bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trap and baiting materials for monitoring tef shoot flies, Atherigona spp. (Diptera: Muscidae) in tef (Eragrostis tef)
Authors: Damte Tebkew , Mitiku Geteneh , Tolera Ashebir 
Published: May 2023

Abstract
In an experiment five color types, four baiting materials and two trap types were consecutively evaluated from 2018/19 to 2022/23 cropping seasons inclusive, to determine their attractiveness to tef shoot fly (Atherigona spp.). Treatments were tested in Latin square design and for the latter two trials a control treatment was included for comparison. It was found that orange and green color water pan traps attracted more number of tef shoot flies than yellow, indigo and blue color traps; although, the number of tef shoot flies caught per trap per day was less than one in all color types. Moreover, more number of tef shoot flies was caught in the morning at 8:00 a.m. than in the later hours. Among the baiting materials that were tested, fish meal and chicken meat attracted greater number of tef shoot flies than lamb and beef. However, a combination of orange water pan trap and a baiting material attracted greater number of tef shoot flies than an orange water pan trap without a baiting material. The trap efficiency of fish meal baited cooking oil container trap relative to the fish meal baited orange water pan trap ranged from 3 to 25%. Depending upon season, tef crop damage by shoot flies varied from trace to 6% and there was positive association between trap catch and damage percentage. Thus traps, particularly cooking oil container trap, baited with fish meal can be used to monitor and assess tef shoot fly population in tef crop.
Cerebrospinal fluid dynamics analysis using Time Spatial Labeling Inversion pulse (Time-SLIP) MR imaging in mice
Authors: Tomita Yusuke , Yagi Mitsuru , Seki Fumiko , Komaki Yuji , Matsumoto Morio , Nakamura Masaya 
Published: May 2023

Abstract
Abnormalities in cerebrospinal fluid (CSF) dynamics cause diverse conditions, such as hydrocephalus and scoliosis, but the underlying mechanism is still unknown. Methods to study CSF dynamics in small animals have not been established due to the lack of an evaluation system. Therefore, the purpose of this research is to to establish the time-spatial labelling inversion pulse (Time-SLIP) MRI technique for the evaluation of CSF dynamics in mice. We performed the Time-SLIP technique on 10 wild-type mice and 20 Tiptoe walking Yoshimura (TWY) mice, a mouse model of ossification of the posterior longitudinal ligament (OPLL). We defined the Stir Distance as the distance of CSF stirring and calculated the mean ± standard deviation. The intraclass correlation coefficient of the intraobserver reliabilities was also calculated. Furthermore, in TWY mice, the correlation coefficient between Stir Distance and Canal Stenosis Ratio (CSR) was calculated. The Stir Distance was significantly lower in TWY mice (p<0.05). The intrarater reliability of the 3 examiners was excellent (>0.90) and there was a strongly negative correlation between Stir Distance and CSR in TWY mice (>-0.80). In this study, we established the Time-SLIP technique in experimental mice. This technique allows a better understanding of CSF dynamics in small laboratory animals.
Mutual Exclusion MAC Protocol for MAC Layer in MANET to Overcome Hidden And Exposed Terminal Problem
Authors: s Hemalatha , Pallathadka Harikumar , Chinhewadi Rajesh P , V Vasudhevan 
Published: May 2023

Abstract
Medium Access layer in Mobile Adhoc Network is responsible of transmission of packet through the wireless medium, while transmitting the packet all nodes has to intellect the medium before sending the packet. Every node can able to transmit the packet when the medium if free otherwise the nodes has to wait until the medium becomes free. Some cases the transmission range of the node can be idle but due to Hidden and Exposed node, the node could not be send the packet to other nodes. Node has to wait unnecessarily event the transmission medium is free. This research article proposed the Mutual Exclusion MAC protocol for avoiding hidden and exposed terminal problem in MANET. The proposed protocol is implemented with the support of Network simulator 3 and results are compared with the existing different protocol proposed for the MAC layer with the parameter and finally the results shown that the proposed protocol overcomes the Hidden and Exposed terminal problem permanently.
Correlation Coefficient-based Breakpoint detection @Piecewise Linear Regression
Authors: Senapati Apurbalal 
Published: May 2023

Abstract
Linear Regression is a traditional supervised Machine Learning technique commonly used for prediction. The approach is frequently used in many applications involving sequencing or forecasts for the time-series domain. The efficiency of the model depends on the characteristics of the data and some other external factors. One notable limitation of this model is its reliance on linear data. When presented with non-linear data, its performance can be severely impacted. To address this issue, researchers have suggested an alternative approach known as piecewise linear regression. Piecewise linear regression is a variant of linear regression that involves partitioning the data points based on their linear characteristics, followed by the application of linear regression within each partition. The primary difficulty associated with piecewise linear regression is identifying the optimal approach for partitioning the data, which can be thought of as locating the breakpoints. The main objective of this paper is to introduce a strategy for partitioning and identifying breakpoints based on the correlation properties of the data points. The effectiveness of this approach was evaluated using open-access COVID-19 data in the context of a prediction model. The findings indicate that the proposed partitioning and breakpoint identification method outperforms other existing systems, suggesting that it is a viable option that is relatively close to the optimal solution.
Effects of pre-rehabilitation program among in-hospital patients undergoing elective coronary artery bypass grafting: study protocol for a single-centric, randomized, controlled trial
Authors: Hu Yu , Guo Xiaochuan , Hu Linzhe , Wang Yuchan , Yao Lingling , Zhao Xin , Yan Ziyan , Liu Yibing , Zhang Sirun , Li Sha 
Published: May 2023

Abstract
Background: Coronary heart disease is considered an epidemic problem with the increasing number of people affected. Coronary artery bypass grafting (CABG) has been the gold standard treatment for revascularization in complex multivessel coronary artery disease. The pre-rehabilitation of CABG aims to improve the functional capacity of patients in the preoperative stage, optimize the physiological reserve to withstand the stress of surgery, and improve the functional status of patients after surgery. Since there are still relatively few studies on preoperative pre-rehabilitation with elective CABG, the main goal of this study is to evaluate whether pre-rehabilitation programs during phase I in-hospital rehabilitation can improve exercise tolerance among patients who undergo elective CABG. Furthermore, this study aims to investigate the effects of pre-rehabilitation program during phase I in-hospital rehabilitation on the inspiratory muscle strength, cardiac function, the conditions of anxiety and depression, health-related quality of life, the rate of pulmonary complication after CABG and the length of inpatient stay.
Methods: This study will be a prospective, single-centre, two parallel groups, randomized controlled clinical trial with blinded evaluators. There are 4 measurement time points: T0= admission, T1 = 1 day before surgery, T2 = 1 day before discharge, T3 = 12- week follow-up. A total of 84 patients who will undergo elective CABG will be included. Interventions include oral education and preoperative pre-rehabilitation delivered by physiotherapists. The primary outcome is exercise tolerance measured by 6minute walking test. Secondary outcomes include inspiratory muscle strength, cardiac function, the conditions of anxiety and depression, health-related quality of life, exercise load, the rate of pulmonary complication after CABG and the length of inpatient stay. Final data collection is expected to be completed by May 2024.
Discussion: This study may provide valuable insights into the effectiveness and safety of preoperative pre-rehabilitation for CABG to improve functional recovery after surgery and provide evidence for standardized preoperative pre-rehabilitation guidelines for CABG.
Trial registration: The trial has been registered with the Chinese Clinical Trial Registry on March 6, 2023 under the registration number: ChiCTR2300069045
The Role of Classifiers and Data Complexity in Learned Bloom Filters: Insights and Recommendations
Authors: Malchiodi Dario , Raimondi Davide , Fumagalli Giacomo , Giancarlo Raffaele , Frasca Marco 
Published: May 2023

Abstract
Bloom Filters, since their introduction over fifty years ago, have become a pillar to handle membership queries in small space, with relevant application in Big Data Mining and Stream Processing. In order to enhance the performances attained by Bloom Filters, with special focus on space occupancy, other approaches from the Artificial Intelligence landscape are used. In particular Learned Bloom Filters have recently been introduced, i.e., models induced from data via Machine Learning techniques and solving the approximate set membership problem. Unlike in the classical case, the “complexity” of the data used to build the learned filter might heavily impact on its performance. Therefore, here we propose the first in-depth analysis, to the best of our knowledge, for the performance assessment of a given Learned Bloom Filter, in conjunction with a given classifier, on a dataset of a given classification complexity. Indeed, we propose a novel methodology, supported by software, for designing, analyzing and implementing Learned Bloom Filters in function of specific constraints on their multi-criteria nature (that is, constraints involving space efficiency, false positive rate, and reject time). Our experiments show that the proposed methodology and the supporting software are valid and useful: we find out that only two classifiers have desirable properties in relation to problems with different data complexity, and, interestingly, none of them has been considered so far in the literature. We also experimentally show that the Sandwiched variant of Learned Bloom filters is the most robust one to data complexity and classifier performance variability, as well as that usually having smaller reject times. The software can be readily used to test new Learned Bloom Filter proposals, which can be compared with the best ones identified here.
miR-106b-5p protects against drug-induced liver injury by targeting vimentin to stimulate liver regeneration
Authors: Lu Xiaoyan , Yu Lingqi , Zheng Jie , Li Anyao , Li Junying , Lou He , Zhang Wentao , Guo Hui , Wang Yuzhen , Li Xuemei , Gao Yue , Fan Xiaohui , Borlak Jürgen 
Published: May 2023

Abstract
Background
Adaptive response to drug-induced liver injury (arDILI) reflects the critical role of restorative tissue repair, and understanding the endogenous mechanism may discover innovative strategies to manage DILI.
Methods
To gain mechanistic insight into arDILI, we investigated exosomal miRNAs in the adaptive response to toosendanin-induced liver injury (TILI) of mice. Exosomal miR-106b-5p was identified as a specific regulator of arDILI by comprehensive miRNA profiling. Its target gene was validated by dual luciferase reporter and siRNA assays. The biological function of miR-106b-5p was determined using agomir and antagomir treatment. Single-cell RNA-sequencing was conducted to identify the key hepatocytes under miR-106b-5p treatment. Knockout mice were used to enable mechanistic insights into arDILI. DILI patient samples were collected to confirm clinical significance.
Results
miR-106b-5p agomir treatment alleviated TILI and other DILI by inhibiting apoptosis and promoting hepatocyte proliferation. Conversely, antagomir treatments had opposite effects. Injured hepatocytes released miR-106b-5p-enriched exosomes taken up by surrounding hepatocytes. Vim (encodes vimentin) was identified as an important target of miR-106b-5p. Single-cell RNA-sequencing of toosendanin-injured mice revealed a cluster of Vim+ hepatocytes; however, declined following miR-106b-5p-agomir co-treatment. Strikingly, liver-specific and/or whole-body Vim knockout protected mice from both TILI and acetaminophen poisoning. Serum miR-106b-5p expression levels correlated with the severity of DILI. Liver biopsies of clinical cases exposed to different DILI causing drugs revealed marked vimentin expression among harmed hepatocytes, inflammatory cell infiltrates and Kupffer cells, therefore demonstrating translational significance.
Conclusion
We report novel mechanisms of arDILI whereby miR-106b-5p safeguards restorative tissue repair by targeting vimentin.
An Efficient LSTM-Based Deep Learning Model for Stock Prediction Analytics and Real-time Visualization
Authors: Saravanan Vijayalakshmi , Paudel Laxman , Acharya Pramod , Paramasivam Praveen , Pillai Anju S 
Published: May 2023

Abstract
Artificial intelligence (AI) is widely implemented in finance for stock price prediction using deep learning techniques. However, stock data’s high volatility and signal-to-noise ratio can make it difficult for neural networks (NN) to make accurate predictions. Nevertheless, even 70% accuracy can provide a good return value, leading to the increasing adoption of AI. In this study, we utilized long short-term memory (LSTM), a time series forecasting method, to predict the future stock market prices of Twitter (TWTR) and Facebook (FB) on NYSE and evaluated the performance of the model. Additionally, sentiment analysis, which involves understanding information from sentiment-rich sources such as news and social media, plays a crucial role in predicting changes in stock market prices. With the abundance of data in modern times, manual data examination is no longer practical, necessitating AI and data mining techniques. The impact of emotion classification on stock market prices was also investigated in this study. The deep learning method involved using an LSTM neural network to learn the features instead of relying on human feature engineering. The NLP model used in this study combines LSTM, an intriguing branch of machine learning that uses past data to predict future values, with real-time visualization for sentiment analysis.
Deep learning-based intraoperative differentiation of primary CNS lymphoma and glioma: a discovery, multicenter validation, and proof-of concept study
Authors: Zhang Xinke , Zhao Zihan , Wang Ruixuan , Chen Haohua , Zheng Xueyi , Liu Lili , Lan Lilong , Li Peng , Wu Shuyang , Cao Qinghua , Luo Rongzhen , Ye Yaping , Wang Yu , Xie Dan , Cai Mu-Yan 
Published: May 2023

Abstract

Intraoperative differentiation of primary central nervous system lymphoma (PCNSL) and glioma is of great importance to decision-making for neurosurgeons. However, distinguishing these two diseases based on frozen sections presents a challenge for pathologists. Here, we aim to develop and validate a deep learning model (LGNet) that could accurately differentiate PCNSL from glioma on haematoxylin and eosin (H&E)-stained frozen whole-slide images. In this study, the LGNet was developed and validated to distinguish PCNSL from glioma on independent cohorts, and its performance was compared to that of three pathologists with varying levels of expertise. Additionally, a human-machine fusion approach was designed to consider the diagnostic results from both pathologist and LGNet, to improve the integrative diagnostic performance. A proof of concept study was further evaluated with an online pathological decision support platform. The LGNet achieved high area under the receiver operating characteristic curves (AUROCs) of 0·965 and 0·972 for discriminating PCNSL and glioma on the two external validation cohorts. Moreover, the LGNet outperformed the three pathologists, and assisted them in making the distinction. The diagnostic performance human-machine fusion was further improved using the human-machine fusion. Notably, the performance of LGNet was verified with the proof of concept cohort, and it was shown that the time-consumption of LGNet was significantly less than that of pathologists (P < 0·001) in practical scenario. Also, the study demonstrated the association between histopathological characteristics and the LGNet’s prediction as derived from the logistic regression model. These findings suggest that the LGNet accurately and timely differentiates PCNSL from glioma based on frozen sections, and adds to the enhancement of pathologists’ diagnostic performance. Thus, our deep learning model LGNet has the application potential during intraoperative diagnosis.
A fluorescent/photoacoustic probe for imaging of hydroxyl radical and high-throughput screening of natural products to attenuate acute kidney injury
Authors: Gao Han , Sun Lei , Li Jiwei , Zhou Qilin , Xu Haijun , Ma Xiao-Nan , Li Renshi , Yu Bo-Yang , Tian Jiangwei 
Published: May 2023

Abstract
The hydroxyl radical (•OH) has been shown to play a crucial role in the occurrence and progression of acute kidney injury (AKI). Therefore, the development of a robust •OH detection tool holds great promise for the early diagnosis of AKI and high-throughput screening (HTS) of inhibitors to attenuate AKI. In this work, we report the design and synthesis of an activatable fluorescent/photoacoustic (PA) probe (CDIA) for sensitive and selective imaging of •OH in AKI. CDIA has near-infrared (NIR) fluorescence/PA channels and fast activation kinetics, enabling the detection of the onset of •OH in an AKI model. The positive detection time of 12 hours using this probe is superior to the 48-hour detection time for typical clinical assays, such as blood urea nitrogen (BUN) and serum creatinine (sCr) detection. Furthermore, a method has been established using CDIA for HTS of natural •OH inhibitors from herbal medicines. Puerarin has been screened out by activating the Sirt1/Nrf2/Keap1 signaling pathway to protect renal cells in AKI. Overall, this work provides a versatile and dual-mode tool for illuminating the •OH-related pathological process in AKI and for screening additional compounds to prevent and treat AKI.
Skillful Multiyear Prediction of Marine Habitat Shifts Jointly Constrained by Ocean Temperature and Dissolved Oxygen
Authors: Chen Zhuomin , Siedlecki Samantha , Long Matthew , Petrik Colleen , Stock Charles , Deutsch Curtis 
Published: May 2023

Abstract
The ability to anticipate marine habitat responses to climate variability has high socioeconomic value. Here we quantify interannual-to-decadal predictability of habitat shifts by combining trait-based aerobic habitat constraints with a suite of initialized retrospective Earth System Model forecasts, for diverse marine species in the North American Large Marine Ecosystems. We find that aerobic habitat viability, defined by joint constraints of temperature and oxygen on organismal energy balance, is potentially predictable in the upper-600 m of the ocean, showing a substantial improvement over a simple persistence forecast. The skillful multiyear predictability is dominated in most regions by the oxygen component, yielding habitat predictions with longer lead time than previously estimated based on temperature alone. Notable predictability differences exist among species differing in temperature sensitivity of hypoxia vulnerability, especially along the northeast coast. This tool will be critical in predicting marine habitat shifts in face of a changing climate.
Enzymic recognition of amino acids drove the evolution of primordial genetic codes
Authors: Bouckaert Remco , Carter Charles , Wills Peter R , Douglas Jordan 
Published: May 2023

Abstract
How genetic information gained its exquisite control over chemical processes needed to build living cells remains an enigma. Today, the aminoacyl-tRNA synthetases (AARS) execute the genetic codes in all living systems. But how did the AARS that emerged over three billion years ago as low-specificity, protozymic forms then spawn the full range of highly-specific enzymes that distinguish between 22 diverse amino acids? A new phylogenetic reconstruction of extant AARS genes, enhanced by analysing modular acquisitions, reveals six AARS with distinct bacterial, archaeal, eukaryotic, or organellar clades, resulting in a total of 33 families of AARS catalytic domains. Small structural modules that differentiate one AARS family from another played pivotal roles in discriminating between amino acid side chains, thereby expanding the genetic code and refining its precision. The resulting model shows a tendency for less elaborate enzymes, with simpler catalytic domains, to activate amino acids that were not synthesised until later in the evolution of the code. The most probable evolutionary route for an emergent amino acid type to establish a place in the code was by recruiting older, less specific AARS, rather than adapting contemporary lineages. This process, parafunctionalisation, differs from previously described mechanisms through which amino acids would enter the code.
Statistical Analysis Plan for a Cluster Randomised trial in Madhya Pradesh, India: support to rural India’s public education system and impact on numeracy and literacy scores (STRIPES2)
Authors: Keddie Suzanne , Fazzio Ila , Shivalli Siddharudha , Magill Nicholas , Elbourne Diana , Sharma Dropti , Shekhawat Sajjan Singh , Banerji Rukmini , Karnati Sridevi , Reddy Harshavardhan , Eble Alex , Boone Peter , Frost Chris 
Published: May 2023

Abstract
Background
India has made steady progress in improving rates of primary school enrolment but levels of learning achievement remain low. The Support To Rural India’s Public Education System (STRIPES) trial provided evidence that an after-school para-teacher intervention improved numeracy and literacy levels in Telangana, India. The STRIPES2 trial investigates whether such an intervention will have a similar effect on the literacy and numeracy of primary school age children in the Satna District of Madhya Pradesh, India.
Methods/design
The STRIPES2 trial forms one part of a cluster-randomised controlled trial with villages (clusters) randomised to receive either a health (CHAMPION2) or education (STRIPES2) intervention. Building on the design of the earlier CHAMPION/STRIPES trial, villages receiving the health intervention are controls for the education intervention and vice versa. The primary outcome is a combined literacy and numeracy score. Secondary outcomes include separate scores for literacy and numeracy; caregivers’ engagement with child’s learning; expenditure on education; enrolment in school; parent’s report of school attendance and the cost effectiveness of the intervention. Over 7000 primary school age children have been recruited and randomised in STRIPES2.
Discussion
This update to the published trial protocol gives a detailed plan for the statistical analysis of the STRIPES 2 trial.
Trial registration. Registry of India: CTRI/2019/05/019296. Registered on 23 May 2019