bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between postural disorders and malocclusion and methods of their detection
Authors:   Kuban State Medical University, Krasnodar, Russian Federation, Ayupova F.S. , Murashkina T.I. ,   Penza State University, Penza, Russian Federation, Gayvoronskaya T.V. ,   Kuban State Medical University, Krasnodar, Russian Federation, Badeeva E.A. ,   Penza State University, Penza, Russian Federation, Arutyunov A.V. ,   Kuban State Medical University, Krasnodar, Russian Federation, Vasiliev Yu.A. ,   Kuban State Medical University, Krasnodar, Russian Federation, Ponomarenko T.V. ,   Kuban State Medical University, Krasnodar, Russian Federation, Gabbasov A.R. ,   Bashkir State Medical University, Ufa, Russian Federation
Published: March 2023

Postural disorders and malocclusion are highly prevalent in children. Malocclusion is found in 90% of children with postural disorders. This problem requires particular attention to identify optimal methods for early diagnosis of and timely treatment for postural disorders and malocclusion, as well as comprehensive health improvement of children. We analyzed the literature data on the types of postural disorders, currently availably methods of its diagnosis, prevention, and treatment. We performed our search using СyberLeninka, E-library, and MEDLINE/PubMed databases with the following key words: malocclusion, diagnosis, treatment, postural disorders, pathological changes in the maxillofacial region, prevention. We selected 57 publications exploring the association between postural disorders and malocclusion and describing main types of postural disorders, their causes, as well as current methods of their detection, prevention, and treatment. We identified difficulties hindering widespread use of the existing methods for the diagnosis of postural disorders. We believe that it is very important to improve available diagnostic methods and create new ones; we discuss the possibility of their development and implementation into routine clinical practice. Key words: malocclusion, diagnosis, treatment, postural disorders, pathological changes in the maxillofacial region, prevention
Housing Instability and Evictions Linked to Elevated Intimate Partner and Workplace Violence Among Women Sex Workers in Vancouver, Canada: Findings of a Prospective, Community-Based Cohort, 2010–2019
Authors: Goldenberg Shira M. , Buglioni Natalie , Krüsi Andrea , Frost Elizabeth , Moreheart Sarah , Braschel Melissa , Shannon Kate
Published: March 2023

Objectives. To model the relationship of unstable housing and evictions with physical and sexual violence perpetrated against women sex workers in intimate and workplace settings.

Methods. We used bivariate and multivariable logistic regression with generalized estimating equations to model the association of unstable housing exposure and evictions with intimate partner violence (IPV) and workplace violence among a community-based longitudinal cohort of cisgender and transgender women sex workers in Vancouver, Canada, from 2010 through 2019.

Results. Of 946 women, 85.9% experienced unstable housing, 11.1% eviction, 26.2% IPV, and 31.8% workplace violence. In multivariable generalized estimating equation models, recent exposure to unstable housing (adjusted odds ratio [AOR] = 2.04; 95% confidence interval [CI] = 1.45, 2.87) and evictions (AOR = 2.45; 95% CI = 0.99, 6.07) were associated with IPV, and exposure to unstable housing was associated with workplace violence (AOR = 1.46; 95% CI = 1.06, 2.00).

Conclusions. Women sex workers face a high burden of unstable housing and evictions, which are linked to increased odds of intimate partner and workplace violence. Increased access to safe, women-centered, and nondiscriminatory housing is urgently needed. (Am J Public Health. 2023;113(4):442–452. https://doi.org/10.2105/AJPH.2022.307207 )
Oxidative Stress Levels of Fine Particulate Matter (PM2.5) and Urinary Glutathione of Microbus Drivers
Authors: Sidebang Purnama , Kusumayati Agustin , Haryanto Budi
Published: March 2023

Urinary glutathione levels are known to be an early indicator of oxidative stress in travelers. This study analyzed the association between particulate matter (PM2.5) exposure on the road and urine glutathione levels in Jakarta’s microbus drivers. This cross-sectional study involved 96 microbuses (one of Jakarta’s public transportations) drivers of nine routes in Kampung Melayu Bus Station, Jakarta, Indonesia. An anthropometric assessment and a structured questionnaire were employed. Along with the participants driving on the road, real-time personal equipment measuring PM2.5 exposure concentrations was used. Total glutathione levels were measured using a colorimetric method. A correlation test and linear regression analysis were used to examine the effect of PM2.5 exposure on total glutathione levels. The average PM2.5 exposure concentration was 90.9±1.8 μg/m3, with a maximum concentration of 114.7 μg/m3. The average urinary glutathione level was 1.3±0.5 μM. The regression analysis showed that PM2.5 was associated with urinary glutathione levels after controlling for body mass index and smoking status. To conclude, the drivers experience exposure to an extremely high level of PM2.5 that could influence the glutathione levels.
Job Satisfaction Model of Primary Health Care Midwives Based on Indonesian Workforce Research in the Health Sector
Authors: Despitasari Mieska , Bachtiar Adang , Hendarwan Harimat , Besral Besral , Yuniar Yuyun
Published: March 2023

Promotive, preventive, curative, and rehabilitative efforts that are comprehensive, integrated, and sustainable are employed to enhance the health state of the global population. Within this context, however, the quality of primary health care depends on job satisfaction, which leads to the happiness of human resources in the health sector. Therefore, this study aimed to analyze and formulate a job satisfaction model among primary health care midwives in Indonesia. This study was an advanced secondary data analysis of a cross-sectional study conducted in 2017 by the National Institute of Health Research and Development, Ministry of Health of the Republic of Indonesia. A total of 87,341 midwives from all 9,669 primary health cares in Indonesia participated in this study. Data were collected by distributing the Minnesota Satisfaction Questionnaire, elaborating on the satisfaction level and relevant contributing factors. The prefilled Likert scale questionnaire was analyzed using logistic regression. The findings suggested a model indicating that motivation, work area (region), history of salary delay, and training received were important for their job satisfaction, whereas the motivation aspect contributed the most. Therefore, the local and central governments must consider these factors in the human resource policymaking process.
Developing a New Tool for Early Detection of the Nutritional and Health Risk Factors of Urban Workers’ Productivity
Authors: Nai Hildagardis Meliyani Erista , Estri Arimbi Karunia , Widianti Christina Ririn
Published: March 2023

Nutrition and health play vital roles in work productivity. This study aimed to develop a risk self-assessment tool called Early Detection of the Nutritional and Health Risk Factors on the productivity of urban workers. This study was conducted in two stages: 1) the development of the tool to determine the nutritional and health risk factors that affect productivity based on literature reviews and scoring systems and 2) the testing of validity and reliability. Finally, the tool contained 63 items, including 28 items on nutritional risk factors and 35 on health risk factors. The validity of the tool was assessed using the content validity index (CVI): item-level CVI (I-CVI) and scale-level CVI (S-CVI), and face validity index (FVI) and reliability using Cronbach’s alpha coefficient. Preliminary versions of this tool showed a high content validity (I-CVI = 1.00; S-CVI based on the average method = 1). The face validity index among urban workers was at least 0.90, and the overall Cronbach’s alpha coefficient was 0.70. The tool developed is acceptable, but revisions are still needed, and sample sizes must be increased.
Presidential Vote Share and COVID-19 Vaccination Rate in Indonesia: A District-level Cross-Sectional Ecological Study
Authors: Wirawan Gede Benny Setia , Gustina Ni Luh Zallila , Valerie Ivy Cerelia , Pradnyani RS I Gusti Ayu Indah , Arifin Muchamad Zaenal , Januraga Pande Putu
Published: March 2023

Political affiliation has been reported as a determinant of COVID-19 vaccine acceptance in some countries, although few studies have examined the Asian context. This study aims to fill this gap by employing an ecological study design using Indonesian regions as data points. Political affiliation was represented by incumbent President Jokowi’s vote share in the 2019 presidential election. Potential confounders included population density, human development index, availability of hospitals and primary health care, 2019–2020 economic growth, COVID-19 mortality rate, and proportion of Muslims in the population. The final analysis included 201 out of 501 districts and cities in Indonesia. Controlling for confounders, multivariate regression found that Jokowi’s vote share was an independent predictor of vaccination rate, with standardized β and R2 values of 0.350 and 0.734 for the first dose vaccination rates and 0.251 and 0.782 for the second dose, respectively. This association may be underpinned by differences in religiosity, public trust, and vulnerability to misinformation between Jokowi’s supporters and the opposition. Improving public trust in a politically polarizing society is crucial to improving future coverage of COVID-19 and other vaccines.
Fine-grained modeling and cost-effectiveness evaluation of public health policies for cervical cancer, with application to a Colombian case study
Authors: Daniela Angulo , Cortes Maria Fernanda , Mura Ivan , Akhavan-Tabatabaei Raha
Published: March 2023

Background: Cervical cancer (CC) is globally ranked fourth in terms of incidence and mortality among women. Vaccination against Human Papilloma Virus (HPV) and screening programs can significantly reduce CC mortality rates. Hence, executing cost-effective public health policies for prevention and surveillance is crucial. However, defining policies that make the best use of the available resources is not easy, as it requires predicting the long-term costs and results of interventions on a changing population. Since the simpler task of predicting the results of public health policies is difficult, devising those that make the best usage of available resources is an arduous challenge for decision-makers.
Methods: We propose a fine-grained epidemiological simulation model based on differential equations, which predicts costs and effectiveness of CC public health policies that include vaccination and screening. The model represents population dynamics, HPV transmission within the population, likelihood of infection/clearance, virus-induced appearance of precancerous lesions and eventually of CC, as well as immunity gained with vaccination and early detection with screening. The model is implemented in an open-source software tool that allows defining and evaluating multiple policies. We validate the modeling approach against real population data and apply it to a case study based on Colombian data.
Results: For the Colombian case, our modeling approach showed minimal deviations with respect to reference population estimates over a 30-year horizon, and an accurate estimation of the age-standardized mortality rate. We analyzed 54 policies and identied 8 with appropriate costs/effectiveness results. We explored the sensitivity of our results to changes in parameters, including the discount rate and the performance of screening tests.
Conclusions: Our modeling approach can provide valuable support for healthcare decision-makers. The implementation into an automated tool allows customizing the analysis with country specific data, flexibly defining public health policies to be evaluated, and conducting disaggregated analysis of their cost and effectiveness.
An in-memory computing architecture based on a duplex 2D material structure for in-situ machine learning
Authors: Wang Xinran , Ning Hongkai , Yu Zhihao , Zhang Qingtian , Wen Hengdi , Gao Bin , Mao Yun , Li Yuankun , Zhou Ying , Zhou Yue , Chen Jiewei , Liu Lei , Wang Wenfeng , Li Taotao , Li Yating , Meng Wanqing , Li Weisheng , Li Yun , Qiu Hao , Shi Yi , Chai Yang , Wu Huaqiang
Published: March 2023

The growing computational demand in artificial intelligence (AI) calls for hardware solutions that are capable of in-situ machine learning, where both training and inference are performed by edge computation. This not only requires extremely energy-efficient architecture (such as in-memory computing, IMC) but also memory hardware with tunable properties to simultaneously meet the demand for training and inference. Here, we report a duplex device structure based on ferroelectric field-effect transistor (FeFET) and atomically thin MoS2 channel and realize a universal IMC architecture for in-situ learning. By exploiting the tunability of ferroelectric energy landscape, the duplex building block demonstrates overall excellent performance in endurance (>1013), retention (>10 years), speed (4.8 ns) and energy consumption (22.7 fJ/(bit·μm2)). We implemented a hardware neural network using arrays of two-transistor-one-duplex-FeFET (2T1D) cells and achieved 99.86% accuracy in non-linear localization task with in-situ trained weights. Simulations show that the proposed device architecture could achieve the same level of performance as graphics processing unit under notably improved energy efficiency. Our device core can be combined with silicon circuitry through three-dimensional heterogeneous integration to give a hardware solution toward general edge intelligence (EI).
Longwave infrared multispectral image sensor system using aluminum-germanium plasmonic filter arrays
Authors: Shaik Noor E Karishma , Widdicombe Bryce , Sun Dechuan , John Sam E , Ryu Dongryeol , Ampalavanapillai Nirmalathas , Unnithan Ranjith R
Published: March 2023

A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and colour filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR; 8 – 14 µm) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum-based plasmonic filter array sandwiched in germanium. To realize the multispectral sensor, the filter arrays are then integrated into a 3D printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-the-art deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.
Surgical Site Infections and Antibiotic Prophylaxis in Orthognathic Surgery
Authors: Naros Andreas , Naros Carola Helene , Awad Daniel , Krimmel Michael , Kluba Susanne
Published: March 2023

Background
This study was conducted to determine surgical site infection (SSI) rates and potential risk factors as well as to evaluate antibiotic prophylaxis in orthognathic surgery.
Methods
This retrospective observational study included patients who received orthognathic surgery. SSIs and their management were assessed for up to one year post-operatively. Various predictor variables were assessed.
Results
In total 291 patient met the inclusion criteria (56.7% female). The mean age at surgery was 25.5 ± 8.5 years. Fifty-four patients (18.6%) were diagnosed with a craniofacial malformation. Relevant previous surgeries were documented in about one quarter of included patients (n = 75). Ninety-two percent of patients (n = 267) received intraoperative single-dose antibiotic prophylaxis. Surgical site infections occurred in 12.4% (n = 36) of patients. There was a significant association between postoperative infections and type of surgery (P = .037) as well as type of drainage (P = .002). Statistical analyses also revealed a higher prevalence of smokers (P = .036) and previous surgically assisted rapid palatal expansion (SARPE) (P = .018) in the infection group. Furthermore, no significant relationships were observed between postoperative infections and various co-factors (i.e. antibiotic regime, age at surgery, gender, associated craniofacial malformations, surgery duration, displacement distances, mandibular setback vs. advancement).
Conclusion
Low rates of SSIs occurred following an intraoperative single-dose antibiotic regime. None of the SSIs had a significant effect on the final surgical outcome. Present data do not warrant escalation of the antibiotic regimen. Postoperative smoking and capillary drainage should be avoided.
A novel fractional time-delayed grey model and its applications in predicting enrollments and educational expenditure in China
Authors: Xie Wanli , Liu Caixia
Published: March 2023

Accurate forecasting of the enrollment scale of higher education and education expenditure is the key to scientific decision-making in education. Nevertheless, education data is affected by economy and education system policies, which result in difficulty in modeling. Grey model can build a model from an unclear system with insufficient overall information, and make a fuzzy long-term description of the development law of things. In order to predict the enrollment scale of higher education and education expenditure while improving the prediction accuracy of the existing grey system, we present a novel time-delayed grey model whose time term is a fractional-order accumulation, along with an optimization method that adaptively changes the data structure based on the characteristics of the data, FPGM(1,1,r). The particle swarm optimization algorithm is used to optimize the model's hyperparameters. Results of the experiment indicate that the FPGM(1,1,r) model can effectively fit and forecast the enrollment scale and educational expenditure. In terms of predicting the sustainable development of higher education in China, it is more accurate than other grey models.
“They have to do something:” News Coverage of Climate Change and Generation Z
Authors: Novak Alison
Published: March 2023

In a January 2021 Pew Research Center report, climate change was singled out as a “defining issue” for Generation Z (Tyson et al., 2021). Featuring the findings of a survey of nearly 10,000 members of Generation Z, climate change was repeatedly identified as a top priority impacting the group’s interests, activism actions, and digital media engagement. The Pew study seemingly confirmed what many journalists, political pundits, and even organizational leaders theorized about the newest generation of citizens: climate change permeated the group unlike any other issue. The Pew study launched hundreds of news articles, covering both its findings as well as the growing climate change movement embraced by Generation Z. News articles have the potential to shape intra and inter-group perceptions of (and actions within) generational political and civic engagement. Thus, understanding the nature of this coverage is critical to anticipating and constructing future climate change activism.
Modelling the suitability of agrivoltaics to deliver sustainable energy and food production in East Africa
Authors: Neesham-McTiernan Talitha , Randle-Boggis Richard , Cinderby Steve , Cotton Matthew , Hartley Sue , Lamanna Christine
Published: March 2023

Agrivoltaics address energy, food, and water security concomitantly, yet little research has addressed the opportunities and barriers for this technology in East Africa, a region facing substantial challenges across the water-energy-food nexus. Here, we identify possible use cases for the technology in five East African nations and determine the factors influencing delivery of potential benefits, before assessing the spatial suitability of the technology for two of the emergent use cases: agribusiness and marginal farming. Our findings show there is a moderate to high suitability for AV across both use cases, with a range of biophysical and social factors driving successful outputs; land tenure and solar radiation were the most important for determining suitability. Our analysis indicates where AVs would be best developed to improve access to, and resilience of, energy and food production in East Africa, and our methods could be applied to other locations to support agrivoltaics development internationally.
Effect of Human Chorionic Gonadotropin Injection before Frozen-Thawed Embryo Transfer
Authors: XIN XIN , DONG LI , GUAN LU , WANG YIXUAN , LI JIAXI , WU HAICUI , LIAN FANG
Published: March 2023

Purpose
To investigate the effect of human chorionic gonadotropin (hCG) in hormone replacement regimen for frozen-thawed embryos
Methods
We performed a retrospective cohort study and included patients who underwent frozen embryo transfer (FET) between January 1, 2015, to May 31, 2021. According to the protocols for the FET cycle, the patients were divided into control (n = 238) and hCG groups (n = 216). The clinical pregnancy rate, live birth rate, early abortion rate, late abortion rate, and ectopic pregnancy rate were compared between the two groups.
Results
There was a significant difference in clinical pregnancy rate between the hCG and control groups (54.6 vs. 45.5%, p = 0.048). The ectopic pregnancy rate decreased (0.4% vs. 1.2%), while the live birth rate increased (35.6 vs. 28.6%, p = 0.107) in the hCG group; however, these differences were not statistically significant.
Conclusion
HCG injection in the hormone replacement regimen for FET increased the pregnancy rate.
Predicting therapeutic responses in head and neck squamous cell carcinoma from TP53 mutation and The mutation of TP53 could be detected by cfDNA
Authors: Wei Mei , Liu Yao , Zhi Jingtai , Li Li , Zhang Shengchi , Wang Wei
Published: March 2023

Objectives: Head and neck squamous cell carcinoma (HNSCC) was increasing globally. The mutation of the TP53 was the most common of all somatic genomic changes in HNSCC, and TP53 mutation was associated with the response of immunotherapeutic and chemotherapeutic. Tumor-derived circulating cell-free DNA (cfDNA) was minimally invasive method to determine genetic alterations for cancer. The study aimed to explore the therapeutic responses of TP53 mutation patients with HNSCC and the accuracy of cfDNA to detect TP53 mutation.
Materials and methods: The information of TP53 mutations and patients’ survival time and clinical data in HNSCC was downloaded from The Cancer Genome Atlas (TCGA) Database. The difference of immune infiltration between TP53 mutant group and wild group was compared. The ssGSEA method applied to the transcriptome of HNSCC samples to assess the distribution of immune cell types between two groups. The chemotherapy response was constructed using the R software package, pRRophetic. GSEA enrichment analysis was performed based on TP53 mutation. The next-generation sequencing (NGS) was executed on cfDNA of 9 patients with HNSCC to detect genetic alterations. Tumor biopsy (n = 9) was sequenced using the same technique.
Results: TP53 was the most frequently mutated gene in HNSCC. TP53 mutation was related to the immune cells and expression of immune-associated genes. The TP53 mutation group showed less response to immunotherapeutic but high sensitivity to some chemotherapies compared to wild-type group. TP53 was the most frequently mutated gene (6/9; 66.67%) in cfDNA. 27.27% of the tissue tumor variants were not detected in cfDNA when all TP53 mutations were considered.
Conclusion: TP53 mutation could be used as a specific predictor of treatment response in patients with HNSCC. It was feasible to detect the TP53 mutations from HNSCC patients by cfDNA. The results suggested that the therapeutic response in patients could be predicted by detecting TP53 mutations in cfDNA, and large-scale and prospective studies were needed to validate it.
Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study
Authors: Dub Timothee , Mäkelä Henna , Kleef Esther van , Leblond Agnes , Mércier Alizé , Hénaux Viviane , Bouyer Fanny , Binot Aurelie , Thiongane Oumy , Lancelot Renaud , Delconte Valentina , Zamuner Léa , Bortel Wim Van , Arsevska Elena
Published: March 2023

Epidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies contacted, 34 agencies (42%) from 26 different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.
Modelling the effects of topographic hetero- geneity on distribution of Nitraria tanguto- rum Bobr. species in deserts using LiDAR-data
Authors: Zhou Huoyan , Feng Linyan , Fu Liyong , Sharma Ram P. , Zhou Xiao , Zhao Xiaodi
Published: March 2023

Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. A number of hot issues can be investigated in desert ecosystems , including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species distribution models (SDMs) can be used to understand these relationships. We used the data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light detection and ranging) data of one square kilometers in the Inner Mongolia region of China to construct the SDMs. We evaluated the performance of the SDMs constructed with both the variants of the parametric and nonpara-metric algorithms (bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, generalized linear model (GLM), generalized additive model (GAM), random forest (RF), and support vector machine (SVM)). The area under the receiver operating characteristic curve was used to evaluate the algorithms. The SDMs constructed with RF appeared to be the best based on the area under the curve (0.7733). We also generated Nitraria tangu-torum Bobr. distribution maps with the constructed SDMs and the suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to the southern part with a slope of 0-20 degree at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on desert species distribution on a small scale. The presented SDMs will have important applications for predicting species distribution and will be useful for preparing conservation and management plans for desert ecosystems on a small scale.
Application and effect simulation of image recognition technology based on machine vision feature parameters in art teaching
Authors: Surong Guo , Jicheng Xu , Chunming Han
Published: March 2023

Under the new era background, the multi cultures of various countries in the world are spreading and infiltrating each other, and the art education at this stage is in the stage of innovation and development. It is necessary to improve the teaching content and teaching methods of schools in an all-round way, deepen the research of education curriculum reform, especially to combine these new technologies with art classroom teaching to achieve the transformation of humanity. Today, with the great development trend of visual media, it has gradually entered the era of reading and map reading, which is a period of information flow and communication. The basic function of image is to transfer and convey image information. Therefore, in art teaching, it and graphics are complementary. In order to fully carry out art teaching, this paper applies the image recognition technology of machine vision feature parameters to art teaching and effect simulation. Machine vision technology analyzes the required resolution and color scale from digital imaging, and uses pre processed images and features to lock the images in the machine vision system, obtain the texture and proportion of art images according to the area, width and proportion of objects, and complete classification and recognition, The whole image production process is more efficient, and finally can produce high-resolution art images. The results show that, due to the application of image recognition technology of machine vision feature parameters in art teaching and effect simulation, it has achieved good performance results. It has maximized the image recognition methods of students, thus cultivating students' artistic perception through artistic literacy, and promoting the balanced development of students.
Atrial Fibrillation Detection Based on Machine Learning From a Large-Scale Data Set of 12-Lead Electrocardiograms
Authors: Chuang Bo Sheng , Yang Albert C.
Published: March 2023

Background:
Atrial fibrillation (AF) is a dangerous cardiac arrhythmia that increases the risk of stroke and heart failure. Despite its severity, AF diagnosis still heavily depends on the expertise of healthcare professionals. The real-time detection of paroxysmal AF is currently limited due to the absence of automated methods. An efficient machine learning algorithm for AF detection would thus be of immense clinical value.
Objective:
The aim of this study is to develop a clinical valuable machine learning algorithm that can accurately detect AF.
Methods:
This study used the 12-lead ECG recordings obtained from 2020 PhysioNet Challenge data sets. We employed the Welch method to extract the power spectral features of 12-lead electrocardiograms (ECGs) in the range of 0.083 to 24.92 Hz and used multiple machine learning methods to classify sinus rhythm and AF on the basis of these power spectral features. Our machine learning approach is based on the observation that normal ECGs present predictable and stable heartbeats, whereas those of AF are more chaotic and feature fibrillatory waves.
Results:
A LightGBM model achieved the highest performance, obtaining an average F1 score of 0.988 across the 12 leads. The interlead variation of accuracy was low, and aVR lead yielded the highest F1 score of 0.993.
Conclusion:
In conclusion, a machine learning model based on LightGBM may provide a real-time approach for AF detection in intensive care units or in wearable devices used in remote settings.
Amyotrophic Lateral Sclerosis in three Italian regions: prevalence and incidence based on health administrative databases
Authors: Bacigalupo Ilaria , Finocchietti Marco , Paoletti Olga , Bargagli Anna Maria , Brunori Paola , Lombardi Niccolò , Sciancalepore Francesco , Agabiti Nera , Kirchmayer Ursula , Group Caesar Study
Published: March 2023

Background
Studies of the epidemiology of amyotrophic lateral sclerosis (ALS) are numerous, with incidence and prevalence estimates varying widely depending on population characteristics, geographical areas, and available data sources. The aim of this study was to estimate prevalence and incidence of ALS in three Italian regions (Latium, Tuscany, and Umbria) using health administrative databases.
Methods
ALS patients residing in Latium, Umbria and Tuscany were identified through an algorithm based on data from three different health administrative databases: hospital discharges, exemptions from health care co-payment, and emergency departments (study period 2014-2019). We calculated crude, age- and gender specific prevalence estimates on December 31, 2019 and standardised incidence rates of ALS by region, year, and sex between 2014-2019. Moreover, using a clinical dataset available in the Lazio region, we calculated the proportion of individuals correctly identified as ALS cases by the algorithm in this region.
Results
A total of 1,031 persons affected by ALS, aged ≥ 18 years at the prevalence day were identified: 408 cases in Tuscany, 546 in Latium, and 77 in Umbria. Diseases specific co-payment exemptions accounted for the biggest contribution for case detection, ranging between 24.7% in Umbria and 30.6% in Latium. The algorithm produced standardised overall ALS prevalence rates similar between the three regions, varying between 12.31/100,000 in Tuscany, 11.52/100,000 in Latium and 9.90/100,000 in Umbria. The 5-year crude rates were higher in men, and in people aged 65-79 years. Among 310 patients included in the clinical dataset, 263 (84.8%) were identified by the algorithm based on health administrative databases.
Conclusions
ALS prevalence and incidence in three regions of Central Italy are rather similar but slightly higher than those previously reported. This finding is plausible given that previous results relate to at least ten years ago and evidenced of increasing trends. Overall, our results encourage the use of administrative data to produce occurrence estimates, useful to both, epidemiological surveillance and research and healthcare policies.
Lenvatinib activates anti-tumor immunity by suppressing immunoinhibitory infiltrates in the tumor microenvironment of advanced hepatocellular carcinoma
Authors: Yamauchi Masami , Ono Atsushi , Amioka Kei , Fujii Yasutoshi , Nakahara Hikaru , Teraoka Yuji , Uchikawa Shinsuke , Fujino Hatsue , Nakahara Takashi , Murakami Eisuke , Okamoto Wataru , Miki Daiki , Kawaoka Tomokazu , Tsuge Masataka , Imamura Michio , Hayes Nelson , Ohishi Waka , Kishi Takeshi , Kimura Mizuki , Suzuki Natsumi , Arihiro Koji , Aikata Hiroshi , Chayama Kazuaki , Oka Shiro
Published: March 2023

Lenvatinib has been expected as an immunomodulator based on its marked anti-angiogenic property in immunotherapy for solid cancers. However, the mechanism by which the tumor immune microenvironment is edited in human cancer tissues remains to be elucidated. Here, we analyzed the multi-omics of serial tumor and blood samples during lenvatinib monotherapy in 51 patients with advanced hepatocellular carcinoma. Lenvatinib yielded survival benefits regardless of baseline immune subtypes as determined by expression of the extracted RNAs. While immune signatures associated with T-cell functions and interferon responses were enriched in the early phase of treatment, signatures associated with immunoinhibitory cells were downregulated along with efficient vascular endothelial growth factor receptor and fibroblast growth factor receptor blockades. These findings were consistently supported by imaging mass cytometry, T-cell receptor repertoire analysis and kinetics of circulating proteins. Our study also identified interleukin-8 and angiopoietin-2 as possible targets of intervention to overcome resistance to existing immunotherapies.
K-means clustering of outpatient prescription claims for health insured in Iran
Authors: Monahhed Shekoofeh , Sefiddashti Sara Emamgholipour , Minaei Behrouz , Shahali Zahra
Published: March 2023

Objective The segmentation of consumers based on their behavior and needs is the most crucial action of the health insurance organization. This study's objective is to cluster Iranian health insureds according to their demographics and data on outpatient prescriptions.
Setting The population in this study corresponded to the research sample. The Health Insurance Organization's outpatient claims were registered consecutively in 2016, 2017, 2018, and 2019 were clustered.
Design The k-means clustering algorithm was used to cross-sectionally and retrospectively analyze secondary data from outpatient prescription claims for secondary care using Python 3.10.
Participants The current analysis transformed 21 776 350 outpatient prescription claims from health insured into 193 552 insureds.
Results Insureds using IQR were split into three classes low, middle, and high risk. Based on the silhouette coefficient the insureds of all classes were divided into three clusters. all data for a period of four years, the first through third clusters, there were 21 799, 7170, and 19 419 insureds in the low risk class. Middle risk class had 48 348,23 321, 25 107 insureds and 14 037, 28 504, 5847 insured in the high risk class were included. For the first cluster of low-risk insureds: the total average cost of prescriptions paid by the insurance for the insureds was 211$, the average age was 26 years, the average franchise 88.5US$, the average number of medications and prescriptions was 409 and 62, the total average costs of prescriptions Outpatient was 302.5 US$, the total average number of medications for acute and chronic disease was 178 and 215 respectively. The majority of insureds were men and those who were part of the householder's family.
Conclusions By segmenting insurance customers insurers can set insurance premium rates, controlling the risk of loss which improves their capacity to compete in the insurance market.
Developing emotional preparedness and mental resilience through high-fidelity simulation: A ‘Bridge Too Far’ for institutions teaching major trauma management and mass-casualty medicine?
Authors: Newton Jon , Smith Andrew
Published: March 2023

Clinical acumen represents only part of being adequately equipped to attend a major incident. The emotive sights, sounds and smells inherent of these dynamic environments are all-encompassing experiences and thus responders must also be armed with the emotional preparedness to perform their clinical or managerial duties effectively; as well as the mental resilience to facilitate professional continuance. Despite this, limited training and a sparsity of evidence exists to guide developments within this domain.
Background: Historically, major incident training has focused on clinical principles; but irrespective of how comprehensive this teaching might be, or how fortuitously the learning materials meet national guidelines, they are of little consequence if tandem steps to cultivate mental resilience and emotional preparedness are absent. Effectively dovetailing these three elements is essential if teaching institutions are to properly prepare students for real-world practice. High-Fidelity Simulation (HFS) has a growing reputation as an effective means of bridging important gaps between theory and practice; and to help identify if this modality could cultivate these advancements, we exposed studentsto a large-scale HFS and conducted an evaluation-based study to better understand their lived experience. The primary aim was to ascertain the impact on clinical acumen, emotional preparedness and mental resilience; to help develop teaching and learning practices
Methods: Quantitative data was obtained from a sample of 108 students undertaking paramedic science, physician associate studies and adult nursing. Self-completed questionnaires incorporating a 7-point Likert scale were utilised; and each student’s strength of agreement was rated against a series of statements surrounding acquisition of clinical acumen, emotional preparedness and mental resilience. Ethical approval was granted and SPSS v28 employed to analyse the data.
Results: 91% of students agreed the combination of theoretical training and HFS provided made them feel clinically prepared to attend a real major incident; 86% agreed this experience had developed their mental resilience and 90% agreed that they felt emotionally prepared to attend a major incident.
Conclusion: HFS is a practicable teaching and learning tool; and effective in developing clinical acumen, mental resilience and emotional preparedness in learners training to work in disaster environments or emergency medicine settings.
GREB1 isoform 4 is specifically transcribed by MITF and required for melanoma proliferation
Authors: Kikuchi Akira , Shinzawa Koei , Matsumoto Shinji , Sada Ryota , Harada Akikazu , Saitoh Kaori , Kato Keiko , Ikeda Satsuki , Hirayama Akiyoshi , Yokoi Kazunori , Tanemura Atsushi , Nimura Keisuke , Ikawa Masahito , Soga Tomoyoshi
Published: March 2023

Growth regulation by estrogen in breast cancer 1 (GREB1) is involved in hormone-dependent and -independent tumor development (e.g., hepatoblastoma). In this study, we found that a GREB1 splicing variant, isoform 4 (Is4), which encodes C-terminal half of full-length GREB1, is specifically expressed via microphthalmia-associated transcription factor (MITF) in melanocytic melanoma, and that two MITF-binding E-box CANNTG motifs at the 5’-upstream region of GREB1 exon 19 are necessary for GREB1 Is4 transcription. MITF and GREB1 Is4 were strongly co-expressed in approximately 20% of the melanoma specimens evaluated (17/89 cases) and their expression was associated with tumor thickness. GREB1 Is4 silencing reduced melanoma cell proliferation in association with altered expression of cell proliferation-related genes in vitro. In addition, GREB1 Is4 targeting by antisense oligonucleotide (ASO) decreased melanoma xenograft tumor formation and GREB1 Is4 expression in a BRAFV600E; PTENflox melanoma mouse model promoted melanoma formation, demonstrating the crucial role of GREB1 Is4 for melanoma proliferation in vivo. GREB1 Is4 bound to CAD, the rate-limiting enzyme of pyrimidine metabolism, and metabolic flux analysis revealed that GREBI Is4 is necessary for pyrimidine synthesis. These results suggest that MITF-dependent GREB1 Is4 expression leads to melanoma proliferation and GREB1 Is4 represents a new molecular target in melanoma.
Energy Band Structure of Relativistic Quantum Plasmon Excitation
Authors: Akbari-Moghanjoughi Massoud
Published: March 2023

In this paper, we use the effective Schrodinger-Poisson and square-root Klein-Gordon-Poisson models to study the quantum and relativistic quantum energy band structure of finite temperature electron gas in a neutralizing charge background. Based on the plasmon band gap appearing above the Fermi level, new definitions on plasmonic excitations and plasma parameters in a wide electron temperature-density regime is suggested. The new equation of state (EoS) for excited electrons to the plasmon band leads to novel aspects of relativistic collective quantum excitations such as the plasmon black-out and quantum pressure collapse, which are studied using both non-relativistic and relativistic quantum models. The plasmon black-out effect may be used to explain why metallic elements do not show collective behavior at low temperatures. The model can be used to predict phases of matter in which the plasmonic activities are shut down, hence, it may behave like a mysterious dark matter. On the other hand, the energy band structure model predicts the plasmon pressure collapse in temperature-density coordinates matching that of a white dwarf star. The prediction of energy band structure of collective quantum excitations may have direct implications for the inertial confinement fusion (ICF), the EoS of warm dense matter (WDM) and evolution of stellar and other unknown cosmological structures. It is found that predictions of non-relativistic and relativistic quantum excitation models closely match up to temperature-density of degenerate stars, which confirms the relevance of non-relativistic plasmon models used in the warm and dense matter regime. The effect of positron on band structure of collective quantum excitations is also studied.
Resolving Quantum Interference Black-Box through Attosecond Photoionization Spectroscopy
Authors: Gong Xiaochun , Jiang Wenyu , Armstrong Gregory , Han Lulu , Xu Yidan , Zuo Zitan , Tong Jihong , Lu Peifen , Dahlström Jan Marcus , Ueda Kiyoshi , Brown Andrew , Hart Hugo van der , Wu Jian
Published: March 2023

Multiphoton light-matter interactions invoke a so-called ‘Black Box’ in which the experimental observations contain the quantum interference between multiple pathways (1–3). Here, we employ attosecond coincidence metrology (4, 5) with a partial wave polarization manipulator to deduce the pathway interference within this quantum ‘Black Box’ (6) for two-photon ionization of neon and argon. The angle-dependent and attosecond time-resolved photoelectron spectra are measured across a broad energy range. Two-photon phase shifts for each partial wave are reconstructed through the comprehensive analysis of these photoelectron spectra. We resolve the quantum interference between the p → d → p and p → s → p ionization pathways, in agreement with our theoretical simulations. Our approach thus provides a microscope to look inside the ‘black box’ of pathway interference in ultrafast dynamics of atoms, molecules, and condensed matter.