Set Function Research Articles

FOXO1 is a master regulator of memory programming in CAR T cells

A major limitation of chimeric antigen receptor (CAR) T cell therapies is the poor persistence of these cells in vivo1. The expression of memory-associated genes in CAR T cells is linked to their long-term persistence in patients and clinical efficacy2–6, suggesting that memory programs may underpin durable CAR T cell function. Here we show that the transcription factor FOXO1 is responsible for promoting memory and restraining exhaustion in human CAR T cells. Pharmacological inhibition or gene editing of endogenous FOXO1 diminished the expression of memory-associated genes, promoted an exhaustion-like phenotype and impaired the antitumour activity of CAR T cells. Overexpression of FOXO1 induced a gene-expression program consistent with T cell memory and increased chromatin accessibility at FOXO1-binding motifs. CAR T cells that overexpressed FOXO1 retained their function, memory potential and metabolic fitness in settings of chronic stimulation, and exhibited enhanced persistence and tumour control in vivo. By contrast, overexpression of TCF1 (encoded by TCF7) did not enforce canonical memory programs or enhance the potency of CAR T cells. Notably, FOXO1 activity correlated with positive clinical outcomes of patients treated with CAR T cells or tumour-infiltrating lymphocytes, underscoring the clinical relevance of FOXO1 in cancer immunotherapy. Our results show that overexpressing FOXO1 can increase the antitumour activity of human CAR T cells, and highlight memory reprogramming as a broadly applicable approach for optimizing therapeutic T cell states.

A microdosimetry analysis of reversible electroporation in scattered, overlapping, and cancerous cervical cells

We present a numerical method for studying reversible electroporation on normal and cancerous cervical cells. This microdosimetry analysis builds on a unique approach for extracting contours of free and overlapping cervical cells in the cluster from the Extended Depth of Field (EDF) images. The algorithm used for extracting the contours is a joint optimization of multiple-level set function along with the Gaussian mixture model and Maximally Stable Extremal Regions. These contours are then exported to a multi-physics domain solver, where a variable frequency pulsed electric field is applied. The trans-Membrane voltage (TMV) developed across the cell membrane is computed using the Maxwell equation coupled with a statistical approach, employing the asymptotic Smoluchowski equation. The numerical model was validated by successful replication of existing experimental configurations that employed low-frequency uni-polar pulses on the overlapping cells to obtain reversible electroporation, wherein, several overlapping clumps of cervical cells were targeted. For high-frequency calculation, a combination of normal and cancerous cells is introduced to the computational domain. The cells are assumed to be dispersive and the Debye dispersion equation is used for further calculations. We also present the resulting strength-duration relationship for achieving the threshold value of electroporation between the normal and cancerous cervical cells due to their size and conductivity differences. The dye uptake modulation during the high-frequency electric field electroporation is further advocated by a mathematical model.

An Inclusive Distance Irregularity Strength of n-ary Tree

An inclusive distance vertex irregular labelling of a simple graph G is a function of the vertex set of to positive integer set such that the sum of its vertex label and the labels of all vertices adjacent to the vertex are distinct. The minimum of maximum label of the vertices is said to be inclusive distance irregularity strength of G, denoted by dis(G). The purpose of this research is showing that dis(T_{n,2})= (n^2+2)/2 where T_{n,2} is a complete n-ary tree to level two.

MATHEMATICAL MODEL OF ECONOMIC INFORMATION

This article shows that economic information can be formally described using an information field, which is a topological space with an introduced system of subsets that is closed with respect to the union operation. This system of subsets consists of information quanta - sets of information that can be used by a subject when making economic decisions. On this information field, a set function is introduced, defined on information quanta, which has the meaning of the economic value of the information quanta when the subject solves an economic problem. Many economic problems can be described by optimization problems, when a subject need to make a certain choice from a set of feasible solutions, and by game-theoretic models, when it is necessary to choose the best strategy in the face of opposition from other subjects. For these models, it is shown how a function can be constructed to evaluate the economic usefulness of information quanta.

Fractional guidance-based level set evolution for noisy image segmentation with intensity inhomogeneity

Intensity inhomogeneity (IIH) and noise are ubiquitous in images acquired by a variety of imaging modalities, which pose an ongoing challenge for segmentation methods used in many applications. This paper proposes a level set evolution equation based on fractional derivative to address the IIH and noise issues in image segmentation, which integrates the information from the input and guidance images. The proposed model is a linear diffusion equation of level set function with nonlinear edge-stopping and guidance sources, in which the edge-stopping source is derived from the input image and a local statistical model (Bayesian formulation) in the level set framework, whereas the guidance source is derived from the guidance image and the fitting term of the popular Chan-Vese model. The guidance source ensures swift contour movement toward the boundary of the objects and enhances the robustness to noise and contour initialization, while the edge-stopping source is used to stop the evolving contour on the boundary of the objects later in the evolution. To tackle the challenge of obtaining accurate guidance images for many applications, an adaptive variable-order of fractional derivative robust to IIH and noise is introduced to generate the desired guidance image from the input image. The proposed diffusion equation with sources is solved numerically by the series splitting method and finite difference scheme. Experimental results on synthetic and real images show that the proposed model has higher performance in terms of accuracy and efficiency of segmentation, compared to several state-of-the-art level set models.

Wegner estimate and localisation for alloy-type operators with minimal support assumptions on the single site potential

Abstract We prove a Wegner estimate for alloy-type models merely assuming that the single site potential is lower bounded by a characteristic function of a thick set (a particular class of sets of positive measure). The proof exploits on one hand recently proven unique continuation principles or uncertainty relations for linear combinations of eigenfunctions of the Laplacian on cubes and on the other hand the well developed machinery for proving Wegner estimates. We obtain a Wegner estimate with optimal volume dependence at all energies, and localization near the minimum of the spectrum, even for some non-stationary random potentials. We complement the result by showing that a lower bound on the potential by the characteristic function of a thick set is necessary for a Wegner estimate to hold. Hence, we have identified a sharp condition on the size for the support of random potentials that is sufficient and necessary for the validity of Wegner estimates.

Abstract 873: Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA profiles

Abstract Background: Despite the recent success in Immune checkpoint blockade (ICB) therapy, limited patients benefit due to immune-related adverse events or treatment resistance, especially in solid tumors. To overcome these hurdles, understanding the molecular mechanisms behind treatment response is essential. However, the current lack of immune gene transcriptional programs (GTPs) hampers data-driven immunological discovery. Constructing immunity-specific knowledgebases with rigorously curated gene sets (irGSs) and rich immunological language can unlock new insights, enabling interpretation of high-throughput immune microenvironment profiling studies and fostering personalized, effective cancer treatments. Methods: We collected 83 BulkRNAseq datasets from the ImmuneSigDB. These datasets contain 1826 samples challenged with infections, cytokines or immunological perturbations of different kinds and magnitude, possessing yet-to-be discovered immune functions that lie beneath the transcriptomic profiles. Using non-negative matrix factorization (NMF), we identified gene sets with coordinated expression. We used CITE-seq and selected scRNAseq to annotate the immunological functions of each gene set and validated the clinical utilities of these gene sets from different aspects using data from Cancer Genome Atlas Program (TCGA) pan-cancer, ICB cohorts and a 10X Genomics Visium FFPE Human Breast Cancer spatial slide. Results: We presented 19 lymphoid-data derived and 9 myeloid-data derived gene sets (irGSs), encompassing a diverse array of immune functions. Through extensive validation using BulkRNAseq, scRNAseq and spatial transcriptomics data, we revealed six irGSs-defined pan-cancer microenvironment subtypes in TCGA with significantly distinct survival patterns regardless of cancer types. Moreover, irGSs are associated with T cell exhaustion phenotypes, which in turn well predicted (classification accuracy of 72%) Nivolumab response using 104 treatment-naïve melanoma patients. Lastly, irGSs simultaneously isolated out tumor regions and highlighted immune infiltration with high specificity in breast cancer spatial transcriptomics H&E image. Conclusions: These pioneering gene sets, originally derived from non-cancerous experiments, hold tremendous promise for cancer research across diverse contexts. Similarities between cancerous and sepsis immune microenvironments underscore their wide applicability. By studying gene set activities, immunologists can gain profound insights into cancer survival drivers, unravel the intricacies of ICB treatment mechanisms, and potentially conquer therapeutic resistance. These translational utilities herald a transformative era in cancer immunotherapy and open new frontiers in the fight against cancer. Citation Format: Shan He, Vakul Mohanty, Matthew Gubin, Hind Rafei, Rafet Basar, Merve Dede, Xianli Jiang, Yukun Tan, Maura Gillison, Katayoun Rezvani, Weiyi Peng, Ken Chen. Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 873.

Abstract 34: Improving CAR T cell efficacy in pancreatic cancer using an in vivo CRISPR Cas9 screen

Abstract Introduction: Chimeric antigen receptor (CAR) T cell therapy has demonstrated remarkable success in the treatment of certain hematologic malignancies. However, efficacy against solid tumors has remained limited and is associated with relatively low T cell persistence. Here, we perform an in vivo CAR T cell CRISPR screen in pancreatic adenocarcinoma using a curated guide library to identify genes that enhance CAR T cell persistence in vivo. Material and Methods: The Mario library consists of 1180 total guide RNAs targeting 135 genes that have been shown to modulate T cell functions in other models. To generate Mario-CART cells, primary human T cells were transduced with two lentiviral vectors, the first encoding an anti-mesothelin CAR and the second containing a fixed guide targeting the TCR alpha gene constant region (TRAC) as well as the Mario library. Mario-CART cells were transduced at low MOI, expanded in vitro and selected for loss of CD3 expression using magnetic selection, to enrich for cells that had a successful gene-edited event. Purified Mario-CART cells were intravenously injected into NSG mice bearing subcutaneous pancreatic adenocarcinoma (ASPC1). Tumor and spleen were harvested 2 or 4 weeks after Mario-CART injection, and engrafted T cells were isolated by CD45 positive selection. Genomic DNA was prepared from retrieved cells and illumina sequencing was used to evaluate guide abundance to identify gene knockouts that had been enriched after tumor exposure in vivo. Results: The transcription factor, GATA3, was consistently identified as the most enriched hit across two donor T cells when Mario-CART was recovered at the 2-week timepoint. When compared to control or a depleted hit, GATA3 KO CAR showed early improvements in tumor clearance in vivo. However, relapse occurred at similar timepoints across groups. At the 4-week timepoint, the most enriched guides comprised a different set, including genes involved in transforming growth factor beta and JAK-STAT signaling pathways as well as T cell trafficking. In vivo anti-tumor control data are being collected. Conclusions: Our findings highlight the dynamic nature of CART-tumor interactions. Gene knockouts that confer enhancements may change over time with varying context (such as in vitro vs in vivo conditions). It may be difficult to identify single gene knockouts that enhance CAR-T cell functions across different conditions and over time, but rather it may be necessary to exert more dynamic functional control of CAR T cells to enhance their function in different settings. Citation Format: Tamina Kienka, Felix Korell, Nelson Knudsen, Kathleen Yates, Andi Cheng, Debattama Sen, Marcela Maus, Robert Manguso. Improving CAR T cell efficacy in pancreatic cancer using an in vivo CRISPR Cas9 screen [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 34.

Construction of an oxidative phosphorylation-related gene signature for predicting prognosis and identifying immune infiltration in osteosarcoma.

Osteosarcoma is a prevalent malignant tumor that originates from mesenchymal tissue. It typically affects children and adolescents. Although it is known that the growth of osteosarcoma relies on oxidative phosphorylation for energy production, limited attention has been paid to exploring the potential of oxidative phosphorylation-related genes in predicting the prognosis of individuals suffering from osteosarcoma. All the data were retrieved from the UCSC Xena and GEO (GENE EXPRESSION OMNIBUS). Identification of the oxidative phosphorylation genes linked to the prognosis of individuals with osteosarcoma was done by means of univariate COX and LASSO regression analyses. Following that, patients were categorized into a high-risk group and a low-risk group as per the risk score determined by the identified oxidative phosphorylation genes. Furthermore, a comparison was made in terms of the survival and immune infiltration between both groups, and the prognostic model was established. Five oxidative phosphorylation genes (ATP6V0D1, LHPP, COX6A2, MTHFD2, NDUFB9) associated with the prognosis of individuals with osteosarcoma were identified and the risk prognostic models were constructed. In the current research, the analysis of the ROC curves indicated a superior predictive accuracy exhibited by the risk model. The prognosis was adversely affected by immune infiltration in the high-risk group in comparison with the low-risk group. The function of the oxidative phosphorylation-related prognostic gene set was verified by GO and KEGG analysis. Furthermore, the link between oxidative phosphorylation-related genes and osteosarcoma immune infiltration was examined by GSEA analysis. In this study, a prognostic model that demonstrated good predictive performance was constructed. Additionally, this study highlighted a correlation between oxidative phosphorylation-related genes and immune infiltration.

A single level set function approach for multiple material-phases applied to full-waveform inversion in the time domain

This paper presents a multiple material-phase level-set approach for acoustic full-waveform inversion in the time domain. By using a single level set (LS) function, several level values are used to define virtual boundaries between material phases with different (and known) wave propagation velocities. The aim of the proposed approach is to provide a suitable framework to identify multiple/nested inclusions or a finite number of almost homogeneous sedimentary layers with sharp interfaces between them. The use of a single LS function provides a significant reduction in the number of variables to be identified, when compared with the usual multi-material phase approaches defined by multiple functions, especially for problems with a high number of degrees of freedom. Numerical experiments show satisfactory results in identifying simultaneously different interfaces. Cases with and without inverse crime are evaluated, showing that the approach is reasonably robust in dealing with such a condition.

Rescheduling with New Orders Under Bounded Disruption

Rescheduling problems arise when unpredicted events occur, such as the arrival of new orders. These new jobs should be integrated in a proper way in the existing schedule of the so-called old jobs, with the aim of minimizing an objective function for the joint set of jobs. To avoid a major disruption of the original schedule, each old job is not allowed to deviate from its original completion time by more than a certain threshold. Filling a gap in the existing literature, we consider the minimization of the total weighted completion time. The resulting rescheduling problem is shown to be weakly NP-hard and several properties of the structure of an optimal schedule are derived. These can be used for the construction of an exact dynamic programming algorithm with pseudo-polynomial running time. A fully polynomial time approximation scheme is obtained from the dynamic program by three different scaling and reduction steps. Finally, for the minimization of the number of late jobs a strong NP-hardness result is derived. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was partially supported by the Ministero dell’Istruzione, dell’Università e della Ricerca [Award TESUN-83486178370409 finanziamento dipartimenti di eccellenza CAP. 1694 TIT. 232 ART. 6]. U. Pferschy acknowledges support by the Field of Excellence COLIBRI at the University of Graz.

A Conservative Solid-Gas Coupling Scheme with Overlapping Meshes for Combustion Simulation of Propellants with Voids

ABSTRACT A conservative coupling scheme with overlapping meshes for the combustion simulation of propellants with voids is established. The two main ingredients of this scheme are the conservative calculation of fluxes of mass, momentum, energy, and species and the robust treatment of voids. Specifically, for the discrete flux calculation, firstly the solid-gas interfaces are captured by the level-set method and then sampling points are generated between the interfaces at consecutive time steps, and these points are used to ensure the conservation of these quantities up to each grid of the gas domain. For the second ingredient, the void before exposure is treated as “solid” with properties of the air. Once an exposure condition is met, the topological change of the void is realized by reinitializing the level set function inside and on the boundary of the void. Overall, the scheme is conservative and robust, which is validated with experimental results.

THE ROLE OF GAMES IN CONTEMPORARY EDUCATION: CHALLENGES FOR THE TEACHERS AND STUDENTS IN THE ERA OF BLENDED LEARNING – CORRELATION BETWEEN GRADES AND GAMES PERFORMANCE

In recent years, there has been a surge in information and communications technology, propelled by the pandemic’s emphasis on remote learning. Interactive technologies like computers, tablets, and mobile phones hold promise for digitizing work and education, fostering creativity and immersion. Educators are increasingly exploring the integration of interactive and game-based tools into learning, inspired by the widespread popularity of gaming across age groups. Immersive game-based platforms, capable of integrating curricula and functioning in multiplayer settings, are emerging as a new trend in online learning, offering enhanced engagement for both students and teachers and improving test results. However, integrating games still presents challenges for teachers and students. Understanding the correlation between students’ grades and their performance in educational games is a significant challenge. While research suggests that game-based learning can positively impact academic achievement, further exploration is needed to grasp the nuanced relationship between in-game performance and traditional assessment metrics. Formative assessments through game-based solutions have gained popularity, but there is a lack of multiplayer classroom tools offering varied gameplay mechanics and content integration for creative learning. Research into the motivational aspects of multiplayer gaming, the impact of different gameplay dynamics on learning outcomes, and a comparison of multiplayer versus traditional approaches are areas for further exploration. Our study introduces a novel multiplayer game-based platform (Multiplayer Team Training Platform or MTT) addressing the challenges of uniform gameplay, offering a variety of gameplay dynamics and automated tracking of student performance in both single and multiplayer modes. A comparison between traditional assessment and game-based assessment in similar settings was undertaken with different classes, subjects, and age groups, and a comparative study was done to analyze the results of both experiments. The investigations showed that the Control Groups consistently outperformed, suggesting potential benefits of traditional teaching methods, especially under teacher’s supervision. However, it was also observed that repeated testing in the control groups did not necessarily result in enhanced retention of knowledge. Conversely, the game-based environment showed gradual improvement with gameplay repetition and reduced concerns about cheating among students. Strong positive correlations between subject grades and performance were found, indicating that higher-graded students excel in both environments, with lower-grade students showing a trend of better and continuous improvement, particularly in the game-based environment.

Integrating Wearable Textiles Sensors and IoT for Continuous sEMG Monitoring.

Surface electromyography is a technique used to measure the electrical activity of muscles. sEMG can be used to assess muscle function in various settings, including clinical, academic/industrial research, and sports medicine. The aim of this study is to develop a wearable textile sensor for continuous sEMG monitoring. Here, we have developed an integrated biomedical monitoring system that records sEMG signals through a textile electrode embroidered within a smart sleeve bandage for telemetric assessment of muscle activities and fatigue. We have taken an "Internet of Things"-based approach to acquire the sEMG, using a Myoware sensor and transmit the signal wirelessly through a WiFi-enabled microcontroller unit (NodeMCU; ESP8266). Using a wireless router as an access point, the data transmitted from ESP8266 was received and routed to the webserver-cum-database (Xampp local server) installed on a mobile phone or PC for processing and visualization. The textile electrode integrated with IoT enabled us to measure sEMG, whose quality is similar to that of conventional methods. To verify the performance of our developed prototype, we compared the sEMG signal recorded from the biceps, triceps, and tibialis muscles, using both the smart textile electrode and the gelled electrode. The root mean square and average rectified values of the sEMG measured using our prototype for the three muscle types were within the range of 1.001 ± 0.091 mV to 1.025 ± 0.060 mV and 0.291 ± 0.00 mV to 0.65 ± 0.09 mV, respectively. Further, we also performed the principal component analysis for a total of 18 features (15 time domain and 3 frequency domain) for the same muscle position signals. On the basis on the hierarchical clustering analysis of the PCA's score, as well as the one-way MANOVA of the 18 features, we conclude that the differences observed in the data for the different muscle types as well as the electrode types are statistically insignificant.

Sustainable Young Nursing Business Manager Leadership Competency: The Philippines Experience

The global shortage of qualified nurse managers, particularly evident in the Philippines, is rooted in the absence of training programs for transitioning nurses into management roles. Recent studies highlight this gap, leading to the promotion of inexperienced nurses to leadership positions and resulting in ineffective management practices and compromised care quality. Recognizing the importance of sustainable leadership competency for the long-term viability of healthcare services, a study in Metro Manila seeks to address these issues. The research focuses on gathering insights from nursing business experts regarding essential skills for sustainable leadership among young nursing business managers. The study aims to not only explore these perspectives but also assess the competency levels of young nurse managers using a newly developed survey tool. The objectives encompass determining expert views on necessary skills, profiling participants based on demographic factors, assessing competency levels, and drawing recommendations from the mixed-methods study results. Employing a mixed-method approach, the study initially conducted interviews with nursing business leaders in Manila. The themes derived from these interviews were then utilized in formulating the questionnaire. The findings revealed demographic variations among participants, influencing their responses. Five major themes emerged, reflecting participant perspectives on the competency of young nurse business managers: professional conduct, educational competency, individual assessment, trans-cultural competency, and informatics tech competency. In conclusion, the study emphasizes the diverse skills and competencies required by young nurse business leaders for effective functioning in occupational and professional settings. These competencies, spanning professional conduct, individual assessment, cultural competence, and technological proficiency, are deemed critical components in assessing and evaluating the effectiveness and efficiency of young nursing leaders, as emphasized in the existing literature. The study underscores the imperative for comprehensive training programs to enhance the competency of young nursing leaders, contributing to the overall quality and sustainability of healthcare services. Keywords: Nursing, leadership, Management, Concepts Investigated: professional conduct, individual assessment, culturally-inclined competency, informatics tech competency, educational competencies, Exploratory sequential Mixed-method , purposive sampling , The Philippines, South East Asia

Fuzzy Mandelbric Set and Its Perturbations by Dynamical Noises

In this paper, we introduce a membership function used to form the fuzzy Mandelbric set and investigate the structural effects of additive and multiplicative dynamic noises on it. The newly defined membership function of this fuzzy set and its perturbations is a generalization of the indicator function for the classical Mandelbric set. We present an algorithm for detecting each complex number’s fuzzy membership degree. Through the use of the membership degrees of each complex number and experimental mathematics based on the visualizations of a variety of versions by utilizing computer-aided design, we gain a deep foresight for the structure characteristics of the additive and multiplicative perturbed fuzzy Mandelbric sets. Our novel approach allows us to identify the symmetry states of the Mandelbric set and its perturbations by the membership degrees of complex numbers, unlike the existing methods described in the literature.

Obstetric imaging practice characteristics associated with prenatal detection of critical congenital heart disease in a rural US region over 20years.

To identify clinical practice characteristics associated with the frequency of prenatal critical congenital heart disease (CCHD) detection (i.e., the number of liveborn infants with postnatally confirmed CCHD identified on prenatal sonography) over 20years in a rural setting comprised of 11 primarily low-volume obstetric hospitals and the single tertiary academic hospital to which they refer. This was a retrospective cohort study of all patients in the referral region with an initial prenatal and/or postnatal diagnosis of CCHD from 01/01/2002 to 12/31/2021. The frequency of prenatal CCHD detection at the time of an obstetric ultrasound was reported, as was the change in detection over time. Critical congenital heart disease detection was assessed as a function of cardiac lesion type, practice setting, and practice characteristics. There were 271 cases with a confirmed postnatal CCHD diagnosis, of which 49% were identified prenatally. The majority of community practices each averaged <10 CCHD cases in total over the study period. Prenatal detection at the tertiary academic hospital's obstetric ultrasound unit was 64%, compared to 22% at the combined referring community practices (p<0.001), though CCHD detection improved over time in both settings. Professional accreditation by the American Institute of Ultrasound in Medicine, image interpretation by radiology or Maternal Fetal Medicine, and use of video clips of ventricular outflow tracts were associated with improved prenatal CCHD detection. Our data demonstrate the infrequency of CCHD cases at small-volume, rural hospitals and the substantial variation in prenatal CCHD detection across practice settings. Our methods allowed for the identification of practice characteristics associated with prenatal CCHD detection.

The Unspoken Wounds: Understanding the Psychological Impact on Healthcare Professionals Fighting COVID-19 in Pakistan

During the COVID-19 pandemic, hospital staff faced numerous mental health challenges. However, limited research focused on anxiety and stress specifically among hospital workers during this time. Therefore, this study aimed to investigate the anxiety levels of healthcare workers during the COVID-19 pandemic. A multidimensional, cross-sectional survey was distributed to healthcare workers and staff at hospitals, COVID-19 laboratories, and healthcare settings. The survey included a total of 625 frontline healthcare workers, with 445 (71.2%) being male and 180 (28.8%) female. There were 405 (64.8%) lab professionals, 90 (14.0%) doctors, and 130 (20.8%) others, including nursing staff, administrative personnel, and supporting staff crucial to the functioning of healthcare settings. Among the lab professionals, 37.0% reported moderate depression levels and 16.0% reported severe depression levels during the pandemic. For doctors, 22.2% experienced mild depression and 33.33% experienced severe depression. Several factors were significantly associated with depression and anxiety among frontline healthcare workers, including physiological and social factors, fear of infection, risk of infecting family members and colleagues, lack of personal protective equipment (PPE), long working hours, untrained staff, social issues, and cooperation problems. These factors collectively contributed to reduced work efficacy during the pandemic. Frontline health workers played a critical role in the fight against COVID-19. The findings from this study have important implications for developing strategies to improve the mental health of healthcare workers during the pandemic and implementing policies that enhance work efficacy, ultimately leading to the improved outcomes.

Closest Farthest Widest

The current paper proposes and tests algorithms for finding the diameter of a compact convex set and the farthest point in the set to another point. For these two nonconvex problems, I construct Frank–Wolfe and projected gradient ascent algorithms. Although these algorithms are guaranteed to go uphill, they can become trapped by local maxima. To avoid this defect, I investigate a homotopy method that gradually deforms a ball into the target set. Motivated by the Frank–Wolfe algorithm, I also find the support function of the intersection of a convex cone and a ball centered at the origin and elaborate a known bisection algorithm for calculating the support function of a convex sublevel set. The Frank–Wolfe and projected gradient algorithms are tested on five compact convex sets: (a) the box whose coordinates range between −1 and 1, (b) the intersection of the unit ball and the non-negative orthant, (c) the probability simplex, (d) the Manhattan-norm unit ball, and (e) a sublevel set of the elastic net penalty. Frank–Wolfe and projected gradient ascent are about equally fast on these test problems. Ignoring homotopy, the Frank–Wolfe algorithm is more reliable. However, homotopy allows projected gradient ascent to recover from its failures.

Continuity of the solution set mappings to parametric unified weak vector equilibrium problems via free-disposal sets

In this paper, the lower and upper semicontinuity of the solution mappings for parametric unified weak vector equilibrium problem (PUWVEP) are established, via freedisposal set and nonlinear scalarization function. Moreover, example is given to illustrate the obtained results. The results improve the corresponding ones in the literature [12-14].