bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aquatic Animal Monitoring System
Authors: Sakirin Jakkawan , Rapeethasanaphong Thaniyaporn , Maleewong Parichat 
Published: August 2022

Abstract
Aquatic Animal Monitoring Systemis initiated as part of PTTEP's Ocean for Life strategy as we thrive in enhancing Ocean Health & Biodiversity Monitoring to ensure that PTTEP's offshore operations are friendly and safe to the surrounding environment and aquatic animals. The basis of the Aquatic Animal Monitoring Systemproject focuses on conservation survey and tracking of rare aquatic animals as well as marinebiodiversity.
As part of the process, an underwater camera was installed on a jacket leg of PTTEP's platform to allow the video recording of underwater lives. The video footage was then analyzed by Artificial Intelligence (AI)software using an object detection method for determining the animal's categorization, then using machine learning algorithm for more accuracy. This concept can visualize aquatic animals around the platform and the surrounding environment. Moreover, the AI software can shorten the video by cutting off any non-life appearing period. Therefore, this technique can support a processor during the video analysis from the platform, contributing to a better work efficiency as it can save time, manpower, and most importantly cost.
For the detection algorithm, all targets generatea large amount of data in the form of images with labels in order to train a software to memorize the target objects. The AI software was able to detect and identify nine species of aquatic animals which are fish, turtle, whale, dolphin, shark, seal, sea lion, stingray, and seahorse. With AI software in place, the video raw file can be shortened up to 85% by removing non-life periods in the original video and tracking only animal life in the video frame. This is a significant milestone for PTTEP in creating sustainable values to the ocean, which is considered as PTTEP's second home.
Adopting artificial intelligence and machine learning technology to this project, it helps categorizing aquatic animal types and shorten a videofile. Moreover, it can save manpower and time.
Selection pressure dominates the use of synonymous codons in genes linked to epilepsy
Authors: Chaudhary Kaustubh Pramod , Sophiarani Yengkhom , Nath Anupam , Tiwari Rahul , Bordoloi Hemashree , Hanjabam Joykishan Sharma , Singh Ashish , Malakar Arup Kumar , Singh Ravail , Gupta Suruchi , Paul Prosenjit 
Published: August 2022

Abstract
Bioinformatic approaches were utilized to investigate the pattern of codon usage in 109 epilepsy-related genes. The genes were found to be rich in G and C nucleotides, with GC usage 7% higher than AT. Preferred codon analysis confirmed the dominance of G and C at the wobble position of codons. CAG is the most frequently used codon. The ENc analysis, on the other hand, did not reveal any exceptional codon usage bias. Correlation of GC content at the first and second codon positions with the third codon position (neutrality plot) suggested the impact of selection pressure in shaping the GC enriched compositional pattern observed in epilepsy-related genes. It was further confirmed by correspondence analysis. Altogether, our findings imply that the pattern of evolutionary processes (especially selection pressure) operating on epilepsy-related genes. This might aid in identifying disease genes compositional signatures and deciphering the genetic mechanisms underlying epilepsy. Further this information would be useful in facilitating the existing epilepsy related therapies like gene therapy, cell therapy etc.
Genome-wide transcriptome and physiological analyses provide new insights into cadmium tolerance to tomato seedlings
Authors: Liang Quanwu , Song Ge , Li Yuqiong , Wang Haihua , Yan Mingli , Zhang Jihong 
Published: August 2022

Abstract
Background
Cadmium (Cd) pollution in agricultural soils has become one of the main environmental problems in China. This study investigated the underlying molecular mechanism controlling the effects of auxin and lignin in response to Cd stress in tomato.
Results
Cadmium treatment significantly inhibited the root growth (by 43.46%) of tomato seedlings, and induced their antioxidant enzyme activities (POD and CAT). Cadmium treatment decreased their shoot dry weight and root dry weight at day 7, 40.42% and 60.40%, respectively. The content of malondialdehyde (MDA) increased and total chlorophyll contents were reduced during the treatment. A comparative transcriptome (RNA-Seq) was de novo assembled and differentially expressed genes (DEGs) with altered expression levels were investigated among wild type cultivars A57 with or without cadmium ion treatment for 24 h. A total of 18,883 unigenes were grouped into 51 Gene Ontology (GO) categories and 89 Kyoto Encyclopedia of Genes and Genomes terms (KEGG). A number of 1,563 differentially expressed genes (DEGs) were found in Cd-exposed tomato seedlings, including 559 down-regulated genes and 1157 up-regulated genes. Go term enrichment analysis indicated that various auxin signaling pathway-related GO terms were significantly enriched in DEGs. Most genes involved in lignin biosynthesis pathway were up-regulated, for instance, COMT and CCOMT.
Conclusions
The transcriptome expression profiles of tomato seedlings under Cd stresses obtained in this study provided a resource for identifying common mechanisms in the lignification formation and the inhibition of plant growth.
Energy Communities Support Climate Policy in Exchange for Transition Assistance
Authors: Gazmararian Alexander 
Published: August 2022

Abstract
Decarbonization to mitigate climate change will require a reduction in fossil fuel consumption. The economic effects of moving away from fossil fuels motivates communities that produce coal, oil and gas to obstruct climate policy. How could climate policy be designed to win support for decarbonization in fossil fuel communities? Answering this question requires public opinion data from these crucial regions and measurement of multi-dimensional preferences, but national surveys often fail to recruit respondents from these areas and studies often focus on individual transition policy instruments. To overcome these limitations, we field surveys at Appalachian county fairs to access this hard-to reach population (N=249) and employing a conjoint experiment designed to elicit multi-dimensional preferences. We find that policies reducing transition costs, such as investments in worker healthcare and pensions and income compensation, increase support for transition assistance. In turn, respondents report willingness to back climate policy coupled with assistance for workers and community members. The findings suggest that smart policy design can build climate coalitions for decarbonization, even in the places most impacted by moving away from fossil fuels.
Characteristics of metastatic breast cancer patients who survived more than 10 years
Authors: KIKUCHI MAMI , FUJII TAKAAKI , HONDA CHIKAKO , TANABE KEIKO , NAKAZAWA YUKO , OGINO MISATO , OBAYASHI SAYAKA , SHIRABE KEN 
Published: August 2022

Abstract
Background/Aim: Despite advances in treatment for breast cancer, metastatic breast cancer (MBC) remains difficult to cure, and few MBC patients survive 10 years after receiving a breast cancer metastasis diagnosis. We collected the cases of MBC patients who survived >10 years post-metastasis diagnosis and assessed the patients’ characteristics. ﻿Results: We retrospectively analyzed the cases of 245 consecutive patients diagnosed with MBC between January 2005 and December 2012 at our institution. Among them, 167 patients with confirmed survival of >10 years (i.e., long-term survival) or confirmed death at ≤10 years post-metastasis diagnosis were enrolled. There were 22 patients with MBC who survived >10 years. Regarding the cancer subtypes, 11 patients (50%) with long-term survival were HER2-positive. Seven of the 11 patients with HER2-positive MBC have been without recurrence although anti-HER2 therapy was discontinued. Triple-negative breast cancer (TNBC) was most common in the patients who survived ≤5 years, but was not present in the >10-year survival group. In the HER2-negative cases, more cases in the long-term survival group were treated with local therapy (34.4% in the <5-year survival group, 43.8% in the 5–10-year group, and 72.7% in the >10-year group).Conclusion: Our analyses revealed that MBC patients who survive >10 years after being diagnosed with metastasis are more likely to be HER2-positive and more likely to be treated with local therapy. This suggests the efficacy of anti-HER2 therapy, and, conversely, clarifies unmet needs in TNBC and luminal-type MBC. The usefulness of local therapy was also supported by our findings.
Genome-Wide Disease Screening of Early Human Embryos at Base-Pair Resolution
Authors: Xia Yuntao , Gonzalez-Pena Veronica , Klein David , Chandramohan Dhruva , Chertman Willy , Bechor Elan , Luquette Lovelace , Schleede Justin , Alvero Ruben , Kort Jonathan , Moon Jeong , Reddy Vik , Park Peter , Siddiqui Noor , Behr Barry , Gawad Charles 
Published: August 2022

Abstract

Whole-genome sequencing of preimplantation human embryos (PGT-WGS) is not currently performed due to the insufficient quality of amplified DNA from embryo biopsies. Here we present a PGT-WGS approach that takes advantage of the improved genome coverage and uniformity of primary template-directed amplification (PTA) to call almost all early embryo genetic variants accurately and reproducibly from a preimplantation biopsy. In a research sibling cohort, we identified clonal and mosaic chromosomal aneuploidy, de novo mitochondrial variants, and variants predicted to cause mendelian and non-mendelian diseases. In addition, we utilized the genome-wide data to compute polygenic risk scores for common diseases. Finally, we performed a clinical study on the parents and 8 sibling embryos of an infant with neonatal-onset seizures and severe developmental delay. We first used low-pass whole genome sequencing to identify embryos most likely to successfully implant based on aneuploidy. We then performed PGT-WGS and evenly covered a mean of 99.1% of the embryo genomes at least 10X, resulting in the detection of 99.5% of parental variants and enabling us to screen genome-wide for inherited and de novo genetic variants in each embryo using ACMG rules. Although numerous computational, interpretive, and ethical challenges remain for widespread implementation, this study establishes the technical feasibility of screening for and preventing numerous debilitating genetic diseases with PGT-WGS.
Mutations involving TGFB and MAPK may be associated with malignancy in granular cell tumors
Authors: Dehner Carina , Moon Tyler , Lyu Yang , Zhang Xiaochun , Zhou Zhaohe , Yang Kuangying , Chrisinger John , Griffin Anthony , Wunder Jay , Dickson Brendan , Hirbe Angela 
Published: August 2022

Abstract
Granular cell tumors are mesenchymal neoplasms of presumed schwannian differentiation that may present as solitary or multifocal lesions with excision usually being curative. A minority of cases, however, shows histological features associated with an increased risk for metastasis and are highly aggressive leading to death in about a third of cases1. While benign and malignant cases have been shown to harbor mutations in the H + ATPase genes, there is only limited data examining molecular aberrations associated with malignancy. The departmental archives were searched for cases of atypical/malignant granular cell tumor. Clinical and histopathological features were noted. Whole exome sequencing was performed. Three cases of malignant granular cell tumor and on atypical granular cell tumor were included. Three tumors metastasized to distant sites with a median disease-free survival of 16 months and an overall follow-up time of 32.5 months. Whole exome sequencing showed mutations involving TGFβ and MAPK pathways in all four tumors. Mutations involving the TGFβ and MAPK pathways may be associated with tumor progression or malignant transformation in granular cell tumor pathogenesis.
High Thermal Conductivity in Wafer-Scale Cubic Silicon Carbide Crystals
Authors: Cheng Zhe , Liang Jianbo , Kawamura Keisuke , Asamura Hidetoshi , Uratani Hiroki , Graham Samuel , Ohno Yutaka , Nagai Yasuyoshi , Shigekawa Naoteru , Cahill David 
Published: August 2022

Abstract
High thermal conductivity electronic materials are critical components for high-performance electronic and photonic devices as both active functional materials and thermal management materials. We report an isotropic high thermal conductivity exceeding 500 W m− 1K− 1 at room temperature in high-quality wafer-scale cubic silicon carbide (3C-SiC) crystals, which is the second highest among large crystals (only surpassed by diamond). Furthermore, the corresponding 3C-SiC thin films are found to have record-high in-plane and cross-plane thermal conductivity, even higher than diamond thin films with equivalent thicknesses. Our results resolve a long-standing puzzle that the literature values of thermal conductivity for 3C-SiC are lower than the structurally more complex 6H-SiC. We show that the observed high thermal conductivity in this work arises from the high purity and high crystal quality of 3C-SiC crystals which avoids the exceptionally strong defect-phonon scatterings. Moreover, 3C-SiC is the only SiC polytype which can be epitaxially grown on Si. We show that the measured 3C-SiC-Si thermal boundary conductance is among the highest for semiconductor interfaces. These findings provide insights for fundamental phonon transport mechanisms, and suggest that 3C-SiC is an excellent wide-bandgap semiconductor for applications of next-generation power electronics as both active components and substrates.
Understanding repertoire sequencing data through a multiscale computational model of the germinal center
Authors: Kampen Antoine H. C. van , García-Valiente Rodrigo , Tejero Elena Merino , Stratigopoulou Maria , Balashova Daria , Jongejan Aldo , Lashgari Danial , Pélissier Aurélien , Caniels Tom G. , Claireaux Mathieu A. F. , Musters Anne , Gils Marit J. van , Martinez Maria Rodriguez , de Vries Niek , Meyer-Hermann Michael , Guikema Jeroen E.J. , Hoefsloot Huub 
Published: August 2022

Abstract
Sequencing of B cell and T cell immune receptor repertoires helps us to understand the adaptive immune response, although it only provides information about the clonotypes and their frequencies. Generally, additional time-consuming or expensive experiments are required to further characterize the identified (dominant) clones by measuring, for example, their affinity or function. Here, we present a multiscale model of the germinal center (GC) to gain general insight in B cell repertoires, to establish the relationship between clonal abundance and affinity, to establish the variability of affinity within a clone, and to establish the extent that plasma cells (PCs) with high B cell receptor (BcR) mRNA content may disturb the identification of dominant clones. Since we simulate B cell repertoires generated from a single GC we also compare the extent that these repertoires deviate from experimental repertoires established from single GCs, blood or tissue. Our simulations show that there is a limited correlation between clonal abundance and affinity and that there is large affinity variability among same-ancestor subclones. Our simulations suggest that low abundant (sub)clones might also be of interest since they may have high affinity for the Ag. We show that the presence of PCs does not significantly affect the number of dominant clones derived from single GCs by sequencing BcR mRNAs. As expected, immune repertoires generated from our single GC model deviate in several aspects from experimental repertoires. At the same time, results from these simulations guide data interpretation and the design of follow-up experiments.
An efficient empirical mode decomposition based feature extraction model for human activity recognition of elderly people using machine learning algorithms
Authors: Gnanadesigan Naveen Sundar , Dhanasegar Narmadha , S Jebapriya , Ramachandran Meenakumari , Muthusamy Suresh , Thangavel Gunasekaran , Veerappan Prasanna Moorthy 
Published: August 2022

Abstract
Many individuals throughout the globe need to be constantly monitored for health reasons, including diabetes patients and individuals with other chronic diseases, the elderly, and the disabled. At any time, these individuals may be at a higher risk of suffering life-threatening falls or experiencing fainting. HAR (Human Activity Recognition) model using machine learning techniques play an important role in observing the activities of the people. The existing methods of activity monitoring lacks accuracy. Hence, the proposed method focuses to improve the prediction accuracy using accelerometer and gyroscope data. The research work analysis accelerometer and gyroscope data using various decomposition techniques such as EMD(Empirical Mode Decomposition), DWT (Discrete Wavelet Transform), FFT (Fast Fourier Transform) to process non-linear data and to split series of signal data into set of IMF(Intrinsic Mode Function), PCA(Principal Component Analysis) was performed for selecting optimal features. Then human activities are recognized by using multi-class classification techniques. The proposed EMD method achieves better performance with 98.4% accuracy, 100% Precision, 100% Recall and 100% F-measure.
Polyol-mediated synthesis of nickel oxide nanoparticles through aqueous sol-gel route
Authors: Zahra Samreen , Shahid Waneeza , Amin Chaudhry Athar , Zahra Sarwat , Kanwal Bushra 
Published: August 2022

AbstractBackground:In this work, nickel oxide nanoparticles were prepared by polyol mediated aqueous route of sol-gel process using nickel nitrate hexahydrate as precursor, a mixture of isopropyl alcohol and water as solvent and glycerol for making polyol medium. The synthesized nanoparticles were calcined at various temperatures ranging from 500 to 900 °C in order to investigate the effect of calcination temperature on their structure and morphology. Characterization was carried out using X-ray diffractometry, differential scanning calorimetry-thermogravimetry, infrared spectroscopy and field emission scanning electron microscopy.Results:The results confirmed the formation of face-cantered cubic structure of nickel oxide with significant variation in the surface morphology with the increasing calcination temperature.Conclusions:The study revealed that the aqueous sol-gel route can be employed successfully for the synthesis of ultrafine nickel oxide.
Color Image Splicing Localization Based On Block Classification Using Transition Probability Matrix
Authors: YILDIRIM ESRA ODABAS , TAHAOGLU Gul , ULUTAS Guzin , USTUBIOGLU Beste , NABIYEV Vasif 
Published: August 2022

Abstract
With the increasing technology, digital images have become a widely used data type in crucial areas such as medical journalism and law. Since it is used in such important areas, it has become questionable whether digital images are original or not. Image splicing forgery is one of the most common forgery types applied to digital images. This work proposes a new image splicing detection and localization method. Our motivation is to reveal the boundaries of forgery by using statistical features of the image blocks. The proposed method has two main stages: training and localizing. In both phases, image blocks that contain edge information are used because the splicing operation causes some inconsistency on the edges. In the training stage, original blocks are selected from the regions that include original boundaries, and forged blocks are selected from the areas that contain splicing operation-induced edges. Transition probability matrices are calculated in eight directions to obtain the correlation of the borders between the neighbor blocks on original and splicing edges. These matrices are used as a feature for each block. The blocks are classified as authentic and spliced using SVM. A new post-processing step has been proposed to eliminate the false positives that may occur due to the presence of original regions that are likely to be detected as spliced edges in the image. The publicly available Columbia dataset has been used to show the effectiveness of the state-of-the-art and proposed method. The results indicate that the proposed method has performed well even under JPEG compression and Gaussian blurring attacks.
Acute heart failure outcome and predictors of outcome among patients admitted to Jimma University Medical Center, Southwest Ethiopia
Authors: Tegene Elsah , Mekonin Gemechis , Dukessa Tadesse , Dube Lamessa , Tadesse Iyasu 
Published: August 2022

Abstract
Background: Acute heart failure (AHF) is a life-threatening medical condition requiring urgent evaluation and treatment. Despite the high burden of the disease in Ethiopia, limited studies and literatures are available that describe the clinical characteristics, and outcome of the disease in the country. Objectives: To determine the etiology, precipitating factors and treatment outcome and predictors of outcome among patients with AHF admitted to Emergency and Medical ward, Jimma university medical center, Southwest Ethiopia from July 12, 2021 to January 10, 2022.Methods: A prospective cohort study was conducted in AHF patients admitted to Emergency and Medical ward. We used Purposive sampling technique to get the sample population. Data were collected using a structured questionnaire developed according to the guidelines of Ethiopian national major non-communicable diseases assessment, European Society of Cardiology, and ACC/AHA. Imaging findings (chest X-ray, electrocardiogram, and echocardiography), treatments given and hospital stay were abstracted from the patient’s card. SPSS software version 26 was used for data analysis. The level of significance was chosen at 5% and p–value ≤ 0.05 was reported statistically significant and results were reported as 95% confidence intervals.Result: We enrolled 184 AHF patients. Ninety eight (53.3%) patients were males. The mean age of the participants was 48.33(±18.855). Out of the total patients, 90 (48.9%) had denovo heart failure, 94 (51.1%) had acute decompensated heart failure (ADHF) and 66 (70.2%) of those with ADHF had readmission history of more than two times in the past 1 year.The most common chest X-ray findings in the patients were cardiomegaly; 168(91.3%). The most common identified precipitating cause for AHF was pneumonia; 54(29.3%). Systemic hypertension was the most common comorbidity being seen in 52 (28.3%) patients. Yet, pulmonary hypertension, chronic kidney disease, chronic obstructive pulmonary disease and tuberculosis were the next common comorbidities being found in 46 (25%), 31(16.8%), 12 (6.5%), 9 (4.9 %) respectively.Smoking history, admission systolic blood pressure, presence of acute coronary syndrome as precipitating factor, presence of chronic kidney disease as a comorbidity, blood urea nitrogen (BUN), and troponin were all predictors in hospital mortality. Conclusion: Pneumonia and hypertension were the most common identified precipitating factors and comorbidities, respectively. Acute coronary syndrome, chronic kidney disease, smoking, and BUN are independent predictors of mortality in hospitalized AHF patients in JUMC.