bluuebunny/crossref_metadata_2025 huggingface dataset:
Revision of zigzag geckos (Diplodactylidae: Amalosia) in eastern Australia, with description of five new species
Authors: HOSKIN CONRAD. J. , COUPER PATRICK J.
Published: September 2023

Geckos of the genus Amalosia Wells & Wellington, 1984 occur across eastern and northern Australia. Only five species are described but additional diversity has been recognised for some time. Here we assess species diversity in eastern Australia, using morphological and genetic (ND4 mtDNA) data. We describe five new species, all morphologically distinct and highly genetically distinct (>25% divergence). Amalosia hinesi sp. nov. is found in woodlands on the western side of the Great Dividing Range in south-east Queensland and north-east New South Wales. Amalosia saxicola sp. nov. is a large species found on rocks in the Mackay–Townsville areas of mid-east Queensland, including on many offshore islands. Amalosia nebula sp. nov. is restricted to rocky areas in upland sclerophyll forest of the Wet Tropics region of north-east Queensland. Amalosia capensis sp. nov. is a small species found in the northern half of Cape York Peninsula. Amalosia queenslandia sp. nov. is a small species that is widespread through woodlands over much of eastern and central Queensland. These species are diagnosed from other Amalosia species in eastern Australia, including A. cf. rhombifer which occurs in north-western Queensland. Amalosia cf. rhombifer is part of the clades comprising the remainder of the ‘A. rhombifer’ complex across the Northern Territory and Western Australia, which will be dealt with separately. Herein, we also we also synonymise the monotypic genus Nebulifera with Amalosia. This revision brings the number of Amalosia species to ten.
A new species of the genus Subniganda Kiriakoff, 1962 (Lepidoptera: Notodontidae) from India and Nepal
Authors: MAZUMDER ARNA , SCHINTLMEISTER ALEXANDER , IRUNGBAM JATISHWOR SINGH , CHANDRA KAILASH
Published: September 2023

The present paper describes a new species of Notodontidae Subniganda bambusoides Mazumder & Schintlmeister sp. nov. from the Himalayan and northeastern landscapes of India and Nepal. It also provides a comparative diagnosis with its congeners S. tristan (Schintlmeister, 1997) and S. isolde (Schintlmeister, 1997) along with detailed morphological and genitalic illustrations.
First records of Pieza Evenhuis, 2002 (Diptera: Mythicomyiidae: Mythicomyiinae) from Colombia, with description of a new species from arid zones of a Tropical dry forest
Authors: RAMOS-PASTRANA YARDANY , CÓRDOBA-SUAREZ ERIC , EVENHUIS NEAL. L
Published: September 2023

Pieza Evenhuis, 2002 has a New World distribution and is a poorly studied group in the Neotropical Region, and completely unknown in Colombia. We recorded for the first time the genus Pieza in Colombia with two species, namely Pieza rafaeli Mendes et al., 2019 and we described Pieza globosa sp. nov. (type-locality: Sabanalarga, Boca de Niquia) from arid zones of a Tropical dry forest. Illustrations and a distribution map are presented.
A new species of Brasixenos Kogan & Oliveira, 1966 (Strepsiptera: Xenidae) from Mexico
Authors: QUINTOS-ANDRADE GERARDO , VALENZUELA-GONZÁLEZ JORGE ERNESTO , PALMEROS-SÁNCHEZ BEATRIZ , TORRES-MORENO RAYMUNDO
Published: September 2023

The first record of the genus Brasixenos Kogan & Oliveira is presented for Mexico with the description of B. mesoamericanus, a species found in localities from central Veracruz state. The males of this species are characterized by the mouthparts shape, wing venation, and genitalia. The females are identified by their reduced size and the mandible morphology. Polybia plebeja is identified as the host of this species. The biology and distribution of Brasixenos, and challenges in the study of this genus in the Neotropical region are discussed.
Epipleoneura parmula sp. nov. (Odonata: Coenagrionidae: Protoneurinae) from Amazonian Ecuador
Authors: TENNESSEN K.J. , GARRISON R.W.
Published: September 2023

Epipleoneura parmula sp. nov. is described and illustrated based on males from eastern Ecuador (holotype: ♂, ECUADOR, Orellana Province, Primavera, Laguna Taracoa {0.4652°S, 76.7638°W}, 247 m, 27 July 1977, Dennis R. Paulson & Susan Hills leg., in FSCA). Using the most recent synopsis of the genus (Pessacq 2014), the new species keys to the couplet separating E. pallida Rácenis and E. ocuene De Marmels, but unlike in those species, the epiproct branches are longer, fused nearly their entire length and their tips are accentuated. Adults were collected at a lake and several ponds, indicating that the habitat is lentic, although the nymph is unknown.
Housing for the poor in India’s Rural Punjab
Authors: Gupta Namita , Gupta Rajiv Kumar
Published: September 2023

This article critically evaluates the effectiveness of recent state-led housing schemes in rural India, with a special focus on the Pradhan Mantri Awaas Yojana (Gramin) programme in one particular district in Punjab. Based on a close examination of official reports and ethnographic research, the article makes recommendations for future policies and programmes for housing the poor in rural India. Importantly, it identifies significant synergies with other government schemes that seek to support the rural poor.
Virtual Teams, Academic Resilience and Mental Wellbeing: A Study of International Students in Virtual Teams in India
Authors: Rana Minakshi , Ryhal Piar Chand , Negi Sunder Kala
Published: September 2023

The study addresses upon the call of mental wellbeing in global virtual teams. The objective is to explore how virtual scenarios of learning process, influence academic resilience and finally form mental wellbeing among the cohort group of different cultural and geographic backgrounds. Using stratified sampling technique, primary data were collected from 367 international students. In the present study, exploratory factor analysis, confirmatory factor analysis and structural equation modeling are used to obtain the results. The results revealed the challenges faced by the virtual teams in online learning setting. These challenges offer a path to both academic practitioners, higher management of the institutions and policymakers to offer an online learning process that is apt and suitable to the team members who participate from different geographical places. It was also found that online learning process is directly linked to academic resilience. Further, academic resilience successfully mediates between online learning process and mental wellbeing. The study opens debates on how online learning process should be made effective and relevant, especially, for those who do not belong to the same geography and cultural backgrounds.
Learning-skill stories completion: A study on the development of 3–6-year-old children's narratives
Authors: Li Rong
Published: September 2023

The development of children's narrative language is an essential part of their language development, which has a unique value to children's future learning and growth. Many research studies focus on children's narrative ability, but few focus on the completion of Chinese children's stories. In this study, 191 Chinese children aged 3–6 years old in the Shanghai area were selected to collect a story corpus by using the international and general completion pictures of learning stories, supplemented with the method of corpus study to explore the characteristics of children's narrative language development in the learning-skill stories.

The study found that the development of vocabulary, syntax, and other aspects of 3–6-year-old children showed an apparent upward trend. The variety of words, the types, and the frequency of nouns and adjectives increased with age. Regarding the perspective of syntactic analysis, the critical stage in the development of syntactic length was about 5 years old. The narrative situation of the learning-skill stories could help children to show a better narrative level. At the age of 3–6, the story structure of children's learning-skill stories gradually became complete and three-dimensional with age, and the richness of children's life experiences was an essential factor in promoting the integrity of the story structure. In the Chinese context, reactive sequences and abbreviated episodes were the main types of story structure in children's narration. The narratives of children's learning-skill stories were influenced by Chinese culture.
Mode and site of action of therapies targeting CGRP signaling
Authors: Labastida-Ramírez Alejandro , Caronna Edoardo , Gollion Cédric , Stanyer Emily , Dapkute Austeja , Braniste Diana , Naghshineh Hoda , Meksa Liga , Chkhitunidze Nino , Gudadze Tamari , Pozo-Rosich Patricia , Burstein Rami , Hoffmann Jan
Published: September 2023

Targeting CGRP has proved to be efficacious, tolerable, and safe to treat migraine; however, many patients with migraine do not benefit from drugs that antagonize the CGRPergic system. Therefore, this review focuses on summarizing the general pharmacology of the different types of treatments currently available, which target directly or indirectly the CGRP receptor or its ligand. Moreover, the latest evidence regarding the selectivity and site of action of CGRP small molecule antagonists (gepants) and monoclonal antibodies is critically discussed. Finally, the reasons behind non-responders to anti-CGRP drugs and rationale for combining and/or switching between these therapies are addressed.
HIV-1-related factors interact with p53 to influence cellular processes
Authors: Liu Shanling , Guo Ting , Hu Jinwei , Huang Weiliang , She Pengfei , Wu Yong
Published: September 2023

Human immunodeficiency virus type 1 (HIV-1) is the primary epidemic strain in China. Its genome contains two regulatory genes (tat and rev), three structural genes (gag, pol, and env), and four accessory genes (nef, vpr, vpu, and vif). Long terminal repeats (LTRs) in thegenome regulate integration, duplication, and expression of viral gene. The permissibility of HIV-1 infection hinges on the host cell cycle status. HIV-1 replicates by exploiting various cellular processes via upregulation or downregulation of specific cellular proteins that also control viral pathogenesis. For example, HIV-1 regulates the life cycle of p53, which in turn contributes significantly to HIV-1 pathogenesis. In this article, we review the interaction between HIV-1-associated factors and p53, providing information on their regulatory and molecular mechanisms, hinting possible directions for further research.
Obstructive jaundice as a rare complication of multiple pancreaticoduodenal artery aneurysms due to median arcuate ligament syndrome: a case report and review of the literature
Authors: Jalili Javad , Javadrashid Reza , Alvandfar Dara , Falahatian Masih , Jafarizadeh Ali , Alihosseini Samin , Hashemizadeh Seyedeh Elnaz
Published: September 2023

Background

Obstructive jaundice has various causes, and one of the rarest is pancreaticoduodenal artery aneurysm (PDAA), which is often associated with celiac axis stenosis caused by median arcuate ligament syndrome (MALS).

Case presentation

The patient was a 77-year-old Azeri woman who presented with progressive jaundice, vague abdominal pain, and abdominal distension from 6 months ago. The intra- and extrahepatic bile ducts were dilated, the liver's margin was slightly irregular, and the echogenicity of the liver was mildly heterogeneous in the initial ultrasound exam. A huge cystic mass with peripheral calcification and compressive effect on the common bile duct (CBD) was also seen near the pancreatic head, which was connected to the superior mesenteric artery (SMA) and had internal turbulent blood flow on color Doppler ultrasound. According to the computed tomography angiography (CTA) findings, the huge mass of the pancreatic head was diagnosed as a true aneurysm of the pancreaticoduodenal artery caused by MALS. Two similar smaller aneurysms were also present at the huge aneurysm's superior margin. Due to impending rupture signs in the huge aneurysm, the severe compression effect of this aneurysm on CBD, and the patient's family will surgery was chosen for the patient to resect the aneurysms, but unfortunately, the patient died on the first day after the operation due to hemorrhagic shock.

Conclusion

In unexpected obstructive jaundice due to a mass with vascular origin in the head of the pancreas, PDAA should be considered, and celiac trunk should be evaluated because the main reason for PDAA is celiac trunk stenosis or occlusion by atherosclerosis or MALS. The treatment method chosen (including transarterial embolization, open surgery, or combined method) depends on the patient's clinical status and radiological findings, but transarterial embolization would be safer and should be used as a first-line method.
The SIRT7-mediated deacetylation of CHD1L amplifies HIF-2α-dependent signal that drives renal cell carcinoma progression and sunitinib resistance
Authors: He Hongchao , Li Jie , Wang Wei , Cheng Jie , Zhou Jian , Li Qunyi , Jin Juan , Chen Li
Published: September 2023

Background

Aberrant interplay between epigenetic reprogramming and hypoxia signaling contributes to renal cell carcinoma progression and drug resistance, which is an essential hallmark. How the chromatin remodelers enhance RCC malignancy remains to be poorly understood. We aimed to elucidate the roles of CHD1L in determining hypoxia signaling activation and sunitinib resistance.

Methods

The qRT-PCR, western blotting, and immunohistochemistry technologies were used to detect CHD1L expressions. Lentivirus transfection was used to generate stable CHD1L-KD cells. The roles of SIRT7/CHD1L were evaluated by CCK-8, wound healing, transwell assays, xenograft models, and tail-vein metastasis models. Co-immunoprecipitation, Chromatin Immunoprecipitation (ChIP), and luciferase reporter assays were conducted to explore epigenetic regulations.

Results

We screened and validated that CHD1L is up-regulated in RCC and correlates with poorer prognosis of patients. CHD1L overexpression notably enhances cell proliferation, migration, and self-renewal capacities in vitro and in vivo. Mechanistically, SIRT7 physically interacts with CHDL1 and mediates the deacetylation of CHD1L. Wild-type SIRT7, but not H187Y dead mutant, stabilizes CHD1L protein levels via attenuating its ubiquitination levels. SIRT7 is increased in RCC and correlates with hazardous RCC clinical characteristics. SIRT7 depends on CHD1L to exert its tumor-promoting functions. Accumulated CHD1L amplifies HIF-2α-driven transcriptional programs via interacting with HIF-2α. CHD1L recruits BRD4 and increases the RNA polymerase II S2P loading. CHD1L ablation notably abolishes HIF-2α binding and subsequent transcriptional activation. CHD1L overexpression mediates the sunitinib resistance via sustaining VEGFA and targeting CHD1L reverses this effect. Specific CHD1L inhibitor (CHD1Li) shows a synergistic effect with sunitinib and strengthens its pharmaceutical effect.

Conclusions

These results uncover a CHD1L-mediated epigenetic mechanism of HIF-2α activation and downstream sunitinib resistance. The SIRT7–CHD1L–HIF-2α axis is highlighted to predict RCC prognosis and endows potential targets.
Sex differences in paternal arsenic-induced intergenerational metabolic effects are mediated by estrogen
Authors: Xue Yanfeng , Gong Yingyun , Li Xin , Peng Fei , Ding Guolian , Zhang Zhao , Shi Junchao , Savul Ilma Saleh , Xu Yong , Chen Qi , Han Leng , Mao Shengyong , Sun Zheng
Published: September 2023

Background

Gene-environment interactions contribute to metabolic disorders such as diabetes and dyslipidemia. In addition to affecting metabolic homeostasis directly, drugs and environmental chemicals can cause persistent alterations in metabolic portfolios across generations in a sex-specific manner. Here, we use inorganic arsenic (iAs) as a prototype drug and chemical to dissect such sex differences.

Methods

After weaning, C57BL/6 WT male mice were treated with 250 ppb iAs in drinking water (iAsF0) or normal water (conF0) for 6 weeks and then bred with 15-week-old, non-exposed females for 3 days in cages with only normal water (without iAs), to generate iAsF1 or conF1 mice, respectively. F0 females and all F1 mice drank normal water without iAs all the time.

Results

We find that exposure of male mice to 250 ppb iAs leads to glucose intolerance and insulin resistance in F1 female offspring (iAsF1-F), with almost no change in blood lipid profiles. In contrast, F1 males (iAsF1-M) show lower liver and blood triglyceride levels than non-exposed control, with improved glucose tolerance and insulin sensitivity. The liver of F1 offspring shows sex-specific transcriptomic changes, with hepatocyte-autonomous alternations of metabolic fluxes in line with the sex-specific phenotypes. The iAsF1-F mice show altered levels of circulating estrogen and follicle-stimulating hormone. Ovariectomy or liver-specific knockout of estrogen receptor α/β made F1 females resemble F1 males in their metabolic responses to paternal iAs exposure.

Conclusions

These results demonstrate that disrupted reproductive hormone secretion in alliance with hepatic estrogen signaling accounts for the sex-specific intergenerational effects of paternal iAs exposure, which shed light on the sex disparities in long-term gene-environment interactions.
Quantitative evaluation of plants on top surface of the Great Wall in Dazhuangke using the analytical hierarchy process
Authors: Pan Jianbin , Xu Nuo , Tang Yuyang , Cheng Meijing , Zhang Lin , Wang Bing , Lan Jingwen
Published: September 2023

The Great Wall of China was a military facility that has been continuously built and used for over thousands of years, and is a world-renowned cultural heritage site today. The plants growing on top surface of the Great Wall caused the damage of the Great Wall, but the process of removing these plants may further damage the body of the Great Wall. In this paper, based on the Analytic Hierarchy Process and the expert Delphi method, we selected 13 specific indices from three Constraint Layer factors, and then estimated and identified 45 species/categories of plants on top surface of the Dazhuangke section of the Great Wall. The results showthe constitution and the evaluation gradeof the evaluation system. The factor of Disruptive Effects plays the main guidance role in the evaluation system of the Constraint Layer; The indices of Impact on the Near Side Wall is the core element of the evaluation model with the highest weight of the Standard Layer. And the “Preservation”, “Partial Preservation”and “Removal” recommendations were given based on three evaluation grade (from Grade I to Grade III). This research provides scientific basis for the protective repair of the Great Wallheritage as well as "Garden on the Great Wall".
Evaluation of sleep quality and depressive symptoms among medical students during COVID-19 pandemic
Authors: Samadi Shahram , Amirifard Hamed , Eslami Mohammad , Khadembashiri Mohammad Amin , Khadembashiri Mohammad Mehdi , Najafi Anahita
Published: September 2023

Background

Depression and sleep disorders are interrelated and have high prevalence. Based on previous findings, medical students experience high rates of depression and low sleep quality, due to the challenges during medical training programs. During the COVID-19 pandemic, medical students faced extra physical and psychological pressure. This study aimed to evaluate sleep quality and depressive symptoms and their possible association among medical students during COVID-19 pandemic.

Results

One hundred twenty-one medical students completed the survey. Twenty-six percent of participants had a good sleep quality. Mean sleep duration of participants was approximately 6 h per day. Moreover, they had a mean sleep latency of 28 min and a mean PSQI score of 7.76. We found the prevalence of moderate, moderately severe, and severe depression among participants to be 22%, 6%, and 7%, respectively. The participants’ mean PHQ-9 score was found to be 8.4.

Conclusions

We found a relatively high depression rate, poor sleep quality, and a significant inverse relationship between sleep quality and depression severity score among medical students who were doing their clinical rotations during the COVID-19 pandemic. Additionally, quarter of moderate to severely depressed participants were taking antidepressants. Our findings highlight the need for future interventional studies that address these issues among medical students.
Bilateral congenital cholesteatoma: a case report
Authors: Nourizadeh Navid , Afzalzadeh Mohamad Reza , Rashed Masoud Mahdavi
Published: September 2023

Background

Cholesteatoma is regarded as a critical health issue in the world. Nevertheless, congenital cholesteatoma (CC) is an unusual problem, and bilateral CC is extremely rare.

Case presentation

The present work reports a young boy with conductive hearing loss because of CC. The left side was operated using a canal-wall-down mastoidectomy method for immense destruction of the middle ear structures and mastoid air cells, and endoscopic ear surgery was performed on the second side.

Conclusion

Although CC is very rare, it should be considered in the differential diagnosis of hearing loss in children. Moreover, it should be carefully followed up because it can probably occur on the second side.
Discovering social determinants of health from case reports using natural language processing: algorithmic development and validation
Authors: Raza Shaina , Dolatabadi Elham , Ondrusek Nancy , Rosella Laura , Schwartz Brian
Published: September 2023

Background

Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available in electronic health records, clinical reports, and social media data, usually in free text format. Extracting key information from free text poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information.

Objective

The objective of this research is to advance the automatic extraction of SDOH from clinical texts.

Setting and data

The case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create ground truth labels, and semi-supervised learning method is used for corpus re-annotation.

Methods

An NLP framework is developed and tested to extract SDOH from the free texts. A two-way evaluation method is used to assess the quantity and quality of the methods.

Results

The proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities.

Conclusions

NLP can be used to extract key information, such as SDOH factors from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.
HERTHENA-Lung01, a Phase II Trial of Patritumab Deruxtecan (HER3-DXd) in Epidermal Growth Factor Receptor–Mutated Non–Small-Cell Lung Cancer After Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Therapy and Platinum-Based Chemotherapy
Authors: Yu Helena A. , Goto Yasushi , Hayashi Hidetoshi , Felip Enriqueta , Chih-Hsin Yang James , Reck Martin , Yoh Kiyotaka , Lee Se-Hoon , Paz-Ares Luis , Besse Benjamin , Bironzo Paolo , Kim Dong-Wan , Johnson Melissa L. , Wu Yi-Long , John Thomas , Kao Steven , Kozuki Toshiyuki , Massarelli Erminia , Patel Jyoti , Smit Egbert , Reckamp Karen L. , Dong Qian , Shrestha Pomy , Fan Pang-Dian , Patel Parul , Sporchia Andrea , Sternberg David W. , Sellami Dalila , Jänne Pasi A.
Published: September 2023

PURPOSE

Patritumab deruxtecan, or HER3-DXd, is an antibody-drug conjugate consisting of a fully human monoclonal antibody to human epidermal growth factor receptor 3 (HER3) attached to a topoisomerase I inhibitor payload via a stable tetrapeptide-based cleavable linker. We assessed the efficacy and safety of HER3-DXd in patients with epidermal growth factor receptor ( EGFR)–mutated non–small-cell lung cancer (NSCLC).

METHODS

This phase II study (ClinicalTrials.gov identifier: NCT04619004 ) was designed to evaluate HER3-DXd in patients with advanced EGFR-mutated NSCLC previously treated with EGFR tyrosine kinase inhibitor (TKI) therapy and platinum-based chemotherapy (PBC). Patients received HER3-DXd 5.6 mg/kg intravenously once every 3 weeks or an uptitration regimen (3.2 → 4.8 → 6.4 mg/kg). The primary end point was confirmed objective response rate (ORR; RECIST 1.1) by blinded independent central review (BICR), with a null hypothesis of 26.4% on the basis of historical data.

RESULTS

Enrollment into the uptitration arm closed early on the basis of a prespecified benefit-risk assessment of data from the phase I U31402-A-U102 trial. In total, 225 patients received HER3-DXd 5.6 mg/kg once every 3 weeks. As of May 18, 2023, median study duration was 18.9 (range, 14.9-27.5) months. Confirmed ORR by BICR was 29.8% (95% CI, 23.9 to 36.2); median duration of response, 6.4 months; median progression-free survival, 5.5 months; and median overall survival, 11.9 months. The subgroup of patients with previous osimertinib and PBC had similar outcomes. Efficacy was observed across a broad range of pretreatment tumor HER3 membrane expression levels and across diverse mechanisms of EGFR TKI resistance. In patients with nonirradiated brain metastases at baseline (n = 30), the confirmed CNS ORR by BICR per CNS RECIST was 33.3% (95% CI, 17.3 to 52.8). The safety profile (National Cancer Institute Common Terminology Criteria for Adverse Events v5.0) was manageable and tolerable, consistent with previous observations.

CONCLUSION

After tumor progression with EGFR TKI therapy and PBC in patients with EGFR-mutated NSCLC, HER3-DXd once every 3 weeks demonstrated clinically meaningful efficacy with durable responses, including in CNS metastases. A phase III trial in EGFR-mutated NSCLC after progression on an EGFR TKI is ongoing (HERTHENA-Lung02; ClinicalTrials.gov identifier: NCT05338970 ).
Progress Toward and Challenges Remaining for Thyroid Tissue Regeneration
Authors: Romitti Mírian , Costagliola Sabine
Published: September 2023

Thyroid hormones play a pivotal role in diverse physiological processes, and insufficient synthesis of these hormones results in hypothyroidism, a prevalent disorder with a significant global impact. Research has shown that the residual thyroid tissue following surgery fails to fully regenerate the gland and restore normal function. The slow turnover rate of the thyroid gland and the presence of resident stem cells, which may contribute to regeneration within adult thyroid tissue, are topics of ongoing debate. This comprehensive review summarizes current research findings concerning the regeneration of the adult thyroid. Investigations have identified potential cellular mechanisms implicated in thyroid regeneration following partial tissue damage, including cells within microfollicles and a cluster of potential thyroid progenitors cells. Nevertheless, the exact mechanisms remain elusive. In cases of complete removal of the thyroid gland, regeneration does not occur, underscoring the necessity for an external source of thyroid tissue. The transplantation of thyroid organoids has emerged as a promising approach to restore thyroid function. Researchers have successfully derived thyroid organoids from various sources and demonstrated their functionality in both in vitro and in vivo animal models. Despite the challenges that still need to be addressed in achieving full maturation and functionality of human thyroid organoids, significant strides have been made in this regard. This review explores the potential of thyroid organoid transplantation and its implications for the field of regenerative medicine.
Ο ρόλος του συνδρόμου βακτηριακής υπερανάπτυξης του λεπτού εντέρου σε ασθενείς με μη-αλκοολική λιπώδη νόσο του ήπατος
Authors: Γκολφάκης Παρασκευάς
Published: September 2023

Εισαγωγή: Η Μη-Αλκοολική Λιπώδης Νόσος του Ήπατος (ΜΑΛΝΗ) είναι μια πολυπαραγοντική διαταραχή με ευρύ φάσμα κλινικών εκδηλώσεων. Το σύνδρομο βακτηριακής υπερανάπτυξης του λεπτού εντέρου (ΣΒΥΛΕ) χαρακτηρίζεται από αυξημένο αριθμό ή παρουσία βακτηρίων τύπου παχέος εντέρου στο λεπτό έντερο. Το ΣΒΥΛΕ μέσω της δημιουργίας περίσσειας ενεργειακών αποθεμάτων και πρόκλησης φλεγμονής θα μπορούσε να αποτελέσει έναν παθοφυσιολογικό μηχανισμό για την ανάπτυξη ΜΑΛΝΗ. Σκοπός – Μέθοδος: Ασθενείς με ιστολογική, βιοχημική ή ακτινολογική διάγνωση ΜΑΛΝΗ [Μη-Αλκοολικό Λιπώδες Ήπαρ (ΜΑΛΗ), Μη-Αλκοολική Στεατοηπατίτιδα (ΜΑΣΗ), κίρρωση] υπεβλήθησαν σε γαστροσκόπηση και δωδεκαδακτυλικό υγρό αναρροφήθηκε σε αποστειρωμένες παγίδες. Το ΣΒΥΛΕ ορίστηκε ως ≥103 CFU / ml αναρρόφησης δωδεκαδακτύλου και / ή παρουσία βακτηρίων παχέος εντέρου. Ασθενείς ελεύθεροι ηπατικής νόσου που υπεβλήθησαν σε γαστροσκόπηση λόγω συμπτωμάτων γαστροοισοφαγικής παλινδρόμησης αποτέλεσαν την ομάδα ελέγχου (ΟΕ). Το πρωτογενές καταληκτικό σημείο ήταν ο επιπολασμός του ΣΒΥΛΕ σε ασθενείς με ΜΑΛΝΗ, ενώ η σύγκριση του επιπολασμού του συνδρόμου στα διάφορα στάδια της νόσου αποτέλεσε ένα από τα δευτερογενή καταληκτικά σημεία της μελέτης. Αποτελέσματα: Συμπεριελήφθησαν 125 ασθενείς (51 ΜΑΛΗ, 27 ΜΑΣΗ, 17 κίρρωση και 30 ΟΕ). Η μέση ηλικία ήταν 54 ± 11,9 έτη. ΣΒΥΛΕ διαγνώστηκε σε 23/125 (18.4%) ασθενείς με υπεροχή των Gram,-αρνητικών βακτηρίων (19/23, 82,6%). Ο επιπολασμός του συνδρόμου ήταν μεγαλύτερος στους ασθενείς με ΜΑΛΝΗ σε σύγκριση με την ΟΕ (22/95, 23,2% έναντι 1/30, 3,3%, p = 0,014). Οι ασθενείς με ΜΑΣΗ έπασχαν πιο συχνά από ΣΒΥΛΕ σε σύγκριση με τους ασθενείς με ΜΑΛΗ (6/27, 22,2% έναντι 8/51, 15,7%) αλλά χωρίς στατιστικά σημαντική διαφορά (p = 0,11). Ο επιπολασμός του ΣΒΥΛΕ ήταν μεγαλύτερος στους ασθενείς με ΜΑΣΗ- κίρρωση σε σύγκριση με τους ασθενείς με ΜΑΛΗ (8/17, 47,1% έναντι 8/51, 15,7%, p = 0,02), ενώ ο επιπολασμός δε διέφερε ανάμεσα σε ασθενείς με κίρρωση και ΜΑΣΗ (8/17, 47,1% έναντι 6/27, 22,2%, p = 0,11).118. Συμπέρασμα: Ο επιπολασμός του ΣΒΥΛΕ είναι σημαντικά υψηλότερος σε ασθενείς με ΜΑΛΝΗ σε σύγκριση με την ομάδα ελέγχου. Επιπρόσθετα, το ΣΒΥΛΕ είναι πιο συχνό σε ασθενείς με ΜΑΣΗ-σχετιζόμενη κίρρωση σε σύγκριση με ασθενείς με ΜΑΛΗ.
Aspect-based Text Classification for Sentimental Analysis using Attention mechanism with RU-BiLSTM
Authors: Yelisetti Sandeep , Nellore Geethanjali
Published: September 2023

Sentiment analysis has gained increasing attention from an educational and social perspective with the huge expansion of user interactions due to the Web’s significant improvement. The connection between an opinion target’s polarity scores and other aspects of the content is defined by aspect-based sentiment analysis. Identifying aspects and determining their different polarities is quite complicated because they are frequently implicit. To overcome these difficulties, efficient hybrid methods are used in aspect-based text classification in sentiment analysis. The existing process evaluates the aspects of polarity by using a Convolutional neural network, and it does not work with Big data. In this work, aspect-based text classification and attention mechanisms are used to assist in filtering out irrelevant information and quickly locating the essential features in big data. Initially, the data is collected, and then the data is preprocessed by using Tokenization, Stop word removal, Stemming, and Lemmatization. After preprocessing, the features are vectorized and extracted using Bag-of-Words and TF-IDF. Then, the extracted features are given into word embeddings by GloVe and Word2vec. It uses Deep Recurrent based Bidirectional Long Short Term Memory (RUBiLSTM) for aspect-based sentiment analysis. The RU-Bi-LSTM method integrates aspect-based embeddings and an attention mechanism for text classification. The attention mechanism focuses on more crucial aspects and the bidirectional LSTM to maintain context in both ways. Finally, the binary and ternary classification outcomes are obtained using the final dense softmax output layer. The proposed RU-BiLSTM uses four reviews and two Twitter datasets. The results of the studies demonstrate the efficacy of the RU-BiLSTM model, which outperformed aspect-based classifications on lengthy reviews and short tweets in terms of evaluation.
Synchronous Federated Learning based Multi Unmanned Aerial Vehicles for Secure Applications
Authors: Sharma Itika , Gupta Sachin Kumar , Mishra Ashutosh , Askar Shavan
Published: September 2023

Unmanned Aerial Vehicles (UAVs), also known as drones, have rapidly gained popularity due to their widely employed applications in various industries and fields, including search and rescue, agriculture, industry, military operations, safety, and more. Additionally, drones assist with tasks such as search and rescue efforts, pandemic virus containment, crisis management, and other critical operations. Due to their unique capabilities in image, video, and information collection, a multi-UAV system plays a crucial role in these activities. However, such images and video data involve individual privacy. Therefore, such multi-UAV applications have an indigenous tradeoff of privacy preservation. We have proposed a Federated Learning (FL) based approach for ensuring privacy in multi-UAV applications. The proposed methodology utilizes a synchronous FL approach and the Convolutional Neural Network (CNN) to ensure security. The model parameters are protected by using a secure aggregation. Results demonstrate that the proposed approach outperforms existing techniques in terms of accuracy and precision.
A Deep LSTM-RNN Classification Method for Covid-19 Twitter Review Based on Sentiment Analysis
Authors: Srikanth Jatla , Shanmugam Avula Damodaram
Published: September 2023

In today’s world, advanced internet technologies have significantly increased people’s affinity towards social networks to stay updated on current events and communicate with others residing in different cities. Social opinion analyses helped determine the optimal public health response during the COVID-19 pandemic. Analysis of articulating tweets from Twitter can reveal the public’s perceptions of social distance. Sentiment Analysis is used for classifying text data and analyzing people’s emotions. The proposed work uses LSTM-RNN with the SMOTE method for categorizing Twitter data. The suggested approach uses increased characteristics weighted by attention layers and an LSTM-RNN-based network as its foundation. This method computes the advantage of an improved information transformation framework through the attention mechanism compared to existing BI-LSTM and LSTM models. A combination of four publicly accessible class labels such as happy, sad, neutral, and angry, is analyzed. The message of tweets is analyzed for polarization and subjectivity using TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet. The model has been successfully built and evaluated using two feature extraction methods, TF-IDF (Term Frequency-Inverse Document Frequency) and Bag of Words (BoW). Compared to the previous methodologies, the suggested deep learning model improved considerably in performance measures, including accuracy, precision, and recall. This demonstrates how effective and practical the recommended deep learning strategy is and how simple it is to employ for sentiment categorization of COVID-19 reviews. The proposed method achieves 97% accuracy in classifying the text whereas, among existing Bi-LSTM, achieves 88% maximum in the text classification.
Vulnerability Detection in Computer Networks using Virtual Reality Technology
Authors: Liu Songlin
Published: September 2023

This paper challenges the time-related challenges inherent in conventional network security detection methodologies. It is achieved by incorporating virtual reality technology into the domain of computer network security detection. The research methodology employs optimization calculations to extract attributes that characterize network security vulnerabilities. Concurrently, the weighting of diverse vulnerability attributes is adjusted using a web crawler, a comprehensive list of injection points, and meticulous analyses of the attacks’ genetic characteristics. This collective approach facilitates the exploration of automated network security vulnerability detection within a virtual reality framework. The study’s empirical results demonstrate that the detection method proposed within this investigation exhibits a notably reduced delay of 75.33 milliseconds. The respective delays observed in the two conventional methods stand at 290.11 milliseconds and 337.30 milliseconds. The substantial decrease in detection delay validates the effectiveness and efficiency of the devised automated network vulnerability detection approach grounded in virtual reality technology.
Computer Malicious Code Signal Detection based on Big Data Technology
Authors: Liu Xiaoteng
Published: September 2023

The article addresses the challenges modelled by the inadequacy of traditional detection methods in effectively handling the substantial volume of software behavior samples, particularly in big data. A novel approach is proposed for leveraging big data technology to detect malicious computer code signals. Additionally, it seeks to attack the issues associated with machine learning-based mobile malware detection, namely the presence of a large number of features, low accuracy in detection, and imbalanced data distribution. To resolve these challenges, this paper presents a multifaceted methodology. First, it introduces a feature selection technique based on mean and variance analysis to eliminate irrelevant features hindering classification accuracy. Next, a comprehensive classification method is implemented, utilizing various feature extraction techniques such as principal component analysis (PCA), Kaehunen-Loeve transform (KLT), and independent component analysis (ICA). These techniques collectively contribute to enhancing the Precision of the detection process. Recognizing the issue of unbalanced data distribution among software samples, the study proposes a multi-level classification integration model grounded in decision trees. In response, the research focuses on enhancing accuracy and mitigating the impact of data imbalance through a combination of feature selection, extraction techniques, and a multi-level classification model. The empirical results highlight the effectiveness of the proposed methodologies, showcasing notable accuracy improvements ranging from 3.36% to 6.41% across different detection methods on the Android platform. The introduced malware detection technology, grounded in source code analysis, demonstrates a promising capacity to identify Android malware effectively.
Deep Learning-Based Education Decision Support System for Student E-learning Performance Prediction
Authors: Jakkaladiki Sudha Prathyusha , Janečková Martina , Krunčík Jan , Malý Filip , Otčenášková Tereza
Published: September 2023

Information Technology (IT) and its advancements change the education environment. Conventional classroom education has been transformed into a modernized form. Education field decision-makers are always searching for new technologies that provide fast solutions to support Education Decision Support Systems (EDSS). There is a significant need for an effective decision support system to utilize student data which helps the university in making the right decisions. The Electronic learning system (e-learning) provides a live forum for faculties and students to connect with learning portals and virtually execute educational activities. Even though these modern approaches support the education system, active student participation still needs to be improved. Moreover, accurately measuring student performance using collected attributes remains difficult for parents and teachers. Therefore, this paper seeks to understand and predict student performance using effective data processing and a deep learning-based decision model. The implementation of EDSS starts with data preprocessing, Extraction-Transformation-Load (ETL), a data mart area to store the extracted data with Online Analytical Processing (OLAP) processing, and decision-making using Deep Graph Convolutional Neural Network (DGCNN). The statistical evaluation is based on the student dataset from the Kaggle repository. The analyzed results depict that the proposed EDSS model on an independent data mart with efficient decision support and OLAP provides a better platform to make academic decisions and help educators to make necessary decisions notified to the students.
Efficient Net-based Transfer Learning Technique for Facial Autism Detection
Authors: Saeed Mian Tariq
Published: September 2023

Autism Spectrum Disorder is a neurological disorder in which an individual faces life-long effects in communication and interaction with others. Nowadays, the Autism Spectrum disorder ratio is increasing drastically more than ever before. Autism can be identified at all developmental levels as a ”behavioural condition,” and its symptoms often arise between the ages of two and four. The ASD issue starts during puberty and persists through adolescence and adulthood. Children with ASD use both nonverbal and verbal behaviour to communicate, and they struggle with joint attention and social reciprocity. Children with autism are frequently socially isolated as a result of these problems. Through very expensive and time-consuming screening exams, autism spectrum features can be identified. As one of the possible mirrors of the brain, children’s faces can be utilised as a biomarker and as a quick and convenient technique for the early identification of ASD. An effective, genuine, and automatic method of face-based spectrum disorder identification is required. In this study we compare the transfer learning approach used for autism identification with the convolutional neural network (CNN)-based efficient-net strategy to identify autistic children using facial images. We used an open-source Kaggle dataset and evaluated the model performance in terms of accuracy, confusion matrix, precision, recall, and F1 measure. Efficient shows an accuracy of 97% on the benchmark dataset and beats the baseline technique of transfer learning-based approaches. This study can be used to help medical professionals validate their initial screening procedures for finding youngsters with ASD disease.
Optimization Algorithm for Urban Rail Transit Operation Scheduling based on Linear Programming
Authors: Wu Shuang , Wu Jinlong , Sun Yifeng , Yao Tong
Published: September 2023

At present, the traditional urban public transportation system cannot meet people’s daily travel needs. Urban Rail Transit (URT) has been rapidly promoted in major cities due to its advantages such as low energy consumption, high frequency, and large traffic volume. To achieve a more excellent and energy-saving operation scheduling strategy, the research first combines the train dynamics model and the energy consumption model. Since the optimization problem of URT is a linear problem, the attraction model of the Firefly algorithm can determine the calculation time consumed by the algorithm, which is very suitable for the complex optimization problem of URT. Therefore, the FA based optimization algorithm for urban rail transit operation scheduling (FURTOSO) based on the Firefly algorithm is studied and designed. Therefore, based on the study of the four working conditions of traction, cruise, coasting, and braking, a Firefly Algorithm for Urban Rail Transit Operation Scheduling (FURTOSO) was designed. Finally, the study optimizes the operation scheduling of Chengdu Metro Line 8 from two aspects: driving strategy and train schedule. The research demonstrates that the FURTOSO algorithm only needs 76 iterations to reach a stable state, with a fitness value of 0.6827. In practical applications, the utilization rate of train RBE is 30.1%, the total energy consumption (TEC) is 2.661 * 1011J, and the energy saving rate is 13.03%. In summary, the FURTOSO algorithm proposed in the study has excellent performance and has better energy-saving effects in Chengdu Metro Line 8.
The Optimal Selection of Ideological and Political Issues in Business Courses Based on Swarm Intelligence Algorithm
Authors: Yin Xuecai
Published: September 2023

The current optimal selection matrix for ideological and political issues of business courses is mostly set as a single objective form, and the topic selection is limited in scope, increasing the mutation rate of the optimal selection of topics. Therefore, the design and analysis of the optimal selection method for ideological and political issues of business courses based on the swarm intelligence algorithm is proposed. According to the actual measurement needs and standards, extract the optimal characteristic of ideological and political issues selection of the curriculum, use a multi-level approach to break the limits of issues selection, establish a multi-level swarm intelligence selection matrix, build an optimal selection model for ideological and political issues of business and trade courses based on swarm intelligence accounting, and achieve the optimal selection of issues through group fixed-point optimization. The novelty of this work lies in the design and analysis of the optimal selection method for ideological and political issues in business courses using a swarm intelligence algorithm. This approach introduces a new way of selecting topics by harnessing the power of collective intelligence inspired by the behavior of insects or animals. The final test results show that the mutation rate of the optimal selection of the three topics finally screened using the swarm intelligence algorithm is better controlled below 0.2 through the measurement of five classes, indicating that the topic is more practical, more targeted, and has better discussion value.
Scalable Computing Infrastructure for Online and Blended Learning Environments
Authors: Xin Liao
Published: September 2023

With the growing popularity of online learning and blended learning, as well as the rapid development of cloud computing and big data technology, scalable computing infrastructure has become an indispensable part of building a modern education platform. Method: Five experiments were conducted to test the scalability and reliability of computing infrastructure based on online and blended learning environments. The experiments include the performance comparison of online learning platforms based on different virtualization technologies, the performance comparison of online and hybrid learning environments under different loads, the comparison of online learning experiences under different bandwidth constraints, the system stability test under different user numbers, and the comparison of access speeds in different regions. Result: The experimental results showed that on an online learning platform using the KVM (Kernel-based Virtual Machine) interface, when the number of concurrent users is 99, the response time is 100.9ms, and the CPU (Central Processing Unit) utilization rate is 60.9%. Under low load conditions, the concurrent access volume is 200; the response time is 50ms, and the throughput is 10.3. When accessing locally, the latency is 9.19ms; the download speed is 500.3KB/s; the network throughput is 399.8KB/s. Conclusion: Exploring the scalability, reliability, performance, stability, and access speed of online learning platforms is crucial for improving platform competitiveness and ensuring user experience.