bluuebunny/crossref_metadata_2025 huggingface dataset:
Changes in Sexuality and Sexual Dysfunction over Time in the First Two Years after Treatment of Head and Neck Cancer
Authors: Stone Margot A. , Lissenberg-Witte Birgit I. , de Bree Remco , Hardillo Jose A. , Lamers Femke , Langendijk Johannes A. , Leemans C. René , Takes Robert P. , Jansen Femke , Verdonck-de Leeuw Irma M.
Published: September 2023

The aim of this study was to investigate changes in sexuality and sexual dysfunction in head and neck cancer (HNC) patients in the first two years after treatment, in relation to the type of treatment. Data were used of 588 HNC patients participating in the prospective NETherlands Quality of life and Biomedical Cohort Study (NET-QUBIC) from diagnosis to 3, 6, 12 and 24 months after treatment. Primary outcome measures were the International Index of Erectile Function (IIEF) and the Female Sexual Function Index (FSFI). The total scores of the IIEF and FSFI were dichotomized into sexual (dys)function. In men, type of treatment was significantly associated with change in erectile function, orgasm, satisfaction with intercourse, and overall satisfaction. In women, type of treatment was significantly associated with change in desire, arousal, and orgasm. There were significant differences between treatment groups in change in dysfunctional sexuality. A deterioration in sexuality and sexual dysfunction from baseline to 3 months after treatment was observed especially in patients treated with chemoradiation. Changes in sexuality and sexual dysfunction in HNC patients were related to treatment, with an acute negative effect of chemoradiation. This effect on the various domains of sexuality seems to differ between men and women.
DKK1 Promotes Epithelial–Mesenchymal Transition and Cisplatin Resistance in Gastric Cancer via Activation of the PI3K/AKT Pathway
Authors: Li Jian , Zhang Yaqiong , Ye Fangzhou , Qian Peiyu , Qin Zhe , Li Deming , Ye Li , Feng Li
Published: September 2023

Chemotherapy is a classical method of cancer treatment. Cisplatin-based chemotherapy is a traditional and essential therapeutic approach in gastric cancer treatment. However, the development of drug resistance during treatment is a major obstacle that limits their further application, and molecular changes have occurred in the development of drug resistance. Here, we found that Dickkopf-related protein 1 (DKK1) is highly expressed in gastric cancer and related to poor prognosis in gastric cancer patients through public database mining. Next, we also identified that DKK1 is highly expressed in CDDP-resistant gastric cancer cell lines, supporting the notion that DKK1 is a necessary regulator of CDDP resistance. In terms of mechanistic research, our data reveal that DKK1 was able to activate the PI3K/AKT pathway and affect epithelial-to-mesenchymal transition, further contributing to CDDP resistance. Genetic knockdown and pharmacological inhibition of DKK1 recovered CDDP sensitivity both in vitro and in vivo. Therefore, our study highlights the potential of targeted inhibition of DKK1 to reverse CDDP resistance and alleviate metastatic properties in gastric cancer.
A Study on Survival Analysis Methods Using Neural Network to Prevent Cancers
Authors: Bae Chul-Young , Kim Bo-Seon , Jee Sun-Ha , Lee Jong-Hoon , Nguyen Ngoc-Dung
Published: September 2023

Background: Cancer is one of the main global health threats. Early personalized prediction of cancer incidence is crucial for the population at risk. This study introduces a novel cancer prediction model based on modern recurrent survival deep learning algorithms. Methods: The study includes 160,407 participants from the blood-based cohort of the Korea Cancer Prevention Research-II Biobank, which has been ongoing since 2004. Data linkages were designed to ensure anonymity, and data collection was carried out through nationwide medical examinations. Predictive performance on ten cancer sites, evaluated using the concordance index (c-index), was compared among nDeep and its multitask variation, Cox proportional hazard (PH) regression, DeepSurv, and DeepHit. Results: Our models consistently achieved a c-index of over 0.8 for all ten cancers, with a peak of 0.8922 for lung cancer. They outperformed Cox PH regression and other survival deep neural networks. Conclusion: This study presents a survival deep learning model that demonstrates the highest predictive performance on censored health dataset, to the best of our knowledge. In the future, we plan to investigate the causal relationship between explanatory variables and cancer to reduce cancer incidence and mortality.
Clonal Extinction Drives Tumorigenesis
Authors: Amaro Adriana , Pfeffer Ulrich
Published: September 2023

Before a tumor is diagnosed and surgically removed, it has been growing for many months or even years [...]
Absolute Quantification of Pan-Cancer Plasma Proteomes Reveals Unique Signature in Multiple Myeloma
Authors: Kotol David , Woessmann Jakob , Hober Andreas , Álvez María Bueno , Tran Minh Khue Hua , Pontén Fredrik , Fagerberg Linn , Uhlén Mathias , Edfors Fredrik
Published: September 2023

Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.
High-Dose Acetaminophen with N-acetylcysteine Rescue Inhibits M2 Polarization of Tumor-Associated Macrophages
Authors: Bryan Allyn , Pingali Pavani , Joslyn Martha , Li Howard , Bernas Tytus , Koblinski Jennifer , Landry Joseph , Lee Won Sok , Patel Bhaumik , Neuwelt Alexander
Published: September 2023

High-dose acetaminophen (AAP) with N-acetylcysteine (NAC) rescue is among the few treatments that has shown activity in phase I trials without achieving dose-limiting toxicity that has not progressed to evaluation in later line studies. While the anti-tumor effects of AAP/NAC appear not to be mediated by glutathione depletion and free radical injury, the mechanism of anti-tumor effects of AAP/NAC has not been definitively characterized. In vitro, the effects of AAP/NAC were evaluated on bone marrow derived macrophages. Effects of AAP on IL-4/STAT6 (M2) or IFN/LPS/STAT1 (M1) signaling and downstream gene and protein expression were studied. NAC reversed the AAP toxicity in the normal liver but did not reverse AAP cytotoxicity against tumor cells in vitro. AAP/NAC selectively inhibited IL-4-induced STAT6 phosphorylation but not IFN/LPS-induced STAT1 phosphorylation. Downstream, AAP/NAC inhibited IL-4 induction of M2-associated genes and proteins but did not inhibit the IFN/LPS induction of M1-associated genes and proteins. In vivo, AAP/NAC inhibited tumor growth in EF43.fgf4 and 4T1 triple-negative breast tumors. Flow cytometry of tumor-associated macrophages revealed that AAP/NAC selectively inhibited M2 polarization. The anti-tumor activity of high-dose AAP/NAC is lost in macrophage-depleted mouse syngeneic tumor models, suggesting a macrophage-dependent mechanism of action. In conclusion, our study is the first to show that high-dose AAP/NAC has profound effects on the tumor immune microenvironment that facilitates immune-mediated inhibition of tumor growth.
DNA Hypomethylation in the TNF-Alpha Gene Predicts Rheumatoid Arthritis Classification in Patients with Early Inflammatory Symptoms
Authors: Pitaksalee Rujiraporn , Parmar Rekha , Hodgett Richard , Emery Paul , Ponchel Frederique
Published: September 2023

Biomarkers for the classification of rheumatoid arthritis (RA), and particularly for anti-citrullinated peptide antibody (ACPA)-negative patients, remain an important hurdle for the early initiation of treatment. Taking advantage of DNA-methylation patterns specific to early RA, quantitative methylation-specific qPCR (qMSP) offers a robust technology for the development of biomarkers. We developed assays and established their value as RA classification biomarkers. Methods: DNA-methylation data were screened to select candidate CpGs to design qMSP assays. Eight assays were developed and tested on two early inflammatory arthritis cohorts. Logistic regression and bootstrapping were used to demonstrate the added value of the qMSP assays. Result: Differentially methylated CpG data were screened for candidate CpG, thereby meeting the qMSP assay requirements. The top CpG candidate was in the TNF gene, for which we successfully developed a qMSP assay. Significantly lower DNA-methylation levels were observed in RA (p < 4 × 10−9), with a high predictive value (OR < 0.54/AUC < 0.198) in both cohorts (n = 127/n = 157). Regression using both datasets showed improved accuracy = 87.7% and AUC = 0.944 over the model using only clinical variables (accuracy = 85.2%, AUC = 0.917). Similar data were obtained in ACPA-negative patients (n = 167, accuracy = 82.6%, AUC = 0.930) compared to the clinical variable model (accuracy = 79.5%, AUC = 0.892). Bootstrapping using 2000 datasets confirmed that the AUCs for the clinical+TNF-qMSP model had significant added value in both analyses. Conclusion: The qMSP technology is robust and can successfully be developed with a high specificity of the TNF qMSP assay for RA in patients with early inflammatory arthritis. It should assist classification in ACPA-negative patients, providing a means of reducing time to diagnosis and treatment.
A Phytochemical Approach to the Removal of Contaminants from Industrial Dyeing Wastewater
Authors: Urbina-Suarez Néstor A. , Salcedo-Pabón Cristian J. , Contreras-Ropero Jefferson E. , López-Barrera German L. , García-Martínez Janet B. , Barajas-Solano Andrés F. , Machuca-Martínez Fiderman
Published: September 2023

This study investigates the influence of photoperiod and wastewater concentration on the growth of microalgae and cyanobacteria for the removal of environmentally significant parameters (COD, BOD, Cr, Fe, color, chlorides, nitrogen compounds, and phosphates) from dyeing wastewater. A two-factor central composite design with surface response was employed, involving two algae species (Chlorella and Scenedesmus sp.) and two cyanobacteria species (Hapalosiphon and Oscillatoria sp.). The findings indicated that extended photoperiods (>13 h) and higher wastewater concentrations (70–80% v/v) enhanced biomass production across all strains. However, Hapalosiphon and Chlorella sp. (1.6 and 0.45 g/L) exhibited better tolerance to the wastewater’s high toxicity, resulting in higher biomass concentrations and improved COD and BOD removal by Hapalosiphon sp. (75% and 80%, respectively). Further analysis of the obtained biomass revealed their potential applications. Among the cyanobacteria, Hapalosiphon sp. synthesized the highest concentrations of total proteins and lipids (38% and 28% w/w, respectively), while Oscillatoria sp. displayed a high protein content (42% w/w). In contrast, the algae demonstrated a strong propensity for storing substantial quantities of total carbohydrates (65% and 57% w/w for Scenedesmus and Chlorella sp., respectively). These results signify the feasibility of cultivating photosynthetic microorganisms in industrial dyeing wastewater as a sustainable source of nutrients for targeted metabolite production.
Enhancing Pediatric Adnexal Torsion Diagnosis: Prediction Method Utilizing Machine Learning Techniques
Authors: Turki Ahmad , Raml Enas
Published: September 2023

This study systematically examines pediatric adnexal torsion, proposing a diagnostic approach using machine learning techniques to distinguish it from acute appendicitis. Our retrospective analysis involved 41 female pediatric patients divided into two groups: 21 with adnexal torsion (group 1) and 20 with acute appendicitis (group 2). In group 1, the average age was 10 ± 2.6 years, while in group 2, it was 9.8 ± 21.9 years. Our analysis found no statistically significant age distinctions between these two groups. Despite acute lower abdominal pain being a common factor, group 1 displayed shorter pain duration (28.9 h vs. 46.8 h, p < 0.05), less vomiting (28% vs. 50%, p < 0.05), lower fever incidence (4.7% vs. 50%, p < 0.05), reduced leukocytosis (57% vs. 75%, p < 0.05), and CRP elevation (30% vs. 80%, p < 0.05) compared to group 2. Machine learning techniques, specifically support vector classifiers, were employed using clinical presentation, pain duration, white blood cell counts, and ultrasound findings as features. The classifier consistently demonstrated an average predictive accuracy of 87% to 97% in distinguishing adnexal torsion from appendicitis, as confirmed across various SVM models employing different kernels. Our findings emphasize the capacity of support vector machines (SVMs) and machine learning as a whole to augment diagnostic accuracy when distinguishing between adnexal torsion and acute appendicitis. Nevertheless, it is imperative to validate these results through more extensive investigations and explore alternative machine learning models for a comprehensive understanding of their diagnostic capabilities.
Investigation of Genetic Factors and Clinical Data in Breast Cancer Highlights the Importance of Breastfeeding and Cancer History
Authors: Mercês Amanda , da-Silva-Cruz Rebecca , Silva Caio S. , Burbano Rommel , Ribeiro-dos-Santos Ândrea , Cavalcante Giovanna C.
Published: September 2023

Breast cancer (BC) is the type of neoplasm that most affects women worldwide. It is known that one of the hallmarks of cancer is the resistance to cell death with the evasion of apoptosis. Considering the relevance of TP53, BCL2, CASP3, and CASP9 genes for the occurrence of the intrinsic apoptosis, this study investigated the distribution of the genetic variants rs17880560 (TP53), rs11269260 (BCL2), rs4647655 (CASP3), rs4645982, and rs61079693 (CASP9), as well as genetic ancestry and clinical data, in a BC cohort from the Brazilian Amazon that other variants in these genes might play a role in this process. In the present study, 22 breast cancer tissues and 10 non-cancerous tissues were used, therefore, 32 samples from different patients were subjected to genotyping. We observed that breastfeeding and cancer history were factors that need to be considered for BC (p = 0.022). Therefore, this study contributed to a greater understanding of intrinsic apoptosis in BC, reinforcing previous data that suggest that the history of cancer might be a condition that affects the development of BC and that breastfeeding may act as a protective factor for this type of cancer. We recommend more studies on the genetic factors investigated here, aiming at a future with tools that can help in the early diagnosis.
Study on Microstructure and Mechanical Properties of A100-Y2O3 Coatings on Low-Carbon Steel by Laser Cladding
Authors: Zhou Kexin , Han Tengfei , Zhu Xinghui , Chen Zhongyu , Zhou Chao , Cao Hanbo , Shen Yifu
Published: September 2023

To enhance the microhardness and wear resistance of low-carbon steel, laser cladding was employed to create A100-Y2O3 cladding coatings that remained free of cracks. The phase composition, microstructure, and element distribution of these coatings were examined using XRD and SEM analyses, respectively. The microhardness and wear resistance of the A100-Y2O3 cladding coatings were tested by an HXS-1000 A type digital liquid crystal intelligent microhardness tester and an ML-10 friction and wear tester, respectively. The XRD results show that the addition of Y2O3 did not change the phase composition of the A100-Y2O3 cladding coatings. With the addition of Y2O3, the grains of the A100-Y2O3 cladding coatings are finer compared with those of the A100-0%Y2O3 cladding coating. The upper part of the A100-Y2O3 cladding coatings were composed of fine equiaxed grains. The average microhardness of the A100-0%Y2O3 cladding coatings was 532.489 HV. With the addition of Y2O3, the microhardness of the A100-Y2O3 cladding coatings was obviously improved, and the average microhardness of A100-1.5%Y2O3 coating reached 617.290 HV. The A100-Y2O3 cladding coatings were reduced, and the worn surface became relatively smooth owing to the addition of Y2O3. The addition of Y2O3 significantly improved the wear resistance of the A100-Y2O3 cladding coatings.
Studies on Optoelectronic and Transport Properties of XSnBr3 (X = Rb/Cs): A DFT Insight
Authors: Behera Debidatta , Akila Boumaza , Mukherjee Sanat Kumar , Geleta Tesfaye Abebe , Shaker Ahmed , Salah Mostafa M.
Published: September 2023

Modern manufacturing is aiming for products that are readily available, environmentally sustainable, and energy efficient. This paper delves into the exploration of compounds meeting these criteria. Specifically, we investigate the structural, elastic, optoelectronic, and transport properties of XSnBr3 (X = Rb/Cs) compounds utilizing the full-potential linearized augmented plane wave program (FP LAPW), a component of Wien2K software. Structural optimization is carried out through the generalized gradient approximation (GGA) approach, yielding lattice constants consistent with preceding numerical and experimental studies. The explored XSnBr3 (X = Rb/Cs) materials exhibit ductility and mechanical stability. Notably, XSnBr3 (X = Rb/Cs) displays a direct bandgap, signifying its semiconducting nature. The bandgap values, as determined by the modified Becke–Johnson (mBJ) approach, stand at 2.07 eV for X = Rb and 2.14 eV for XSnBr3 (X = Rb/Cs). Furthermore, utilizing the BoltzTraP software’s transport feature, we investigate thermoelectric properties. Remarkably, XSnBr3 (X = Rb/Cs) demonstrates impressive figures of merit (ZT) at room temperature, implying its potential to serve as a material for highly efficient thermoelectric devices. This research holds promise for contributing to the development of environmentally friendly and energy-efficient technologies.
The Impact of Variant Histology in Patients with Urothelial Carcinoma Treated with Radical Cystectomy: Can We Predict the Presence of Variant Histology?
Authors: Prijovic Nebojsa , Acimovic Miodrag , Santric Veljko , Stankovic Branko , Nikic Predrag , Vukovic Ivan , Radovanovic Milan , Kovacevic Luka , Nale Petar , Babic Uros
Published: September 2023

Considering the divergent biological behaviors of certain histological subtypes of urothelial carcinoma, it would be of great importance to examine the impact of variant histology and to predict its presence in patients with bladder cancer. A single-center cohort study included 459 patients who underwent radical cystectomy for urothelial carcinoma between 2017 and 2021. Patients were followed up with until July 2022. We compared clinical, laboratory, and histopathologic characteristics and the overall survival between patients with pure urothelial carcinoma and variant histologies. Our results showed that the patients with variant histology were older and preoperatively more frequently had hydronephrosis and higher values of leukocytes and neutrophils. Also, we found a significant association between variant histology and an advanced stage of tumor disease, the presence of lymphovascular invasion, positive surgical margins, and metastases in surgically resected lymph nodes. The number of neutrophils was identified as an independent preoperative predictor of the presence of variant histology after a radical cystectomy. The overall survival of the patients with variant histology was significantly lower compared to the patients with pure urothelial carcinoma. According to our results, the presence of variant histology represents a more aggressive form of the disease. Preoperative neutrophil counts may indicate the presence of variant histology of urothelial carcinoma in patients with bladder cancer.
Ecosystem Management Policy Implications Based on Tonga Main Tuna Species Catch Data 2002–2018
Authors: Vaihola Siosaia , Kininmonth Stuart
Published: September 2023

Despite the crucial role played by international and regional tuna fisheries in facilitating the successful implementation of the ecosystem approach to fisheries management, there exist disparities in viewpoints among these stakeholders, resulting in gaps between regional fisheries management and local communities. Nevertheless, the Tongan government, under the Ministry of Fisheries, is dedicated to the efficient management of its tuna resources, aiming to establish it as the preferred and optimal approach for ensuring the long-term sustainability of its tuna fisheries and the ecosystem services they provide to the community. Recognizing that an appropriate legal, policy and institutional framework is in place for sustainable management of tuna, the first part of this paper presents a review of current Tonga fisheries laws and policies for its tuna fisheries. This review reflects the implementation of an information-based management framework, namely the Tonga National Tuna Fishery Management and Development Plan. The tuna fisheries in Tonga mainly catch albacore (Thunnus alalunga), bigeye (Thunnus obesus), skipjack (Katsuwonus pelamis), and yellowfin (Thunnus albacares) tuna. These tuna species are caught within Tonga’s exclusive economic zones and play a crucial role in the country’s economy; hence, it is crucial to examine the spatio-temporal distributions of their catch in relation to their environmental conditions. In pursuit of this goal, the tasks of mapping (i) the spatio-temporal distribution of catch landed at ports and (ii) the spatio-temporal of environmental conditions were performed. The study utilizes longline catch per unit effort data spanning from 2002 to 2018 for albacore, bigeye, skipjack, and yellowfin tuna. It also incorporates data on environmental conditions, including sea surface temperature, sea surface chlorophyll, sea surface current, and sea surface salinity. Additionally, the El Nino Southern Oscillation Index is mapped in relation to catch data to examine the potential effects of climate change on the tuna catch. Results show that bigeye, skipjack, and yellowfin CPUE show a central–northernmost distribution and are primarily caught between latitudes 14° S–22° S, while albacore, shows a central–southern distribution. The highest CPUE for all species are in latitudes 15.5° S–22.5° S and longitudes 172.5° W–176.5° W. The data indicate that sea surface current velocities range from −0.03 to 0.04 ms−1, sea surface salinity ranges from 34.8 to 35.6 PSU, sea surface chlorophyll concentration varies from 0.03 to 0.1 mg m−3, and sea surface temperature fluctuates seasonally, ranging from 18 °C to 30 °C. Mapping also reveals that times of reduced catches in Tonga coincide with periods of moderate to strong El Nino events from 2002 to 2018.
The Addition of a High Concentration of Phosphorus Reduces the Diversity of Arbuscular Mycorrhizal Fungi in Temperate Agroecosystems
Authors: Miao Feng , Wang Senlin , Yuan Yuxiang , Chen Yun , Guo Erhui , Li Yuan
Published: September 2023

Phosphorus (P) is an essential macronutrient crucial for both plant growth and crop production, playing a pivotal role in agriculture since the early 20th century. The symbiotic relationship between AMF and plants serves as a classic illustration. These fungi play a regulatory role in the growth and development of plants, especially in facilitating the absorption of P and carbon molecules by plants. While there has been a growing body of research on the community assembly of arbuscular mycorrhizal fungi (AMF) in recent decades, our knowledge of the processes governing the coexistence of these AMF communities influenced by P in agroecosystems remains limited. To investigate the impact of various P fertilizers on AMF communities in temperate agroecosystems, this study was conducted using soils sourced from wheat–maize rotation farmland at Henan Agricultural University Yuanyang Base. With the Illumina MiSeq high-throughput sequencing technique, we systematically examined the taxonomic composition of soil AMF at the Yuanyang Base of Henan Agricultural University in a wheat–maize rotation agricultural field. Our primary objective was to unravel the mechanisms behind AMF community assembly and stability under varying P gradient fertilization conditions. Nonmetric Multidimensional Scaling (NMDS) analysis revealed significant differences among AMF communities in field soil subjected to various treatments (p < 0.05). A torus translations test demonstrated positive associations with the three treatments in 36 out of the 51 examined AMF operational taxonomic units (OTUs), making up 70.59% (p < 0.05) of the results. Furthermore, 37.84% (14/37) of the OTUs displayed preferences for the low P concentration treatment, while 34.29% (12/35) and 32.26% (10/31) favored medium and high P concentrations, respectively. An analysis of the Normalized Stochasticity Ratio (NST) and Checkerboard Score (C-score) indicated that in temperate agroecosystems, deterministic processes predominantly governed AMF in all treatment groups, with high P conditions exerting a stronger influence than low or medium P conditions. This study underscores the profound impact of long-term P fertilizer application on AMF community structures within temperate agricultural systems employing wheat–maize rotation. Additionally, it highlights the dominant role played by deterministic processes in shaping the assembly of AMF communities in these temperate agricultural systems that use P fertilizers. These findings emphasize the need for balanced nutrient management, particularly concerning P, to ensure the stability of AMF communities.
Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost
Authors: Tomita Katsuyuki , Yamasaki Akira , Katou Ryohei , Ikeuchi Tomoyuki , Touge Hirokazu , Sano Hiroyuki , Tohda Yuji
Published: September 2023

An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. We used two decision-tree classifiers to identify the diagnostic algorithms: RF and XGBoost. Bayesian optimization was used to optimize the hyperparameters of RF and XGBoost. Accuracy and area under the curve (AUC) were used as evaluation metrics. The XGBoost classifier outperformed the RF classifier with an accuracy of 81% and an AUC of 85%. A combination of symptom–physical signs and lung function tests was successfully used to construct a diagnostic algorithm on importance features for diagnosing adult asthma. These results indicate that the proposed model can be reliably used to construct diagnostic algorithms with selected features from objective tests in different settings.
Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring
Authors: Hasasneh Ahmad , Hijazi Haytham , Talib Manar Abu , Afadar Yaman , Nassif Ali Bou , Nasir Qassim
Published: September 2023

Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detection and monitoring. Our unsupervised framework employs interpretable clusters of normal and abnormal measures, facilitating disease progression detection. Additionally, we enhance result interpretation by leveraging the language model Davinci GPT-3 to gain deeper insights into the underlying data patterns and relationships. Our results demonstrate the effectiveness of unsupervised learning, achieving a Silhouette score of 0.55. Furthermore, validation using supervised learning techniques yields high accuracy (0.884 ± 0.005), precision (0.80 ± 0.112), and recall (0.817 ± 0.037). These promising findings indicate the potential of unsupervised techniques for identifying inflammatory markers, contributing to the development of efficient and reliable COVID-19 detection and monitoring methods. Our study shows the capabilities of AI and wearables, reflecting the pursuit of low-cost, accessible solutions for addressing health challenges related to inflammatory diseases, thereby opening new avenues for scalable and widely applicable health monitoring solutions.
Resting-State Functional Connectivity Difference in Alzheimer’s Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement
Authors: Lama Ramesh Kumar , Kwon Goo-Rak
Published: September 2023

The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer’s disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We used the threshold-free cluster enhancement (TFCE) method to explore the connectivity from resting-state functional MR images (rs-fMRIs). We conducted the study on a total of 32 AD, 32 HC, and 31 MCI subjects. We modeled the brain as a graph-based network to study these impairments, and pairwise Pearson’s correlation-based functional connectivity was used to construct the brain network. The study found that connections in the sensory motor network (SMN), dorsal attention network (DAN), salience network (SAN), default mode network (DMN), and cerebral network were severely affected in AD and MCI. The disruption in these networks may serve as potential biomarkers for distinguishing AD and MCI from HC. The study suggests that alterations in functional connectivity in these networks may contribute to cognitive deficits observed in AD and MCI. Additionally, a negative correlation was observed between the global clinical dementia rating (CDR) score and the Z-score of functional connectivity within identified clusters in AD subjects. These findings provide compelling evidence suggesting that the neurodegenerative disruption of functional magnetic resonance imaging (fMRI) connectivity is extensively distributed across multiple networks in individuals diagnosed with AD.
Metalinguistic Awareness as Self-Management in People with Apraxia of Speech and Aphasia: Lived Experience from a Longitudinal Case Series
Authors: Mumby Katharyn
Published: September 2023

People with acquired apraxia of speech (AOS) and aphasia commonly experience long-term communication disability without support for their ongoing recovery or self-management. Little is known about their lived experience of metalinguistic abilities and capacity to harness them for self-management of speech production. The author, a speech and language therapist (SLT), revisited her previous qualitative study after her own more recent lived experience of recovering from mild aphasia. Participant perspectives were explored from a longitudinal case series (eleven people with confirmed AOS and aphasia discharged from SLT), with full ethical approval. The anonymized data comprised detailed transcripts from videoed semi-structured interviews, participant assessments, field notes and reflections, member checking, and reflexivity. The original systematic thematic analysis of these data in NVivo software was re-interrogated by the author, deriving three overarching themes: Metalinguistic awareness of spoken communication breakdown, Self-management, and Therapeutic assessment. The participants conveyed the nature, occurrence, context, mechanism, and purpose underlying errors in spoken communication (themes of What, When, Where, How and Why). They generated compensatory strategies, elucidated via subthemes with quotations, verified contemporaneously by an original participant volunteer. The findings support the value of metalinguistic co-construction during in-depth assessments of communication disability, offering fresh avenues for long-term self-management in aphasia and AOS.
A Social Recommendation Model Based on Basic Spatial Mapping and Bilateral Generative Adversarial Networks
Authors: Zhang Suqi , Zhang Ningjing , Wang Wenfeng , Liu Qiqi , Li Jianxin
Published: September 2023

Social recommender systems are expected to improve recommendation quality by incorporating social information when there is little user–item interaction data. Therefore, how to effectively fuse interaction information and social information becomes a hot research topic in social recommendation, and how to mine and exploit the heterogeneous information in the interaction and social space becomes the key to improving recommendation performance. In this paper, we propose a social recommendation model based on basic spatial mapping and bilateral generative adversarial networks (MBSGAN). First, we propose to map the base space to the interaction and social space, respectively, in order to overcome the issue of heterogeneous information fusion in two spaces. Then, we construct bilateral generative adversarial networks in both interaction space and social space. Specifically, two generators are used to select candidate samples that are most similar to user feature vectors, and two discriminators are adopted to distinguish candidate samples from high-quality positive and negative examples obtained from popularity sampling, so as to learn complex information in the two spaces. Finally, the effectiveness of the proposed MBSGAN model is verified by comparing it with both eight social recommendation models and six models based on generative adversarial networks on four public datasets, Douban, FilmTrust, Ciao, and Epinions.
A New Approach Using BMI and FMI as Predictors of Cardio-Vascular Risk Factors among Mexican Young Adults
Authors: Salinas-Mandujano Rocío Guadalupe , Reynoso-Camacho Rosalía , Salgado Luis Miguel , Ramos-Gomez Minerva , Pérez-Ramírez Iza F. , Aguilar-Galarza Adriana , Moreno-Celis Ulisses , Anaya-Loyola Miriam Aracely
Published: September 2023

The study aimed to identify accurate cut-off points for waist circumference (WC), body fat percentage (BF%), body mass index (BMI), fat mass index (FMI), and fat-free mass index (FFMI), and to determine their effective accuracy to predict cardiovascular risk factors (CVRFs) among Mexican young adults. A cross-sectional study was conducted among 1730 Mexican young adults. Adiposity measures and CVRFs were assessed under fasting conditions. The optimal cut-off points were assessed using the receiver operating characteristic curve (ROC). Age-adjusted odds ratios (OR) were used to assess the associations between anthropometric measurements and CVRFs. The cut-off values found, in females and males, respectively, for high WC (≥72.3 and ≥84.9), high BF% (≥30 and ≥22.6), high BMI (≥23.7 and ≥24.4), high FMI (≥7.1 and ≥5.5), and low FFMI (≤16 and ≤18.9) differ from those set by current guidelines. High BMI in women, and high FMI in men, assessed by the 50th percentile, had the best discriminatory power in detecting CVRFs, especially high triglycerides (OR: 3.07, CI: 2.21–4.27 and OR: 3.05, CI: 2.28–4.08, respectively). Therefore, these results suggest that BMI and FMI measures should be used to improve the screening of CVRFs in Mexican young adults.
Human Activity Recognition Based on Continuous-Wave Radar and Bidirectional Gate Recurrent Unit
Authors: Zhou Junhao , Sun Chao , Jang Kyongseok , Yang Shangyi , Kim Youngok
Published: September 2023

The technology for human activity recognition has diverse applications within the Internet of Things spectrum, including medical sensing, security measures, smart home systems, and more. Predominantly, human activity recognition methods have relied on contact sensors, and some research uses inertial sensors embedded in smartphones or other devices, which present several limitations. Additionally, most research has concentrated on recognizing discrete activities, even though activities in real-life scenarios tend to be continuous. In this paper, we introduce a method to classify continuous human activities, such as walking, running, squatting, standing, and jumping. Our approach hinges on the micro-Doppler (MD) features derived from continuous-wave radar signals. We first process the radar echo signals generated from human activities to produce MD spectrograms. Subsequently, a bidirectional gate recurrent unit (Bi-GRU) network is employed to train and test these extracted features. Preliminary results highlight the efficacy of our approach, with an average recognition accuracy exceeding 90%.
A 10-Bit 400 MS/s Dual-Channel Time-Interleaved SAR ADC Based on Comparator Multiplexing
Authors: Wang Cheng , Yang Zhanpeng , Xing Xinpeng , Duan Quanzhen , Zheng Xinfa , Gielen Georges
Published: September 2023

This paper proposes a 10-bit 400 MS/s dual-channel time-interleaved (TI) successive approximation register (SAR) analog-to-digital converter (ADC) immune to offset mismatch between channels. A novel comparator multiplexing structure is proposed in our design to mitigate comparator offset mismatch between channels and improve ADC dynamic performance. Compared to traditional TI-SAR ADC utilizing offset calibration technique, hardware and power consumption overhead are minimized in our design. In addition, a split capacitive digital-to-analog converter (CDAC) and a double-tail dynamic comparator using the clock decoupling technique were applied to eliminate comparator common mode input voltage shift, ensuring conversion accuracy and boosting speed. A 400 MS/s 10-bit dual-channel TI-SAR ADC with comparator multiplexing was designed in 40 nm CMOS and compared to the conventional one. The simulated ADC ENOB and SFDR with 6σ offset mismatch were improved from 5.0-bit and 32.2 dB to 9.7-bit and 77.2 dB, respectively, confirming the merits of the proposed design compared to current state-of-the-art works.
Image Inpainting Based on Multi-Level Feature Aggregation Network for Future Internet
Authors: Wang Dong , Hu Liuqing , Li Qing , Wang Guanyi , Li Hongan
Published: September 2023

(1) Background: In the future Internet era, clarity and structural rationality are important factors in image inpainting. Currently, image inpainting techniques based on generative adversarial networks have made great progress; however, in practical applications, there are still problems of unreasonable or blurred inpainting results for high-resolution images and images with complex structures. (2) Methods: In this work, we designed a lightweight multi-level feature aggregation network that extracts features from convolutions with different dilation rates, enabling the network to obtain more feature information and recover more reasonable missing image content. Fast Fourier convolution was designed and used in the generative network, enabling the generator to consider the global context at a shallow level, making it easier to perform high-resolution image inpainting tasks. (3) Results: The experiment shows that the method designed in this paper performs well in geometrically complex and high-resolution image inpainting tasks, providing a more reasonable and clearer inpainting image. Compared with the most advanced image inpainting methods, our method outperforms them in both subjective and objective evaluations. (4) Conclusions: The experimental results indicate that the method proposed in this paper has better clarity and more reasonable structural features.
Determination and Modeling of Proximate and Thermal Properties of De-Watered Cassava Mash (Manihot esculenta Crantz) and Gari (Gelatinized cassava mash) Traditionally Processed (In Situ) in Togo
Authors: Mwape Mwewa Chikonkolo , Parmar Aditya , Roman Franz , Azouma Yaovi Ouézou , Emmambux Naushad M. , Hensel Oliver
Published: September 2023

The roasting process of Gari (Gelatinized cassava mash), a shelf-stable cassava product, is energy-intensive. Due to a lack of information on thermal characteristics and scarcity/rising energy costs, heat and mass transfer calculations are essential to optimizing the traditional gari procedure. The objective of this study was to determine the proximate, density, and thermal properties of traditionally processed de-watered cassava mash and gari at initial and final processing temperatures and moisture contents (MCwb). The density and thermal properties were determined using proximate composition-based predictive empirical models. The cassava mash had thermal conductivity, density, specific heat capacity, and diffusivity of 0.34 to 0.35 W m−1 °C−1, 1207.72 to 1223.09 kg m−3, 2849.95 to 2883.17 J kg−1 °C, and 9.62 × 10−8 to 9.76 × 10−8 m2 s−1, respectively, at fermentation temperatures and MCwb of 34.82 to 35.89 °C and 47.81 to 49%, respectively. The thermal conductivity, density, specific heat capacity and diffusivity of gari, ranged from 0.27 to 0.31 W m−1 °C−1, 1490.07 to 1511.11 kg m−3, 1827.71 to 1882.61 J kg−1 °C and 9.64 × 10−8 to 1.15 × 10−8 m2 s−1, respectively. Correlation of all the parameters was achieved, and the regression models developed showed good correlation to the published models developed based on measuring techniques.
Comparative Evaluation of PSA, PVSA, and Twin PSA Processes for Biogas Upgrading: The Purity, Recovery, and Energy Consumption Dilemma
Authors: Golmakani Ayub , Wadi Basil , Manović Vasilije , Nabavi Seyed Ali
Published: September 2023

The current challenges of commercial cyclic adsorption processes for biogas upgrading are associated with either high energy consumption or low recovery. To address these challenges, this work evaluates the performance of a range of configurations for biogas separations, including pressure swing adsorption (PSA), pressure vacuum swing adsorption (PVSA), and twin double-bed PSA, by dynamic modelling. Moreover, a sensitivity analysis was performed to explore the effect of various operating conditions, including adsorption time, purge-to-feed ratio, biogas feed temperature, and vacuum level, on recovery and energy consumption. It was found that the required energy for a twin double-bed PSA to produce biomethane with 87% purity is 903 kJ/kg CH4 with 90% recovery, compared to 961 kJ/kg CH4 and 76% recovery for a PVSA process. With respect to minimum purity requirements, increasing product purity from 95.35 to 99.96% resulted in a 32% increase in energy demand and a 23% drop in recovery, illustrating the degree of loss in process efficiency and the costly trade-off to produce ultra-high-purity biomethane. It was concluded that in processes with moderate vacuum requirements (>0.5 bar), a PVSA should be utilised when a high purity biomethane product is desirable. On the other hand, to minimise CH4 loss and enhance recovery, a twin double-bed PSA should be employed.
Influence of Transport Demand Parameters on Environmental Pollution for Deliveries by Cargo Bikes in City Areas with Traffic Restrictions
Authors: Vasiutina Hanna , Naumov Vitalii , Szarata Andrzej , Rybicki Stanisław
Published: September 2023

In light of global environmental degradation, which is largely affected by the transport sector, increasing attention is paid to enhancing the quality of life in urban areas. Policymakers are taking steps to reduce transport-related pollution and accelerate the shift to sustainable city development. They introduce stricter requirements for fuel quality and transport emissions, impose fines for traffic congestion, increase parking fees, establish low-pollution zones, etc. The implementation of measures that may not be perceived positively requires careful planning and analysis. This involves using suitable techniques and software to evaluate and support planned strategies. We propose a comprehensive approach to assess the ecological effect of using a delivery fleet that incorporates cargo bikes, operating in conjunction with light vans within a restricted urban area. The presented methodology was implemented using Python programming language and includes simulation tools for the execution of alternative delivery scenarios and a model for determining emissions generated by the analyzed supply scheme. The developed approach was applied to selected areas of Krakow (Poland) and San Sebastian (Spain), and the obtained models were used to study the influence of transport demand on the decrease of transport-caused environmental pollution. Simulation results indicate a statistically significant influence of the mean consignment weight on the emission reduction obtained due to the use of cargo bikes.
Airtightness Assessment under Several Low-Pressure Differences in Non-Residential Buildings
Authors: Shim Chanhyung , Hong Goopyo
Published: September 2023

The thermal performance of building envelopes is significantly affected by building insulation and airtightness. However, most studies have focused on improving thermal performance in building envelopes, while few studies on improving airtightness in buildings have been conducted. The present study measured airtightness and infiltration in non-residential buildings using fan pressurization and tracer gas methods. By analyzing the results obtained from both methods, the distribution of the correlation factors was identified, which can be used for the air leakage rates obtained from the blower door test to estimate the infiltration rates under natural airflow conditions. Since it is difficult to get the values of ACH50 through the blower door test in buildings of large volume or where large air leakages occur, the study proposed a method to convert the values of airtightness under several low-pressure differences of 20 Pa, 25 Pa, 30 Pa and 35 Pa into ACH50 using conversion coefficient. By dividing the air leakage rate under 20 Pa pressure difference by the conversion coefficient of 0.60, the values of ACH50 can be estimated. Results converted to ACH50 using conversion coefficient for various pressure differences of 20 Pa, 25 Pa, 30 Pa, and 35 Pa showed an error of 0.1–4.4%, respectively, compared to actual ACH50 measurement results.