Standard Format Research Articles

GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system

Abstract. A geospatial data processing tool, GEO4PALM, has been developed to generate geospatial static input for the Parallelized Large-Eddy Simulation (PALM) model system. PALM is a community-driven large-eddy simulation model for atmospheric and environmental research. Throughout PALM's 20-year development, research interests have been increasing in its application to realistic conditions, especially for urban areas. For such applications, geospatial static input is essential. Although abundant geospatial data are accessible worldwide, geospatial data availability and quality are highly variable and inconsistent. Currently, the geospatial static input generation tools in the PALM community heavily rely on users for data acquisition and pre-processing. New PALM users face large obstacles, including significant time commitments, to gain the knowledge needed to be able to pre-process geospatial data for PALM. Expertise beyond atmospheric and environmental research is frequently needed to understand the data sets required by PALM. Here, we present GEO4PALM, which is a free and open-source tool. GEO4PALM helps users generate PALM static input files with a simple, homogenised, and standardised process. GEO4PALM is compatible with geospatial data obtained from any source, provided that the data sets comply with standard geo-information formats. Users can either provide existing geospatial data sets or use the embedded data interfaces to download geo-information data from free online sources for any global geographic area of interest. All online data sets incorporated in GEO4PALM are globally available, with several data sets having the finest resolution of 1 m. In addition, GEO4PALM provides a graphical user interface (GUI) for PALM domain configuration and visualisation. Two application examples demonstrate successful PALM simulations driven by geospatial input generated by GEO4PALM using different geospatial data sources for Berlin, Germany, and Ōtautahi / Christchurch, New Zealand. GEO4PALM provides an easy and efficient way for PALM users to configure and conduct PALM simulations for applications and investigations such as urban heat island effects, air pollution dispersion, renewable energy resourcing, and weather-related hazard forecasting. The wide applicability of GEO4PALM makes PALM more accessible to a wider user base in the scientific community.

AI and machine learning in climate change research: A review of predictive models and environmental impact

The burgeoning threat of climate change has spurred an increased reliance on advanced technologies to comprehend and mitigate its far-reaching consequences. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as indispensable tools in climate change research, offering unprecedented capabilities for predictive modeling and assessing environmental impact. This review synthesizes the current state of AI and ML applications in climate change research, emphasizing their role in predictive modeling and understanding environmental repercussions. Predictive models leveraging AI and ML algorithms have demonstrated remarkable efficacy in forecasting climate patterns, extreme weather events, and sea-level rise. These models incorporate vast datasets encompassing meteorological, geospatial, and oceanic information, enabling more accurate predictions of future climate scenarios. Moreover, AI-driven models excel in recognizing intricate patterns and non-linear relationships within climate data, enhancing their capacity to simulate complex environmental systems. Environmental impact assessment stands as a critical facet of climate change research, and AI and ML techniques are proving instrumental in this regard. These technologies facilitate the analysis of diverse ecological parameters, including deforestation rates, biodiversity loss, and carbon sequestration dynamics. By discerning nuanced patterns within immense datasets, AI systems contribute to a more nuanced understanding of the direct and indirect consequences of climate change on ecosystems. Despite these advancements, challenges persist, such as the need for standardized data formats, model interpretability, and ethical considerations. Additionally, the integration of AI and ML findings into policy frameworks remains a crucial frontier. As the intersection of AI, ML, and climate change research evolves, continuous interdisciplinary collaboration is essential to harness the full potential of these technologies in safeguarding our planet's future. This review illuminates the current landscape of AI and ML applications, providing insights into their efficacy, challenges, and potential contributions to advancing climate change research and environmental sustainability.

Classification of BI-RADS using convolutional neural network and effecientNet-B7

From its development, image processing technology has become increasingly complex, such as detecting cancer in an image. In this context, the American College of Radiology (ACR) has developed a standard format and terminology called the breast imaging reporting and data system (BI-RADS). The aim of this research is to determine whether CNN can classify BI-RADS types (C1, C2, C3, C4), the best model, and the classification results of BI-RADS types on mammograms using CNN, and the best values for accuracy, sensitivity, specificity, and precision. Google Colaboratory and machine learning libraries were used in this research to create machine learning for BI-RADS classification. Then, a dataset from Kaggle titled "Mini-DDSM: Mammography-based Automatic Age Estimation" by Lekamlage et al., from the Association for Computing Machinery (ACM), Kyoto, Japan, was utilized. Based on the research, an accuracy of 85.9% was achieved for Category 1 classification. Category 2 classification attained an accuracy of 77.9%. The accuracy for Category 3 classification was 81.4%, and for Category 4, it reached 90.8%.

Systematic review protocol for complications following surgical decompression of degenerative cervical myelopathy.

Degenerative Cervical Myelopathy (DCM) is one of the most common degenerative disorders of cervical spine and sources of cord dysfunction in adults. It usually manifests with neurologic presentations such as loss of dexterity and gait issues. Treatment for moderate and severe cases of DCM is surgical decompression of the region. There are many approaches available for surgical intervention which could be categorized into anterior and posterior based on the side of neck where operation takes place. Additionally, for certain cases the hybridized anterior-posterior combined surgery is indicated. While there are many technical differences between these approaches with each having its own advantages, the complications and safety profiles of them are not fully disclosed. This protocol aims to systematically search for current reports on complications of surgical decompression methods of DCM and pool them for robust evidence generation. Search will be carried out in PubMed, Scopus, and Cochrane databases for retrospective and prospective surgical series, cohorts, or trials being performed for DCM with at least a sample size of 20 patients. Query strings will be designed to capture reports with details of complications with no year limit. Studies not being original (e.g., review articles, case reports, etc.), not in English, having patients younger than 18-years-old, and not reporting at least one complication will be excluded. Two independent reviewers will review the titles and abstracts for first round of screening. Full text of retrieved studies from previous round will be screened again by the same reviewers. In case of discrepancy, the third senior reviewer will be consulted. Eligible studies will then be examined for data extraction where data will be recorded into standardized form. Cumulative incidence and 95% confidence intervals of complication will be then pooled based on generalized linear mixed models with consideration of approach of surgery as subgroups. Heterogeneity tests will be performed for assessment of risk of bias. This systematic review is aimed at providing practical information for spine surgeons on the rates of complications of different surgical approaches of DCM decompression. Proper decision-making regarding the surgical approach in addition to informing patients could be facilitated through results of this investigation.

Implementing Many-Lights Rendering with IES-Based Lights

In recent years, many research projects on real-time rendering have focused on the introduction of global illumination effects in order to improve the realism of a virtual scene. The main goal of these works is to find a compromise between the achievable quality of the resulting rendering and the intrinsic high computational cost of global illumination. An established approach is based on the use of Virtual Point Lights, i.e., “fictitious” light sources that are placed on surfaces in the scene. These lights simulate the contribution of light rays emitted by the light sources and bouncing on different objects. Techniques using Virtual Point Lights are often called Many-Lights techniques. In this paper, we propose an extension of a real-time Many-Lights rendering technique characterized by the integration of photometric data in the process of Virtual Point Lights distribution. We base the definition of light sources and the creation of Virtual Point Lights on the description provided in the IES standard format, created by the Illuminating Engineering Society (IES).

Help biocurators to maximize the reach of your data.

Curated scientific databases catalogue and amplify research findings to maximize their reach. Authors should write their papers with this in mind, ensuring that data are accurate, easy to extract, and presented in standardized formats.

Short anesthesia without intravenous fluid therapy in children: Results of a prospective non-interventional multicenter observational study.

The German guidelines recommend that intravenous fluid therapy should not be mandatorily performed in children with short fasting times undergoing short anesthesia, but there is a lack of clinical studies including a large number of pediatric patients. Therefore, we performed a prospective non-interventional multicenter observational study to evaluate the perioperative hemodynamic and metabolic stability of children undergoing short anesthesia without intravenous fluid therapy. The primary aim was to assess the incidence of hypotension and the secondary aim was to assess the real preoperative fasting times, the incidence of hypoglycemia and the impact on ketone bodies and acid-base balance. Children aged 1 month-18 years undergoing short anesthesia (<1 h) without intravenous fluid therapy were enrolled. Patient demographics, the surgical or diagnostic procedure performed, anesthesia, hemodynamic, laboratory data, and adverse events were documented using a standardized case report form. Four hundred and twenty seven children that were investigated at three pediatric centers from July 2021 to June 2022 (mean age 83.4 ± 58.9 months, body weight 27.9 ± 19.8 kg) were included in the analysis. The real preoperative fasting times were 14.2 ± 3.6 h for solids, 7.2 ± 3.5 h for milk and 5 ± 4.8 h for clear fluids. During the course of anesthesia, hypotension (<2.5th percentile) was detected in 3 of 427 cases (0.7%), hypoglycemia (glucose <3.0 mmol L-1) in 1 of 355 cases (0.3%), and ketosis (ketone bodies ≥0.6 mmol L-1) in 51 of 233 cases (21.9%). The occurrence of ketosis was associated with lower body weight (p <.001) and longer fasting times for solids or milk (p =.021), but not for clear fluids (p =.69). Our study supported the German guidelines recommendation that perioperative intravenous fluid therapy is not mandatory in children beyond the neonatal period with short pre- and postoperative fasting times undergoing short anesthesia (<1 h).

User Centered Rare Disease Clinical Trial Knowledge Graph (RCTKG).

Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases.

Large-Scale Standardized Image Integration for Secondary Use Research Projects.

Imaging techniques are a cornerstone of today's medicine and can be crucial for a successful therapy. But in addition, the generated imaging series are an important resource for new informatics' methods, especially in the field of artificial intelligence. This paper describes the success of integrating clinical routine imaging data into a standardized format for research purposes. Thus, we designed an integration flow and successfully implemented it in the local data integration center of University Hospital Schleswig-Holstein. The flow integrates imaging series and radiological reports from the primary system into an openEHR repository with enrichment by semantic codes for better findability and retrieval using HL7 FHIR. As a result, 6.6 million radiological studies with 29 million image series are now available for further medical (informatics) research.

An International Standard RWD Database Designed - Taiwan Experience.

The use of Real-World Data (RWD) in medical data analysis is nowadays required. RWD may come from electronic health records (EHRs), insurance claims, medical products, Internet of Things sensors, health screenings, etc. The goal of RWD is that the data used for analysis should not be affected by environmental variables, experimental control, research context settings, etc. RWD can effectively reduce the cost and improve the accuracy of medical research. The clinical data and patient self-report are integrated, cleaned and pre-processed, and the data format is unified and standardized by international standard formats to provide a structured database for clinical research by this study. An international standard real-world research database was established through the breast cancer database of a medical center in Taipei.

Potential of Assistive Robots in Clinical Nursing: An Observational Study of Nurses' Transportation Tasks in Rural Clinics of Bavaria, Germany.

Transportation tasks in nursing are common, often overlooked, and directly impact patient care time in the context of staff shortages and an aging society. Current studies lack a specific focus on transportation tasks, a gap our research aims to fill. By providing detailed data on transportation needs in nursing, our study establishes a crucial foundation for the development and integration of assistive robots in clinical settings. In July and September 2023, we conducted weekly observations of nurses to assess clinical transportation needs. We aim to understand the economic impact and the methods nurses use for transportation tasks. We conducted a participant observation using a standardized app-based form over a seven-day observation period in two rural clinics. N = 1830 transports were made by nurses and examined by descriptive analysis. Non-medical supplies account for 27.05% (n = 495) of all transports, followed by medical supplies at 17.32% (n = 317), pharmacotherapy at 14.10% (n = 258) and other other categories like meals or drinks contributing 12.68% (n = 232). Most transports had a factual transport time of under a minute, with patient transport and lab samples displaying more variability. In total, 77.15% of all transports were made by hand. Requirements to collect items or connect transports with patient care were included in 5% of all transports. Our economic evaluation highlighted meals as the most costly transport, with 9596.16 € per year in the observed clinics. Budget-friendly robots would amortize these costs over one year by transporting meals. We support understanding nurses' transportation needs via further research on assistive robots to validate our findings and determine the feasibility of transport robots.

Australian Ocean surface waves dataset from SAR

AbstractIn this article, a regional ocean surface waves dataset from Sentinel‐1 A and B Synthetic Aperture Radar (SAR) satellites has been described. The ocean wave data have been extracted from the Sentinel‐1 level‐2 OCN (ocean) product as provided by the European Space Agency and downloadable for this region from the Copernicus Australasia regional data hub. The source OCN data have been produced by evolving versions of Sentinel‐1 Instrument Processing Facility (IPF). The structure of the source OCN NetCDF files changes over time and presents a challenge in performing long duration, time series analyses, including the examination of potential inconsistencies in OCN wave data, due to employment of different IPF versions over the duration of the satellite missions. Here, the input OCN wave data have been homogenized to a single, easily usable standard format after applying a quality assurance and control procedure that removes various inconsistencies in variables, coordinates, dimensions and land flag, and through the addition of new auxiliary variables. The new format has the desirable properties of being compact in size, consistent in structure, and scalable in temporal and spatial coverage. It is also convenient to use and offers opportunities to perform fast, multi‐year regional processing and analysis for calibration and validation studies and scientific applications. No re‐processing of Sentinel‐1 level‐1 data has been carried out in this work.

Inflammatory Biomarkers and Gait Impairment in Older Adults: A Systematic Review.

Peripheral inflammation and gait speed alterations are common in several neurological disorders and in the aging process, but the association between the two is not well established. The aim of this systematic literary review is to determine whether proinflammatory markers are a positive predictor for gait impairments and their complications, such as falls in older adults, and may represent a risk factor for slow gait speed and its complications. The systematic review was performed in line with the Preferred Report Items for Systematic Review and Meta-Analyses (PRISMA). A protocol for literature searches was structured a priori and designed according to the International Perspective Register of Systemic Review (PROSPERO: CRD42023451108). Peer-reviewed original articles were identified by searching seven electronic databases: Excerpta Medica Database (EMBASE), SciVerse (ScienceDirect), Scopus, PubMed, Medline, Web of Science, and the Cochrane Library. The search strategy was formulated based on a combination of controlled descriptors and/or keywords related to the topic and a manual search was conducted of the reference lists from the initially selected studies to identify other eligible studies. The studies were thoroughly screened using the following inclusion criteria: older adults, spatiotemporal gait characteristics, and proinflammatory markers. A meta-analysis was not performed due to the heterogeneity of the studies, and the results were narratively synthesized. Due to the clinical and methodological heterogeneity, the studies were combined in a narrative synthesis, grouped by the type of biomarkers evaluated. A standardized data extraction form was used to collect the following methodological outcome variables from each of the included studies: author, year, population, age, sample size, spatiotemporal gait parameters such as gait velocity, and proinflammatory markers such as TNF-α, high sensitivity C-reactive (CRP) proteins, and IL-6. We included 21 out of 51 studies in our review, which examined the association between inflammatory biomarkers and gait impairment. This review highlights the role of TNF-α, CRP, and IL-6 in gait impairment. Biomarkers play an important role in the decision-making process, and IL-6 can be an effective biomarker in establishing the diagnosis of slow gait speed. Further longitudinal research is needed to establish the use of molecular biomarkers in monitoring gait impairment.

Mpox Virus in the Pharynx of Men Having Sex with Men: A Case Series.

The recent Mpox virus (MPV) outbreak in Europe and North America, primarily among men who have sex with men (MSM), raised concerns about various transmission sources. We examined patients with Mpox from an urban STI center in Lombardy, Italy, between May and August 2022. Demographic, transmission, and clinical data were collected using a standardized form. Initial and subsequent tests were conducted using the RealStar Orthopoxvirus PCR Kit 1.0 (Altona Diagnostics, Hamburg, Germany) for skin lesions and oropharyngeal swabs. A total of 15 patients were recruited, all MSM, with 40% being HIV-positive. Almost all reported recent unprotected sexual activity. Oropharyngeal symptoms were observed in a minority, and oral cavity lesions were present in 20% of cases. MPV DNA was detected in skin lesions of 93% of patients and in oropharyngeal swabs of 87%. Skin samples exhibited a higher viral load than pharyngeal samples, with the latter persisting longer. Prospective follow-up of 11 individuals revealed an average pharyngeal persistence of 5.3 days beyond skin lesion clearance, reaching up to 80 days in an immunosuppressed case. Our findings indicate that MPV replication can persist in the pharynx asymptomatically and for an extended period.

Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD).

Age-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade. We collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework. Our findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal "Retina"; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy). The introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns-cited, active, and citing research categories-offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.

Gestational weight gain and its association with maternal and neonatal outcomes among pregnant women with normal pre-pregnancy body mass index from a Middle Eastern country

BackgroundInappropriate gestational weight gain (GWG) is one of the leading health problems during pregnancy. Little is known about the association of GWG with the pregnancy outcomes among Middle Eastern pregnant women. PurposeTo examine the relationship between GWG and maternal and neonatal outcomes among pregnant women with normal pre-pregnancy BMI. MethodsThis retrospective cohort study was conducted in Oman among 763 healthy women with normal pre-pregnancy BMI between August 1, 2021, and June 30, 2022. Maternal and neonatal outcomes were retrieved from participants’ medical records using a standardized form. The analytical tools included descriptive statistics and the chi-square test. ResultsDescriptive statistics showed 22.6 % and 21.4 % of the study participants gained weight above or below the guidelines, respectively. The findings from the chi-square test showed a significant association between total GWG and the incidence of emergency caesarean section (p < .01), gestational diabetes mellitus (p < .01), and gestational hypertension (p < .01), macrosomia (p < .01), small for gestational age (p < .01), large for gestational age (p < .01), low birth weight (p < .01), intrauterine growth retardation (p < .01), and preterm birth (p < .01). Conclusion and implications for practiceOur study found that inappropriate gestational weight gain (GWG) contributes to several adverse maternal and neonatal outcomes. We recommend that Ministries of Health in Middle East and North Africa area includes GWG as a risk factor for pregnancy-related adverse outcomes. Midwives should closely monitor and manage GWG to ensure optimal pregnancy outcomes. In addition, midwives should encourage mothers to maintain GWG within the recommended range using various strategies such as continuous counselling and motivational health promotion interventions.

A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions

Deep learning has seen significant growth recently and is now applied to a wide range of conventional use cases, including graphs. Graph data provides relational information between elements and is a standard data format for various machine learning and deep learning tasks. Models that can learn from such inputs are essential for working with graph data effectively. This paper identifies nodes and edges within specific applications, such as text, entities, and relations, to create graph structures. Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. The paper delves into specific GNN models like graph convolution networks (GCNs), GraphSAGE, and graph attention networks (GATs), which are widely used in various applications today. It also discusses the message-passing mechanism employed by GNN models and examines the strengths and limitations of these models in different domains. Furthermore, the paper explores the diverse applications of GNNs, the datasets commonly used with them, and the Python libraries that support GNN models. It offers an extensive overview of the landscape of GNN research and its practical implementations.

Effect of Nutritional Interventions on Serum C-reactive Protein Levels in Patients with Periodontitis: A Comprehensive Meta-Analysis

Elevated level of serum C-reactive protein, a systemic inflammation biomarker, is associated with periodontitis, a common inflammatory condition. This meta-analysis aimed to determine the effects of nutritional interventions on serum C-reactive protein levels in patients with periodontitis. We searched the Cochrane Library, PubMed, Scopus, and Web of Science databases according to PRISMA guidelines, including articles published until December 2023. The articles were selected according to predetermined inclusion criteria, and data extraction and quality assessment were conducted utilizing the Newcastle-Ottawa Scale and standardized forms. Seven out of 438 identified articles met the inclusion criteria. We found that patients with periodontitis had a statistically significant correlation between nutritional interventions and decreased serum C-reactive protein levels (p &lt; 0.05). The interventions included specified dietary modifications and dietary supplements, including vitamin C and folic acid. Diverse patient demographics and intervention categories were observed across the identified articles. Thus, this meta-analysis confirmed that nutritional interventions can reduce serum C-reactive protein levels in patients with periodontitis; therefore, dietary modification is essential in managing the systemic inflammation associated with periodontal disease.

The COVID-19 Severity and Its Association with Intestinal Parasite Coinfection and Urine Biochemical Parameters among COVID-19-Confirmed Patients Admitted to Debre Markos University COVID-19 Center, Northwest Ethiopia.

Though most people with COVID-19 disease show asymptomatic to mild illness, a substantial number of patients are at high risk of developing severe disease and adverse outcomes with long COVID-19 and death. Even though some studies showed that previously existing infections with parasites amend the host's body defenses to increase resistance to infection with SARS-CoV-2, there is limited data in Ethiopia. This study is aimed at determining the COVID-19 disease severity and its association with intestinal parasite coinfection and urine biochemical parameters among COVID-19-confirmed patients admitted at Debre Markos University COVID-19 Center, 2021. A prospective cohort study was conducted on 136 RT-qPCR-confirmed COVID-19 patients admitted at Debre Markos University COVID-19 Center from January 1 to March 30, 2021. Sociodemographic and clinical data were collected by using standardized data collection forms. A urine biochemical test was performed using a dry urine dipstick kit and stool examination using direct wet mount microscopic examination and formalin-ether concentration method. The chi-square test, Fisher exact test, and ordinal logistic regression analysis were computed to assess association with outcome variables using Statistical Package for Social Science software (version 24). A total of 136 COVID-19-confirmed patients participated in this study. The median age of the participants was 48 years. The majority (86 (62.5%)) of them were male in sex. Of the 136 cases, 39 (28.7%) had died. Among the 136 patients, 22 (16.2%) were coinfected with intestinal parasites. COVID-19 patients who have intestinal parasite coinfection had lower odds of developing clinically severe COVID-19 compared to noninfected (AOR = 0.37; 95% CI = 0.147-0.944; P = 0.037). The majority (104 (76.5%)) of them have abnormal urine biochemical results. From the abnormal urine biochemical tests observed, the urine blood, glucose, and ketone tests were positive for 54 (39.7%), 36 (26.5%), and 30 (21.1%) patients, respectively. Among the 31 critical COVID-19 patients, 25 (80.6%) showed abnormal urine biochemical parameters. Age and comorbidity were significantly associated with COVID-19 severity (P < 0.05). Patients with old age and comorbidity had an increased risk of developing severe COVID-19 disease. Patients having SARS-CoV-2 and intestinal parasitic coinfections demonstrated mild COVID-19 disease severity. Abnormal urine biochemical results were common among critical COVID-19 patients. Thus, advanced study on the effect of the interaction among intestinal parasites on COVID-19 clinical severity and its mechanisms is essential.

A reversible data hiding method based on bitmap prediction for AMBTC compressed hyperspectral images

In the transmission of hyperspectral images that have been compressed using absolute moment block truncation coding (AMBTC), confidentiality and security of crucial information is often a concern. Although many data hiding (DH) methods based on AMBTC work well in guaranteeing a large amount of secret information can be embedded, the requirements of actual user scenarios, such as reversibility and imperceptibility, are degraded sometimes. To address these challenges, we propose an embedding pattern that utilizes bitmap prediction to embed secret information within the bitmaps while preserving the standard format of AMBTC codes and enabling recovery of the cover image without loss. Our proposed method, therefore, belongs to the category of reversible data hiding (RDH) techniques. Since the embedding distortion (ED) reduction is an essential object, an adaptive embedding order based on the features of AMBTC codes is conducted. Furthermore, we propose a dynamical embedding scheme to reduce ED when we are striving to achieve a larger embedding capacity (EC).