bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Analysis of Municipal Solid Waste to RDF Pretreatment Methods in Indonesia
Authors: Widyarsana I M W , Saraswati D 
Published: August 2022

Abstract
Municipal solid waste in Indonesia has a high potential to be used as an alternative energy source. One of the methods is by producing refuse-derived fuel (RDF) as coal substitution. Pre-treatment is needed to reduce high water content in domestic waste, especially in Indonesia, to produce high-quality RDF. This study compares and analyzes the optimal pre-treatment method to produce RDF from domestic waste based on each RDF product characteristic. The research reviewed and analyzed the data of RDF characteristics of various pre-treatment methods applied in Indonesia from pilot-scale experiments as primary data and previous research as secondary data. The methods compared are fermentation method, sun-drying, and bio drying. RDF from each method was tested to collect the proximate and ultimate characteristics data. Statistical analysis of RDF characteristics is carried out to determine whether there is a significant difference from different pre-treatment methods. This analysis shows no significant effect on the difference in the characteristics of the RDF. RDF from bio drying provides the highest average number of calorific values and lowest moisture content. The study concluded that the bio drying method is recommended in more waste treatment facilities in Indonesia to increase waste as an alternative energy resource.
Coastal protection system design at the Indonesian Mangrove Center in Pekalongan, Central Java – Indonesia
Authors: Yola  , Yuanita N , Kurniawan A , Nugroho E O , Martin R J , Muhammad I F , Wijaya F S , Fauzi N 
Published: August 2022

Abstract
The Pekalongan area suffered from coastal flooding and heavy coastal erosion due to land subsidence and increasing wave condition in the last decade. Pekalongan government built a land dike in 2018 to protect Pekalongan city from coastal flooding. This study aims to determine a suitable coastal protection system, consisting of hard-structures and mangroves, to solve the erosion problem at the Pekalongan coast, specifically at the future Pekalongan Mangrove Information Centre (PIM). Environmental data, i.e., wind, wave, current, tidal, sediment, and soil data, were obtained by field survey and secondary data collection from open-source data. The selection of the most suitable coastal protection design concepts was performed by comparing several alternatives of structure materials. Coastal protection layout was obtained based on a numerical sediment transport model result using Delft3D software. From environmental data processing and analysis with various design aspects, the top elevation of the coastal protection structure is +4.5 m MSL (Mean Sea Level). The configuration of the mangrove planting media consists of ‘guludan’ structure technique with dimension 10 m x 5 m x 4 m.
Health risk-based prioritization approaches of pharmaceuticals in the Upper Citarum River Basin
Authors: Utami R R , Salami I R S , Geerling G W , Notodarmojo S , Ragas A M J 
Published: August 2022

Abstract
Two methods were developed to prioritize human health risks of pharmaceuticals based on defined daily doses (DDDs) obtained from the WHO and acute toxicity data on pharmaceuticals (TDLo) obtained from the PubChem database. A major advantage of these methods is that it requires no acceptable daily intake (ADI), reference dose (RfD), or tolerable daily intake (TDI) values, which are often unavailable or difficult to obtain for pharmaceuticals group. Both methods were applied to prioritize 16 pharmaceuticals detected by passive samplers in the water source of Ciwalengke Village, a peri-urban area in the Upper Citarum River Basin. Ten out of 16 pharmaceuticals could be prioritized by using both methods. The risk indicator values (RIhum) showed good agreement between the two approaches, especially for Caffeine and Trimethoprim. The priority ranking of Caffeine and Lidocaine were always in the top 3 highest human health risks. The ranking comparison showed considerable consistency, indicating that both prioritization methods are reasonably in line with each other. The pharmaceutical intake through the exposure of contaminated river water was expected to pose negligible human health risks (i.e. RIhum < 1) but can be refined more by conducting supplementary effects studies for other individual pharmaceuticals or pharmaceuticals mixtures.
Preliminary studies of Bandung City Health System Resilience (case study : Covid-19 pandemic)
Authors: Roosmini D , Kanisha T F , Nastiti A , Kusumah S W , Salami I R S 
Published: August 2022

Abstract
The SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) virus causes the COVID-19 pandemic globally. The city of Bandung is the city that became the cluster of Covid-19 spread after DKI Jakarta at the beginning of the pandemic. In 2019, the Global Health Security Index (GHSI) was developed to assess countries’ readiness to face a pandemic. The index obtained shows that globally the value of this readiness is still minimal. In improving community health system services, health system resilience has been developed and studied in recent years. This study conducted a preliminary evaluation of the Bandung City health system in dealing with the Covid-19 pandemic using the concept of resilience. This research uses the West Java and Bandung City Health Office website. Methods used for evaluating the health system resiliency are comparing the cases of Covid-19 at the sub-district to the health facilities, the incidence of infectious diseases in 2019, and the socio-economic conditions of each sub-district. Research shows that the health system in Bandung City has not met the targets in the 2018-2023 Regional Medium-Term Development Plan. Overall, the initial research on the Bandung City health system still does not meet the criteria for resilience.
Emerging machine learning strategies for diminishing measurement uncertainty in SPM nanometrology
Authors: Nguyen Linh Thi Phuong , Liu Bernard Haochih 
Published: August 2022

AbstractScanning probe microscopy (SPM) is an outstanding nanometrology tool for characterizing the structural, electrical, thermal, and mechanical properties of materials at the nanoscale. However, many challenges remain in the use of SPM. Broadly speaking, these challenges are associated with the acquisition of the SPM data and the subsequent analysis of this data, respectively. Both problems are related to the inherent uncertainty of the data obtained in SPM-based measurements due to the nanoscale geometry of the SPM probe tip, the state of the sample imaging region, the data analysis methods themselves, and the experience of the users. Machine learning (ML) approaches have been increasingly applied to address these problems in recent years. In general, ML approaches involve constructing a well-organized and representative SPM dataset from experimental and theoretical trials, and then using the data features of this dataset for ML models to learn and produce appropriate predictions. Herein, this review examines the development of recent ML strategies for reducing measurement uncertainty in SPM-based measurements. The review commences by introducing the ML models and algorithms commonly used in SPM-related applications. Recent approaches for collecting and preprocessing the SPM data to extract significant data features for further ML processing are then introduced. A review of recent proposals for the applications of ML to the improvement of SPM instrumentation and the enhancement of data processing and overall understanding of the material phenomena is then presented. The review concludes by presenting brief perspectives on future opportunities and open challenges in the related research field.
Performance evaluation of computational methods for splice-disrupting variants and improving the performance using the machine learning-based framework
Authors: Liu Hao , Dai Jiaqi , Li Ke , Sun Yang , Wei Haoran , Wang Hong , Zhao Chunxia , Wang Dao Wen 
Published: August 2022

Abstract
A critical challenge in genetic diagnostics is the assessment of genetic variants associated with diseases, specifically variants that fall out with canonical splice sites, by altering alternative splicing. Several computational methods have been developed to prioritize variants effect on splicing; however, performance evaluation of these methods is hampered by the lack of large-scale benchmark datasets. In this study, we employed a splicing-region-specific strategy to evaluate the performance of prediction methods based on eight independent datasets. Under most conditions, we found that dbscSNV-ADA performed better in the exonic region, S-CAP performed better in the core donor and acceptor regions, S-CAP and SpliceAI performed better in the extended acceptor region and MMSplice performed better in identifying variants that caused exon skipping. However, it should be noted that the performances of prediction methods varied widely under different datasets and splicing regions, and none of these methods showed the best overall performance with all datasets. To address this, we developed a new method, machine learning-based classification of splice sites variants (MLCsplice), to predict variants effect on splicing based on individual methods. We demonstrated that MLCsplice achieved stable and superior prediction performance compared with any individual method. To facilitate the identification of the splicing effect of variants, we provided precomputed MLCsplice scores for all possible splice sites variants across human protein-coding genes (http://39.105.51.3:8090/MLCsplice/). We believe that the performance of different individual methods under eight benchmark datasets will provide tentative guidance for appropriate method selection to prioritize candidate splice-disrupting variants, thereby increasing the genetic diagnostic yield.
ProteinPrompt: a webserver for predicting protein–protein interactions
Authors: Canzler Sebastian , Fischer Markus , Ulbricht David , Ristic Nikola , Hildebrand Peter W , Staritzbichler René 
Published: August 2022

Abstract

Motivation
Protein–protein interactions (PPIs) play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine learning algorithms to calculate specific, currently unknown PPIs. Our tool is designed to quickly and reliably predict contact propensities based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.

Results
We collected and thoroughly filtered a comprehensive database of known binders from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest (RF) algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network (GNN) implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for binders. To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the RF method achieved an accuracy rate of 0.88 and an area under the curve of 0.95. The GNN achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of RF and GNNs reduces the likelihood of errors. The consensus reached an accuracy of 0.89.

Availability and implementation
ProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt, where training and test data used to optimize the methods are also available. The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.
STAT3 in porcine endometrium during early pregnancy induces changes in extracellular matrix components and promotes angiogenesis
Authors: Jalali Beenu Moza , Likszo Pawel , Lukasik Karolina 
Published: August 2022

Abstract
A molecular interaction between maternal endometrium and implanting conceptus can lead to activation of a variety of transcription factors that regulate expression of several genes necessary for the process of embryo implantation. While, signal transducer and activator of transcription 3 (STAT3) is responsible for decidualization and epithelial remodeling in humans and mice, its role in porcine endometrium has not been explored before. In the present study, we observed a pregnancy dependent increase in gene and protein expression of STAT3. Phosphorylated STAT3 was predominantly present in the endometrium of pregnant animals in luminal and glandular epithelium and in the endothelium of blood vessels with a weak staining in stromal cells. Interleukins, IL-1β and IL-6, and epidermal growth factor (EGF)-induced STAT3 expression and phosphorylation in endometrial explants collected on Day 13 of the estrous cycle. Biological significance of STAT3 was evaluated by blocking its phosphorylation with STAT3-specific inhibitor, Stattic. Using porcine extracellular matrix (ECM) and adhesion molecule array, EGF was shown to induce changes in gene expression of ECM components: MMP1, MMP3, MMP12, LAMA1, SELL, and ICAM1, which was abrogated in the presence of Stattic. Transcriptional activity of STAT3 was observed in promoter regions of MMP3 and MMP12. Additionally, IL-6-induced STAT3 phosphorylation upregulated VEGF and VCAM1 abundances in endometrial-endothelial cells (EEC). Moreover, IL-6 resulted in an increase in EEC proliferation and capillary formation which was reversed in the presence of Stattic. Results of present study reveal a role for STAT3 phosphorylation in regulating extracellular matrix remodeling and angiogenesis in porcine endometrium to facilitate embryo implantation.
On boosting the power of Chatterjee’s rank correlation
Authors: Lin Z , Han F 
Published: August 2022

SummaryThe ingenious approach of Chatterjee (2021) to estimate a measure of dependence first proposed by Dette et al. (2013) based on simple rank statistics has quickly caught attention. This measure of dependence has the appealing property of being between 0 and 1, and being 0 or 1 if and only if the corresponding pair of random variables is independent or one is a measurable function of the other almost surely. However, more recent studies (Cao & Bickel 2020; Shi et al. 2022b) showed that independence tests based on Chatterjee’s rank correlation are unfortunately rate inefficient against various local alternatives and they call for variants. We answer this call by proposing an improvement to Chatterjee’s rank correlation that still consistently estimates the same dependence measure, but provably achieves near-parametric efficiency in testing against Gaussian rotation alternatives. This is possible by incorporating many right nearest neighbours in constructing the correlation coefficients. We thus overcome the ‘ only one disadvantage’ of Chatterjee’s rank correlation (Chatterjee, 2021, § 7).
Kernel two-sample tests in high dimensions: interplay between moment discrepancy and dimension-and-sample orders
Authors: Yan Jian , Zhang Xianyang 
Published: August 2022

Summary
Motivated by the increasing use of kernel-based metrics for high-dimensional and large-scale data, we study the asymptotic behaviour of kernel two-sample tests when the dimension and sample sizes both diverge to infinity. We focus on the maximum mean discrepancy using an isotropic kernel, which includes maximum mean discrepancy with the Gaussian kernel and the Laplace kernel, and the energy distance as special cases. We derive asymptotic expansions of the kernel two-sample statistics, based on which we establish a central limit theorem under both the null hypothesis and the local and fixed alternatives. The new nonnull central limit theorem results allow us to perform asymptotic exact power analysis, which reveals a delicate interplay between the moment discrepancy that can be detected by the kernel two-sample tests and the dimension-and-sample orders. The asymptotic theory is further corroborated through numerical studies.
Thoracoabdominal aneurysm causing functional mitral valve stenosis after total arch replacement
Authors: Hugas Maria , Schoenhoff Florian , Schmidli Jürg , Weiss Salome 
Published: August 2022

Abstract
Large thoracic and thoraco-abdominal aneurysms may compress adjacent mediastinal structures. We present a case of a large thoraco-abdominal aneurysm compressing the left atrium and leading to functional mitral valve stenosis after total aortic arch repair, requiring urgent open thoraco-abdominal aneurysm repair.
Neuromodulation devices for heart failure
Authors: Dusi Veronica , Angelini Filippo , Zile Michael R , De Ferrari Gaetano Maria 
Published: August 2022

Abstract
Autonomic imbalance with a sympathetic dominance is acknowledged to be a critical determinant of the pathophysiology of chronic heart failure with reduced ejection fraction (HFrEF), regardless of the etiology. Consequently, therapeutic interventions directly targeting the cardiac autonomic nervous system, generally referred to as neuromodulation strategies, have gained increasing interest and have been intensively studied at both the pre-clinical level and the clinical level. This review will focus on device-based neuromodulation in the setting of HFrEF. It will first provide some general principles about electrical neuromodulation and discuss specifically the complex issue of dose-response with this therapeutic approach. The paper will thereafter summarize the rationale, the pre-clinical and the clinical data, as well as the future prospectives of the three most studied form of device-based neuromodulation in HFrEF. These include cervical vagal nerve stimulation (cVNS), baroreflex activation therapy (BAT), and spinal cord stimulation (SCS). BAT has been approved by the Food and Drug Administration for use in patients with HfrEF, while the other two approaches are still considered investigational; VNS is currently being investigated in a large phase III Study.
Meta-analysis of larvae of the black soldier fly (Hermetia illucens) microbiota based on 16S rRNA gene amplicon sequencing
Authors: IJdema Freek , De Smet Jeroen , Crauwels Sam , Lievens Bart , Van Campenhout Leen 
Published: August 2022

Abstract
Black soldier fly larvae (BSFL) belong to the most widely reared insects as an alternative protein source at industrial scale. Bacteria in the larval gut can provide benefits for the animal, though some bacteria can also be pathogenic for the insect. Accurate characterization of the BSFL microbiota is important for the production of BSFL in terms of yield and microbiological safety. In this study, 16S ribosomal RNA gene sequence data sets from 11 studies were re-analysed to gain better insights in the BSFL gut microbiota, potential factors that influence their composition, and differences between the gut and the whole larvae microbiota. A core gut microbiota was found consisting of members of Enterococcus, Klebsiella, Morganella, Providencia, and Scrofimicrobium. Further, the factors ‘Study’, ‘Age’ and ‘Feed’ (i.e. rearing substrate of the larvae) significantly affected the microbiota gut composition. When compared to whole larvae, a significantly lower diversity was found for gut samples, suggesting that the larvae harboured additional microbes on their cuticle or in the insect body. Universal choices in insect sample type, primer selection and bio-informatics analysis pipeline can strengthen future meta-analyses and improve our understanding of the BSFL gut microbiota towards the optimization of insect rearing conditions and substrates.
A continental-scale survey of Wolbachia infections in blue butterflies reveals evidence of interspecific transfer and invasion dynamics
Authors: Shastry Vivaswat , Bell Katherine L , Buerkle C Alex , Fordyce James A , Forister Matthew L , Gompert Zachariah , Lebeis Sarah L , Lucas Lauren K , Marion Zach H , Nice Chris C 
Published: August 2022

Abstract
Infections by maternally inherited bacterial endosymbionts, especially Wolbachia, are common in insects and other invertebrates but infection dynamics across species ranges are largely under studied. Specifically, we lack a broad understanding of the origin of Wolbachia infections in novel hosts, and the historical and geographical dynamics of infections that are critical for identifying the factors governing their spread. We used Genotype-by-Sequencing data from previous population genomics studies for range-wide surveys of Wolbachia presence and genetic diversity in North American butterflies of the genus Lycaeides. As few as one sequence read identified by assembly to a Wolbachia reference genome provided high accuracy in detecting infections in host butterflies as determined by confirmatory PCR tests, and maximum accuracy was achieved with a threshold of only 5 sequence reads per host individual. Using this threshold, we detected Wolbachia in all but 2 of the 107 sampling localities spanning the continent, with infection frequencies within populations ranging from 0% to 100% of individuals, but with most localities having high infection frequencies (mean = 91% infection rate). Three major lineages of Wolbachia were identified as separate strains that appear to represent 3 separate invasions of Lycaeides butterflies by Wolbachia. Overall, we found extensive evidence for acquisition of Wolbachia through interspecific transfer between host lineages. Strain wLycC was confined to a single butterfly taxon, hybrid lineages derived from it, and closely adjacent populations in other taxa. While the other 2 strains were detected throughout the rest of the continent, strain wLycB almost always co-occurred with wLycA. Our demographic modeling suggests wLycB is a recent invasion. Within strain wLycA, the 2 most frequent haplotypes are confined almost exclusively to separate butterfly taxa with haplotype A1 observed largely in Lycaeides melissa and haplotype A2 observed most often in Lycaeides idas localities, consistent with either cladogenic mode of infection acquisition from a common ancestor or by hybridization and accompanying mutation. More than 1 major Wolbachia strain was observed in 15 localities. These results demonstrate the utility of using resequencing data from hosts to quantify Wolbachia genetic variation and infection frequency and provide evidence of multiple colonizations of novel hosts through hybridization between butterfly lineages and complex dynamics between Wolbachia strains.