bluuebunny/crossref_metadata_2025_split huggingface dataset:
Site Suitability Analysis for Sea Turtle Nesting Area by using AHP and GIS
Authors: Azizan Nor Hidayah , Naharudin Nabilah , Hashim Nurhanisah , Rusli Mohd Uzair 
Published: August 2023

Abstract
Sea turtles are among the endangered marine life not only in Malaysia but also in the world. There are various criteria required in finding a suitable turtle nest site, it may be necessary to suit the needs of the environment in the study area, and it was found that there are four (4) most important criteria in studying turtle nesting suitability site namely (1) sand temperature, (2) vegetation, (3) beach slope, and (4) sand particle size. The use of GIS with the help of AHP can get a better result in finding the site suitability for sea turtle nesting by using weighted overlay analysis. The highest weight value is sand particle size with 0.616, followed by beach slope with 0.220, sand temperature with 0.114, and vegetation with 0.049 was derived from the use of AHP techniques. As a result, the suitability index in the study area was measured and analyzed with the sea turtles’ activities. As a result, the suitability index in the study area was measured and analyzed with the sea turtles’ activities. It was found that there is a low correlation between the site suitability index and the sea turtles’ activities which matches the expert’s opinion that though the site is deemed suitable for sea turtles nesting, they may not be crawled up and nest due to other external factors. The sites with the highest index were verified to be the most suitable by the experts as it is true that sea turtles were ascending to the sites for nesting. In conclusion, integrating GIS with the help of AHP can be an important technique to find a suitable site for the sea turtle nesting area.
Analyzing the Effectiveness of Support Vector Machine and Random Forest Classifiers in Delineating the Green Area
Authors: Nadzri Iklil Faqihah Mohamad , Khalid Nafisah , Wahab Warishah Abdul , Hashim Nurhanisah 
Published: August 2023

Abstract
Due to human limitations in exploring the world, the existence of remote sensing technology has made it possible and affordable for humans to study the green cover in the modern world, especially over a large region. This is so that details of the objects can be captured and monitored by satellites or other aircraft by measuring the wavelengths of radiation that are both emitted and reflected from the area. For the past decades, various approaches have been utilized by researchers to detect green areas such as deep learning, machine learning, object based and pixel-based classification. Thus, this study aims to evaluate the effectiveness of machine learning classifiers such as Support Vector Machine (SVM) and Random Forest (RF) in detecting and delineating the green area in Universiti Teknologi Mara (UiTM) Shah Alam. Based on the study, the overall accuracy obtained by the SVM classifier is 80% with a 0.75 kappa coefficient, whereas the RF classifier managed to get 79% with a 0.74 kappa. Even though the result of both classifiers is almost the same, the accuracy of detecting green area by the SVM classifier is 93% which outperforms the RF classifier with an accuracy of 88%. This shows that the SVM classifier is more effective than the RF classifier. The detection and delineation of the green area using both machine learning approaches also showed using a map so that it is easier to be analyzed and observed.
Ecological risk assessment of Kaeng Krachan National Park emphasis on Land Use/Land Cover (LULC)
Authors: Rathnayake R M P J , Rattanapun P , Chaisri B , Dubsok A , Wachirasirodom R , Kittipongvises S 
Published: August 2023

Abstract
Land use/land cover (LULC) changes with time, signify disturbances and conservation patterns of landscape types in an environment. Kaeng Krachan National Park (KKNP) is a UNESCO world heritage site with rich biodiversity and provides habitat for indigenous communities who are relying on farming. The study was conducted by calculating landscape ecological risk indices of classified Landsat images based on five landscape classes (Water, Grassland, Built-up-land, Forest, and Cultivated land). Landscape loss indices were calculated based on classified Landsat images. Assigning landscape loss indices in grid cells calculated the ecological risk indices of the area. Ecological risk indices were lowest (ERI ≤ 0.0005), lower (ERI ≤ 0.001), medium (ERI ≤ 0.005), higher (ERI ≤ 0.01), and highest (ERI ≤ 0.015). Ecological risk indices were higher on the east border of KKNP proximate to the semi-urban settlements. The highest risk area demarcates waterbodies and extensively cultivated land. The LULC areas which show the higher variation of changes from 2013 to 2022 are grasslands and forest cover. The changes in the LULC pattern have increased the risk in cultivated land areas and grasslands due to farming obligations. The provision of land permits and land use limitations are addressed issues to conserve KKNP for conservation.
Biofilm Fabrication from Cellulose Acetate of Oil Palm Empty Fruit Bunch and Corn Starch as Bio-polybag Material for Eco-friendly Plantation
Authors: Purba Ibnu Tryansar , Sani Kalimaya Qolbi , Sayekti Nur , Ramadhani Sabela Sanata , Waluyo Joko , Pranolo Sunu Herwi , Kaavessina Mujtahid 
Published: August 2023

Abstract
The utilization of polybags is prevalent among agriculturalists as a nursery container in one of the processes in agricultural and plantation activities. The polybag currently used is derived from petroleum and tough to decompose, causing various environmental problems such as soil contamination which interferes with plant growth. On the other hand, Indonesia has a superior palm oil commodity that increases annually. This accumulation provokes an increasing quantity of palm oil waste. Oil palm empty fruit bunch (OPEFB) is the highest considerable solid waste in the CPO production (0.23 kg/kg fresh fruit bunch) containing cellulose (40.37%). It is potentially used as bio-polybags decomposable easily. This study aims to determine the preparation method and the composition’s consequence on the characterization of OPEFB cellulose-based-bio-polybags. The technique used in producing bio-polybags is solution casting. This study’s concentration variants were adjusted to the ANOVA (23 factorial design) statistical results by conversion regression equation concerning corn starch, cellulose acetate, and CaCO3. Mechanical assessments include tensile strength (1.461 – 8.442 MPa) and elongation (2.611% – 8.808%). The structural test might consider the quality of the biofilm. This innovation is desired to decrease conventional polybag usage and promote sustainable bio-polybag as eco-friendly nursery containers in the vegetable plantation.
Preparation of nanoporous SiO2/C derived from rice husk as anode material in SiO2/C||LiFePO4 full-cell through alkaline activation treatment
Authors: Pham Thanh Liem , Le Huu Phuoc , Le My Loan Phung , Vu Tan Phat , Tran Van Man 
Published: August 2023

Abstract
Silicon-based materials such as pure silicon (Si), silicon monoxide (SiO), silica (SiO2), are considered promising anode for future high power energy Li-ion batteries. Among them, SiO2 has garnered attention owing to its outstanding features such as high theoretical capacity (1961 mAh g−1), abundant reserve, and low-cost processing. However, the large expansion and shrinkage of the Si and SiO2 volume during lithiation/delithiation reaction are still the main barriers for practical application. In this study, SiO2 derived from rice husks and activated by KOH displayed a nanoporous structure with a porous matrix carbon that can absorb the volume expansion during lithiation process and facilitate the diffusion of Li+ ion along the pores to minimise the dendrite growth at the local area. Through activation treatment, the surface area of SiO2 increases up to 278.875 m2 g−1 with a pore volume of 0.191 cm3 g−1 and the average pore diameter is about 0.771 nm. The cycling results showed that rice husk ash mixed with KOH at a ratio of 1:0.5 offered the best capacity retention of SiO2/C anode material in half-cell. In full-cell configuration of SiO2/C||LiFePO4, the the negative electrode/positive electrode capacity ratio (N/P) ratio of 1.2 exhibited the most stable performance with the highest capacity retention.
Integrating multiple regulations on enzyme activity: the case of phosphoenolpyruvate carboxykinases
Authors: Rojas Bruno E , Iglesias Alberto A 
Published: August 2023

Abstract
Abstract. Data on protein post-translational modifications (PTMs) increased exponentially in the last years due to the refinement of mass spectrometry techniques and the development of databases to store and share datasets. Nevertheless, these data per se do not create comprehensive biochemical knowledge. Complementary studies on protein biochemistry are necessary to fully understand the function of these PTMs at the molecular level and beyond, for example, designing rational metabolic engineering strategies to improve crops. Phosphoenolpyruvate carboxykinases (PEPCKs) are critical enzymes for plant metabolism with diverse roles in plant development and growth. Multiple lines of evidence showed the complex regulation of PEPCKs, including PTMs. Herein, we present PEPCKs as an example of the integration of combined mechanisms modulating enzyme activity and metabolic pathways. PEPCK studies strongly advanced after the production of the recombinant enzyme and the establishment of standardized biochemical assays. Finally, we discuss emerging open questions for future research and the challenges in integrating all available data into functional biochemical models.
An Exploratory Pilot Study of Neuropsychological Performance in Two Huntington Disease Centers of Excellence Clinics
Authors: Rossetti M Agustina , Anderson Kendra M , Hay Kaitlyn R , Del Bene Victor A , Celka Andrea S , Piccolino Adam , Nelson Sheese Amelia L , Huynh Melissa , Zhu Liang , Claassen Daniel O , Furr Stimming Erin , Considine Ciaran M 
Published: August 2023

Abstract

Objectives
To describe the characteristics of patients receiving a clinical referral for neuropsychological evaluation in two Huntington’s Disease Society of America Centers of Excellence (HDSA COE). In this exploratory pilot study, we used an empirically supported clinical neuropsychological battery to assess differences in cognitive performance between premanifest and manifest HD patient groups (compared with each other and normative expectations).

Method
Clinical data from 76 adult genetically confirmed patients referred for neuropsychological evaluations was retrospectively collected from two HDSA COEs. ANOVA and Chi-square tests were used to compare variables between pre-manifest (n = 14) and manifest (n = 62) groups for demographic, cognitive, neuropsychiatric, and disease severity variables.

Results
Our clinics serviced a disproportionate number of motor manifest patients. Six measures were excluded from analyses due to infrequent administration. The full WAIS-IV Digit Span was disproportionately administered to the manifest group. The premanifest group showed stronger cognitive performance with effect sizes in the large range on subtests of the WAIS-IV Digit Span, HVLT-R, SDMT, and verbal fluency.

Conclusions
This is the first study to assess an empirically supported neuropsychological research battery in a clinical setting with a relatively large sample size given the rarity of HD. The battery adequately captured areas of impairment across the disease spectrum. Application of the current battery with larger premanifest samples is warranted.
Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion
Authors: Wang Weixu , Zhou Xiaolan , Wang Jing , Yao Jun , Wen Haimei , Wang Yi , Sun Mingwan , Zhang Chao , Tao Wei , Zou Jiahua , Ni Ting 
Published: August 2023

AbstractSingle-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for uncovering cellular heterogeneity. However, the high costs associated with this technique have rendered it impractical for studying large patient cohorts. We introduce ENIGMA (Deconvolution based on Regularized Matrix Completion), a method that addresses this limitation through accurately deconvoluting bulk tissue RNA-seq data into a readout with cell-type resolution by leveraging information from scRNA-seq data. By employing a matrix completion strategy, ENIGMA minimizes the distance between the mixture transcriptome obtained with bulk sequencing and a weighted combination of cell-type-specific expression. This allows the quantification of cell-type proportions and reconstruction of cell-type-specific transcriptomes. To validate its performance, ENIGMA was tested on both simulated and real datasets, including disease-related tissues, demonstrating its ability in uncovering novel biological insights.
Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases
Authors: Sheng Nan , Wang Yan , Huang Lan , Gao Ling , Cao Yangkun , Xie Xuping , Fu Yuan 
Published: August 2023

AbstractMotivationIdentifying the relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating and prognosing diseases. The development of effective computational prediction methods can reduce experimental costs. While numerous methods have been proposed, they often to treat the prediction of lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) as separate task. Models capable of predicting all three relationships simultaneously remain relatively scarce. Our aim is to perform multi-task predictions, which not only construct a unified framework, but also facilitate mutual complementarity of information among lncRNAs, miRNAs and diseases.ResultsIn this work, we propose a novel unsupervised embedding method called graph contrastive learning for multi-task prediction (GCLMTP). Our approach aims to predict LDAs, MDAs and LMIs by simultaneously extracting embedding representations of lncRNAs, miRNAs and diseases. To achieve this, we first construct a triple-layer lncRNA–miRNA-disease heterogeneous graph (LMDHG) that integrates the complex relationships between these entities based on their similarities and correlations. Next, we employ an unsupervised embedding model based on graph contrastive learning to extract potential topological feature of lncRNAs, miRNAs and diseases from the LMDHG. The graph contrastive learning leverages graph convolutional network architectures to maximize the mutual information between patch representations and corresponding high-level summaries of the LMDHG. Subsequently, for the three prediction tasks, multiple classifiers are explored to predict LDA, MDA and LMI scores. Comprehensive experiments are conducted on two datasets (from older and newer versions of the database, respectively). The results show that GCLMTP outperforms other state-of-the-art methods for the disease-related lncRNA and miRNA prediction tasks. Additionally, case studies on two datasets further demonstrate the ability of GCLMTP to accurately discover new associations. To ensure reproducibility of this work, we have made the datasets and source code publicly available at https://github.com/sheng-n/GCLMTP.
Effects of lactate, super-GDF9, and low oxygen tension during bi-phasic in vitro maturation on the bioenergetic profiles of mouse cumulus–oocyte complex
Authors: Akin Nazli , Ates Gamze , von Mengden Lucia , Herta Anamaria-Cristina , Meriggioli Cecilia , Billooye Katy , Stocker William A , Ghesquiere Brecht , Harrison Craig A , Cools Wilfried , Klamt Fabio , Massie Ann , Smitz Johan , Anckaert Ellen 
Published: August 2023

Abstract
In vitro maturation (IVM) is an alternative assisted reproductive technology with reduced hormone-related side effects and treatment burden compared to conventional IVF. Capacitation (CAPA)-IVM is a bi-phasic IVM system with improved clinical outcomes compared to standard monophasic IVM. Yet, CAPA-IVM efficiency compared to conventional IVF is still suboptimal in terms of producing utilizable blastocysts. Previously, we have shown that CAPA-IVM leads to a precocious increase in cumulus cell (CC) glycolytic activity during cytoplasmic maturation. In the current study, considering the fundamental importance of CCs for oocyte maturation and cumulus–oocyte complex (COC) microenvironment, we further analyzed the bioenergetic profiles of maturing CAPA-IVM COCs. Through a multi-step approach, we (i) explored mitochondrial function of the in vivo and CAPA-IVM matured COCs through real-time metabolic analysis with Seahorse analyzer, and to improve COC metabolism (ii) supplemented the culture media with lactate and/or super-GDF9 (an engineered form of growth differentiation factor 9) and (iii) reduced culture oxygen tension. Our results indicated that the pre-IVM step is delicate and prone to culture-related disruptions. Lactate and/or super-GDF9 supplementations failed to eliminate pre-IVM-induced stress on COC glucose metabolism and mitochondrial respiration. However, when performing pre-IVM culture under 5% oxygen tension, CAPA-IVM COCs showed similar bioenergetic profiles compared to in vivo matured counterparts. This is the first study providing real-time metabolic analysis of the COCs from a bi-phasic IVM system. The currently used analytical approach provides the quantitative measures and the rational basis to further improve IVM culture requirements.
τ-censored weighted Benjamini–Hochberg procedures under independence
Authors: Zhao Haibing , Zhou Huijuan 
Published: August 2023

Abstract
In the field of multiple hypothesis testing, auxiliary information can be leveraged to enhance the efficiency of test procedures. A common way to make use of auxiliary information is by weighting p-values. However, when the weights are learned from data, controlling the finite-sample false discovery rate becomes challenging, and most existing weighted procedures only guarantee false discovery rate control in an asymptotic limit. In a recent study conducted by Ignatiadis & Huber (2021), a novel τ-censored weighted Benjamini–Hochberg procedure was proposed to control the finite-sample false discovery rate. The authors employed the cross-weighting approach to learn weights for the p-values. This approach randomly splits the data into several folds and constructs a weight for each p-value Pi using the p-values outside the fold containing Pi. Cross-weighting does not exploit the p-value information inside the fold and only balances the weights within each fold, which may result in a loss of power. In this article, we introduce two methods for constructing data-driven weights for τ-censored weighted Benjamini–Hochberg procedures under independence. They provide new insight into masking p-values to prevent overfitting in multiple testing. The first method utilizes a leave-one-out technique, where all but one of the p-values are used to learn a weight for each p-value. This technique masks the information of a p-value in its weight by calculating the infimum of the weight with respect to the p-value. The second method uses partial information from each p-value to construct weights and utilizes the conditional distributions of the null p-values to establish false discovery rate control. Additionally, we propose two methods for estimating the null proportion and demonstrate how to integrate null-proportion adaptivity into the proposed weights to improve power.
Blurring cluster randomized trials and observational studies: Two-Stage TMLE for subsampling, missingness, and few independent units
Authors: Nugent Joshua R , Marquez Carina , Charlebois Edwin D , Abbott Rachel , Balzer Laura B 
Published: August 2023

Summary
Cluster randomized trials (CRTs) often enroll large numbers of participants; yet due to resource constraints, only a subset of participants may be selected for outcome assessment, and those sampled may not be representative of all cluster members. Missing data also present a challenge: if sampled individuals with measured outcomes are dissimilar from those with missing outcomes, unadjusted estimates of arm-specific endpoints and the intervention effect may be biased. Further, CRTs often enroll and randomize few clusters, limiting statistical power and raising concerns about finite sample performance. Motivated by SEARCH-TB, a CRT aimed at reducing incident tuberculosis infection, we demonstrate interlocking methods to handle these challenges. First, we extend Two-Stage targeted minimum loss-based estimation to account for three sources of missingness: (i) subsampling; (ii) measurement of baseline status among those sampled; and (iii) measurement of final status among those in the incidence cohort (persons known to be at risk at baseline). Second, we critically evaluate the assumptions under which subunits of the cluster can be considered the conditionally independent unit, improving precision and statistical power but also causing the CRT to behave like an observational study. Our application to SEARCH-TB highlights the real-world impact of different assumptions on measurement and dependence; estimates relying on unrealistic assumptions suggested the intervention increased the incidence of TB infection by 18% (risk ratio [RR]=1.18, 95% confidence interval [CI]: 0.85–1.63), while estimates accounting for the sampling scheme, missingness, and within community dependence found the intervention decreased the incident TB by 27% (RR=0.73, 95% CI: 0.57–0.92).
Faecal Bacteriome and Metabolome Profiles Associated with Decreased Mucosal Inflammatory Activity Upon Anti-TNF Therapy in Paediatric Crohn’s Disease
Authors: Hurych Jakub , Mascellani Bergo Anna , Lerchova Tereza , Hlinakova Lucie , Kubat Michal , Malcova Hana , Cebecauerova Dita , Schwarz Jan , Karaskova Eva , Hecht Tomas , Vyhnanek Radim , Toukalkova Lenka , Dotlacil Vojtech , Greinerova Katerina , Cizkova Anabela , Horvath Rudolf , Bronsky Jiri , Havlik Jaroslav , Hradsky Ondrej , Cinek Ondrej 
Published: August 2023

Abstract

Background and Aims
Treatment with anti-tumour necrosis factor α antibodies [anti-TNF] changes the dysbiotic faecal bacteriome in Crohn’s disease [CD]. However, it is not known whether these changes are due to decreasing mucosal inflammatory activity or whether similar bacteriome reactions might be observed in gut-healthy subjects. Therefore, we explored changes in the faecal bacteriome and metabolome upon anti-TNF administration [and therapeutic response] in children with CD and contrasted those to anti-TNF-treated children with juvenile idiopathic arthritis [JIA].

Methods
Faecal samples collected longitudinally before and during anti-TNF therapy were analysed with regard to the bacteriome by massively parallel sequencing of the 16S rDNA [V4 region] and the faecal metabolome by 1H nuclear magnetic resonance imaging. The response to treatment by mucosal healing was assessed by the MINI index at 3 months after the treatment started. We also tested several representative gut bacterial strains for in vitro growth inhibition by infliximab.

Results
We analysed 530 stool samples from 121 children [CD 54, JIA 18, healthy 49]. Bacterial community composition changed on anti-TNF in CD: three members of the class Clostridia increased on anti-TNF, whereas the class Bacteroidia decreased. Among faecal metabolites, glucose and glycerol increased, whereas isoleucine and uracil decreased. Some of these changes differed by treatment response [mucosal healing] after anti-TNF. No significant changes in the bacteriome or metabolome were noted upon anti-TNF in JIA. Bacterial growth was not affected by infliximab in a disc diffusion test.

Conclusions
Our findings suggest that gut mucosal healing is responsible for the bacteriome and metabolome changes observed in CD, rather than any general effect of anti-TNF.