bluuebunny/crossref_metadata_2025_split huggingface dataset:
A pilot study of the metabolic profiles of apheresis platelets modified by donor age and sex and in vitro short‐term incubation with sex hormones
Authors: Hadley Jamie B. , Kelher Marguerite R. , D'Alessandro Angelo , Gamboni Fabia , Hansen Kirk , Coleman Julia , Jones Kenneth , Cohen Mitchell , Moore Ernest E. , Banerjee Anirban , Silliman Christopher C. 
Published: October 2022

AbstractBackgroundPlatelets are part of innate immunity and comprise the cellular portion of hemostasis. Platelets express sex hormone receptors on their plasma membrane and sex hormones can alter their function in vitro. Little is known about how age and sex may affect platelet biology; thus, we hypothesized that platelets from males and females have different metabolomic profiles, which may be altered by age and in vitro treatment with sex hormones.MethodsDay 1 apheresis platelets were drawn from five 18–53‐year‐old, premenopausal younger females (YF), five ≥54‐year‐old, postmenopausal, older females (OF), five 18–44‐year‐old younger males (YM), and four ≥45‐year‐old older males (OM). Platelets were normalized to a standard concentration and metabolomics analyses were completed. Unsupervised statistical analyses and hierarchical clustering with principal component analyses were completed.ResultsPlatelets from OM had (1) elevated mono‐, di‐ and tri‐carboxylates, (2) increased levels of free fatty acids, acyl‐carnitines, and free amino acids, and (3) increased purine breakdown and deamination products. In vitro incubation with sex hormones only affected platelets from OM donors with trends towards increased ATP and other high‐energy purines and decreases in L‐proline and other amino acids.ConclusionPlatelets from OM's versus YF, OF, and YM have a different metabolome implying increased energy metabolism, more free fatty acids, acylcarnitines, and amino acids, and increased breakdown of purines and deamination products. However, only platelets from OM were affected by sex hormones in vitro. Platelets from OM are metabolically distinct, which may impart functional differences when transfused.
Vehicle make and model recognition using mixed sample data augmentation techniques
Authors: Anwar Talha , Zakir Seemab 
Published: October 2022

<span lang="EN-US">Vehicle identification based on make and model is an integral part of an intelligent transport system that helps traffic monitoring and crime control. Much research has been performed in this regard, but most of them used manual feature extraction or ensemble convolution neural networks that result in increased execution time during inference. This paper compared three deep learning models and utilized different augmentation techniques to achieve state-of-the-art performance without ensembling or fusing the models. Experimentations are made without any augmentation, with standard augmentation, and by mixed sample data augmentation techniques. Gradient accumulation and stochastic weighted averaging with mixed precision are used to have a large batch size that helped to reduce training time. The dataset comprised 48 vehicles’ models running on the road of Pakistan. The highest accuracy and F1 score of 97% and 95% using the FMix augmentation technique with EfficientNetV2-S architecture gave the confidence that the proposed solution can be implemented in production. </span>
Multimedia information retrieval using artificial neural network
Authors: Mahmood Maha , AL-kubaisy Wijdan Jaber , Al-Khateeb Belal 
Published: October 2022

<span lang="EN-US">The importance of the multimedia information retrieval (MIR) is highlighted by the extensive amount of the information on the internet. Image, audio, video, and text are all examples of the characteristics of the raw multimedia data. It is greatly challenging to represent a concept of human perception and how the machine-level language can grasp it (semantic gap of MIR). However, this paper aims to improve the information retrieval model that retrieves data from multimedia. This can be implemented by leveraging the use of variety of algorithms that go through training and testing to extract the model. One of these algorithms extracts text information based on the query language's nature as the vector space model (VSM) and the latent semantic index (LSI) were used. The other technique uses curvelet decomposition and statistic parameters like mean, standard deviation, and signal energy to recover these properties. Additionally, a discrete wavelet transforms (DWT) and signal characteristics-based method is used to retrieve audio signals. Finally, the neural network learning is modeled and trained on a collection of different multimedia images. The learned features have been utilized for presenting a highly sufficient system of multimedia retrieval which operates for a large set of multi-modal datasets. </span>
A hybrid composite features based sentence level sentiment analyzer
Authors: Maree Mohammed , Eleyat Mujahed , Rabayah Shatha , Belkhatir Mohammed 
Published: October 2022

<div align="left"><span lang="EN-US">Current lexica and machine learning based sentiment analysis approaches still suffer from a two-fold limitation. First, manual lexicon construction and machine training is time consuming and error-prone. Second, the prediction’s accuracy entails sentences and their corresponding training text should fall under the same domain. In this article, we experimentally evaluate four sentiment classifiers, namely Support Vector Machines, Naive Bayes, Logistic Regression and Random Forest. We quantify the quality of each of these models using three real-world datasets that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic movie reviews. Specifically, we study the impact of a variety of natural language processing (NLP) pipelines on the quality of the predicted sentiment orientations. Additionally, we measure the impact of incorporating lexical semantic knowledge captured by WordNet on expanding original words in sentences. Findings demonstrate that the utilizing different NLP pipelines and semantic relationships impacts the quality of the sentiment analyzers. In particular, results indicate that coupling lemmatization and knowledge-based n-gram features proved to produce higher accuracy results. With this coupling, the accuracy of the support vector machine (SVM) classifier has improved to 90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three other classifiers. </span></div>
An adaptive metaheuristic approach for risk-budgeted portfolio optimization
Authors: Kumar Gandikota Naga Sunil , Hasan Mohd Hilmi , Jaafar Jafreezal 
Published: October 2022

<div align="left"><span lang="EN-US">An investment portfolio implies the assortment of assets invested in the commodity market and equity funds across global markets. The critical issue associated with any portfolio under its optimization entails the achievement of an optimal Sharpe ratio related to risk-return. This issue turns complex when risk budgeting and other investor preferential constraints are weighed in, rendering it difficult for direct solving via conventional approaches. As such, this present study proposes a novel technique that addresses the problem of constrained risk budgeted optimization with multiple crossovers (binomial, exponential &amp; arithmetic) together with the hall of fame via differential evolution (DE) strategies. The proposed automated solution facilitates portfolio managers to adopt the best possible portfolio that yields the most lucrative returns. In addition, the outcome coherence is verified by monitoring the best blend of evolution strategies. As a result, imminent outcomes were selected based on the best mixture of portfolio returns and Sharpe ratio. The monthly stock prices of Nifty50 were included in this study.</span></div>
Hybridizing genetic algorithm and single-based metaheuristics to solve unrelated parallel machine scheduling problem with scarce resources
Authors: Abed Munther H. , Mohmad Kahar Mohd Nizam 
Published: October 2022

<span lang="EN-US">This paper focuses on solving unrelated parallel machine scheduling with resource constraints (UPMR). There are </span><em><span lang="EN-US">j</span></em><span lang="EN-US"> jobs, and each job needs to be processed on one of the machines aim at minimizing the </span><em><span lang="EN-US">makespan</span></em><span lang="EN-US">. Besides the dependence of the machine, the processing time of any job depends on the usage of a rare renewable resource. A certain number of those resources R</span><em><span lang="EN-US">max</span></em><span lang="EN-US"> can be disseminated to jobs for the purpose of processing them at any time, and each job j needs units of resources (</span><em><span lang="EN-US">rjm</span></em><span lang="EN-US">) when processing in machine m. When more resources are assigned to a job, the job processing time minimizes. However, the number of resources available is limited, and this makes the problem difficult to solve for a good quality solution. Genetic algorithm shows promising results in solving UPMR. However, genetic algorithm suffers from premature convergence, which could hinder the resulting quality. Therefore, the work hybridizes genetic algorithm with a single-based metaheuristics (SBH) to handle the premature convergence in the genetic algorithm with the aim to escape from the local optima and improve the solution quality further. The single-based metaheuristics replaces the mutation in the genetic algorithm. The evaluation of the algorithm performance was conducted through extensive experiments. </span>
Multiple face mask wearer detection based on YOLOv3 approach
Authors: Ge Cheng Xiao , As’ari Muhammad Amir , Jasmin Sufri Nur Anis 
Published: October 2022

<div align="left"><a name="_Hlk108683337"></a><span lang="EN-US">The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. </span></div>
Autonomous driving system using proximal policy optimization in deep reinforcement learning
Authors: Yazid Imam Noerhenda , Rachmawati Ema 
Published: October 2022

<span>Autonomous driving is one solution that can minimize and even prevent accidents. In autonomous driving, the vehicle must know the surrounding environment and move under the provisions and situations. We build an autonomous driving system using proximal policy optimization (PPO) in deep reinforcement learning, with PPO acting as an instinct for the agent to choose an action. The instinct will be updated continuously until the agent reaches the destination from the initial point. We use five sensory inputs for the agent to accelerate, turn the steer, hit the brakes, avoid the walls, detect the initial point, and reach the destination point. We evaluated our proposed autonomous driving system in a simulation environment with several branching tracks, reflecting a real-world setting. For our driving simulation purpose in this research, we use the Unity3D engine to construct the dataset (in the form of a road track) and the agent model (in the form of a car). Our experimental results firmly indicate our agent can successfully control a vehicle to navigate to the destination point. </span>
Substantial adaptive artificial bee colony algorithm implementation for glioblastoma detection
Authors: Ibrahim Shafaf , Fariza Abu Samah Khyrina Airin , Hamzah Raseeda , Mohd Ali Nurul Amira , Aminuddin Raihah 
Published: October 2022

<p><span lang="EN-US">Glioblastoma multiforme (GBM) is a high-grade brain tumor that is extremely dangerous and aggressive. Due to its rapid development rate, high-grade cancers require early detection and treatment, and early detection may possibly increase the chances of survival. The current practice of GBM detection is performed by a radiologist; due to the enormous number of cases, it is nevertheless tedious, intrusive, and error-prone. Thus, this study attempted a substantial adaptive artificial bee colony (a-ABC) algorithm implementation in providing a non-invasive approach for GBM detection. The basic statistical intensity-based analysis of minimum (minGL), maximum (maxGL), and mean (meanGL) of grey level data was employed to investigate the GBM's feature properties. The a-ABC's performance for adaptive GBM detection identification was evaluated using T1-weighted (T1), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and T1-contrast (T1C) which are four different magnetic resonance imaging (MRI) imaging sequences. Hundred and twenty MRI of GBM images were assessed in total, with 30 images per imaging sequence. The overall mean of GBM detection accuracy percentage was 93.67%, implying that the proposed a-ABC algorithm is capable of detecting GBM brain tumors. Other feature extraction strategies, on the other hand, may be added in the future to enhancee the performance of feature extraction. </span></p>
Comparative evaluation for detection of brain tumor using machine learning algorithms
Authors: Kareem Shahab Wahhab , Abdulrahman Bikhtiyar Friyad , Hawezi Roojwan Sc. , Khoshaba Farah Sami , Askar Shavan , Muheden Karwan Muhammed , Abdulkhaleq Ibrahim Shamal 
Published: October 2022

<span lang="EN-US">Automated flaw identification has become more important in medical imaging. For patient preparation, unaided prediction of tumor (brain) detection in the magnetic resonance imaging process (MRI) is critical. Traditional ways of recognizing z are intended to make radiologists' jobs easier. The size and variety of molecular structures in brain tumors is one of the issues with MRI brain tumor diagnosis. Deep learning (DL) techniques (artificial neural network (ANN), naive Bayes (NB), multi-layer perceptron (MLP)) are used in this article to detect brain cancers in MRI data. The preprocessing techniques are used to eliminate textural features from the brain MRI images. These characteristics are then utilized to train a machine-learning system.</span>
Forecasting the number of dengue fever based on weather conditions using ensemble forecasting method
Authors: Nabilah Mursyidatun , Tyasnurita Raras , Mahananto Faizal , Anggraeni Wiwik , Vinarti Retno Aulia , Muklason Ahmad 
Published: October 2022

<p><span lang="EN-US">Dengue fever is still a crucial public health problem in Indonesia, with the highest fatality rate (CFR) is 1.01% in East Java, Malang Regency. One of the solutions to control the death rate and cases is to forecast the cases number. This study proposed ensemble forecasting that build from several penalized regressions. Penalized regressions are able to overcome linear regression analysis’ shortcomings by using penalty values, that will affect regression’s coefficient, resulting on regression model with a slight bias in order to reduce parameter estimations and prediction values' variances. Penalized regressions are evaluated and built as ensemble forecasting method to minimize the shortcomings of other existing model, so it could produce more accurate values comparing to single penalized regression model. The result showed that the ensemble model `consists of smoothly clipped absolute deviation (SCAD) and Elastic-net is sufficient to capture data patterns with root mean squared error (RMSE) 6.38. </span></p>
Genome-wide analysis of polyamine biosynthesis genes in wheat reveals gene expression specificity and involvement of STRE and MYB-elements in regulating polyamines under drought
Authors: Ebeed Heba Talat 
Published: October 2022

Abstract
Background
Polyamines (PAs) are considered promising biostimulants that have diverse key roles during growth and stress responses in plants. Nevertheless, the molecular basis of these roles by PAs has not been completely realized even now, and unfortunately, the transcriptional analyses of the biosynthesis pathway in various wheat tissues have not been investigated under normal or stress conditions. In this research, the findings of genome-wide analyses of genes implicated in the PAs biosynthesis in wheat (ADC, Arginine decarboxylase; ODC, ornithine decarboxylase; AIH, agmatine iminohydrolase; NPL1, Nitrlase like protein 1; SAMDC, S-adenosylmethionine decarboxylase; SPDS, spermidine synthase; SPMS, spermine synthase and ACL5, thermospermine synthase) are shown.

Results
In total, thirty PAs biosynthesis genes were identified. Analysis of gene structure, subcellular compartmentation and promoters were discussed. Furthermore, experimental gene expression analyses in roots, shoot axis, leaves, and spike tissues were investigated in adult wheat plants under control and drought conditions. Results revealed structural similarity within each gene family and revealed the identity of two new motifs that were conserved in SPDS, SPMS and ACL5. Analysis of the promoter elements revealed the incidence of conserved elements (STRE, CAAT-box, TATA-box, and MYB TF) in all promoters and highly conserved CREs in >80% of promoters (G-Box, ABRE, TGACG-motif, CGTCA-motif, as1, and MYC). The results of the quantification of PAs revealed higher levels of putrescine (Put) in the leaves and higher spermidine (Spd) in the other tissues. However, no spermine (Spm) was detected in the roots. Drought stress elevated Put level in the roots and the Spm in the leaves, shoots and roots, while decreased Put in spikes and elevated the total PAs levels in all tissues. Interestingly, PA biosynthesis genes showed tissue-specificity and some homoeologs of the same gene family showed differential gene expression during wheat development. Additionally, gene expression analysis showed that ODC is the Put biosynthesis path under drought stress in roots.

Conclusion
The information gained by this research offers important insights into the transcriptional regulation of PA biosynthesis in wheat that would result in more successful and consistent plant production.
Population genetic analyses inferred a limited genetic diversity across the pvama-1 DI domain among Plasmodium vivax isolates from Khyber Pakhtunkhwa regions of Pakistan
Authors: Ullah Ibrar , Afridi Sahib Gul , Israr Muhammad , Khan Hizbullah , Shams Sulaiman , Zaib Komal , Le Huong Giang , Kang Jung-Mi , Na Byoung-Kuk , Khan Asifullah 
Published: October 2022

Abstract
Background
Plasmodium vivax apical membrane antigen-1 (pvama-1) is an important vaccine candidate against Malaria. The genetic composition assessment of pvama-1 from wide-range geography is vital to plan the antigen based vaccine designing against Malaria.

Methods
The blood samples were collected from 84 P. vivax positive malaria patients from different districts of Khyber Pakhtunkhwa (KP) province of Pakistan. The highly polymorphic and immunogenic domain-I (DI) region of pvama-1 was PCR amplified and DNA sequenced. The QC based sequences raw data filtration was done using DNASTAR package. The downstream population genetic analyses were performed using MEGA4, DnaSP, Arlequin v3.5 and Network.5 resources.

Results
The analyses unveiled total 57 haplotypes of pvama-1 (DI) in KP samples with majorly prevalent H-14 and H-5 haplotypes. Pairwise comparative population genetics analyses identified limited to moderate genetic distinctions among the samples collected from different districts of KP, Pakistan. In context of worldwide available data, the KP samples depicted major genetic differentiation against the Korean samples with Fst = 0.40915 (P-value = 0.0001), while least distinction was observed against Indian and Iranian samples. The statistically significant negative values of Fu and Li’s D* and F* tests indicate the evidence of population expansion and directional positive selection signature. The slow LD decay across the nucleotide distance in KP isolates indicates low nucleotide diversity. In context of reference pvama-1 sequence, the KP samples were identified to have 09 novel non-synonymous single nucleotide polymorphisms (nsSNPs), including several trimorphic and tetramorphic substitutions. Few of these nsSNPs are mapped within the B-cell predicted epitopic motifs of the pvama-1, and possibly modulate the immune response mechanism.

Conclusion
Low genetic differentiation was observed across the pvama-1 DI among the P. vivax isolates acquired from widespread regions of KP province of Pakistan. The information may implicate in future vaccine designing strategies based on antigenic features of pvama-1.
Tertiary lymphoid structures associate with improved survival in early oral tongue cancer
Authors: Almangush Alhadi , Bello Ibrahim O. , Elseragy Amr , Hagström Jaana , Haglund Caj , Kowalski Luiz Paulo , Nieminen Pentti , Coletta Ricardo D. , Mäkitie Antti A. , Salo Tuula , Leivo Ilmo 
Published: October 2022

Abstract
Background
The clinical significance of tertiary lymphoid structures (TLSs) is not well-documented in early oral tongue squamous cell carcinoma (OTSCC).

Methods
A total of 310 cases of early (cT1-2N0) OTSCC were included in this multicenter study. Assessment of TLSs was conducted on hematoxylin and eosin-stained sections. TLSs were assessed both in the central part of the tumor and at the invasive front area.

Results
The presence of TLSs associated with improved survival of early OTSCC as presented by Kaplan–Meier survival analyses for disease-specific survival (P = 0.01) and overall survival (P = 0.006). In multivariable analyses, which included conventional prognostic factors, the absence of TLSs associated with worse disease-specific survival with a hazard ratio (HR) of 1.96 (95% CI 1.09–3.54; P = 0.025) and poor overall survival (HR 1.66, 95% CI 1.11–2.48; P = 0.014).

Conclusion
Histological evaluation of TLSs predicts survival in early OTSCC. TLSs showed superior prognostic power independent of routine WHO grading and TNM staging system.