bluuebunny/crossref_metadata_2025 huggingface dataset:
Surrounding greenness is associated with lower risk and burden of low birth weight in Iran
Authors: Luo Siqi , Wang Yaqi , Mayvaneh Fatemeh , Relvas Helder , Baaghideh Mohammad , Wang Kai , Yuan Yang , Yin Zhouxin , Zhang Yunquan
Published: November 2023

The nexus between prenatal greenspace exposure and low birth weight (LBW) remains largely unstudied in low- and middle-income countries (LMICs). We investigated a nationwide retrospective cohort of 4,021,741 live births (263,728 LBW births) across 31 provinces in Iran during 2013–2018. Greenness exposure during pregnancy was assessed using satellite-based normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). We estimated greenness-LBW associations using multiple logistic models, and quantified avoidable LBW cases under scenarios of improved greenspace through counterfactual analyses. Association analyses provide consistent evidence for approximately L-shaped exposure-response functions, linking 7.0–11.5% declines in the odds of LBW to each 0.1-unit rise in NDVI/EVI with multiple buffers. Assuming causality, 3931–5099 LBW births can be avoided by achieving greenness targets of mean NDVI/EVI, amounting to 4.4–5.6% of total LBW births in 2015. Our findings suggest potential health benefits of improved greenspace in lowering LBW risk and burden in LMICs.
Pertuzumab plus high-dose trastuzumab for HER2-positive breast cancer with brain metastases: PATRICIA final efficacy data
Authors: Lin Nancy U. , Kumthekar Priya , Sahebjam Solmaz , Ibrahim Nuhad , Fung Anita , Cheng Anna , Nicholas Alan , Sussell Jesse , Pegram Mark
Published: November 2023

The PATRICIA study (NCT02536339) examined the efficacy and safety of pertuzumab plus high-dose trastuzumab in patients with HER2-positive metastatic breast cancer (MBC) with progressive central nervous system (CNS) metastases following radiotherapy. Primary analysis confirmed CNS objective response rate (ORR) was 11% (95% confidence interval [CI]: 3–25); clinical benefit rate (CBR) was 68% (4 months) and 51% (6 months). We report final efficacy data after a further 21-months of follow-up, updated safety, survival, and patient-reported outcomes (PROs). Patients received standard-dose pertuzumab plus high-dose trastuzumab (6 mg/kg weekly) until CNS or systemic disease progression or unacceptable toxicity. Primary endpoint: confirmed ORR (CNS) per Response Assessment in Neuro-Oncology Brain Metastases criteria. Secondary endpoints were response duration, CBR, progression-free survival (PFS), overall survival (OS), safety, and PROs. By clinical cut-off, 39 patients had completed or discontinued treatment. Confirmed ORR (CNS) was 11% (95% CI: 3.0–25.4). Median CNS-PFS was 4.6 months (95% CI: 4.0–8.9), as was median CNS-PFS or systemic PFS (95% CI: 4.0–8.9); median OS was 27.2 months (95% CI: 16.1–not reached). CBR in the CNS was 51% (19 patients, 95% CI: 34.4–68.1) at 6 months. Two patients remained on treatment until study closure, achieving stable disease for 4.1 and 4.8 years. Treatment-related grade 3/4 adverse events occurred in 7.7% of patients. Patients with confirmed partial response or stable disease (≥4 months) in the CNS had stable PROs over time. Pertuzumab plus high-dose trastuzumab represents a reasonable non-chemotherapeutic treatment option for selected patients with HER2-positive MBC with CNS metastases.
Predicting water quality through daily concentration of dissolved oxygen using improved artificial intelligence
Authors: Yang Jiahao
Published: November 2023

As an important hydrological parameter, dissolved oxygen (DO) concentration is a well-accepted indicator of water quality. This study deals with introducing and evaluating four novel integrative methods for the prediction of DO. To this end, teaching–learning-based optimization (TLBO), sine cosine algorithm, water cycle algorithm (WCA), and electromagnetic field optimization (EFO) are appointed to train a commonly-used predictive system, namely multi-layer perceptron neural network (MLPNN). The records of a USGS station called Klamath River (Klamath County, Oregon) are used. First, the networks are fed by the data between October 01, 2014, and September 30, 2018. Later, their competency is assessed using the data belonging to the subsequent year (i.e., from October 01, 2018 to September 30, 2019). The reliability of all four models, as well as the superiority of the WCA-MLPNN, was revealed by mean absolute errors (MAEs of 0.9800, 1.1113, 0.9624, and 0.9783) in the training phase. The calculated Pearson correlation coefficients (RPs of 0.8785, 0.8587, 0.8762, and 0.8815) plus root mean square errors (RMSEs of 1.2980, 1.4493, 1.3096, and 1.2903) showed that the EFO-MLPNN and TLBO-MLPNN perform slightly better than WCA-MLPNN in the testing phase. Besides, analyzing the complexity and the optimization time pointed out the EFO-MLPNN as the most efficient tool for predicting the DO. In the end, a comparison with relevant previous literature indicated that the suggested models of this study provide accuracy improvement in machine learning-based DO modeling.
Speech emotion analysis using convolutional neural network (CNN) and gamma classifier-based error correcting output codes (ECOC)
Authors: Zhao Yunhao , Shu Xiaoqing
Published: November 2023

Speech emotion analysis is one of the most basic requirements for the evolution of Artificial Intelligence (AI) in the field of human–machine interaction. Accurate emotion recognition in speech can be effective in applications such as online support, lie detection systems and customer feedback analysis. However, the existing techniques for this field have not yet met sufficient development. This paper presents a new method to improve the performance of emotion analysis in speech. The proposed method includes the following steps: pre-processing, feature description, feature extraction, and classification. The initial description of speech features in the proposed method is done by using the combination of spectro-temporal modulation (STM) and entropy features. Also, a Convolutional Neural Network (CNN) is utilized to reduce the dimensions of these features and extract the features of each signal. Finally, the combination of gamma classifier (GC) and Error-Correcting Output Codes (ECOC) is applied to classify features and extract emotions in speech. The performance of the proposed method has been evaluated using two datasets, Berlin and ShEMO. The results show that the proposed method can recognize speech emotions in the Berlin and ShEMO datasets with an average accuracy of 93.33 and 85.73%, respectively, which is at least 6.67% better than compared methods.
Moderate intensity walking exercises reduce the body mass index and vascular inflammatory factors in postmenopausal women with obesity: a randomized controlled trial
Authors: Son Woo-Hyeon , Park Hyun-Tae , Jeon Byeong Hwan , Ha Min-Seong
Published: November 2023

Postmenopause, the secretion of female hormones changes, causing excessive fat accumulation in the body and leading to chronic inflammation, which increases the incidence of cardiovascular diseases (CVD). Walking is an easily accessible daily exercise and effective non-pharmacological treatment for reducing obesity and the incidence of CVD. The aim of this study was to investigate the effect of moderate intensity walking exercises on body composition, vascular inflammatory factors, and vascular endothelial growth factor (VEGF) in postmenopausal women with obesity. Twenty-six older postmenopausal women with obesity (ages 68–72) were randomly assigned to control (n = 12, BMI 26.06 ± 1.37) or exercise (n = 14, BMI 26.04 ± 1.94) groups. Following a 12-week moderate intensity walking exercise program, we measured the participants’ body composition with an InBody S10 analyzer and assessed blood sera using enzyme-linked immunosorbent assays. There was a significant clustering by weight (p < 0.01), body mass index (p < 0.01), percentage body fat (p < 0.001), high-sensitivity C-reactive protein (p < 0.05), interleukin-6, and tumor necrosis factor-α (p < 0.05) being significantly decreased in the exercise group. Although VEGF levels did not change significantly, a tendency to increase was observed in participants that exercised. Our results indicate that walking exercise may help prevent CVD in postmenopausal women with obesity by reducing obesity and vascular inflammatory factors.
Additive manufacturing of poly (lactic acid)/hydroxyapatite/carbon nanotubes biocomposites for fibroblast cell proliferation
Authors: da Luz Belo Francilene , Vasconcelos Esleane Vilela , Pinheiro Miriane Alexandrino , da Cruz Barbosa Nascimento Damares , Passos Marcele Fonseca , da Silva Alisson Clay Rios , dos Reis Marcos Allan Leite , Monteiro Sérgio Neves , Brígida Rebecca Thereza Silva Santa , Rodrigues Ana Paula Drummond , Candido Verônica Scarpini
Published: November 2023

Bone tissue is one of the most important in the human body. In this study, scaffolds of poly (lactic acid) PLA reinforced with hydroxyapatite (HA) and carbon nanotubes (CNT) were manufactured, evaluating their mechanical and biological properties. HA was synthesized by wet method and characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The scaffolds were produced using additive manufacturing and characterized by optical microscopy, SEM, thermogravimetric analysis (TGA), Raman spectroscopy and biological tests. The SEM results showed that the PLA surface was affected by the incorporation of CNT. TG showed that the incorporation of HA into the polymer matrix compromised the thermal stability of PLA. On the other hand, the incorporation of CNT to the polymer and the impregnation with HA on the surface by thermal effect increased the stability of PLA/CNT scaffolds. Raman spectra indicated that HA impregnation on the surface did not modify the polymer or the ceramic. In the compression tests, PLA and PLA/CNT scaffolds displayed the best compressive strength. In the biological tests, more than 85% of the cells remained viable after 48 h of incubation with all tested scaffolds and groups with CNT in the composition disclosing the best results.
Evaluating automatic sentence alignment approaches on English-Slovak sentences
Authors: Forgac Frantisek , Munkova Dasa , Munk Michal , Kelebercova Livia
Published: November 2023

Parallel texts represent a very valuable resource in many applications of natural language processing. The fundamental step in creating parallel corpus is the alignment. Sentence alignment is the issue of finding correspondence between source sentences and their equivalent translations in the target text. A number of automatic sentence alignment approaches were proposed including neural networks, which can be divided into length-based, lexicon-based, and translation-based. In our study, we used five different aligners, namely Bilingual sentence aligner (BSA), Hunalign, Bleualign, Vecalign, and Bertalign. We evaluated both, the performance of the Bertalign in terms of accuracy against the up to now employed aligners as well as among each other in the language pair English-Sovak. We created our custom corpus consisting of texts collected in 2021 and 2022. Vecalign and Bertalign performed statistically significantly best and BSA the worst. Hunalign and Bleualign achieved the same performance in terms of F1 score. However, Bleualign achieved the most diverse results in terms of performance.
The combination of Ephedrae herba and coixol from Coicis semen attenuate adiposity via glucocorticoid receptor regulation
Authors: Yu Ga-Ram , Kim Jai-Eun , Lim Dong-Woo , Park Won-Hwan
Published: November 2023

The enhanced therapeutic effects and mechanisms of certain herbal combination in various herbal prescriptions are mostly unclear. A combination of two herbs, namely Ephedrae herba (EH) and Coicis semen (CS), has been commonly prescribed for obesity. In our previous work, the combination of EH and CS was studied using network pharmacological approach to predict its pharmacological targets and in vitro experiments to evaluate its efficacy on obesity. Although we demonstrated enhanced anti-adiposity effects of the combination on matured adipocytes, the molecular mechanisms and contributing compounds underlying the effects of EH-CS combination on adiposity or adipogenesis were not fully elucidated. The current study adopted integrated bioinformatics analysis to precisely validate potential targets of EH-CS by screening differentially expressed genes (DEGs) of morbid obesity patients from NCBI gene expression omnibus (GEO). Based on the functional cluster analysis of down-regulated DEGs, the anti-adipogenesis mechanism of EH-CS combination was speculated with KEGG enrichment analysis. Furthermore, we investigated the combinational effects of EH and coixol, or stigmasterol, the two compounds in CS which were expected to have main beneficial effects in metabolic diseases. Moreover, distinct effect of the combination on transcriptional activity of glucocorticoid receptor (GR) was investigated using electrophoretic mobility shift assay (EMSA). The EH-CS combination was predicted to modulate down-regulated genes which are involved in KEGG pathways crucial to metabolic disease in morbidly obese individuals. The combination of EH with CS compounds significantly increased the phosphorylation of acetyl-coA carboxylase (ACC), AMP-activated protein kinase (AMPK), and protein kinase B (AKT) in 3T3-L1 cells and decreased intracellular lipid accumulation. The two CS compounds significantly increased the anti-adipogenesis/lipogenesis effects of EH by inhibiting the gene expression levels. Finally, the combination of EH and coixol inhibited dexamethasone-induced GR translocation to the nucleus and transcriptional binding activity in adipocytes. The combination of EH and CS could be considered a therapeutic strategy for treating metabolic diseases, including obesity.
Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies
Authors: Roointan Amir , Ghaeidamini Maryam , Shafieizadegan Saba , Hudkins Kelly L. , Gholaminejad Alieh
Published: November 2023

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease’s underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ − 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.
Fuzzy inference-based LSTM for long-term time series prediction
Authors: Wang Weina , Shao Jiapeng , Jumahong Huxidan
Published: November 2023

Long short-term memory (LSTM) based time series forecasting methods suffer from multiple limitations, such as accumulated error, diminishing temporal correlation, and lacking interpretability, which compromises the prediction performance. To overcome these shortcomings, a fuzzy inference-based LSTM with the embedding of a fuzzy system is proposed to enhance the accuracy and interpretability of LSTM for long-term time series prediction. Firstly, a fast and complete fuzzy rule construction method based on Wang–Mendel (WM) is proposed, which can enhance the computational efficiency and completeness of the WM model by fuzzy rules simplification and complement strategies. Then, the fuzzy prediction model is constructed to capture the fuzzy logic in data. Finally, the fuzzy inference-based LSTM is proposed by integrating the fuzzy prediction fusion, the strengthening memory layer, and the parameter segmentation sharing strategy into the LSTM network. Fuzzy prediction fusion increases the network reasoning capability and interpretability, the strengthening memory layer strengthens the long-term memory and alleviates the gradient dispersion problem, and the parameter segmentation sharing strategy balances processing efficiency and architecture discrimination. Experiments on publicly available time series demonstrate that the proposed method can achieve better performance than existing models for long-term time series prediction.
A dynamic link between spring Arctic sea ice and the Tibetan Plateau snow increment indicator
Authors: Zhang Chao , Duan Anmin , Jia XiaoJing , Wang Zhibiao , Pan Zhulei
Published: November 2023

Tibetan Plateau snow has long been recognized as a sensitive indicator of climate variability. However, the commonly used snow indicator cannot fully represent the interannual variability in late autumn Tibetan Plateau snow. Here, we establish a snow indicator for Tibetan Plateau snow variability: the snow cover extent increment within late autumn. This snow increment indicator improves the standard deviation by 72.6% and diabatic cooling by 89.7% over the entire Tibetan Plateau, which shows a robust link to the subsequent spring Arctic sea-ice concentration. Faster Tibetan Plateau snow cover extent increment leads to stronger Tibetan Plateau cooling and disturbance along the subtropical westerly jet, thereby inducing a Tibetan Plateau-Arctic wave train and a spring Arctic cyclonic anomaly. This cyclonic system favors the reduction in spring Arctic sea-ice by equatorward sea ice drift, implying a link between spring Arctic sea-ice and the Tibetan Plateau snow increment indicator through the wave train and sea-ice dynamic processes.
Ternary solvent based homogeneous liquid–liquid microextraction for the preconcentration of organochlorine pesticides from water and apple juice samples
Authors: Ago Kero Assefa , Kitte Shimeles Addisu , Chirfa Gadisa , Gure Abera
Published: November 2023

Organochlorine pesticide residues in water and apple juice samples have been analysed using a ternary solvents-based homogeneous liquid–liquid microextraction method. The method has satisfactory EFs, low LODs, and a short extraction time.
Direct immunoassay on a polyester microwell plate for colorimetric detection of the spike protein in swab and saliva samples
Authors: Moreira Nikaele S. , Baldo Thaisa A. , Duarte Lucas C. , Lopes-Luz Leonardo , Oliveira Karoliny A. , Estrela Paulo F. N. , Simões Amanda M. , Bührer-Sékula Samira , Duarte Gabriela R. M. , Coltro Wendell K. T.
Published: November 2023

This study presents the development of a polyester microplate for detecting the S-protein of the SARS-CoV-2 virus in saliva and nasopharyngeal swab samples using direct enzyme-linked immunosorbent assay (ELISA) technology.
Co-crystallization: a green approach for the solubility enhancement of poorly soluble drugs
Authors: Bhatia Meenakshi , Devi Sunita
Published: November 2023

The co-crystallization of pharmaceutical drugs is gaining consideration because it is an environmentally friendly and potentially effective technique to improve the solubility and bioavailability of poorly soluble drugs.
A minimal kinetic model for the interpretation of complex catalysis in single enzyme molecules
Authors: Kundu Prasanta , Saha Soma , Gangopadhyay Gautam
Published: November 2023

A three-state stochastic kinetic model is the minimal requirement to interpret the results of single β-galactosidase-driven conversions of resorufin-β-d-galactopyranoside into fluorogenic product resorufin.
Various states of water species in an anion exchange membrane characterized by Raman spectroscopy under controlled temperature and humidity
Authors: Wakolo Solomon Wekesa , Tryk Donald A. , Nishiyama Hiromichi , Miyatake Kenji , Iiyama Akihiro , Inukai Junji
Published: November 2023

A combination of Raman spectroscopy of an anion exchange membrane (AEM), Raman spectroscopy of alkaline solutions and Density Functional Theory calculations is used to elucidate the OH species in an AEM. The behavior of the species is discussed.
Mechanistic insight into the N2O + O(1D,3P) reaction: role of post-CCSD(T) corrections and non-adiabatic effects
Authors: Anand Vishva Jeet , Kumar Pradeep
Published: November 2023

In the present work, we have studied the N2O + O(1D,3P) reaction using high level quantum chemical calculations along with non-adiabatic kinetics.
Enantiomorphic kagome bands in a two-dimensional covalent organic framework with non-trivial magnetic and topological properties
Authors: Gao Quan , Sun Xuelian , Xu Xuhui , Jiang Xinxin , Wang Zhikuan , Yang Lei , Li Dongmei , Cui Bin , Liu Desheng
Published: November 2023

Non-trivial magnetic and topological properties are presented for a two-dimensional covalent organic framework, COF-SH, that has enantiomorphic kagome bands.
Chemical characterization of urban aerosols in Abidjan and Korhogo (Côte d'Ivoire) from 2018 to 2020 and the identification of their potential emission sources
Authors: Gnamien Sylvain , Liousse Cathy , Keita Sékou , Silué Siélé , Bahino Julien , Gardrat Eric , Kassamba-Diaby Mohamed , Ochou Arsène , Yoboué Véronique
Published: November 2023

As part of PASMU project, equipment was installed in urban sites of Abidjan and Korhogo (Cote d'Ivoire) in West Africa with the aim of studying the chemical composition of PM2.5 aerosols and identifying the contributing emissions sources.
In vitro digestion of high-lipid emulsions: towards a critical interpretation of lipolysis
Authors: Okuro Paula K. , Viau Michèle , Marze Sébastien , Laurent Sophie , Cunha Rosiane L. , Berton-Carabin Claire , Meynier Anne
Published: November 2023

A holistic interpretation including FFA titration and evaluation of lipolytic products appears of great relevance to capture the complexity of in vitro lipid digestion.
Towards scalable reductive etherification of 5-hydroxymethyl-furfural through iridium-zeolite-based bifunctional catalysis
Authors: Sun Zehui , Chen Mugeng , Wang Kaizhi , Chen Chen , Fei Jiachen , Guo Wendi , Zhu Conglin , He Heyong , Liu Yongmei , Cao Yong
Published: November 2023

Ir/Na-ZSM-5, featuring ultra-small Ir NPs and suitable surface acidity resulted from alkali exchange, could deliver impressive BMMF yields from concentrated HMF (up to 22.6 wt%) via reductive etherification under mild conditions (50 °C, 1 MPa H2).
One-step synthesized Nb2O5−y-decorated spinel-type (Ni,V,Mn)3O4−x nanoflowers for boosting electrocatalytic reduction of nitrogen into ammonia
Authors: Gemeda Tadele Negash , Kuo Dong-Hau , Ha Quoc-Nam
Published: November 2023

Two renewable methods for reducing greenhouse gas emissions associated with ammonia (NH3) production are the renewable H2-combined Haber–Bosch process and the electrochemical nitrogen reduction reaction (eNRR).
Cobalt macrocyclic complex-catalyzed selective electroreduction of CO2 to CO
Authors: Xie Wen-Jun , Chen Jin-Mei , Yang Zhi-Wen , He Liang-Nian
Published: November 2023

A novel member of the metal porphyrinoids family, cobalt 5,15-diazaporphyrin, as a molecular catalyst immobilized onto carbon nanotubes, was developed for highly efficient ECO2R to CO.
ZIF-8-based core/shell nanocarriers for relieving multidrug resistance in cancer therapy
Authors: Gong Jinsong , Li Xiaobin , Li Shaoping , Xu Man , Dai Wubin
Published: November 2023

Treatment of cancer cells mediated via VER.
Development and validation of a LC-MS/MS method for the simultaneous determination of cycloicaritin and its carbamate prodrug in rat plasma: application to pharmacokinetic study
Authors: Wang Weiping , Li Fengxiao , Fan Jiaqi , Gan Shuo , Zhang Jiaming , Jiang Qikun , Zhang Tianhong
Published: November 2023

This work aimed to develop an accurate and selective LC-MS/MS method for simultaneous determination of CICT and its carbamate prodrug (3-L) in rat plasma, and the proposed method was validated and successfully applied to a pharmacokinetic study.
Quantified instant conjugation of peptides on a nanogold surface for tunable ice recrystallization inhibition
Authors: Yang Shixuan , Ding Zhongxiang , Chu Leiming , Su Mengke , Liu Honglin
Published: November 2023

The adverse effects of recrystallization limit the application of cryopreservation in many fields.
Highly efficient and stable tandem luminescent solar concentrators based on carbon dots and CuInSe2−xSx/ZnS quantum dots
Authors: Wang Lianju , Chen Yiqing , Lai Yueling , Zhao Xianglong , Zheng Kanghui , Wang Ruilin , Zhou Yufeng
Published: November 2023

In this work, we fabricated the among green, environment-friendly tandem LSCs using CDs and near-infrared CuInSeS/ZnS QDs as PL emitters. Tandem LSCs exhibits high PCE (0.46%) and ηext (2.57%) and improve the stability of bottom LSCs.
An expeditive and green chemo-enzymatic route to diester sinapoyl-l-malate analogues: sustainable bioinspired and biosourced UV filters and molecular heaters
Authors: Rioux Benjamin , Mouterde Louis M. M. , Alarcan Jimmy , Abiola Temitope T. , Vink Matthias J. A. , Woolley Jack M. , Peru Aurélien A. M. , Mention Matthieu M. , Brunissen Fanny , Berden Giel , Oomens Jos , Braeuning Albert , Stavros Vasilios G. , Allais Florent
Published: November 2023

A highly regioselective lipase-catalyzed transesterification enabled the synthesis of sinapoyl malate diesters with fatty chains that proved to be exceptional UV filters and molecular heaters for plants.
Native mass spectrometry of proteoliposomes containing integral and peripheral membrane proteins
Authors: Zhu Yun , Yun Sangho D. , Zhang Tianqi , Chang Jing-Yuan , Stover Lauren , Laganowsky Arthur
Published: November 2023

Cellular membranes are critical to the function of membrane proteins, whether they are associated (peripheral) or embedded (integral) within the bilayer.
Synthesis of substituted benzylboronates by light promoted homologation of boronic acids with N-sulfonylhydrazones
Authors: Valdés-Maqueda Álvaro , López Lucía , Plaza Manuel , Valdés Carlos
Published: November 2023

The homologation of boronic acids with diazoalkanes obtained by photochemical decomposition of N-tosylhydrazones leads to substituted benzylboronates, previously unavailable under thermal conditions. Batch and continuous flow reactions are described.
Endothelial progenitor cells in pregnancy-related diseases
Authors: Chen Yangyang , Wan Gui , Li Zeyun , Liu Xiaoxia , Zhao Yin , Zou Li , Liu Weifang
Published: November 2023

Placental neovascularization plays a crucial role in fetomaternal circulation throughout pregnancy and is dysregulated in several pregnancy-related diseases, including preeclampsia, gestational diabetes mellitus, and fetal growth restriction. Endothelial progenitor cells (EPCs) are a heterogeneous population of cells that differentiate into mature endothelial cells, which influence vascular homeostasis, neovascularization, and endothelial repair. Since their discovery in 1997 by Asahara et al., the role of EPCs in vascular biology has garnered a lot of interest. However, although pregnancy-related conditions are associated with changes in the number and function of EPCs, the reported findings are conflicting. This review discusses the discovery, isolation, and classification of EPCs and highlights discrepancies between current studies. Overviews of how various diseases affect the numbers and functions of EPCs, the role of EPCs as biomarkers of pregnancy disorders, and the potential therapeutic applications involving EPCs are also provided.
Evaluation of Lifetime Noise Exposure History Reporting
Authors: De Poortere Nele , Verhulst Sarah , Degeest Sofie , Keshishzadeh Sarineh , Dhooge Ingeborg , Keppler Hannah
Published: November 2023

Purpose:

The purpose of this study is to critically evaluate lifetime noise exposure history (LNEH) reporting. First, two different approaches to evaluate the cumulative LNEH were compared. Second, individual LNEH was associated with the subjects' hearing status. Third, loudness estimates of exposure activities, by means of Jokitulppo- and Ferguson-based exposure levels, were compared with dosimeter sound-level measurements.

Method:

One hundred one young adults completed the questionnaires, and a subgroup of 30 subjects underwent audiological assessment. Pure-tone audiometry, speech-in-noise intelligibility, distortion product otoacoustic emissions, auditory brainstem responses, and envelope following responses were included. Fifteen out of the 30 subjects took part in a noisy activity while wearing a dosimeter.

Results:

First, results demonstrate that the structured questionnaire yielded a greater amount of information pertaining to the diverse activities, surpassing the insights obtained from an open-ended questionnaire. Second, no significant correlations between audiological assessment and LNEH were found. Lastly, the results indicate that Ferguson-based exposure levels offer a more precise estimation of the actual exposure levels, in contrast to Jokitulppo-based estimates.

Conclusions:

We propose several recommendations for determining the LNEH. First, it is vital to define accurate loudness categories and corresponding allocated levels, with a preference for the loudness levels proposed by Ferguson et al. (2019), as identified in this study. Second, a structured questionnaire regarding LNEH is recommended, discouraging open-ended questioning. Third, it is essential to include a separate category exclusively addressing work-related activities, encompassing various activities for more accurate surveying.
Polyvinylchloride surface modified with polymer brushes for reduced protein, cell and bacteria attachment
Authors: Almousa Rashed , Xie Dong , Li Jiliang , Anderson Gregory G.
Published: November 2023

The objective of this study was to coat negatively charged polymer brushes by a covalent bond onto the surface of polyvinylchloride using a simple conventional surface free radical polymerisation technique. The coated surfaces were assessed with contact angle, protein adsorption, cell adhesion, and bacterial adhesion. Bovine serum albumin and bovine fibrinogen were used for protein adsorption evaluation. Mouse fibroblast (NIH‐3T3) cells and Pseudomonas aeruginosa were used to assess surface adhesion. Results show that the surface modified with all the attached polymers exhibited significantly reduced contact angle, protein adsorption, and cell as well as bacterial adhesion among which the negatively charged polymers showed extremely low values in all the tests. The negatively charged polymer shows its contact angle at 5° as compared to 70° for original polyvinylchloride. Its bovine serum albumin, bovine fibrinogen, 3T3 adhesion, and P. aeruginosa adhesion were 93%, 84%, 92%, and 92% lower than the original PVC, respectively. Furthermore, the polyvinylchloride surface coated with negatively charged polymer brushes exhibited a hydrogel‐like property. The results indicate that coating a polyvinylchloride surface with acrylic acids using a simple surface‐initiated free radical polymerisation and then converting those to negatively charged salts can be an effective and efficient route for fouling‐resistant applications.
Bit error rate performance of underwater optical wireless communication with hybrid modulation
Authors: Luo Qiong , Chen Chao , Ji Jianhua , Xu Ming
Published: November 2023

Turbulence effects can cause performance degradation for underwater optical wireless communication (UOWC) system. In this paper, a hybrid modulation (modulation–modulation, M–M) is presented to resist turbulence‐induced fading and improve the transmission performance of UOWC systems. The proposed differential phase shift keying‐multiple pulse position modulation (DPSK‐mPPM) is adopted as hybrid modulation to improve the bit error rate (BER) performance over UOWC turbulence channel of Gamma–Gamma distribution. Subsequently, the closed‐form average BER expressions of mPPM and hybrid DPSK‐mPPM modulation are derived and investigated, which are related to the oceanic turbulence parameters. The influence of the variation of anisotropy factor of ocean turbulence is analysed for the UOWC system over strong Gamma–Gamma turbulent channels. Finally, the numerical simulation results demonstrate the effectiveness of proposed DPSK‐mPPM to improve the BER performance compared to the traditional single modulation over UOWC channel under the same conditions. Furthermore, the work of this paper improves the practicality of the proposed DPSK‐mPPM scheme for underwater communication in complex oceanic environments.
Lag‐related noise shrinkage stacked LSTM network for short‐term traffic flow forecasting
Authors: Li Kai , Bai Weihua , Huang Shaowei , Tan Guanru , Zhou Teng , Li Keqin
Published: November 2023

For the transport networks only equipped with sparse or isolated detectors, short‐term traffic flow forecasting faces the following problems: (1) there are only temporal information and no spatial information; (2) the noises in the traffic flow significantly affect the forecasting performance. In this paper, a lag‐related noise shrinkage stacked long short‐term memory (LSTM) network is proposed for the traffic flow forecasting task only related to temporal information. To extract effective temporal features, the optimal time lags are selected in the traffic flow and converted into lag‐related multi‐dimensional data. Then, a discrete wavelet threshold denoising shrinkage algorithm is designed to filter the noises to construct a more reliable training set. A multi‐level stacked LSTM network is employed to learn the features of the training set to map the past traffic flow to the future flow. Four benchmark datasets are to evaluate the forecasting performance by extensive experiments. The comparison with the state‐of‐the‐art models demonstrates an average improvement of 7.28% in MAPE and 6.02% in RMSE. In addition, the proposed method has been applied in the Guilin Travel Network Bus Intelligent Dispatching System. It improves the utilization of the vehicles and reduces operating costs.
Frequency stabilization for interconnected renewable based power system using cascaded model predictive controller with fractional order PID controller
Authors: Sibtain Daud , Rafiq Turab , Bhatti Mehdi Hassan , Shahzad Sulman , Kilic Heybet
Published: November 2023

In today's electrical grid, frequency cannot be ignored. The proliferation of renewable energy sources into a power system degrades its frequency; hence, the need for frequency regulation has been increased as a result. The second major factor is the perturbation in the load that originates the frequency fluctuation which needs to be suppressed in minimum time to avoid any system collapse. The designing of an optimal controller is indispensable because of increasing power system complexity. In order to maintain the stability of the power system, this paper presents the cascaded design of the model predictive controller with fractional order PID controller (MPC‐FOPIDN) to mitigate the frequency oscillations due to disruption in load. The combination of the predictive capabilities of model predictive control (MPC) and fractional order control enhances control abilities, making it an optimal control strategy for load frequency control (LFC). The grasshopper optimization algorithm (GOA) is applied to obtain optimal gains values for the FOPID controller. The efficacy of the controller has effectively mitigated frequency fluctuations caused by a change in demand or any uncertainty in the power system.
Rolling bearing weak fault detection using transient structure‐optimal VMD and adaptive group sparse coding
Authors: Yuan Xing , Liu Hui , Zhang Huijie
Published: November 2023

Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non‐stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure‐optimal variational mode decomposition (TS‐OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state‐of‐the‐art method.
The need for spatially resolved observations of polycyclic aromatic hydrocarbons in protoplanetary discs
Authors: Lange K. , Dominik C. , Tielens A. G. G. M.
Published: November 2023

Context. The signatures of polycyclic aromatic hydrocarbons (PAHs) have been observed in protoplanetary discs, and their emission features obtained from spectral energy distributions (SED) have been used in the literature to characterise their size and determine their abundance.

Aims. Two simple disc models (uniform PAH distribution against a PAH gap in the inner disc) are compared to investigate the difference of their SED and obtainable information.

Methods. We used the radiative transfer code RADMC-3D to model the SED of two protoplanetary discs orbiting a typical Herbig star, one of which features a depletion of PAHs in the inner disc. We further created artificial images of the discs at face-on view to extract radial profiles of the PAH emission in the infrared.

Results. We find that the extracted PAH features from an SED provide limited information about the PAHs in protoplanetary disc environments, except for the ionisation state. The distribution of PAHs in a protoplanetary disc influences the total observed PAH luminosity in a non-linear fashion and alters the relative strength between the 3.3 µm and 11.3 µm features. Furthermore, we produced radial profiles at the 3 µm, 6 µm and, 11 µm PAH emission features and find that they follow a double power-law profile where the slope reflects the radiative environment (single photon regime vs. multi-photon regime) in which the PAHs lie.

Conclusions. Using spatially resolved techniques such as IFU or imaging in the era of the James Webb Space Telescope, we find that multi-wavelength radial emission profiles will not only provide information on the spatial distribution of the PAHs, but may also provide information on their size and underlying UV environment, which is crucial for photo-evaporative disc wind models.
Effects of infrasound on the perception of a low-frequency sound
Authors: Friedrich Björn , Joost Holger , Fedtke Thomas , Verhey Jesko L.
Published: November 2023

The study investigated the effects of an 8 Hz infrasound sinusoid, presented at a sensation level (SL) of 9 decibels (dB), on the detection of i) a 64 Hz pure tone and ii) an 8 Hz sinusoidal amplitude modulation imposed on a 64 Hz sinusoidal carrier, presented at an SL of 25 dB. Two phase relations between infrasound and amplitude modulation were used: 0° (in phase) and 180° (in antiphase). Detection thresholds of the 64 Hz pure tone were, on average across 19 normal-hearing listeners, 4.6 dB higher in the presence than in the absence of the infrasound. Modulation detection thresholds also increased in the presence of the infrasound, on average by about 3 dB. Large individual differences in modulation detection thresholds were observed with respect to the two phase relations. On average across all listeners, however, the difference in modulation detection thresholds between in-phase and antiphase infrasound was not significant. The study shows that supra-threshold infrasound masks frequency components in the low audio-frequency range and affects the perception of amplitude modulations imposed on them.
Comparison of Simple Matching Coefficient and Euclidean Distance In K-Means Algorithm for Tourism Destination Classification
Authors: Agustina Candra , Purwanto Purwanto , Farikhin Farikhin
Published: November 2023

The tourism sector is currently experiencing accelerated growth after being slumped due to the Covid 19 pandemic for 3 years. government assistance continues to be disbursed to restore the tourism industry. this is responded by tourism businesses by developing tourist destinations. The development includes improving existing destinations, as well as by creating new tourist destinations. in various regions, the government and the private sector are competing to build new tourist attractions, usually located near tourist destinations that are well known to the public. These tourist destinations need to be recorded by the government and tourists. in collecting data, it is necessary to make groupings based on the characteristics of these tourist destinations. To assess a destination is done using the 6A framework, namely attractions, amenities, access, availability, activities, Ancillary. The data obtained is then clustered using the K-Means algorithm, using Simple Matching coefficient and Euclidean Distance. SMC uses the principle of similarity while Euclidean uses the principle of calculating the distance between data. from the calculation results of the two methods, the resulting calculator is the same but when the Davies Bouldin Index is calculated, Euclidean Distance shows better performance.
Investigation of the Impact of the Peer-To-Peer Lending Market on the Membership Motivation of the MSME
Authors: Purwanto Iwan , Isnanto R. Rizal , Puji Widodo Aris
Published: November 2023

FinTech has several activities in it, one of which is Peer-to-Peer lending. P2P (Peer-to-Peer lending) financing is an alternative for micro, small and medium enterprises (MSMEs) to obtain funding. This study identifies the benefits of MSME P2P lending. Analyzing credible sources through an interview process with MSME actors who have utilized peer to peer lending facilities. This study uses a qualitative method. Analysis shows that P2P lending benefits MSME. First, P2P lending offers easier and faster access to financing, which helps MSME get loans from traditional banks. Second, the P2P lending platform offers flexibility in loan terms that can be tailored to the unique needs of each MSME, increasing their chances of obtaining financing. In general, peer-to-peer lending requires very appropriate benefits for MSME actors.
Classification of Heart Disease Using Linear Discriminant Analysis Algorithm
Authors: Isnanto R. Rizal , Rashad Ibnu , Edi Widodo Catur
Published: November 2023

Ischaemic coronary heart disease is the number one cause of death globally. Detecting this disease can only be done by consulting directly with a cardiologist at a cost that is certainly not small. Therefore, is a need for a system to detect heart disease in patients with accuracy but low cost. With the development of technology, especially in artificial intelligence area, there was machine learning techniques to enhance automatic detection capabilities. Linear Discriminant Analysis are one of machine learning method for prediction to detect heart disease as early as possible. In this study, linear discriminant analysis algorithm was implemented to classify heart disease. Dataset used are from the UCI machine learning repository. This study carried out two experimental conditions, classifying heart disease based on suffer or not, other is classifying heart disease by 5 level stage. Result proves that the performance of the classifier with LDA with 2 classes is better than 5 classes. Performance of the LDA algorithm in classifying heart disease with 2 labels that are used as targets or outputs. From these results, the precision value is 0.82, the recall value is 0.81, the F1 score value is 0.81, with an accuracy of 81.22%.
Harnessing Collective Capacities: A Sociological Exploration of Stakeholder Contributions in Community-Based Environmental Stewardship and Empowerment
Authors: Hilmi Pujihartati Sri , Dwi Astuti Nurhaeni Ismi , Tri Kartono Drajat , Demartoto Argyo
Published: November 2023

This study examines the role of diverse stakeholders in the Kampung Harmoni Empowerment Program, a community-based initiative aimed at environmental preservation in Indonesia. It utilizes Interpretative Phenomenological Analysis (IPA) to examine the contributions of the local community, the Environmental Agency (DLH), and CSR PT Indaco Warna Dunia, as they navigate the complexities of sustainable community development. The stakeholders' contributions are categorized into five types: physical labor, financial resources, material resources, moral support, and decision-making. Utilizing social capital theory, collective action theory, and the theory of planned behavior, the study provides a detailed analysis of the contributions and their impact on the program's success. The results reveal that the community's investment in social capital, the collective action exhibited by all stakeholders, and the moral guidance and motivation provided have fostered a communal vision for environmental preservation and community empowerment. This intricate interplay of stakeholder contributions provides a robust model for sustainable community development. The study concludes that successful community-based environmental preservation programs necessitate collaboration, shared resources, and an underpinning of sociological understanding. The findings provide valuable insights for similar initiatives and underscore the power of community-led, multi-stakeholder collaboration for sustainable development. This research enriches the field of sociology by providing an in-depth examination of how the collective efforts of various stakeholders can result in the effective management and preservation of the environment.