bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pan-Cancer transcriptomics reveals diverse R-loop events based on multiple machine learning algorithms
Authors: Jiang Bincan , Zhang Yuhan , Chen Ziyang 
Published: December 2023

Abstract
Background:
R-loops are three-stranded RNA-DNA hybrids which play an important role in various cellular and chromosomal function including transcriptional regulation and genome instability. Due to the absence of approach characterizing R-Loops events at a larger scale, we developed a computational metrics as R-Loops score to decipher the landscape of R-Loops events at a pan-cancer level and within LUAD patients.
Methods:
We developed a computational metric, R-Loops Score (RS), to provide a quantified approach of evaluating the R-Loop events. Then, Unsupervised clustering of diverse R-Loops pattern with LUAD patients revealed intratumoral heterogeneity, on the basis of which did we construct a prognostic model and corresponding R-Loops Related Score (RRS) via the multi-machine learning framework for the prediction of clinical outcome.
Results:
Comparing with patients with low RS, the high RS group had significantly lower survival, higher detectable chromosomal instability (CIN), alongside various oncogenic pathway activities. LUAD patients with lower RS exhibited distinct immune infiltration pattern, better clinical outcomes, and different mutation landscapes.
Conclusion:
The RS could function as a quantified method to evaluate R-Loops events across individual cancer types. The RRS provided the LUAD patients with a R-Loops-based prognostic model indicating how CIN involves cancerous peculiarities and immune patterns.
Time series analysis of malaria cases to assess the impact of various interventions over the last three decades and forecasting India’s malaria towards 2030 elimination goals
Authors: Singh Mrigendra P. , Rajvanshi Harsh , Bharti Praveen K. , Anvikar Anup R. , Lal Altaf A. 
Published: December 2023

Abstract
Background
Despite the progress made in this decade towards malaria elimination, it remains a significant public health concern in India and many other countries in South Asia and Asia Pacific region. Understanding the historical trends of malaria incidence in relation to various commodity and policy interventions and identifying the factors associated with its occurrence can inform future intervention strategies for malaria elimination goals.
Methods
This study analysed historical malaria cases in India from 1990 to 2022 to assess the annual trends and the impact of key anti-malarial interventions on malaria incidence. Factors associated with malaria incidence were identified using univariate and multivariate linear regression analyses. Generalized linear, smoothing, autoregressive integrated moving averages (ARIMA) and Holt’s models were used to forecast malaria cases from 2023 to 2030.
Results
The reported annual malaria cases in India during 1990–2000 were 2.38 million, which dropped to 0.73 million cases annually during 2011–2022. The overall reduction from 1990 (2,018,783) to 2022 (176,522) was 91%. The key interventions of the Enhanced Malaria Control Project (EMCP), Intensified Malaria Control Project (IMCP), use of bivalent Rapid Diagnostic Tests (RDT-Pf/Pv), Artemisinin-based Combination Therapies (ACT), and involvement of the Accredited Social Health Activists (ASHAs) as front line workers were found to result in the decline of malaria significantly. The ARIMA and Holt’s models projected a continued decline in cases with the potential for reaching zero indigenous cases by 2027–2028. Important factors influencing malaria incidence included tribal population density, literacy rate, health infrastructure, and forested and hard-to-reach areas.
Conclusions
Studies aimed at assessing the impact of major commodity and policy interventions on the incidence of disease and studies of disease forecasting will inform programs and policymakers of steps needed during the last mile phase to achieve malaria elimination. It is proposed that these time series and disease forecasting studies should be performed periodically using granular (monthly) and meteorological data to validate predictions of prior studies and suggest any changes needed for elimination efforts at national and sub-national levels.
MASC: A Dataset for the Development and Classification of Mobile Applications Screens
Authors: ahmed ali , Zaki Alaa , elgeldawi enas , Abdallah Mohamed , girgis moheb 
Published: December 2023

Abstract

Mobile applications have become an integral part of our daily lives, offering a wide range of functionalities and services. Understanding the diversity of mobile application screens is crucial for optimizing user experience and delivering personalized content. This paper presents a novel dataset, calledMASC(Mobile App Screens Classification) consisting of 7065 images, representing various types of mobile apps screens. MASC dataset is collected from the well-known Rico dataset. These screens were carefully manually classified into ten unique classes to capture the diverse nature of app interfaces. By employing advanced feature extraction techniques, we extracted key characteristics from each screenshot image of app screens related to visual elements, text, and keywords. Based on this dataset, this paper presents a proposed framework for applying machine learning algorithms to the classification of mobile apps screens. Using this framework, the paper also presents a comprehensive study of the classification of mobile apps screens using machine learning algorithms. Several classification algorithms including XGBoost, Gradient Boosting, Random Forest, SVM, Logistic Regression, and others were trained and evaluated on MASC. Results showed high accuracy rates above 93% for top models like Gradient Boosting, indicating that machine learning provides an effective approach to mobile app screen classification. This study contributes to the field of mobile application analysis and user interface understanding. In addition, the proposed mobile app screens classification framework is a promising development that can enhance the accuracy and efficiency of mobile app screens classification.
Green synthesis of Russian knapweed aqueous extract iron oxide nanoparticles and their applications for weed management in wheat
Authors: Souha Leyli Nabati , Alebrahim Mohammad Taghi , Feizpoor Solmaz , Afrouz Mehdi , Zangoueinejad Rouzbeh , Kalkhoran Elham Samadi 
Published: December 2023

Abstract

Plant-derived substances are becoming increasingly popular as eco-friendly alternatives to conventional chemical herbicides in crop production. A promising weed control strategy for achieving sustainable agriculture is integrated weed management using bioherbicides, which inhibit the germination and growth of weed seeds. The properties of such bioherbicides have only rarely been studied in vitro. An extract from Russian knapweed (Acroptilon repense L.) is used here to synthesize iron oxide nanoparticles. Biodegradable plant components and other biodegradable waste materials can be utilized in green synthesis to produce nanoparticles. The resulting nanostructures were characterized in terms of magnetic properties, chemical structure, morphology, thermal stability, and crystal structure using a vibrating sample magnetometer (VSM), Fourier-transform infrared spectroscopy (FT-IR), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and X-ray diffraction (XRD), respectively. Wild mustard, wild oats, and wheat seedlings were exposed to different concentrations of aerial parts extract of Russian knapweed formulated with iron oxide. Germination percentage, seedling length, and dry weight of both the pure and formulated Russian knapweed extract treated seedlings were decreased trend. Reduced germination of wild mustard (zero), wild oats (zero), and wheat (65%) was achieved after treatment with 20% aqueous extract of Russian knapweed formulated as iron oxide nanoparticles. The results suggest that Russian knapweed extracts formulated with iron oxide can be used as a bioherbicide.
Heteroatoms (-B-N-) doped graphene nanoflakes for the sensor mechanism of Carbon and Nitrogen dioxide adsorption: Approach from Theoretical Calculation
Authors: Ashishie Providence , Louis Hitler , Edet Henry , Offiong Offiong E. , Adeyinke Adepapo 
Published: December 2023

Abstract
Density functional theory (DFT) calculations were carried out to study the adsorption of selected greenhouse gases (CO2 and NO2). The graphene doped with Heteroatoms (Boron and Nitrogen) atoms induces a surface character of electron delocalization arising from the sp2 carbon of graphene to oxygen bond which aids high surface specificity. The orbital analysis, such as the density of states as well as frontier molecular orbital, has been studied indicating the kind of interaction (physisorption or chemisorption). It can be seen clearly that electrical conductivity is significantly enhanced by a decrease in energy gap (E.g.) which makes proposed surfaces suitable for the adsorption of CO2 and NO2. A negative value for adsorption energy indicates that the process of adsorption is thermodynamically favoured. The B3LYP and PBE0 functional were employed for a benchmark study on adsorption energies. The adsorption energies (Eads) for the B3LYP functional ranged from -6.42 eV to -20.03 whereas -7.20eV to -30.90eV. the obtained adsorption energies (Eads) forPBE0 functional are more negative than that of the B3LYP functional, which reveals that, PBE0 functional shows better performance in the estimation of such weak interactions. The highest Eads of -30.90 eV was observed for GPQD_B for CO2 adsorption. Thus, CO2 is better adsorbed than NO2 on the studied surface. Non-covalent interactions were observed for interaction between surfaces and probed molecules. The calculated results indicate that the graphene-doped surface is sensitive to CO2 and NO2 gas molecules.
Traditional craniotomy versus current minimally invasive surgery for spontaneous supratentorial intracerebral haemorrhage: a propensity-matched analysis
Authors: Xiao Zhen Kun , Duan Yong Hong , Mao XinYu , Liang RiChu , Zhou Min , Liu Ai-Hua , Yang Yong-Mei 
Published: December 2023

Abstract
Background
Minimally invasive surgery (MIS) and craniotomy are the current treatments for spontaneous supratentorial cerebral haemorrhage (SSTICH). In this study, we aimed to compare the efficacy and safety of MIS and craniotomy for the treatment of SSTICH.
Methods
Clinical and imaging data of 557 consecutive patients with SSTICH who underwent MIS or craniotomy between January 2017 and December 2022 were retrospectively analysed. The patients were divided into two subgroups: the MIS group (MI) and craniotomy group (CI). Propensity score matching was performed to minimise case selection bias. The primary outcome was a dichotomous prognostic (favourable or unfavourable) outcome based on the modified Rankin Scale (mRS) scores at 3 months; an mRS score of 0–2 was considered favourable.
Results
In both conventional statistical and binary logistic regression analyses, the MI group had a better outcome. The outcome of propensity score matching was unexpected (odds ratio = 0.582; 95% confidence interval = 0.281–1.204; P = 0.144), which indicated that after excluding the interference of each confounder, different surgical modalities were more effective, and there was no significant difference in their prognosis.
Conclusions
The decision to choose MIS or craniotomy should be made based on the individual patient, considering the hematoma size, the degree of midline shift, cerebral swelling, and the preoperative Glasgow Coma Scale score.
Climate change mitigation: Which strategies work?
Authors: de Uribarri Pilar Monsalvete Alvarez , Menon Ramanunni , Eicker Ursula 
Published: December 2023

Abstract

Many key studies in recent times have proposed integration of renewable energy and transition to 100% renewable energy as a solution to tackling climate change. However, there is little consensus and a wide range of assumptions made across the various studies. This study attempts to draw the most reliable set of parameters identified and numbers available from reputed sources and use them to provide incisive insights on the impact of Renewable Energy (RE) integration towards climate change mitigation with special attention paid to the speed of implementation, EROI of the sources utilized, associated carbon emissions and the resulting temperature increase. These are studied across pathways suggested within literature and contrast them for pessimistic, neutral and optimistic transformation scenarios. The temporal impacts of the transition over time, from today to 2100 have been elaborated upon. Results across the board show the difficulty in achieving 100% renewable energy integration and their consequent impact on the temperature increases, a large majority of them failing to stop at the oft-quoted 1.5 °C mark. These results underline the difficulty of the task at-hand, especially if decarbonisation and temperature increase goals are to be successfully met.
Perinatal anxiety disorders screening study: A study protocol
Authors: Fairbrother Nichole , Stagg Bryn , Scoten Olivia , Keeney Cora , Cargnelli Claudia 
Published: December 2023

Abstract

Background:The anxiety and their related disorders (AD) are the most common of all mental health conditions, and affect approximately 20% of pregnant and postpartum people. They are associated with significant distress and life interference for sufferers, as well as negative consequences for fetal and infant development. At present, little if any routine screening for prenatal AD is being conducted and data regarding the most effective tools to screen for these disorders is lacking. The majority of screening studies suffer from methodological difficulties which undermine the confidence needed to recommend measures for population distribution. The primary purpose of this research is to identify the most accurate self-report tool(s) to screen for perinatal AD.Methods:A large, prospective cohort of pregnant people (N=1,000) is being recruited proportionally across health service delivery regions in British Columbia (BC). The screening accuracy of a broad range of perinatal AD self-report measures are being assessed using gold standard methodology. Consenting individuals are administered online questionnaires followed by a semi-structured diagnostic interview between 16- and 36-weeks’ gestation, and again between 6 and 20 weeks postpartum. Questionnaires include all screening measures, measures of sleep and unpaid family work, and questions pertaining to demographic and reproductive history, COVID-19, gender role burden, and mental health treatment utilization. Interviews assess all current anxiety disorders, as well as obsessive-compulsive disorder, and posttraumatic stress disorder.Discussion:This research is in response to an urgent demand for accurate perinatal AD screening tools based on high quality evidence. AD among perinatal people often go unidentified and untreated, resulting in continued suffering and life impairment. Findings from this research will inform healthcare providers, policymakers, and scientists, about the most effective approach to screening for anxiety and related disorders in pregnancy in the postpartum period.
Medical image compression and restoration using a hybrid neural network combining Huffman and meta-heuristic techniques
Authors: Khalifeh Mohammad Hossein , Taghizadeh Mehdi , Ghanbarian Mohammad Mehdi , Jamali Jasem 
Published: December 2023

Abstract
In order to compress medical pictures for long-term storage, two methods are used in this work. The first step is to use a neural network–based categorization system to simplify images using a hierarchical modeling technique. The Huffman cipher is then used to compress the reduced images. In the second method, a deep neural network is trained to make predictions. This method can potentially reduce the amount of data needed to describe a picture by using a trained neural network to make intelligent guesses about the location of individual pixels. Huffman compression is used to encrypt the remaining data. By using an improved spatial filtering method to the picture data, we can decode it and then use meta-heuristic algorithms like gray wolf optimization (GWO) and wild horse optimization (WHO) to rebuild the image. Without sacrificing data compression efficacy, this paves the way for a more practical implementation of the proposed techniques in cases when outcomes are uncertain. Images can be simplified using the suggested approaches, leading to faster decoding. Afterwards, performance metrics were taken and evaluated following predetermined daily procedures. The suggested approaches outperformed state-of-the-art deep learning-based systems in compressing medical images while maintaining an exceptionally high quality level.
Blackspot Analysis and Treatment measures at Penamaluru and Gosala intersections on NH-65 – A Case Study Vijayawada
Authors: Narra Malathi , Ch Srinivas , Mummaneni Shobana , Ventrapragada Hari Chandana , Kammila Naga Priyanka 
Published: December 2023

Abstract
The complex flow pattern of vehicle traffic and the presence of pedestrians and mixed traffic, the transportation industry is plagued by accidents. Property and lives are lost in traffic accidents. Most road accidents are caused by four interconnected conditions, human behavior, vehicle efficiency, and infrastructure characteristics. The aim of the project is to collect the accident data from the respective police station, analyze the data obtained, identify the blackspot regions, conduct field studies and proposal of respective measures which reduce the accident severity. The analysis of traffic accidents is based on vehicle type, location (urban or rural), age, gender, driver behavior, alcohol consumption, overloading, and other factors. We employ statistical procedures like frequency analysis, clustering with a two-step algorithm, and the ASI to identify the blackspot areas from collected data in accident analysis. The individual field examination is performed. Inventory investigations, traffic studies like volume studies and speed studies, and geometric investigations are all part of the field investigations. Lastly, the analysis and field research lead to the proposal of Highway safety measures to reduce the severity of accidents in blackspot areas by providing physical and non-physical engineering measures there. These measures are intended to improve road traffic safety and smoothness in the near future.
Vi-DiSC: A novel dataset and framework for extracting information from Vietnamese signposts images
Authors: Nguyen Minh Tam , Tran Hoang Anh , Nguyen Truong Thinh , Tran Gia Phu Pham , Quach Co Thai , Do Trong-Hop 
Published: December 2023

Abstract
With the development of autonomous vehicle technologies, a vision-based vehicle guidance system should be able to understand traffic signs, especially directional signposts. Directional signposts are not as specific as the warning and compulsory signs since they include many different variations. More specifically, they have special arrangements, but still follow some principles. It is difficult for captioning models to tell precisely what information the sign road includes. From the above difficulty, we propose to build a framework that simulates the process of reading a human traffic sign, which we named Vi-DiSC. The framework includes a directional sign recognition model, an optical character recognition system in parallel with a directional arrow recognition model, and finally a rule-based model based on the information extracted from the above models. We also built two Deep Learning models for the Image Captioning task to compare the generated results with the rule-based system. Our team also collects, filters, labels, and augments the directional signposts images to create the dataset for the image captioning task. In addition, we also propose TRID, a suitable metric for evaluating the directional signposts image captioning task. The framework is trained and evaluated on our dataset using various metrics, including our proposed metric TRID, and achieved promising resutls. The dataset is available at \url{https://github.com/thinhnt19393/ViDirectionSignpostCaptioning}.
Recognition of trimeric anion-water clusters by peptide-based supramolecular capsules
Authors: Granja Juan , Lopez-Corbalán Victoria , Fuertes Aberto , Llamas-Saiz Antonio , Amorín Manuel 
Published: December 2023

Abstract

The biological and technological importance of anion-mediated processes has made the development of improved methods for the selective recognition of anions one of the most relevant research topics today. The hydration sphere of anions plays an important role in the functions performed by anions by forming a variety of cluster complexes. Here we describe a supramolecular capsule that recognizes new hydrated anion clusters. These clusters are composed of three ions forming hydrated C3 symmetry complexes that are entrapped within the supramolecular capsule of the same symmetry. The capsule is made of self-assembled Acp-based cyclic peptide equipped with a tris(triazolylethyl)amine cap. To recognise the hydrated anion clusters, the hexapeptide capsule must disassemble to entrap them between its two subunits.
Cross-continental shifts of ecological strategy in a global plant invader
Authors: Irimia Ramona E. , Zhao Weihan , Cao Peipei , Parepa Madalin , Liao Zhiyong , Wang Shengyu , Mounger Jeannie M. , Richardson Conner , Elkott Fatima , Zhuang Xin , Bi Jingwen , Zhao Yujie , Kuglar Elodie , Rafalski Julia , Schloter Eva , Wu Jihua , Ju Ruiting , Yang Ji , Li Bo , Bossdorf Oliver , Richards Christina L. 
Published: December 2023

Abstract

Plant invasions are a global problem that requires studying plants and their environmental associations across native and introduced ranges. We surveyed 150 populations of Japanese knotweed, a key invader of the temperate zone, along 2000 km transects in native China and the introduced ranges of Europe and North America. We found that larger plants and denser populations in the introduced ranges were associated with shifts in leaf economy and chemical defenses. Introduced knotweed populations had higher SLA but reduced leaf chlorophyll, lignin, C:N and leaf toughness along with altered leaf tannins, flavonoids and alkaloids. We found three distinct multivariate knotweed phenotypes primarily in the introduced ranges, and two phenotype clusters mainly in native populations. Decreased herbivore and pathogen impacts in invasive populations and changes in environmental associations indicate that enemy release and novel habitat conditions might have driven the emergence of novel ecological strategies in this global plant invader.