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BackgroundThe COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. ObjectiveThis paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data. MethodsWe applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19–related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes. ResultsA total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. ConclusionsWe uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.

Nowadays, dust storms are an important environmental problem in Iran (especially in Khuzestan Province). The Hawr-al-Azim Wetland is a source for generation of dust storms. One-third area of this wetland is located in Iran. In this study, dust discharge is calculated using wind velocity, mean soil grain size (D50) and soil dry density. Soil characteristics were determined by data collected from 16 boreholes drilled in this wetland. Distributions of D50 and soil dry density in the wetland were determined using ordinary kriging method. Then critical wind speed was calculated in different regions of the wetland. The wetland soil is composed of medium and coarse silty soil. The dust mass discharge has reduced from 2003. The maximum monthly dust discharge mass occurs in June and July (along with a mean monthly wind velocity of 4.42 m s -1 and 4.27 m s -1, respectively). Because of the little amount of clay particles and the high wind velocity, increased soil moisture content cannot help in neither raise inthe critical shear velocity nor decreased dust mass discharge. Also, the produced dust is transported towards Northwest Iran because the dominant direction of wind is 270 to 300 relative to the north.

BackgroundConventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lead to characteristic patterns in the results of widely available, routine blood tests that could be identified with machine learning methodologies. Machine learning modalities integrating findings from these common laboratory test results might accelerate ruling out COVID-19 in emergency department patients. ObjectiveWe sought to develop (ie, train and internally validate with cross-validation techniques) and externally validate a machine learning model to rule out COVID 19 using only routine blood tests among adults in emergency departments. MethodsUsing clinical data from emergency departments (EDs) from 66 US hospitals before the pandemic (before the end of December 2019) or during the pandemic (March-July 2020), we included patients aged ≥20 years in the study time frame. We excluded those with missing laboratory results. Model training used 2183 PCR-confirmed cases from 43 hospitals during the pandemic; negative controls were 10,000 prepandemic patients from the same hospitals. External validation used 23 hospitals with 1020 PCR-confirmed cases and 171,734 prepandemic negative controls. The main outcome was COVID 19 status predicted using same-day routine laboratory results. Model performance was assessed with area under the receiver operating characteristic (AUROC) curve as well as sensitivity, specificity, and negative predictive value (NPV). ResultsOf 192,779 patients included in the training, external validation, and sensitivity data sets (median age decile 50 [IQR 30-60] years, 40.5% male [78,249/192,779]), AUROC for training and external validation was 0.91 (95% CI 0.90-0.92). Using a risk score cutoff of 1.0 (out of 100) in the external validation data set, the model achieved sensitivity of 95.9% and specificity of 41.7%; with a cutoff of 2.0, sensitivity was 92.6% and specificity was 59.9%. At the cutoff of 2.0, the NPVs at a prevalence of 1%, 10%, and 20% were 99.9%, 98.6%, and 97%, respectively. ConclusionsA machine learning model developed with multicenter clinical data integrating commonly collected ED laboratory data demonstrated high rule-out accuracy for COVID-19 status, and might inform selective use of PCR-based testing.

The purpose of this qualitative phenomenographical article was to explore the different ways that elementary assistant principals experienced mentoring from their elementary principals as a way to provide the requisite leadership development that would lead them to become a principal. This research study was conducted using in-depth semi-structured interviews as the primary data source. Ancillary data sources such as demographic surveys, reviewing district leadership secondary source documents, and researcher journal notes aided in the data triangulation and analysis. This study was guided by one main research question: What are the different ways elementary assistant principals experience mentoring from their elementary principals? The research question posed allowed participants to illuminate their lived experiences about how elementary assistant principals experienced mentorship directly from their elementary principals. The prominent themes that emerged from the data were: (1) assistant principal leadership development; (2) principal leadership development; and (3) mentorship perceptions.

Optimization is a vital mathematical concept widely applied in various engineering fields. However, teaching this subject to engineering students often involves abstract materials, making it challenging for them to grasp. To address this issue, a group of five mathematics lecturers collaborated on designing a practical lesson on optimization for first-year engineering students. The aim was to present the concept through a real-world problem, making it more accessible and applicable. The lesson focused on the design of water channels used for transferring water from sources to farms, showcasing how engineers can create optimal water channels with different geometrical cross-sections for agricultural purposes. This approach allowed students to see the direct application of mathematics in the real-world. The lesson was conducted as a three-hour workshop and attended by 38 volunteer first-year engineering students at a Malaysian university. Data were collected through observations and interviews and analyzed using the thematic analysis method. Feedback from both the lecturers and the participating students indicated that this teaching method significantly enhanced their conceptual understanding of mathematical optimization. Moreover, it fostered the development of problem-solving skills in real-world scenarios, bridging the gap between engineering and mathematics.

BACKGROUND: Vertebral artery loops are a rare cause of cervical radiculopathy. Surgical options for nerve root decompression include an anterior or posterior approach, with or without additional microvascular decompression. OBSERVATIONS: The authors describe a case of a 49-year-old man with a long-standing history of left-sided neck pain and migraines, who was found to have a vertebral artery loop in the left C3-4 neural foramen compressing the left C4 nerve root. The patient underwent a posterior cervical decompression with instrumented fusion and macrovascular decompression of the left C4 nerve root via Teflon felt insertion. In a literature review, we identified 20 similar cases that had also been managed surgically. LESSONS: Although the anterior approach is more frequently described in the literature, a posterior approach for nerve compression by a vertebral artery loop is also a safe and effective treatment. The authors report the third case of this surgical approach with a good outcome.

The purpose of this study is to analyse the content of primary school textbooks in Iran for their cultural values and characteristics, based on the individualism-collectivism dimension. Six primary school Let's Read Persian (LRP) textbooks were assessed using quantitative content analysis using two lists of individualistic and collectivistic values and cognitive tendencies derived from previous literature. The results showed that Iranian LRP textbooks emphasised themes of individualism, such as self-direction, success, and happiness, and manifested individualistic cognitive tendencies, such as focusing on central objects, making dispositional attributions, and expressing positive emotions. The findings suggest that Iranian LRP textbooks tend to teach children to be independent, strong, self-reliant, and unique individuals, rather than interdependent, cooperative, and harmonious members of a group. The implications of these findings for the education system and the cultural identity of Iranian students are discussed.

Biodiesel predicted to replace diesel fuel in the future that comes from the transesterification process of plant oils, animal fats, or used oils. One of the potential raw materials is nyamplung seeds because they are scattered in Indonesia, easy to cultivate, and bear fruit throughout the year [1]. The purpose of this study was to determine the effect of mixing pure diesel fuel and nyamplung biodiesel with the fuel composition B0, B20, B30, and B100 on the spray tip characteristics penetration, spray angle, and sauter mean diameter.This study focussed on simulation. The simulation method is carried out to predict the distribution and mean diameter of droplet using commercial fluent software. Fuel spray is carried out in free air with an injection pressure of 15 MPa, fuel temperature 28.2 ℃ and free air temperature 30.1 ℃. The spray process was taken at 6.25 ms and then the fuel spray characteristics were measured using the ImageJ application.The results of testing the fuel spray characteristics indicate that the amount of biodiesel content in the fuel is 20-100%. It can increase the length of spray tip penetration about 0.7-4.9%, a decrease in spray angle of 9.2-32.1% and the sauter mean diameter value increased by 20.1-84.6%. It caused by increasing the amount of biodiesel content in the fuel which results in an increase in the value of density, viscosity and surface tension of the fuel.

Abstract The COVID-19 pandemic brought forth an urgent need for widespread genomic surveillance for rapid detection and monitoring of emerging SARS-CoV-2 variants. It necessitated design, development, and deployment of a nationwide infrastructure designed for sequestration, consolidation, and characterization of patient samples that disseminates de-identified information to public authorities in tight turnaround times. Here, we describe our development of such an infrastructure, which sequenced 594,832 high coverage SARS-CoV-2 genomes from isolates we collected in the United States (U.S.) from March 13th 2020 to July 3rd 2023. Our sequencing protocol (‘Virseq’) utilizes wet and dry lab procedures to generate mutation-resistant sequencing of the entire SARS-CoV-2 genome, capturing all major lineages. We also characterize 379 clinically relevant SARS-CoV-2 multi-strain co-infections and ensure robust detection of emerging lineages via simulation. The modular infrastructure, sequencing, and analysis capabilities we describe support the U.S. Centers for Disease Control and Prevention national surveillance program and serve as a model for rapid response to emerging pandemics at a national scale.

Abstract We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.

Abstract Background Studies have been conducted worldwide to investigate the level of adherence to personal protective measures or fear of COVID-19 among healthcare providers. However, few studies have examined the relationship between adherence to personal protective measures and fear of COVID-19. There is also a need for more information on this topic from Iran. This study investigated the relationship between adherence to personal protective measures against COVID-19 and fear of COVID-19 in the healthcare providers at Pastor Hospital of Bam, Iran, in 2022. Methods This cross-sectional study was conducted in August and September 2022 with 199 healthcare providers of Pastor Hospital of Bam, Iran. The study included medical, nursing, and paramedical staff at Pastor Hospital at the time of the study. Incomplete responses and failure to return the questionnaire to the researcher were exclusion criteria. The fear of COVID-19 scale and a checklist of personal protective measures were used to collect data. Descriptive statistics, t-tests, analysis of variance, and Pearson’s correlation coefficient were used to analyze the data. Results Of the 199 participants, 67.3% were female, and their mean age was 31 ± 4.55 years. The mean score for adherence to personal protective measures was 14.46 ± 3.39 (out of 23), and the mean score for fear of COVID-19 was 17.04 ± 4.58 (out of 35). Adherence to personal protective measures was higher among females than males (14.96 ± 2.99 vs. 13.43 ± 3.92, p = 0.003), in individuals who had attended infection control courses than in those who had not (15.57 ± 2.88 vs. 13.30 ± 3.50, p

Abstract Wellbore stability analysis is a critical component of petroleum engineering, evaluating the risks of sanding, reservoir compaction, and casing failures. Laboratory rock mechanical measurements must be scaled up to reservoir scales to achieve accurate results. One challenge lies in upscaling dynamic measurements from petrophysical logs to pseudo-static elastic properties, which has significant implications for oil and gas operations. We present a novel approach that combines laboratory rock mechanical measurements with well-log data to develop a mechanical earth model (MEM) for an Iranian oilfield with over 350 wells. We conducted static elastic property measurements on 40 core samples from various layers and depths of carbonate and sandstone rocks, demonstrating the practical application of our approach. By integrating these measurements with dynamic log data and static-dynamic correlations, we established a framework for evaluating the mechanical properties of different layers. Our findings indicate that the safe mud weight window ranges from 41.5 to 118.59 pcf, while the stable mud weight window ranges from 41.5 to 156 pcf. We demonstrate the importance of conducting parallel rock mechanical studies on cores and logs to reduce uncertainties, costs, and risks during oil and gas operations. We also propose a novel methodology combining lithological characteristics, abnormally high pressure, and borehole instability mechanisms to evaluate the stability of borehole walls. This framework provides a fresh perspective on wellbore stability analysis and offers practical solutions for the industry. Essential novel techniques include developing a geomechanical model that integrates laboratory rock mechanical measurements with well-log data to evaluate mechanical properties and calculate safe and stable mud-weight windows. Our study advances wellbore stability analysis by providing a new method for addressing this long-standing challenge. It offers valuable insights for petroleum engineers working in the oil and gas industry.

Abstract High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.

Abstract Purpose Child marriage remains a significant socio-cultural phenomenon with profound implications for the quality of marital relationships, particularly among young women. This study investigates the association between child marriage and the quality of marriage among young married women in Iran. Methods This study employed a population-based cross-sectional research design. A total of 70 young women, aged 18–20 years, who had married before reaching 18 years of age, were selected from four primary health care (PHC) centers in Papi District, Lorestan Province, Iran, employing a systematic random sampling approach. Subjects were categorized into women experiencing relationship distress and those without such distress using the Quality of Marriage Index (QMI). Concurrently, their level of sexual satisfaction was assessed utilizing the Hudson’s Index of Sexual Satisfaction (ISS). Furthermore, Social Support was evaluated using the Social Support Questionnaire (SSQ). Results The mean age (SD) of women was 18.9 (0.7) years, and their mean age (SD) of marriage was 15.2 (2.1) years. Women experiencing distress showed significant differences in age of menarche (P = 0.006), education levels (P = 0.039), spouses’ education (P = 0.025), spouses’ occupations (P = 0.004), household income satisfaction (P = 0.041), and household structure (P = 0.045). Pearson’s correlation coefficient analysis revealed significant and positive correlations between sexual self-efficacy and social support with marital quality (r = 0.73, p

Context: Triple-negative breast cancer (TNBC) has poor prognosis, high mortality, and recurrence rates. Ixabepilone or ixempra could be a promising agent for TNBC, especially in the taxane- and/or anthracycline-resistant population. Aims: To evaluate the efficacy and safety of ixabepilone-based chemotherapy compared to ixabepilone-free chemotherapy in TNBC patients. Methods: This review followed the preferred reporting items for systematic reviews and meta-analysis guidelines. The eligibility criteria included randomized control trials (RCTs) in 2013-2023 comparing ixabepilone-based with ixabepilone-free chemotherapy regimen in TNBC. Results: This study identified four eligible RCTs. One study showed that patients treated with ixabepilone had a significant improvement in disease-free survival, had a better response, and a lower risk of disease recurrence compared to those treated with taxane-based regimen. However, two studies showed no differences in terms of overall survival (OS), disease-free survival, and pathological complete responses rates in both treatment arms. Ixabepilone addition to capecitabine significantly prolonged progression-free survival and objective response rate in TNBC patients, but did not significantly improve OS (n = 1). Conclusions: The effectiveness and safety of ixabepilone-based chemotherapy versus ixabepilone-free treatment for triple-negative breast cancer (TNBC) patients vary among studies. More research is needed to better understand the effectiveness and safety of ixabepilone-based chemotherapy versus ixabepilone-free treatment in TNBC patients.