Separate Groups Research Articles

The influence of changes in the economy structure on social sustainability in developing countries—A spatial approach

AbstractThis research presents an analysis of the influence of changes in the economy structure on social sustainability in developing countries. In this paper, this relationship is considered on a macroeconomic scale. The research utilises data from 98 selected developing countries located in Europe, Asia, Americas, and Africa from 2013 to 2020. The social conditions in developing countries, particularly located in Africa (due to health and hunger problems), are extensively discussed. The main goal of the study is to compare whether the economy servitisation in Africa affects same on the social sustainability as in the countries situated on other continents. This study describes social sustainability using selected determinants outlined in the first, second, and third goals of sustainable development (SD) formulated by the United Nations—SDG1 (no poverty), SDG2 (zero hunger), and SDG3 (good health and well‐being). The level of social sustainability is evaluated using a synthetic measure by Perkal. Moreover, spatio‐temporal sensitivity models are estimated and verified. The sensitivity parameter in these models demonstrates the impact of progress in the servitisation process on social sustainability. Furthermore, this connection is considered within separate groups of countries that are divided into four clusters depending on the continent on which they are located. The analysis presents that in the African countries the social conditions are the worst, but focus on the economy servitisation there can rapidly improve this situation. This results from the most sensitive social sustainability in the African countries caused by the economy servitisation.

Empagliflozin ameliorates olfactory bulbectomy-induced depression by mitigating oxidative stress and possible involvement of brain derived neurotrophic factor in diabetic rats

Empagliflozin, a sodium-glucose co-transporter 2 (SGLT2) inhibitor, has recently reported to prevent the depression as chronic animal model. However, its impact on neuroinflammation-mediated depression were remains unexplored. The present study aimed to explore the antidepressant potential of empagliflozin using a neuroinflammation-mediated depression involving the olfactory bulbectomy (OBX) model in rats. To establish this model, initially a low dose of streptozotocin was injected to induce diabetes in all group of animals. Following the confirmation of hyperglycemia, OBX surgery was performed. Post-surgery, the drug treatments were administered orally for 14 consecutive days. The study evaluated the effects of daily oral administration of empagliflozin at doses of 5 and 10 mg/kg, alongside metformin (200 mg/kg) and clomipramine (50 mg/kg), on OBX-induced behavioral depression in rats. Separate sham and vehicle control groups were also maintained. Behavioral parameters in open field, forced swim test, elevated plus maze and splash test were recorded on 28th day. Results showed that empagliflozin, particularly at the higher at the higher dose, significantly enhanced behavioral outcomes, evidenced by increased distance traveled, greater open arm entries, and reduced immobility, alongside a notable reduction in grooming time. Moreover, empagliflozin significantly restored the antioxidants level specifically Glutathione (GSH) and Catalase (CAT) in OBX insulted rat brain and reversed Lipid peroxidase (LPO). Notably, molecular docking study demonstrated a good binding affinity of empagliflozin for Brain-Derived Neurotrophic Factor (BDNF), suggesting that its antidepressant effects may be mediated through the modulation of the BDNF pathway. These findings support the potential therapeutic application of empagliflozin for depression, particularly in cases associated with neuroinflammation and oxidative stress.

Diversity of Northwestern and North Caucasian Populations of Puccinia striiformis f. sp. tritici by Virulence and Microsatellite Loci in 2022.

Yellow rust (Puccinia striiformis f. sp. tritici (Pst)) is a potentially dangerous disease of wheat. Genetic protection of wheat is an environmentally safe method to control the disease, but its successful application requires information on the structure of regional populations of the pathogen. Virulence and molecular polymorphism were characterized in two Russian Pst populations in 2022. Wheat leaves with Pst urediniopustules were collected from the North Caucasus (Krasnodar Krai, Dagestan, and Kalmykia) and Northwestern Russia (Leningrad Oblast). Virulence was tested on 14 isogenic lines (AvocetNIL) and 15 differentiator cultivars. Polymorphism at 20 microsatellite loci was evaluated in molecular analyses. The SSR markers were as recommended by the Global Rust Reference Center. Virulence was assessed in 74 monopustular isolates, including 29 Dagestan, 10 Krasnodar, 5 Kalmyk, and 30 Northwestern ones. Resistance to all isolates was observed in lines with the genes Yr5, Yr10, Yr15, Yr24, and Yr26 and the cultivars Moro (Yr10, YrMor) and Nord Desprez (Yr3, YrND, Yr+). Isolates virulent to the AvYr17 line were detected for the first time in the Dagestan and Krasnodar populations. Their occurrence was moderate (13%) in the Northwestern population. A significant variation was observed in lines and cultivars with the Yr1 and Yr3 genes. Virulence to Yr7 and YrSp was found to be lower than in 2019 to 2021. In total, 28 phenotypes (races) were determined in the virulence analysis (15 in Dagestan, 11 in the Northwestern region, 3 in Krasnodar, and 2 in Kalmykia). A common phenotype was detected in three North Caucasian Pst samples. Genetic distances between the phenotypes were estimated. Most phenotypes grouped together in a multidimensional diagram, with the exception of three Dagestan phenotypes with the fewest virulence alleles. Based on the Fst index, the Dagestan and Kalmyk Pst collections were high similar to each other and differed moderately from the other collections. Long-term trends in virulence (from 2019 to 2022) were assessed in the Pst populations. A high similarity was observed between all regional population samples in 2019 and 2020. The Northwestern and Dagestan populations slightly differed from the other populations in 2021 and 2022. In 2022, the Krasnodar and Kalmyk populations formed separate groups, which differed from each other and from the main group. The long-term virulence analysis indicated that the structure of Pst populations is highly dynamic in Russia. All North Caucasian isolates and 23 Northwestern isolates were used in the SSR analysis. Six loci (RYN3, RYN9, RYN12, WU6, RJO21, and RJO24) were monomorphic. Three polymorphic alleles were identified in each of the RYN13 and RJO27 loci; two alleles, in each of the remaining loci examined. Significant deviations from Hardy-Weinberg equilibrium were observed for most loci. The observed heterozygosity exceeded the expected one, suggesting a clonal origin of the Pst population. Twenty multilocus genotypes (MGs) were found in the total isolate collection (11 in Dagestan, 11 in the Northwestern region, 3 in Krasnodar, and 1 in Kalmykia). Common MGs were detected in the Dagestan, Krasnodar, and Northwestern populations (MG_1); Dagestan, Kalmyk, and Northwestern populations (MG_2); and Dagestan and Krasnodar populations (MG_3 and MG_4). Genetic distances between MGs were estimated. MGs formed four groups in a multidimensional diagram. A major group included 80% of MGs. One Dagestan MG, two Northwestern MGs, and MG_3 common for the Dagestan and Krasnodar collections were significantly differentiated from the major group and differed from each other. Based on Fst, most regional Pst collections were moderately differentiated from each other, with the exception of the Dagestan and Kalmyk collections. The finding was consistent with the virulence analysis results. The Mantel test detected a moderate correlation between the virulence and SSR data (r = 0.6). This indicates that both analyses can be used to assess genetic polymorphism in Pst. The high variability of the virulence and microsatellite loci warrants annual monitoring of regional Pst populations in Russia.

“We’re still seeing people on the screen, we still keeping our bodies moving”: exploring the transition from face to face to online dance classes for community-dwelling older adults during the COVID-19 pandemic

ABSTRACT Background The COVID-19 outbreak meant that people were unable to attend art-based community classes such as dance. In response, activities transitioned to digital modes of delivery. In this study, we aimed to explore the feasibility, inclusivity, and impact of transitioning to an online dance class. Methods Thirty community-dwelling older adults (59–84 years old) were recruited from community groups in Yorkshire (UK). Participants completed an online survey at baseline and after attending 8 weeks of online dance classes. The survey captured perceived benefits on physical activity and mental wellbeing. Two separate focus groups (N = 12) were conducted to explore if the online dance transition could be an opportunity to keep this group active and socially connected. Results Survey data revealed no significant difference in the amount of time engaged in physical activity, but mental wellbeing significantly improved. Themes from the focus groups included holistic wellbeing related to the experience of dance, inclusive participation of online dance, and online dance facilitating social connection and sense of belonging. Conclusions The research provided insight into the online transition of a dance class for older adults, especially regarding inclusivity, sense of belonging and social connection.

Industry 4.0 and Agile Project Management: Evolution of Leadership Concepts and Roles

This study examines the impact of Industry 4.0 on the concept of leadership approaches. The research aims to compare leadership theories before and after Industry 4.0, determine how leadership roles have evolved in agile project management, and identify effective leadership styles. The study employs literature review and qualitative research methods, specifically using focus group studies. Two separate focus group studies were conducted through online meetings and face-to-face sessions to thoroughly examine the approaches of executives from companies who have mature project management offices in Turkey. Data were obtained by transcribing the audio recordings of discussions during focus group sessions and comparing them with researcher notes. The study extensively examines the impacts of identified leadership styles, determined through literature review, on the management of agile project teams. Findings indicate that leadership styles such as visionary, organizing, relational, and social leadership play significant roles in agile project management. Additionally, leadership personality traits observed in each of these styles have been identified. The results underscore the need to redefine leadership approaches and highlight the effects of Industry 4.0 on project management. By linking the characteristics of leadership styles and their impacts on agile teams with the literature, the study contributes to future leadership research and practices. In this context, it emphasizes the development of technological competencies for organizations to succeed and the need for flexible and innovative organizational structures. Furthermore, it suggests that leaders in the digital transformation processes brought by Industry 4.0 should possess skills to facilitate their employees' adaptation processes. This study contributes significantly to academic literature and business by exploring leadership evolution in Industry 4.0 and redefining roles in agile project management, emphasizing ongoing focus on development and innovation in leadership.

Application of the Decision Tree Method for Differentiating Human Groups

One of the tasks of modern biological anthropology is to develop a system that could objectively classify humanity on the basis of measurements. Here, the decision tree algorithm was chosen to create a classification of groups. The method helps to evaluate the differentiating power of specific dimensions for separating samples and to assess the composition of clusters at each step of the analysis. Standard cranial measurements were used, and the entropy index was chosen as a heterogeneity measure. Classification units were 39 ethno-territorial groups from 13 major regions of the Old World. At the first step, differentiation is made between broad-faced and narrow-faced groups, demonstrating the classificatory value of this trait. The first cluster includes only Mongoloids, admixed Southern Siberian populations, and Ainu. The second cluster is heterogeneous, but its further subdivision is more in line with the traditional classification. Traits underlying the branching of the tree may be the same in different branches, evidencing their taxonomic importance. Capabilities of the decision tree method proved sufficient to construct a system largely similar to the traditional one. Certain traits separate large groups of populations, while others are efficient at the regional level. The method, therefore, can be recommended as a supplementary tool at the intraspecific level.

Microstructural Study of Medieval Crucible Steels from Archaeological Sites in Central and Northwest Asia: Identifying the Bulat

The microstructure of 9th–15th century artifacts made of crucible steel, found at sites in Central and Northwest Asia, is described. Metallographic study of items from settlements and burials with precise data on chronology, location, and accompanying artifacts is important for reconstructing the history of bulat steel and the technology of melting and processing ultra-high-carbon crucible steel. The study of the macro- and microstructure, and the chemical analysis of such items indicate an extremely high content of carbon—1.7–2.1 %. The characteristic feature of their microstructure is a dark matrix with white inclusions of ledeburite and iron carbides. The combination of structural components is reflected in the patterned structure of the metal. These properties suggest that such metal is identical to bulat steel. Findings of macrostructural analysis extend our knowledge of the varieties of this metal, its structural features, phase composition of separate groups of ultra-high-carbon crucible steel, smelting technology, plastic and thermal treatment, and physical properties.

An observational study on the use of low-molecular-weight heparin for venous thromboembolism prophylaxis in acute stroke patients

Venous thromboembolism (VTE) including deep vein thrombosis (DVT) and pulmonary embolism (PE) are common complications post-stroke. The UK stroke guidelines recommend the early use of intermittent pneumatic compression devices (IPCDs). At our center, we noticed poor compliance with the use of these devices and sought to develop a pathway for early implementation of low-molecular-weight heparins (LMWH) instead. We retrospectively analyzed the data of 2351 patients in two separate groups and compared both groups to check the incidence of PEs and DVTs as stated on the discharge documentation. Group A consists of 1599 patients who used IPCDs as VTE prophylaxis, whereas Group B involved 752 patients who were placed on the new VTE prophylaxis protocol, which involved the early use of LMWH with enoxaparin delivered subcutaneously. It was observed that IPCDs were not well tolerated by patients, leading to poor compliance with VTE prophylaxis in Group A. VTE compliance was noted to be better in Group B in which the patients were given LMWH in the hospital. Furthermore, symptomatic DVT and PE were not found to be higher in Group B patients, but in fact, the incidence of these conditions was lower in this group. In conclusion, compared to IPCDs, LMWH appears to be well tolerated by patients during the admission period owing to acute stroke. It was observed that the incidence of VTE was reduced in patients who started on early LMWH in the post-stroke period. However, the long-term effects of LMWH prophylaxis, in terms of mortality and morbidity, need to be delineated. Therefore, an additional large trial on the early use of LMWH in comparison to IPCDs is warranted.

7515 Plasma proteomic signatures for type 2 diabetes mellitus and related traits in the UK Biobank

Abstract Disclosure: T. Gupte: None. Z. Azizi: None. K. Nzenkue: None. P. Kho: None. M. Chen: None. J. Zhou: None. R. Guarischi-Sousa: None. D.J. Panyard: None. F. Abbasi: None. K. Watson: None. S. Clarke: None. P.S. Tsao: None. T.L. Assimes: None. Background: The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to associate with two primary risk factors of type 2 diabetes mellitus (T2DM) and to predict incident T2DM. Methods: Clinical, genetic, and high-throughput proteomic data from three subcohorts of 5586, 5584, and 36339 individuals in the UK Biobank were analyzed for association with DXA-measured truncal fat, maximum VO2, and incident T2DM, respectively. Exclusion criteria included prevalent T2DM, history of T1DM, or elevated A1c (>48 mmol/mol) values. We used LASSO to assess relative ability of 13 clinical variables including the Q-Diabetes (Q-D) score, 36 polygenic risk scores (PRSs), and 2923 proteins to associate with each trait by comparing variances explained (R2) and AUC statistics between data types. Stability selection with randomized LASSO identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over the Q-D score was evaluated through the derivation of Δ AUC values. Separate case-control groups were developed to evaluate T2DM prediction in the adiposity and fitness cohorts. Results: Across all three subcohorts, the mean age was 56.6 years, 54.9% were female, and 5.4% developed T2DM over a median follow-up of 7.7 years. In the adiposity cohort, a protein-only LASSO model explained 73.2% of the variance in truncal fat and GLIPR1, CLMP, LEP, FABP4, and FAM3C were among the top proteins selected. In the fitness cohort, a protein-only LASSO model explained 61.9% of the variance in maximum VO2 and LEP, CD99, AGRN, ELN, and CA14 were among the top proteins selected. LASSO-derived PSs increased the R2 of truncal fat and maximum VO2 over clinical and genetic factors by 13% and 1%, respectively, with the former also improving risk prediction of T2DM over Q-D [Δ AUC: 0.026 (95% CI -0.007, 0.060)]. In the T2DM cohort, a protein-only LASSO model returned an AUC of 0.856 (95% CI 0.840, 0.871) and PLXNB2, PODXL, PLA2G7, FGFR2, and NFASC were among the top proteins selected. We observed a similar improvement in T2DM prediction over Q-D [Δ AUC: 0.015 (95% CI 0.009, 0.023)] when using a PS derived strictly from the T2DM outcome and augmented with non-overlapping proteins associated with fat and fitness. A few proteins (ranging between 15-33 for each trait) identified by stability selection algorithms offered most of the improvement in risk prediction. Conclusion: Plasma PS modestly improves the prediction of T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting T2DM risk over the standard practice of using the Q-D score. Presentation: 6/2/2024

8302 A Novel Tool to Map Estrogen Responsiveness Within Estrogen Sensitive Hypothalamic Neurons in Mice

Abstract Disclosure: A.L. Cara: None. A.L. Hall: None. K.K. Soma: None. J. van Veen: None. S.M. Correa: None. Identification of hormone sensitive cells has been traditionally evaluated by hormone receptor expression. Receptor positive cells can be labeled via expression of mRNA transcript via in situ hybridization, presence of protein via immunohistology, or via conditionally-dependent Cre recombinase driven reporter expression. However, these methods used to label and manipulate cells only indicate which cells are capable of sensing hormones, and not necessarily which cells are responding to a hormonal signal in a specific physiologic or behavioral context. Furthermore, broad labeling of all receptor positive cells may not accurately reflect the heterogeneous nature of certain cell populations, particularly in the brain. The hypothalamus is a brain region with well characterized hormone receptor expression, including estrogen receptors, such as ERα (encoded by the Esr1 gene). Estrogens have a broad array of physiologic and behavioral functions which are mediated through hypothalamic nuclei, including thermoregulation, metabolism, and reproduction. Neuronal populations mediating these functions have been characterized, targeted, and manipulated based on ERα expression, but the dynamic response to estrogens in these highly heterogeneous populations has yet to be uncovered. We developed a custom reporter virus that fluorescently labels estrogen-sensitive and responsive cells. The virus contains a flip-excision switch (FLEX) to drive Cre-dependent expression of a FusionRed fluorescent reporter, eight tandem estrogen response elements (ERE), and a destabilized enhanced green fluorescent protein (EGFP) with a 2-hour half-life. The Cre-dependent expression of FusionRed identifies all Esr1-Cre cells at the time of transduction, while the ERE-EGFP identifies a subset of Esr1-Cre+ cells that are actively responding to estrogens via nuclear receptor signaling. We stereotactically injected the ERE-EGFP virus to distinct hypothalamic nuclei of Esr1-Cre female mice. Following transduction, mice were sacrificed during different stages of the estrous cycle. Spatial analysis of ERE-EGFP expression revealed dynamic changes across the estrous cycle, with responses differing based on hypothalamic nucleus. Reporter expression dynamics were compared to estrogen levels measured by LC-MS/MS in ovary, serum, and microdissected brain tissue. A separate group of mice were ovariectomized and given estradiol benzoate (EB) or vehicle control, and neurons were flow sorted for bulk RNA sequencing. We discovered transcriptomic differences in FusionRed+ cells with high or low ERE-EGFP expression, including upregulation of estrogen target genes (e.g., Pgr, encoding progesterone receptor) with EB treatment in ERE-EGFP+ cells. In summary, this new viral reporter reveals divergent responses to estrogens across subsets of estrogen sensitive neurons, brain regions, and hormonal states. Presentation: 6/2/2024

Towards an environmental quality standard (EQS) for lithium in fresh water

Lithium (Li), because of its widespread and increasing use in Li-ion batteries, is an element of vital importance to the energy transition away from fossil fuels and toward clean, renewable energy (green energy). Extraction and refining operations, as well as utilisation in a growing number of applications, are expanding and thus increasing the environmental releases of Li. In order to evaluate consequences of such releases, and to risk assess the accumulation of Li in aquatic ecosystems, environmental quality standards (EQS) are needed so that measured concentrations can be evaluated. Satisfactory EQS for Li are yet to be established and therefore derivation of Predicted No Effect Concentrations (PNECs), or interim EQS, are vital for environmental protection. This study compiled Li freshwater ecotoxicology data from global literature, screened and evaluated it, and then employed it to derive PNECs for short term exposures (i.e. concentrations unlikely to cause harm if their duration is short) and PNECs for standard condition exposures (i.e. concentrations unlikely to cause harm if their duration is continuous) in freshwater ecosystems. The generated dataset comprised 194 individual ecotox endpoints from 31 reference sources, spanning 28 species from 10 separate taxonomic groups (broad organism types). The derived PNECs recommended for consideration in environmental regulation are 0.622 mg/L for short term exposures and 15.2 μg/L for standard condition exposures, but with potential adjustment for site specific water conditions incorporating water hardness, dissolved organic carbon concentration and pH.

COVID-19 pandemic impact on gynecologic cancer treatment pathways in a Finnish tertiary center.

COVID-19 and new guidelines during the pandemic affected the gynecologic cancer treatment pathways, resulting in recorded delays and modifications in the treatment protocols. The aim of this study was to determine the impact of the COVID-19 pandemic in one of the major gynecologic cancer care centers in Finland, Tampere University Hospital. Our retrospective register study included 909 patients that were new gynecologic cancer cases (uterine, cervical, vulvar, vaginal, or ovarian) referred to the Tampere University Hospital Gynecologic Oncology Outpatient Clinic between March 17th, 2018, and March 15th, 2022. The patients were divided into two separate groups depending on their time of referral: time before COVID (March 17th, 2018, to March 15th, 2020), and during COVID (March 16th, 2020, to March 15th, 2022). These groups were compared in terms of patient characteristics, different cancer types and stages, symptoms, and treatment methods. During the COVID-19 pandemic, patients generally suffered from cancer symptoms longer (p < 0.003) and were more likely to be overweight (p = 0.035). The improved multidisciplinary team meeting gave the patients a faster route to their first intervention during COVID (p < 0.05). An insignificant shift toward nonsurgical first interventions and non-curative intent was seen during COVID, but the multidisciplinary team treatment plans were mostly implemented accordingly on both eras. No decrease was seen in the number of new gynecologic cancer cases, and the one-year overall survival remained the same in both groups. Overall, the COVID-19 pandemic did not significantly alter treatment pathways in gynecologic cancer care at Tampere University Hospital. The number of new patients and given treatments remained relatively stable. During COVID, access from referral to cancer treatment was significantly accelerated, which is likely confounded by changes to the multidisciplinary team protocol made in early 2021.

Epidemiology of major heart rhythm and conduction disorders

BackgroundHeart rhythm and conduction disorders cover a variety of pathologies, ranging from the benign to the immediately life threatening. AimsTo describe the epidemiology of patients hospitalized for arrhythmias in France, and to estimate the prevalence and mortality associated with these disorders, divided into three separate groups: atrial fibrillation and flutter; conduction disorders; and ventricular tachycardia/cardiac arrest. MethodsWe looked in the National Health Data System and selected patients who had been hospitalized at least once in 2022 for these diseases and patients who died as a result of these diseases in 2021. The prevalence of these disorders among people alive on 1st January 2023 was estimated by combining previous hospitalizations and people in receipt of 100% coverage for a registered long-term disease. ResultsAt 1st January 2023, the prevalence of patients who had been hospitalized with major rhythm and conduction disorders was 2,740,141: 2,027,900 with atrial fibrillation/flutter; 999,692 with conduction disorders; and 214,989 with ventricular tachycardia/cardiac arrest. In 2022, respectively 90,502, 48,268 and 16,930 were hospitalized for these conditions, which equate to rates of 169.5, 68.3, and 31.7 per 100,000 inhabitants, respectively. Several departments in the Hauts-de-France and Grand-Est regions had rates>20% above the national rate. The rate of ventricular tachycardia/cardiac arrest was 40% higher among residents of the most deprived municipalities than among residents of the least deprived municipalities. Mortality at the end of hospitalization reached 44% for patients hospitalized for ventricular tachycardia/cardiac arrest. ConclusionsArrhythmias and conduction disorders affect a significant proportion of the population, leading to a large number of hospitalizations and procedures, particularly ablation techniques and pacemaker/defibrillator implantation. Given the extent of regional disparities and the impact of the socioeconomic status of the municipality of residence, targeted prevention and screening strategies should be implemented.

The impact of adjuvant antibiotic hydrogel application on the primary stability of uncemented hip stems

ObjectivesTo assess the effect of adjuvant antibiotic-loaded hydrogel application on the primary stability of implanted uncemented hip stems.DesignBiomechanical study.SettingAn electro-mechanic material test system (#5866, Instron, Norwood, MA, USA) equipped with...

ELECTRONIC ASSESSMENT ANXIETY SCALE: DEVELOPMENT, VALIDITY AND RELIABILITY

The widespread availability of internet access in daily life has resulted in a greater acceptance of online assessment methods. E-assessment platforms offer various features such as randomizing questions and answers, utilizing extensive question banks, setting time limits, and managing access during online exams. Electronic assessment enables real-time monitoring, customization, and scalability of feedback, benefiting students, academic staff, and administrative personnel. However, students encounter specific challenges in the electronic assessment environments. These challenges include limited control over test settings and the isolated nature of taking exams without peers. Furthermore, the technological proficiency of both instructors and students, along with resource constraints (computers, mobile devices, internet), can impede the effective utilization of these assessment tools. Technical issues like slow internet connection or disconnections can have significant consequences, especially in online exams, posing difficulties for corrections. The main goal of this study is to develop a Likert-type scale capable of measuring anxiety related to technical issues, social isolation, and the test interface experienced in e-assessment contexts. The study consists of two separate groups: the first group comprising 359 participants and the second group consisting of 356 participants. Both groups include undergraduate and pedagogical formation certificate program students from a university in the Eastern Anatolia Region of Turkiye. Construct validity was assessed through exploratory and confirmatory factor analyses. Item parameters were examined using item analysis based on classical test theory. As a result of the study, a two-factor scale structure comprising 21 items measuring social and technical anxiety was developed. These two dimensions account for 59.89% of the total variance. The Cronbach’s alpha coefficient for the entire scale was 0.93, the McDonald’s omega coefficient was 0.93, and the construct reliability was 0.99.

Functional diversification within the heme-binding split-barrel family

Due to neofunctionalization, a single fold can be identified in multiple proteins that have distinct molecular functions. Depending on the time that has passed since gene duplication and the number of mutations, the sequence similarity between functionally divergent proteins can be relatively high, eroding the value of sequence similarity as the sole tool for accurately annotating the function of uncharacterized homologs. Here, we combine bioinformatic approaches with targeted experimentation to reveal a large multi-functional family of putative enzymatic and non-enzymatic proteins involved in heme metabolism. This family (homolog of HugZ (HOZ)) is embedded in the “FMN-binding split barrel” superfamily and contains separate groups of proteins from prokaryotes, plants, and algae, which bind heme and either catalyze its degradation or function as non-enzymatic heme sensors. In prokaryotes these proteins are often involved in iron assimilation, whereas several plant and algal homologs are predicted to degrade heme in the plastid or regulate heme biosynthesis. In the plant Arabidopsis thaliana, which contains two HOZ subfamilies that can degrade heme in vitro (HOZ1 and HOZ2), disruption of AtHOZ1 (AT3G03890) or AtHOZ2A (AT1G51560) causes developmental delays, pointing to important biological roles in the plastid. In the tree Populus trichocarpa, a recent duplication event of a HOZ1 ancestor has resulted in localization of a paralog to the cytosol. Structural characterization of this cytosolic paralog and comparison to published homologous structures suggests conservation of heme-binding sites. This study unifies our understanding of the sequence-structure-function relationships within this multi-lineage family of heme-binding proteins and presents new molecular players in plant and bacterial heme metabolism.

Barriers to Effective Infection Prevention in the Neonatal Intensive Care Unit: A Qualitative Study.

Infection prevention (IP) behaviors such as hand hygiene (HH) and mobile device disinfection are important to reduce the risk of infection transmission from both family members and hospital staff to critically ill neonates. To inform the design of educational interventions to improve both patient family and staff IP behaviors, we engaged separate groups of nurses and family members to understand perceptions about the spread of infection and barriers to implementing effective IP strategies. This was a qualitative study using focus groups to gather data from neonatal nurses and patient family members. Data were triangulated with hospital-wide survey data and analyzed using inductive content analysis. Twelve nurses and 4 patient family members participated. Themes related to communication about IP between staff and family members emerged: stakeholders expressed discomfort with the timing and nature of just-in-time HH education. These communication challenges contributed to stress levels within the neonatal intensive care unit. This finding was reflected in the hospital-wide survey. Steps should be taken to improve communication about IP behaviors between patient family members and frontline staff. Reducing nurse burden of providing just-in-time HH reminders to patient family members through increased IP education may decrease stress and facilitate IP behaviors. This has the potential to decrease infection spread and improve patient outcomes. The development of interventions targeting stakeholder communication is therefore warranted, but additional research is needed to understand the timing and process for delivery of the educational material.

Deep brain stimulation mitigates memory deficits in a rodent model of traumatic brain injury

BackgroundTraumatic brain injury (TBI) is a major life-threatening event. In addition to neurological deficits, it can lead to long-term impairments in attention and memory. Deep brain stimulation (DBS) is an established therapy for movement disorders that has been recently investigated for memory improvement in various disorders. In models of TBI, stimulation delivered to different brain targets has been administered to rodents long after the injury with the objective of treating motor deficits, coordination and memory impairment. ObjectiveTo test the hypothesis that DBS administered soon after TBI may prevent the development of memory deficits and exert neuroprotective effects. MethodsMale rats were implanted with DBS electrodes in the anterior nucleus of the thalamus (ANT) one week prior to lateral fluid percussion injury (FPI). Immediately after TBI, animals received active or sham stimulation for 6 h. Four days later, they were assessed in a novel object/novel location recognition test (NOR/NLR) and a Barnes maze paradigm. After the experiments, hippocampal cells were counted. Separate groups of animals were sacrificed at different timepoints after TBI to measure cytokines and brain derived neurotrophic factor (BDNF). In a second set of experiments, TBI-exposed animals receiving active or sham stimulation were injected with the tropomyosin receptor kinase B (TrkB) antagonist ANA-12, followed by behavioural testing. ResultsRats exposed to TBI given DBS had an improvement in several variables of the Barnes maze, but no significant improvements in NOR/NLR compared to Sham DBS TBI animals or non-implanted controls. Animals receiving stimulation had a significant increase in BDNF levels, as well as in hippocampal cell counts in the hilus, CA3 and CA1 regions. DBS failed to normalize the increased levels of TNFα and the proinflammatory cytokine IL1β in the perilesional cortex and the hippocampus of the TBI-exposed animals. Pharmacological experiments revealed that ANA-12 administered alongside DBS did not counter the memory improvement observed in ANT stimulated animals. ConclusionsDBS delivered immediately after TBI mitigated memory deficits, increased the expression of BDNF and the number of hippocampal cells in rats. Mechanisms for these effects were not related to an anti-inflammatory effect or mediated via TrkB receptors.

Drone Detection Using Dynamic-DBSCAN and Deep Learning in an Indoor Environment

Drones have found extensive utility in both public and personal places. Consequently, the accurate detection and tracking of drones have emerged as pivotal endeavors in terms of ensuring their optimal performance. This research paper introduces a novel application for discerning the movements of humans and drones from cloud points through the utilization of frequency-modulated continuous wave radar. The dynamic density-based spatial clustering of applications with noise (Dynamic-DBSCAN) algorithm was employed to classify cloud points into separate groups corresponding to the number of objects within the tracking area. Compared to the original DBSCAN algorithm, this method increased accuracy by about 16.8%, achieving an accuracy of up to 93.99%. Subsequently, a trio of deep learning algorithms— long short-term memory, deep neural network, and residual network (ResNet)—were harnessed to ascertain the categorization of each group as either human or drone. According to the results, ResNet achieved the best accuracy rate of 97.72%. Overall, this study underscores the efficacy of the proposed method in accurately and efficiently distinguishing between human and drone entities for effective monitoring and management.

Optimizing Underground Coal Mine Safety: Leveraging Advanced Computational Algorithms for Roof Fall Rate Prediction and Risk Mitigation

AbstractThe utilization and consumption of coal in various nations have emphasized the pivotal role played by coal mines. However, aside from the substantial contribution of coal mines, miners, engineers, and craftsmen in this industry have long been exposed to numerous risks and financial losses resulting from roof collapses in underground coal mines. Hence, due to the heightened sensitivity surrounding this issue, the accurate and low-error forecasting and assessment of the roof fall rate (RFR) are deemed crucial and of utmost importance. Nonetheless, due to the intricate and uncertain inherent characteristics of the rock formations, assessing the RFR has encountered multiple challenges that cannot be precisely approximated through traditional methods. In this paper, algorithms such as the harmony search algorithm (HS) and the invasive weed Optimization algorithm (IWO) are harnessed to address the aforementioned challenges. To model the RFR, a total of 109 data points were used, incorporating input parameters such as primary roof support (PRSUP), depth of cover (D), coal mine roof rating (CMRR), mine height (MH), and intersection diagonal span (IS). For effective data analysis and model development, the dataset was split into two separate groups: one for training and the other for testing. Specifically, 80% of the data was used to build the model, while the remaining 20% was allocated for model evaluation and validation. Based on the outcomes of three statistical metrics R2, MSE, and RMSE, it is evident that the deployment of HS and IWO algorithms demonstrates high performance, with predicted values closely aligning with actual ones. Consequently, the utilization of intelligent algorithms in the field of rock engineering is positioned as a potent tool for researchers and engineers. In conclusion, a sensitivity analysis is carried out with the help of the @RISK software as a means of ranking the influence that the input parameters have on the output of the model. Its results indicate that among different parameters, the CMRR parameter with a sensitivity degree of 0.11 has the most impact on the model, even with the smallest change in this parameter, a significant change is made in the model output.