic performance of the proposed model was evaluated with MIAS data and In comparison to other techniques for spotting microcalcifications in mammograms. These are Local Contrast Method (LCM), Relative Local Contrast Measure Method (RLCMM), and High-Boost-Based Multiscale Local Contrast Measure (HBBMLCM) techniques used to identify cancer microcalcification on mammography imgaes. The LFWT technique was found to be the most effective for the detection of microcalcification of breast cancer compared to the other three methods. The proposed LFWT technique detects all small and tiny spots. The images used in the LFWT method are taken from the MIAS dataset of the microcalcification for breast cancer detection. Before deploying the images, several steps were carried out to remove artifacts such as pectorals and clipping etc. The result is a breast cancer with nice smooth, safe margins and high quality. All MIAS breast cancer images were recorded and in each image, all microcalcifying spots were detected. In every image, where one or more microcalcifications were found in the mammography images, Microcalcifications were detected in the Mammogram images. Microcalcifications were found in either tumor or non-tumour images.
The processing of spatial frequencies through time in visual word recognition
Authors: Pilon Clémence Bertrand , Arguin Martin 
Published: October 2023

Abstract

This study examined the temporal profile of spatial frequency processing in a word reading task in 16 normal adult readers. They had to report the word presented in a 200 ms display using a four-alternative forced-choice task (4AFC). The stimuli were made of an additive combination of the signal (i.e. the target word) and of a visual white noise patch wherein the signal-to-noise ratio varied randomly across stimulus duration. Four spatial frequency conditions were defined for the signal component of the stimulus (bandpass Butterworth filters with center frequencies of 1.2, 2.4, 4.8 and 9.6 cycles per degree). In contrast to the coarse-to-fine theory of visual recognition, the results show that the highest spatial frequency range dominates early processing, with a shift toward lower spatial frequencies at later points during stimulus exposure. This pattern interacted in a complex way with the temporal frequency content of signal-to-noise oscillations. The outcome of individual data patterns classification by a machine learning algorithm according to the corresponding spatial frequency band further shows that the most salient spatial frequency signature is obtained when the time dimension within data patterns is recoded into its Fourier transform.
Fact-checking about the COVID-19 Pandemic on Social Networking Sites: The Moderating Role of Gender
Authors: Xia Jiabei , Wu Tailai , Chen Zhuo , Zhou Liqin 
Published: October 2023

Abstract
This study aims to explore the factors related to fact-checking about the pandemic and the moderating role of gender on the effects of explored factors. Based on social cognitive theory and social role theory, we established a research model to explore the factors of fact-checking on the pandemic and the role of gender. To validate the research model, data were collected by survey method and analyzed by partial least squares technique. The analysis results present that personal factor (self-efficacy and outcome expectancy) and environmental factors (perceived skepticism and perceived ambiguity) have significant impacts on fact-checking about the COVID-19 pandemic. Meanwhile, our results reveal that gender moderates the effects of outcome expectancy, perceived skepticism, and perceived ambiguity on users’ fact-checking. Our study uses social cognitive theory to explore the factors impacting fact-checking, while we combine with social role theory to understand the gender differences in the process of COVID-19 fact-checking in SNSs.
The use of infrared thermography as an indicator of methane production in hair sheep
Authors: Barbosa Maíra de Carvalho Porto , Paim Tiago do Prado , Adballa Adibe , Louvandini Helder , de Faria Danielle Assis , Pimentel Felipe , Pimentel Daniel , Peripolli Vanessa , Ferreira Josiel , McManus Concepta Margaret 
Published: October 2023

Abstract

Current methodologies to predict methane (CH4) production have disadvantages such as high acquisition and implementation costs, the impossibility of reconciling animal evaluation at pasture with more accurate results, and time needed for measurements. Infrared thermography may be an alternative technology for measuring the amount of CH4 produced and has the advantages of low cost, speed and efficiency in obtaining results. The objective of the study was to determine if the infrared thermography is adequate for predicting the emission of CH4 in hair sheep and the best time after feeding to carry out the measurement. Twelve Santa Inês lambs remained for two days in respirometric chambers, in a semi-closed system, to determine the CH4 production. The animals were divided into two treatments, according to the diet provided. During this period, seven thermographic photographs were taken, at times − 1h, -0.5h, 0h, 0.5h, 1h, 2h, 3h, 5h, and 7h, according to the feeding time, defined as 0h. CH4 production was measured over 24 hours. Thermographic images were used to measure the maximum, minimum, average and point temperatures at the left and right flanks. At each time, the temperature difference between the left and right flanks (left minus right) was calculated. Pearson correlation coefficients, multiple regression and principal component analysis, were carried out in SAS®. For sheep, the time three hours after feeding was considered ideal to predict the CH4 production with the use of infrared thermography images. At hour seven post-feeding, a significant increase in temperature differences were observed, suggesting a new peak of CH4 production.
CHOP-mediated Gasdermin E expression promotes pyroptosis, inflammation, and mitochondrial damage in renal ischemia-reperfusion injury
Authors: Zhang Tao , ma nannan , Lu Hao , Li Ning , Ni Weijian , Zhang Wenbo , Liu Qiang , Wu Wenzheng , Xia Shichao 
Published: October 2023

Abstract
In clinical practice, renal ischemia-reperfusion injury (IRI) is a common cause of acute kidney injury (AKI), often leading to acute renal failure or end-stage renal disease (ESRD). The current understanding of renal IRI mechanisms remains unclear, and effective therapeutic strategies and clear targets are lacking. Therefore, the need to find explicit and effective ways to reduce renal IRI remains a scientific challenge. The current study explored pyroptosis, a type of inflammation-regulated programmed cell death, and the role of Gasdermins E (GSDME)-mediated pyroptosis, mitochondrial damage, and inflammation in renal IRI. The analysis of human samples showed that the expression levels of GSDME in normal human renal tissues were higher than those of GSDMD. Moreover, our study demonstrated that GSDME played an important role in mediating pyroptosis, inflammation, and mitochondrial damage in renal IRI. Subsequently, GSDME-N accumulated in the mitochondrial membrane, leading to mitochondrial damage and activation of caspase3, which generated a feed-forward loop of self-amplification injury. However, GSDME knockout resulted in the amelioration of renal IRI. Moreover, at the low expression levels of GSDME, activated caspase3 mediated apoptosis, while at the high expression levels of GSDME, apoptosis was switched to pyroptosis. The current study found that the transcription factor CHOP was activated much earlier in renal IRI. On one hand, CHOP inhibited BCL-2, leading to mitochondrial damage and apoptosis, while on the other hand, it was positively regulated GSDME expression, enabling the conversion of apoptosis to pyroptosis.Therefore, this study explored the transcriptional mechanisms of GSDME during IRI development and the important role of CHOP/Caspase3/GSDME mechanistic axis in regulating apoptosis and pyroptosis in renal IRI. This axis might serve as a potential therapeutic target.
Streptococcus ruminantium-associated sheep mastitis outbreak detected in Italy is distinct from bovine isolates
Authors: Rosa Maria Nives , Vezina Ben , Marogna Gavino , Canu Antonella , Molotzu Monica , Tola Sebastiana 
Published: October 2023

Abstract
Streptococcus ruminantium is the causative agent of several bovine and ovine diseases, however reports are uncommon and application of whole genome sequencing to identify is rare. We report for the first time, a severe ovine mastitis outbreak caused by S. ruminantium in Italy, 2022. S. ruminantium was isolated from 12 adult lactating ewes with diffuse nodules in the mammary parenchyma and predominantly serous and clotted milk. All outbreak isolates, along with five additional historical Italian isolates (between 2011 and 2017), were genomically characterised and then analysed in the context of all publicly available S. ruminantium genomes. Antimicrobial susceptibility testing was performed to determine the MICs of 16 antibiotics. The results showed that all isolates were susceptible to all antimicrobials tested except kanamycin. Single Nucleotide Variant analysis confirmed this as a clonal outbreak across 10 sheep (≤ 15 SNVs), while the two others were colonised by more distantly related clones (≤ 53 pairwise SNVs), indicating the presence of multiple infecting lineages. The five historical S. ruminantium isolates were comprised of genetically-distant singletons (between 1259–5430 pairwise SNVs to 2022 outbreak isolates). Ovine isolates were found to be genetically distinct to bovine isolates, forming monophyletic groups. Bovine isolates were similarly made up of singleton clones in all but two isolates. Taken together, our genomic analysis using all globally available genomes is consistent with general opportunistic pathogenesis of S. ruminantium. We encourage future genomic surveillance efforts to facilitate outbreak detection, as well as improve our understanding of this poorly-understood, multi-host, zoonotic pathogen.
Effect of Certain Genetic Variants in Nicotinic AChR on Substance Addictions: A Structural Equation Modeling Approach
Authors: Albayrak Muammer , Turhan Kemal 
Published: October 2023

Abstract
Nicotine addiction, like many other addictions, is a complex form of behavior with both genetic and environmental aspects. Although there is a need for studies investigating the interaction between environmental and genetic factors in the field of substance addiction, the number of studies that consider both aspects is limited. This study explored how certain genetic variants in the nicotine acetylcholine receptor (AChR) genes interact with nicotine and other types of substance addiction and environment. From the genetic data of 66936 variants, scores generated by Polygenic Risk Score (PRS) analysis for certain genetic variants found in nicotinic AChR genes. The relationship between this score and Wisconsin Index of Smoking Dependence Motives (WISDM), socio-economic status (SES), nicotine and other substance addictions was examined using structural equation modeling (SEM). The data set contains information of 2969 subjects (Female (1786) Male (1168)). PRS had a significant effect on the FTND as expected. PRS also showed a significant effect on alcohol addiction and marijuana addiction with higher coefficients than FTND. SES significantly affects WISDM. WISDM showed a significant effect on alcohol, marijuana, cocaine and opiate addiction. Thus, WISDM was examined as mediator of the relationship between SES and substance addictions. Alcohol addiction significantly affects nicotine, cocaine and marijuana addiction. These findings suggest that certain genetic variants in nicotinic AChR known to affect one type of addiction may also have an effect on other types of substance addictions. In addition, nicotinic AChR may be a clinically significant target for prevention of substance addictions other than nicotine.
Automatic Clone Detection in Wireless Sensor Networks using ANN
Authors: V Sathya , David Preetha Evangeline , M Rajakumaran , S Hariprasath 
Published: October 2023

Abstract
In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor node. Decreasing the measurement of information transmission in sensor networks becomes an important issue. Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks. Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, ANN algorithm has been implemented to detect the clone nodes. The performance has been compared with the existing work. The comparison has been done with time and efficiency of the existing and the proposed work. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. This algorithm is used to detect the clone nodes easily and it is time effective.
Correlation Analysis Between Six Air Pollutants and Emergency Visit Rate in Shandong, China
Authors: Zhao Hanzhu , Chen Cai , Chen Liang , Pinkerton Kent E. , Zhang Yanbing , An Haoyuan , Zhang Zhenlei , Tian Chongxuan , Song Junlin , Li Wei 
Published: October 2023

Abstract
Background
Although air pollution has improved, instances of extreme weather pollution have been observed. Impacts of air pollution on the human body have become a significant focus of research.
Purpose
Previous research primarily focused on selecting specific pollutants as exposure factors or investigating the association with mortality. This study intends to examine the correlation between exposure to six national standard pollutants (sulfur dioxide, nitrogen dioxide, ozone, carbon monoxide, Particulate matter with particle size below 10 microns, Particulate matter with particle size below 2.5 microns) and visiting rate. The findings will provide valuable insights for optimizing hospital resource allocation based on air quality conditions.
Method
In this study, a generalized additive model based on Poisson distribution was employed and the full pollution model, single pollution lag model, and gender-stratified single pollution lag model were utilized to analyze the effects of six pollutants exposure on the visiting rate, delayed visiting rate and the male to female ratio. The study was conducted in Shandong Province, China.
Result
The exposure of PM10, PM2.5 and NO2 was positively correlated with the visiting rate of the population in Jinan, different departments have different sensitivity to pollutants. The exposure to all six pollutants exhibited a noticeable lag effect on the visiting rate in the hospital. However, the optimal lag period of pollutants varied across different departments and differed to some extent between different genders. In general, women were more sensitive to the effects of pollutant exposure, specifically to PM10, SO2, PM2.5, and NO2, than men.
" Evaluating the Impact of Adaptive External Dictionaries on Cyberbullying Detection using Machine Learning: A Review"
Authors: Jehad Hamzeh , Alhija Mwaffaq Abu , Tarawneh Hassan 
Published: October 2023

Abstract

Cyberbullying has escalated due to social media's rapid growth, endangering internet security. Correct these harmful habits. ML is used to research cyberbullying on Twitter. This model is enhanced with adaptive external dictionary (AED). Terms that are negative and positive are produced manually. The dynamic lists of positive and negative words produced by AED sentiment analysis. The dataset has positive and negative tweet columns. Social media's fast expansion has increased cyberbullying, threatening online safety. Recognizing and addressing these risky activities quickly requires a comprehensive system. Uses ML to detect Twitter cyberbullying (ML). This model detects better using Adaptive External Dictionary.47K Kaggle tweets made the AED. Manual refinement only produces negative and positive phrases in the first portion, relevant to our topic. AED sentiment analysis creates dynamic lists of Positive Words (PW) and Negative Words (NW) in this study. Tweets are columns. Combining internet data with positive and negative word counts identifies cyberbullying.
J-Shaped association between heart rate and in-hospital mortality in patients with congestive heart failure in intensive care unit: a retrospective observational study
Authors: Zhang Kai , Han Yu , Gu Fangming , Gu Zhaoxuan , liang Jiaying , Zhao Jiayu , zhang Tianqi , Gao Min , Hou Zhengyan , Cai Tianyi , Gao Yafang , Hu Rui , Liu Tianzhou , Cui Dan 
Published: October 2023

Abstract
Background
Despite extensive evidence linking heart rate (HR) to the risk of all-cause mortality, little attention has been given to exploring this association in patients with congestive heart failure (CHF). This study aimed to assess the relationship between HR and in-hospital mortality in CHF patients using data from a large clinical population-based sample.
Methods
This retrospective observational study utilized the Medical Information Mart for Intensive Care IV database to extract all relevant data. In-hospital mortality served as the primary outcome measure. Data analyses involved restricted cubic spline regression, piecewise logistic regression, and multiple logistic regression models. Additionally, subgroup analysis was performed to examine the robustness of the main findings.
Results
The study included 15,983 participants with CHF, aged 72.9 ± 13.4 years. After adjusting for all factors, with each unit increase in HR, there was a 1% risk increase of patient death (95% confidence interval: 1.01 ~ 1.01, P < 0.001). Compared with individuals with HR Q2 (72–81 beats per minute (bpm) ), the adjusted OR values for HR and in-hospital mortality in Q1 (≤ 72 bpm), Q3 (81–93 bpm), and Q4 (＞93 bpm) were 1.18 (95% CI: 0.99 ~ 1.41, p = 0.07), 1.24 (95% CI: 1.04 ~ 1.47, p = 0.014), and 1.64 (95% CI: 1.39 ~ 1.94, p < 0.001), respectively. A dose-response relationship revealed an J-shaped curve between HR and the risk of in-hospital mortality, with an inflection point at approximately 76 bpm. Stratified analyses confirmed the robustness of this correlation.
Conclusions
In patients with CHF, there exists a J-shaped relationship between heart rate and in-hospital mortality, with an inflection point at 76 bpm. Nonetheless, further investigation through large randomized controlled trials is warranted in the future.
An In-depth Evaluation of Metagenomic Classifiers for Soil Microbiomes
Authors: Edwin Niranjana Rose , Fitzpatrick Amy Heather , Brennan Fiona , Abram Florence , O'Sullivan Orla 
Published: October 2023

Abstract
Background:
Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.
Results:
In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 with Bracken (both with a custom database derived from GTDB-TK genomes and its default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. We found that taxonomic classifiers limited bacteria, archaea and fungi yielded fewer errors than broader databases. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this database classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.
Conclusion:
This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.