bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vivo Assessment of Skin Surface Pattern: Exploring Its Potential as an Indicator of Bone Biomechanical Properties
Authors: Aurégan Jean-Charles , Bosser Catherine , Bachy-Razzouk Manon , Bensidhoum Morad , Hoc Thierry
Published: November 2023

The mechanical properties of bone tissue are the result of a complex process involving collagen–crystal interactions. The mineral density of the bone tissue is correlated with bone strength, whereas the characteristics of collagen are often associated with the ductility and toughness of the bone. From a clinical perspective, bone mineral density alone does not satisfactorily explain skeletal fragility. However, reliable in vivo markers of collagen quality that can be easily used in clinical practice are not available. Hence, the objective of the present study is to examine the relationship between skin surface morphology and changes in the mechanical properties of the bone. An experimental study was conducted on healthy children (n = 11), children with osteogenesis imperfecta (n = 13), and women over 60 years of age (n = 22). For each patient, the skin characteristic length (SCL) of the forearm skin surface was measured. The SCL quantifies the geometric patterns formed by wrinkles on the skin’s surface, both in terms of size and elongation. The greater the SCL, the more deficient was the organic collagen matrix. In addition, the bone volume fraction and mechanical properties of the explanted femoral head were determined for the elderly female group. The mean SCL values of the healthy children group were significantly lower than those of the elderly women and osteogenesis imperfecta groups. For the aged women group, no significant differences were indicated in the elastic mechanical parameters, whereas bone toughness and ductility decreased significantly as the SCL increased. In conclusion, in bone collagen pathology or bone aging, the SCL is significantly impaired. This in vivo skin surface parameter can be a non-invasive tool to improve the estimation of bone matrix quality and to identify subjects at high risk of bone fracture.
Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI
Authors: Kasabov Nikola K. , Bahrami Helena , Doborjeh Maryam , Wang Alan
Published: November 2023

Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.
Dupilumab-Associated Blepharoconjunctivitis: Clinical and Morphological Aspects
Authors: Serino Federica , Dattilo Valeria , Cennamo Michela , Roszkowska Anna Maria , Gola Massimo , Magliulo Manfredi , Magnaterra Elisabetta , Mencucci Rita
Published: November 2023

Purpose: To describe the clinical and morphologic changes in the ocular surface microstructure of patients affected with moderate-to-severe Atopic Dermatitis (AD) before and during Dupilumab treatment. Methods: This is a monocentric observational study on thirty-three patients affected with AD before and during Dupilumab treatment. All patients underwent a slit-lamp examination: complete clinical assessment, Break Up Time test (BUT), Schirmer test, and corneal staining grading (Oxford scale) were performed. Meibomian Glands Dysfunction (MGD) evaluation (Meibography), Non-invasive Keratograph Break Up Time test (NIKBUT), Tear Meniscus Height (TMH), and ocular Redness Score (RS) have been investigated using an OCULUS Keratograph. In vivo images of the conjunctiva, cornea, and meibomian glands have been acquired by confocal microscopy. Results: Sixty-six eyes were included in our study: twenty-two eyes of 11 naive patients with indication for treatment but not in therapy yet (Group 1) and forty-four eyes of 22 patients treated with Dupilumab for at least 4 months (subcutaneous administration of 300 mg every 2 weeks) (Group 2). Either patients treated with Dupilumab or naive patients with moderate-to-severe forms of AD had a tear film instability (TBUT and NIKBUT reduced), whereas the quantity of the tear film was overall normal (Schirmer test and TMH), without statistically significant differences between the two groups. When Meibography was performed with the Keratograph, the difference between Group 1 and Group 2 was statistically significant in terms of Meiboscore (p = 0.0043 and p = 0.0242, respectively), as well as the difference in terms of mean RS. These results paired well with the confocal microscopy results in which we found a decrease in the goblet cell population in the conjunctival epithelium in the treated group (5.2 cells/mm), along with inflammatory cells that were more concentrated around the adenoid lumina of the meibomian glands. Conclusions: In recent years, the use of Dupilumab has been increasing, but mild-to-severe conjunctivitis is a common side effect. Our major results demonstrate a loss of meibomian glands at the Keratograph examination: we can assume a reduced meibum secretion and an evaporative dry eye with MGD. We suggest that the inflammation of the ocular surface may involve not only the cornea and the conjunctiva, but also the meibomian glands, and Dupilumab may play a role. However, the frequency of clear conjunctivitis is not as common as reported in the literature.
Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis
Authors: Giriyappagoudar Muttanagouda , Vastrad Basavaraj , Horakeri Rajeshwari , Vastrad Chanabasayya
Published: November 2023

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein–protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.
Efficacy of Transcranial Direct Current Stimulation (tDCS) on Cognition, Anxiety, and Mobility in Community-Dwelling Older Individuals: A Controlled Clinical Trial
Authors: Rodrigues Nathalia Oliveira , Vidal Bravalhieri Anna Alice , Moraes Tatiane Pereira de , Barros Jorge Aparecido , Ansai Juliana Hotta , Christofoletti Gustavo
Published: November 2023

Transcranial direct current stimulation (tDCS) has gained popularity as a method of modulating cortical excitability in people with physical and mental disabilities. However, there is a lack of consensus on its effectiveness in older individuals. This study aimed to assess the efficacy of a 2-month tDCS program for improving physical and mental performance in community-dwelling older individuals. In this single-blinded, controlled clinical trial, forty-two participants were allocated to one of three groups: (1) the tDCS group, which received, twice a week, 20 min sessions of 2 mA electric current through electrodes placed on the dorsolateral prefrontal cortex; (2) the tDCS-placebo group, which underwent the same electrode placement as the tDCS group but without actual electric stimulation; and (3) the cognitive-control group, which completed crossword puzzles. Main outcome measures were cognition, mobility, and anxiety. Multivariate analyses of variance were employed. Significance was set at 5% (p < 0.05). Regarding the results, no significant benefits were observed in the tDCS group compared with the tDCS-placebo or cognitive-control groups for cognition (p = 0.557), mobility (p = 0.871), or anxiety (p = 0.356). Cognition exhibited positive oscillations during the assessments (main effect of time: p = 0.001). However, given that all groups showed similar variations in cognitive scores (main effect of group: p = 0.101; group × time effect: p = 0.557), it is more likely that the improvement reflects the learning response of the participants to the cognitive tests rather than the effect of tDCS. In conclusion, a 2-month tDCS program with two sessions per week appears to be ineffective in improving physical and mental performance in community-dwelling older individuals. Further studies are necessary to establish whether or not tDCS is effective in healthy older individuals.
Regulation of Vascular Endothelial Growth Factor Signaling by Nicotine in a Manner Dependent on Acetylcholine-and/or β-Adrenergic-Receptors in Human Lung Cancer Cells
Authors: Al Khashali Hind , Darweesh Ban , Ray Ravel , Haddad Ben , Wozniak Caroline , Ranzenberger Robert , Goel Stuti , Khalil Jeneen , Guthrie Jeffrey , Heyl Deborah , Evans Hedeel Guy
Published: November 2023

In addition to binding to nicotinic acetylcholine receptors (nAChRs), nicotine is known to regulate the β-adrenergic receptors (β-ARs) promoting oncogenic signaling. Using A549 (p53 wild-type) and H1299 (p53-null) lung cancer cells, we show that nicotine treatment led to: increased adrenaline/noradrenaline levels, an effect blocked by treatment with the α7nAChR inhibitor (α-BTX) but not by the β-blocker (propranolol) or the α4β2nAChR antagonist (DhβE); decreased GABA levels in A549 and H1299 cell media, an effect blocked by treatment with DhβE; increased VEGF levels and PI3K/AKT activities, an effect diminished by cell co-treatment with α-BTX, propranolol, and/or DhβE; and inhibited p53 activity in A549 cells, that was reversed, upon cell co-treatment with α-BTX, propranolol, and/or DhβE or by VEGF immunodepletion. VEGF levels increased upon cell treatment with nicotine, adrenaline/noradrenaline, and decreased with GABA treatment. On the other hand, the p53 activity decreased in A549 cells treated with nicotine, adrenaline/noradrenaline and increased upon cell incubation with GABA. Knockdown of p53 led to increased VEGF levels in the media of A549 cells. The addition of anti-VEGF antibodies to A549 and H1299 cells decreased cell viability and increased apoptosis; blocked the activities of PI3K, AKT, and NFκB in the absence or presence of nicotine; and resulted in increased p53 activation in A549 cells. We conclude that VEGF can be upregulated via α7nAChR and/or β-ARs and downregulated via GABA and/or p53 in response to the nicotine treatment of NSCLC cells.
Age and Post-Lobectomy Recurrence after Endoscopic or Robotic Thyroid Surgery: A Retrospective Cohort Study of 2348 Papillary Thyroid Carcinoma Patients
Authors: Cho Jin-Seong , Na Yong-Min , Kim Hee Kyung
Published: November 2023

The biology of papillary thyroid carcinoma (PTC) in young patients is poorly understood, and there are conflicting data regarding the recurrence for younger patients compared to older patients. We retrospectively analyzed 2348 clinically node-negative (cN0) PTC patients who underwent a thyroid lobectomy between 2008 and 2017. Young age was defined as less than 35 years old. The clinicopathological characteristics and oncologic outcomes of the young age group were compared to those of the older age group. The number of young age cN0 PTC patients accounted for 20.7% of the enrolled patients, and 24.2% were upstaged into pathologic N1a. The young age group had a significantly larger proportion of females, endoscopic/robotic thyroid lobectomies, stage N1a, and larger tumor sizes. Post-lobectomy recurrences were significantly higher in the young age group. In the Cox analysis, young age, large tumor size, and stage N1a were significant risk factors. The multivariate analysis reveals that young age and stage N1a are significant risk factors. Conversely, minimally invasive or robot-endoscopic thyroidectomies were not risk factors for post-lobectomy recurrence compared to conventional thyroidectomies. While young patients with a stage N1a had a significant risk factor for post-lobectomy recurrence, endoscopic/robotic thyroidectomy was as feasible and safe as conventional thyroidectomies in the median seven-year oncologic follow-up. Further high-quality studies are needed to elucidate the relationship between age and the risk of post-lobectomy recurrence.
Sleep and Inflammation during COVID-19 Virtual Learning in Adolescents with Overweight or Obesity
Authors: Stager Lindsay M. , Morgan Casie H. , Watson Caroline S. , Fobian Aaron D.
Published: November 2023

(1) Background: Adolescents present as a high-risk group for a range of adverse physical health outcomes during the pandemic, including sleep and C-reactive protein (CRP) levels. As adolescents with overweight or obesity (OWOB) present as an even higher risk group, the present study assessed relationships between sleep and CRP levels before and during COVID-19 in adolescents with OWOB. (2) Methods: Fourteen adolescents with OWOB participated in a pre-COVID1, pre-COVID2, and during-COVID-19 lab visit, measuring sleep and CRP levels. The sample size was limited by the number of participants who provided data before COVID-19 and who were enrolled in virtual school during the recruitment phase. However, our power analyses indicated needing a minimum of 10 participants to achieve adequate power. Pre-COVID1, pre-COVID2, and during-COVID-19 normative expected CRP levels were calculated based on age, sex, race, and body mass index percentile-matched data. Analyses compared pre-COVID1 and pre-COVID2 sleep with during-COVID-19 sleep, during-COVID-19 sleep and during-COVID-19 CRP levels, during-COVID-19 CRP levels with normative expected during-COVID-19 CRP levels, change in CRP levels from pre-COVID1 and pre-COVID2 to during-COVID-19 with normative expected CRP levels during those time periods, and change in CRP levels before COVID-19 with change in CRP levels during COVID-19. (3) Results. During COVID-19, participants experienced decreased sleep efficiency (p = 0.001), later wake time (p < 0.001), longer time in bed (p = 0.021), and onset latency (p = 0.004), compared to pre-COVID1, and decreased sleep efficiency (p = 0.002), longer onset latency (p = 0.006), and later wake time (p < 0.001) and bedtime (p = 0.016) compared with pre-COVID2. During-COVID-19 CRP levels were positively correlated with during-COVID-19 wake times (p = 0.01) and times in bed (p = 0.008). During-COVID-19 CRP levels were greater than normative expected CRP levels (p < 0.001). CRP levels increased more from pre-COVID1 and pre-COVID2 to during-COVID-19 than normative expected changes in CRP levels (p < 0.003). Changes in CRP levels before and during COVID-19 were not significantly different. (4) Conclusions. These findings highlight the consequential effects of COVID-19, including impairments in sleep, on adolescents with OWOB. CRP levels increased more (~5 mg/L) during COVID-19 than normative expected change.
Revealing People’s Sentiment in Natural Italian Language Sentences
Authors: Calvagna Andrea , Tramontana Emiliano , Verga Gabriella
Published: November 2023

Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.
Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques
Authors: Anđelić Nikola , Baressi Šegota Sandi , Car Zlatan
Published: November 2023

Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were utilized to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection.
Multi-Wavelength Coherent-Perfect-Absorption−Laser Points and Half-Wave Phase Shift in Parity-Time-Reciprocal Scaling Symmetry Structure
Authors: Xiao Yongjun , Hu Haiyang , Chen Xiaoling , Dong Junjie , Zhao Dong , Liu Fangmei
Published: November 2023

Two slabs of dielectrics with different values of refractive index are synthesized to form a composite structure and the whole system satisfies parity-time-reciprocal scaling symmetry. Four coherent-perfect-absorber-laser points are induced by modulating the incident wavelength of lightwaves and the loss/gain factor of materials. The locations of coherent-perfect-absorber-laser points in parameter space, which manifests the multi-wavelengths of coherent-perfect-absorber-lasers could be regulated by the incidence angle. Especially, a value of π phase shift in the transmitted/reflected light wave has been observed as well. This research has potential applications in multi-wavelength absorbers/lasers and half-wave phase shifters.
Assembly of Multi-Dimensional Microstructures of MXene towards Wearable Electromagnetic Attenuating Devices
Authors: Zhang Min , Cao Mao-Sheng
Published: November 2023

MXene is a competitive and attractive 2D material used in wearable electromagnetic devices due to its laminated nanostructure, abundant surface terminations and high conductivity. Assembling MXene nanosheets into multi-dimensional microstructures is considered to be an effective method for improving the overall performance of MXene-based composites, especially their electromagnetic attenuation and wearability performance. This article focuses on the strategies for assembling multi-dimensional MXene microstructures, from 0D spheres and 1D fibers to 2D films and 3D architectures. The origin of the electromagnetic response of MXene microstructures is revealed, and the corresponding electromagnetic absorption and interference shielding performance are shown. Furthermore, additional extended functions that serve wearable electromagnetic attenuation devices are highlighted. Finally, the application prospects and challenges of wearable electromagnetic devices, as well as the function integration of multi-dimensional MXene composites, are summarized.
Design and Investigation of a Side-Chain Liquid Crystalline Polysiloxane with a Ntb-Phase-Forming Side Chain
Authors: Jiang Wanhe , Mehl Georg H.
Published: November 2023

A new mesogenic non-symmetric dimeric monomer with a terminal olefin function, forming a twist bend nematic (Ntb) as well as a nematic (N) phase, was synthesized, using an enhanced synthetic methodology, which avoids isomerization of the terminal double bond in the preparation of the dimer. This monomer was attached to a pentamethyldisiloxane group, resulting in the SmA LC phase behavior of the ensuing material. Linking the monomer to a siloxane main chain resulted in nematic phase behavior. Detailed studies with the Ntb phase forming dimer DTC5C7 show full miscibility of the dimer and the new LC polymer in the LC state, suggesting that the side-chain LC polymer forms a Ntb phase as the low-temperature nematic phase. Copolymerizing the monomer with a cyanobiphenyl-based monomer allows us to tune the glass transition and phase behavior further.
An Analysis of Clinical and Systemic Factors Associated with Palliative Radiotherapy Delivery and Completion at the End of Life in Alberta, Canada
Authors: Goutam Siddhartha , Ghosh Sunita , Stosky Jordan , Tam Alexander , Quirk Sarah , Fairchild Alysa , Wu Jackson , Kerba Marc
Published: November 2023

Radiotherapy (RT) is often utilized for symptom control at the end of life. Palliative RT (pRT) may not be taken to completion by patients, thus decreasing clinical benefits and adversely impacting resource allocation. We determined rates of incomplete pRT and examined predictors of non-completion using an electronic questionnaire. Methods: A questionnaire was embedded within the RT electronic prescribing system for all five cancer centers of Alberta, Canada, between 2017 and 2020. Prescribing radiation oncologists (ROs) were tasked with completing the questionnaire. Treatment variables were collected for 2040 patients prescribed pRT. Details on pRT courses delivered and completed were used to determine rates of incomplete RT. Electronic medical records of a subset of 367 patients randomly selected from the 2040 patients were then analyzed to examine for association of non-completion of RT with patient, disease, and therapy-related factors. Results: Overall, 10% of patients did not complete pRT. The rate of single fractions prescribed as a proportion of all RT fractions increased from 18% (pre-2017: pre-study era) to 29% (2017–2020: study era) (p < 0.0001). After conducting multivariate analysis on the overall group, multiple lifetime malignancies (OR:0.64) or increasing the number of pRT fractions (OR:0.08–0.17) were associated with non-completion. Being selected for stereotactic RT (OR:3.75) or survival > 30 days post-RT prescription (OR:2.20–5.02) were associated with greater rates of RT completion. The ROs’ estimates of life expectancy at the time of RT prescription were not predictive of RT completion. In the multivariate analysis of the 367-patient subset, the presence of hepatic metastases (OR 2.59), survival 30–59 days (OR 6.61) and survival 90+ days (OR 8.18) post-RT prescription were associated with pRT completion. Only increasing pRT fractionation (OR:0.05–0.2) was associated with non-completion. Conclusion: One in ten patients prescribed pRT did not complete their treatment course. Decreasing pRT fractionation and improving prognostication in patients near the end of life may decrease rates of incomplete RT courses.
Description of Pseudocalidococcus azoricus gen. sp. nov. (Thermosynechococcaceae, Cyanobacteria), a Rare but Widely Distributed Coccoid Cyanobacteria
Authors: Luz Rúben , Cordeiro Rita , Kaštovský Jan , Fonseca Amélia , Urbatzka Ralph , Vasconcelos Vitor , Gonçalves Vítor
Published: November 2023

Coccoid cyanobacteria represent an important part of cyanobacterial freshwater diversity, with many studied strains in public databases identified as Synechococcus. This is a diverse genus, both morphologically and ecologically, with a global distribution. However, many of the so-called Synechococcus-like cyanobacteria strains could represent several independent genera that require further studies. In this work, four strains of a Synechococcus-like cyanobacteria isolated from freshwater lakes and terrestrial atmophytic habitats on São Miguel and Flores Islands (Azores archipelago) were studied genetically using the 16S rRNA and 16S–23S rRNA ITS, morphologically with light and transmission electron microscopy, and ecologically. A draft genome was produced from the reference strain by Illumina sequencing, which allowed a more complete phylogenetic study and a deeper taxonomic analysis, revealing a divergent phylogenetic evolution and low ANI and AAI values (69.4% and 66.3%, respectively) to Thermosynechococcus, the closest phylogenetic genus. Although morphologically similar to Synechococcus, the 16S rRNA and genome phylogenetic analysis placed the studied strains in a clade sister to Thermosynechococcus, inside the Thermosynechococcaceae. Thus, Pseudocalidococcus azoricus gen. sp. nov. is described as a new coccoid freshwater genus and species from the Azores archipelago. A detailed comparison with similar morphological taxa is provided, supporting the separation of the new genus. The 16S rRNA with a high genetic similarity to other strains from several continents identified as Synechococcus sp. suggests that the new genus probably has a worldwide distribution. Future studies should be performed to clarify the taxonomic identity of those strains.
Genetic Diversity of Yuca (Manihot esculenta esculenta; Cassava, Manioc), an Indigenous Crop in the Peruvian Amazon
Authors: Wooding Stephen Park , Peña César Rubén
Published: November 2023

Yuca (Manihot esculenta esculenta; cassava, manioc) is a native Amazonian crop represented by myriad landraces. To investigate human influences on its diversification, we conducted field observations and analyzed 13 short tandem repeat (STR) loci in 43 landraces in the Peruvian Amazon. We found a different multilocus genotype (MLG) in every landrace. However, tests for Hardy–Weinberg equilibrium found a deficit of heterozygosity at every locus (p < 0.001 for 12 of 13 loci). Further, the fraction of genetic variance due to landrace differences was greater than expected (38.84%; p = 0.001). This suggested that landrace hybridization is restricted, a finding consistent with our field observations. However, we found an excess of within-landrace heterozygosity (p < 0.001) in 39 of 43 landraces, suggesting they originated through hybridization. Mantel tests identified associations between genetic and geographic distances (p < 0.001), but their correlation coefficients were low (Mantel’s r < 0.21). In addition, AMOVA analyses revealed that differences between landraces collected from five sampled rivers accounted for just 3.05% of observed genetic variance (p < 0.001). Neighbor joining and principal components analyses also revealed little evidence of differentiation between rivers. Finally, in a comparison with a secondary sample, we found that the closest relative of 27 of 28 specimens had a landrace name different from their own, suggesting that traditional nomenclature is a poor indicator of genetic relatedness.
Clinical Applicability of Ultrasound Shear Wave Elastography in Patients under Hypoglossal Nerve Stimulation Therapy
Authors: Arens Philipp , Fischer Thomas , Fietze Ingo , Penzel Thomas , Dommerich Steffen , Olze Heidi , Lerchbaumer Markus Herbert
Published: November 2023

Relationship between stiffness of genioglossi (GG) and geniohyoidei (GH) muscles under electric hypoglossal nerve stimulation therapy (HNS) in relation to success of therapy was investigated with additional special focus on tongue movement. Patients and Methods: Clinical and sleep laboratory parameters of a cohort of 18 patients with known shear wave velocity (SWV) data of the ipsilateral and contralateral musculi GG and GH (sGG, sGH and nGG, nGH) before and under HNS therapy were analyzed. The SWV was already determined using the ultrasonic shear wave elastography (US-SWE) technique. Results: Median Epworth Sleepiness Scale (ESS) was 8 (IQR 12), median baseline Apnoe–Hypopnoe Index (AHI) 31.65 (IQR 25.1), median AHI under HNS therapy 16.3 (IQR 20.03). Therapy success: 9/18 patients (AHI during therapy < 15/h). There was no significant difference in SWV (sGG, sGH, nGG and nGH) between therapy responders and non-responders during therapy. Also, no difference could be seen with respect to the difference and increase in SWV values without and with stimulation. Examination of SWV values (sGG, sGH, nGG, nGH during stimulation, difference of SWV values stimulation − no stimulation, increase factor of SWV) revealed a significant negative correlation between the AHI under therapy and the measured SWV of the musculus GH of the contralateral side during stimulation (−0.622, p = 0.006). Patients with bilateral protrusion of the tongue differed regarding to therapy success in increase in SWV in sGG (p = 0.032). Tongue protrusion to contralateral: A significant difference between patients with AHI during therapy < 15/h in SWV values at sGG without stimulation (p = 0.021) was seen, with also a correlation to the current AHI under therapy (p = 0.047) and the change factor of the AHI (p = 0.015). Conclusion: Stiffness of the target muscle does not appear to be an isolated measure of the success of HNS therapy. This observation may have implications for future decision-making processes in the process of titrating electrical therapy parameters. But the technique of US-SWE may be useful for future research of the neurophysiology of the tongue and OSA phenotyping.
An Explainable Artificial Intelligence Model Proposed for the Prediction of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and the Identification of Distinctive Metabolites
Authors: Yagin Fatma , Alkhateeb Abedalrhman , Raza Ali , Samee Nagwan , Mahmoud Noha , Colak Cemil , Yagin Burak
Published: November 2023

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating illness with a significant global prevalence, affecting over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. This study uses explainable artificial intelligence and machine learning techniques to identify discriminative metabolites for ME/CFS. Material and Methods: The model investigates a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22–72. The dataset encapsulated 768 metabolites into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics. Random forest methods together with other classifiers were applied to the data to classify individuals as ME/CFS patients and healthy individuals. The classification learning algorithms’ performance in the validation step was evaluated using a variety of methods, including the traditional hold-out validation method, as well as the more modern cross-validation and bootstrap methods. Explainable artificial intelligence approaches were applied to clinically explain the optimum model’s prediction decisions. Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis. The random forest model outperformed the other classifiers in ME/CFS prediction using the 1000-iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC. According to the obtained results, the bootstrap validation approach demonstrated the highest classification outcomes. Conclusion: The proposed model accurately classifies ME/CFS patients based on the selected biomarker candidate metabolites. It offers a clear interpretation of risk estimation for ME/CFS, aiding physicians in comprehending the significance of key metabolomic features within the model.
Changes in Soluble Serum CD81 Concentration during an Oral Glucose Tolerance Test in Patients with Diabetes Mellitus and Individuals with Normal Glucose Tolerance
Authors: Kang Seon Mee , Lee Jun Choul , Ku Bon Jeong
Published: November 2023

Aim: Cluster of differentiation 81 (CD81) is a cell surface protein involved in cell development, activation, growth, and motility. Recent studies have suggested that CD81 is a marker of dedifferentiated β-cells under conditions of metabolic stress, such as progressive diabetes. However, the clinical significance of changes in soluble serum CD81 (sCD81) in diabetic individuals remains unknown. The aim of this study was to investigate whether serum sCD81 concentrations differ between subjects with diabetes and normal glucose tolerance (NGT), and whether sCD81 changes during a 75 g oral glucose tolerance test (OGTT). Materials and methods: We recruited 101 subjects who had completed an OGTT. According to the test results, the participants were divided into diabetes mellitus (DM) and NGT groups. Participants with prediabetes were excluded from the analysis. During the OGTT, sCD81 levels were measured at 0 and 120 min. We compared changes in sCD81 between the groups. Results: In the DM group, soluble sCD81 levels were significantly higher at baseline and 120 min in the OGTT compared with the normal group (0.59 (0.22–1.05) ng/mL vs. 0.25 (0.81–0.67) ng/mL, 0.55 (0.17–0.96) ng/mL vs. 0.21 (0.92–0.78) ng/mL, p = 0.006 and 0.029, respectively). The soluble sCD81 levels in the NGT group remained unchanged (p = 0.658), while those in the DM group were significantly decreased during the OGTT (p = 0.003). Conclusion: Soluble sCD81 levels were elevated in individuals with type 2 diabetes, such that changes in sCD81 were only observed during the OGTT in the DM group. Soluble sCD81 may have potential as a new diagnostic marker for type 2 diabetes.
A Numerical Study on the Capacity Region of a Three-Layer Wiretap Network
Authors: Wu Jiahong , Liu Nan , Kang Wei
Published: November 2023

In this paper, we study a three-layer wiretap network including the source node in the top layer, N nodes in the middle layer and L sink nodes in the bottom layer. Each sink node recovers the message generated from the source node correctly via the middle layer nodes that it has access to. Furthermore, it is required that an eavesdropper eavesdropping a subset of the channels between the top layer and the middle layer learns absolutely nothing about the message. For each pair of decoding and eavesdropping patterns, we are interested in finding the capacity region consisting of (N+1)-tuples, with the first element being the size of the message successfully transmitted and the remaining elements being the capacity of the N channels from the source node to the middle layer nodes. This problem can be seen as a generalization of the secret sharing problem. We show that when the number of middle layer nodes is no larger than four, the capacity region is fully characterized as a polyhedral cone. When such a number is 5, we find the capacity regions for 74,222 decoding and eavesdropping patterns. For the remaining 274 cases, linear capacity regions are found. The proving steps are: (1) Characterizing the Shannon region, an outer bound of the capacity region; (2) Characterizing the common information region, an outer bound of the linear capacity region; (3) Finding linear schemes that achieve the Shannon region or the common information region.
Small Stochastic Data Compactification Concept Justified in the Entropy Basis
Authors: Kovtun Viacheslav , Zaitseva Elena , Levashenko Vitaly , Grochla Krzysztof , Kovtun Oksana
Published: November 2023

Measurement is a typical way of gathering information about an investigated object, generalized by a finite set of characteristic parameters. The result of each iteration of the measurement is an instance of the class of the investigated object in the form of a set of values of characteristic parameters. An ordered set of instances forms a collection whose dimensionality for a real object is a factor that cannot be ignored. Managing the dimensionality of data collections, as well as classification, regression, and clustering, are fundamental problems for machine learning. Compactification is the approximation of the original data collection by an equivalent collection (with a reduced dimension of characteristic parameters) with the control of accompanying information capacity losses. Related to compactification is the data completeness verifying procedure, which is characteristic of the data reliability assessment. If there are stochastic parameters among the initial data collection characteristic parameters, the compactification procedure becomes more complicated. To take this into account, this study proposes a model of a structured collection of stochastic data defined in terms of relative entropy. The compactification of such a data model is formalized by an iterative procedure aimed at maximizing the relative entropy of sequential implementation of direct and reverse projections of data collections, taking into account the estimates of the probability distribution densities of their attributes. The procedure for approximating the relative entropy function of compactification to reduce the computational complexity of the latter is proposed. To qualitatively assess compactification this study undertakes a formal analysis that uses data collection information capacity and the absolute and relative share of information losses due to compaction as its metrics. Taking into account the semantic connection of compactification and completeness, the proposed metric is also relevant for the task of assessing data reliability. Testing the proposed compactification procedure proved both its stability and efficiency in comparison with previously used analogues, such as the principal component analysis method and the random projection method.
A Fine-Grained Approach for EEG-Based Emotion Recognition Using Clustering and Hybrid Deep Neural Networks
Authors: Zhang Liumei , Xia Bowen , Wang Yichuan , Zhang Wei , Han Yu
Published: November 2023

Emotion recognition, as an important part of human-computer interaction, is of great research significance and has already played a role in the fields of artificial intelligence, healthcare, and distance education. In recent times, there has been a growing trend in using deep learning techniques for EEG emotion recognition. These methods have shown higher accuracy in recognizing emotions when compared with traditional machine learning methods. However, most of the current EEG emotion recognition performs multi-category single-label prediction, and is a binary classification problem based on the dimensional model. This simplifies the fact that human emotions are mixed and complex. In order to adapt to real-world applications, fine-grained emotion recognition is necessary. We propose a new method for building emotion classification labels using linguistic resource and density-based spatial clustering of applications with noise (DBSCAN). Additionally, we integrate the frequency domain and spatial features of emotional EEG signals and feed these features into a serial network that combines a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network (RNN) for EEG emotion feature learning and classification. We conduct emotion classification experiments on the DEAP dataset, and the results show that our method has an average emotion classification accuracy of 92.98% per subject, validating the effectiveness of the improvements we have made to our emotion classification method. Our method for emotion classification holds potential for future use in the domain of affective computing, such as mental health care, education, social media, and so on. By constructing an automatic emotion analysis system using our method to enable the machine to understand the emotional implications conveyed by the subjects’ EEG signals, it can provide healthcare professionals with valuable information for effective treatment outcomes.
Prediction of the Deformation of Heritage Building Communities under the Integration of Attention Mechanisms and SBAS Technology
Authors: Ma Chong , Lu Baoli
Published: November 2023

The protection of heritage building communities is of important historical significance, the occurrence of a landslide is related to the safety and stability of the heritage building, and ground monitoring and forecasting are the key steps for the early warning and timely restoration of the heritage building. This study utilizes remote sensing technology to monitor the ground of a cultural heritage building, and employs a Long Short-Term Memory (LSTM) network for prediction. Firstly, we conducted ground subsidence monitoring within a specific time series of the study area using heritage remote sensing images and SBAS-InSAR technology. Following the subsidence monitoring, and incorporating an attention mechanism, we effectively localized and extracted features of heritage building clusters within the region. This approach efficiently addresses the challenge of feature identification resulting from the dense distribution of buildings and the similarity between various objects. The results indicate that the maximum subsidence rate in the research area reached −60 mm/year, reached a maximum uplift rate of 45 mm/year, and that the maximum cumulative subsidence reached −65 mm. Secondly, for the multi-level, multi-scale, and class-specific objects in remote sensing images, the LSTM network enables adaptive contextual information during deep and shallow feature extraction. This allows for better contextual modeling and the correlation between predicted and actual results reaches a 0.95 correlation, demonstrating the accurate predictive performance of the LSTM network. In conclusion, both LSTM and SBAS technologies play a crucial role in decision-making for heritage buildings, facilitating effective early warning and disaster mitigation.
A Novel Energy Management Control Scheme for a Standalone PV System in a DC Nanogrid
Authors: Nkembi Armel Asongu , Santoro Danilo , Cova Paolo , Delmonte Nicola
Published: November 2023

Distributed energy resources (DERs), such as photovoltaic (PV) sources, together with storage systems, such as battery energy storage systems (BESS), are increasingly present and necessary in our electricity distribution networks. Furthermore, the need for efficient use of energy from DERs, especially in developing countries and remote communities, must be addressed with the development of nanogrids (NGs), particularly DC NGs, and standalone PV systems with adequate control strategies. This paper investigates the control and dynamic operation of a standalone PV system. It consists mainly of three DC–DC power converters for the PV source interface, battery management, and load voltage control. A two-level modulation scheme is applied to each of these converters to switch them ON and OFF. A maximum power point tracking (MPPT) closed-loop voltage control system is implemented to make sure that the PV operates at optimum power regardless of the irradiance level or temperature, while battery voltage and load-side voltage control are also implemented to indirectly provide the required load power. The control of each of the converters is achieved by deriving their small-signal models using a state-space approach from which various control objectives are implemented. The DC-link is clamped by a BESS which acts as a backup source to provide power to the DC load in the absence of sufficient power from the PV panel. The dynamic operation of the whole system is enhanced by proposing a robust feedforward scheme that improves the response of the system in the presence of disturbances. The models are analyzed and implemented using PLECS, and numerical simulations are performed to validate the developed models and control schemes.