Miracle-dz/UniFAR-FTUs huggingface dataset:
Monocytes in tumor: The perspectives of single-cell analysis

Infiltration of mononuclear phagocytes into the tumor microenvironment (TME) is known to orchestrate the tumor progression and is generally considered to interfere with the efficacy of immune checkpoint blockade therapies. For a more precise definition of monocytes, we review some recent advances in the functions of monocytes and macrophages in TME based on single-cell analysis. We also summarize the details of the different subpopulations of monocytes and macrophages involved in immunotherapy and their potential applications in clinical studies. In this review, we briefly introduce the developmental trajectory of mononuclear phagocytes, including monocytes, tumor-associated macrophages, dendritic cells and myeloid-derived suppressive cells, and their functions in TME. In this review, the potential of monocytes and their derived cells as diagnostic and therapeutic targets, with regard to the interaction between monocytes and immune checkpoint therapies, is also summarized.
The Convivial Conservation Imperative: Exploring “ Biodiversity Impact Chains ” to Support Structural Transformation

The forest-risk commodity sector operations of the 8 companies pro ﬁ led in this report have been enabled by at least 6.38 billion USD in bond- and shareholdings at the most recent ﬁ ling date in May 2017 by institutional investors (asset managers, insurance companies, pension funds) and have received more than 32.67 billion USD in loans and underwriting facilities between 2010 and 2016.
Social Demographic Characteristics and Teachers’ Performance in Government Aided Secondary Schools: Evidence from Uganda

- In this study, we aimed at examining the association between social demographic characteristics of teachers and their performance in government-aided secondary school Kasese district southwestern Uganda. Using purposive and stratified for both schools and teachers in this quantitative study, we recruited 234 teachers from 25 government aided secondary in Kasese district southwestern Uganda Data was analysed using SPSS version 24, and presented using both descriptive and inferential statistics. Our findings indicate that social demographic characteristics of respondents such as gender, age and education levels are significant predictors of teachers’ performance in secondary schools of Kasese district southwestern Uganda. We conclude that some teachers’ social demographic characteristics have a significant influence on teachers’ performance in government-aided secondary schools with in Kasese district south western Uganda. We recommend that policy makers design the most appropriate interventions entailing human resource practices aimed at streamlining the needs of teachers from different demographic cohorts.
Applicability and Usefulness of the HFACS-GMI* and Usefulness of the

To present information such as causes of accidents and their consequences on the Ghanaian mining industry in the safety literature, classification schemes for incident analysis within the safety literature were studied. Human Factor Analysis and Classification Scheme (HFACS) emerged suitable for incident analysis. Base on its suitability for incident analysis within the Ghanaian Mining Industry (GMI), a derivative of the HFACS, namely HFACS-GMI, was proposed. This research seeks to study the usefulness and the applicability of the HFACS-GMI. Collectively, 56 incident investigation reports were obtained from an open cast gold mine in Ghana and analysed using the HFACS-GMI. Two cases, an equipment damage incident and an injury incident, were used to demonstrate the coding processing in identifying the causal factors. The analysis shows that most mishaps are associated with adverse workplace/operator conditions (151 references), with the physical environment (72.2%) being cited as the major causal code under the tier. Management decision showed a major contribution (74.1%) to mishap under the causal codes. Most cases were attributed to mistake error (57.4%) followed by the contravention (51.1%) of set rules and procedures with the operator's act tiers. Inadequate work standards (27.8%) and failure to ensure competency (24.1%) under the operational process and leadership flaw causal codes, respectively, were identified as the most cited nanocode. Management decision is critical in a mishap and should be given much attention in developing accident prevention strategies . The study has demonstrated that HFACS-GMI is very useful and applicable for incident analysis within the mining industry and is recommended to study causal factors across the mines.
Medical Image Dataset Rebalancing via Conditional Deep Convolutional Generative Adversarial Networks (cDCGANs)

Training deep learning models on imbalanced data sets is difficult in practice because models tend to be biased towards predicting the majority class [21]. This poses a particular challenge in the medical field, not only because the minority class may often be a disease we are trying to diagnose, but also because of the high risk nature of medical artificial intelligence (AI). In this paper we propose to re-balance imbalanced datasets by oversampling using Gener-ative Adversarial Networks (GANs) [11]. We intentionally create an imbalanced dataset consisting of chest x-ray images labeled normal or pneumonia, with pneumonia images being the minority class. In particular, we consider Conditional Deep Convolutional Generative Adversarial Networks (cDCGANs), Deep Convolutional Generative Adversarial Networks (DCGANs) [26] conditioned on class labels [23]. For cDCGANs, we propose a new parameter α that indicates the probability of a randomly generated class label of 0. We then use these GANs to generate new synthetic pneumonia images. We train a ResNet18 model [15] on the new rebalanced datasets and compare their results to baseline, as well as some more common machine learning methods like undersampling, oversampling, class weights, and compare precision, recall/sensitivity, specificity, and the F1 score. Our results show that DCGAN and cDCGAN-based oversampling methods can outperform class weights and classic resampling methods in terms of F1 score, and out-perform the baseline across the board, making GANs a valid method to rebalance datasets. Additionally, we found that our proposed α hyperparameter can potentially be used to tweak the sensitivity and specificity of the ResNet18 model.
Transcriptional regulation and responses in filamentous fungi exposed to lignocellulose

Biofuels derived from lignocellulose are attractive alternative fuels but their production suffers from a costly and inefficient saccharification step that uses fungal enzymes. One route to improve this efficiency is to understand better the transcriptional regulation and responses of filamentous fungi to lignocellulose. Sensing and initial contact of the fungus with lignocellulose is an important aspect. Differences and similarities in the responses of fungi to different lignocellulosic substrates can partly be explained with existing understanding of several key regulators and their mode of action, as will be demonstrated for Trichoderma reesei, Neurospora crassa and Aspergillus spp. The regulation of genes encoding Carbohydrate Active enZymes (CAZymes) is influenced by the presence of carbohydrate monomers and short oligosaccharides, as well as the external stimuli of pH and light. We explore several important aspects of the response to lignocellulose that are not related to genes encoding CAZymes, namely the regulation of transporters, accessory proteins and stress responses. The regulation of gene expression is examined from the perspective of mixed cultures and models are presented for the nature of the transcriptional basis for any beneficial effects of such mixed cultures. Various applications in biofuel technology are based on manipulating transcriptional regulation and learning from fungal responses to lignocelluloses. Here we critically access the application of fungal transcriptional responses to industrial saccharification reactions. As part of this chapter, selected regulatory mechanisms are also explored in more detail.
Does media coverage of firms’ environment, social, and governance (ESG) incidents affect analyst coverage and forecasts?

Media coverage of environment, social, and governance (ESG) issues provides useful information for analysts as corporate social irresponsibility events potentially influence corporate performance and risks. Our study explores whether and how analysts respond to media coverage of corporate social irresponsibility by examining its relationship with analyst coverage and forecasts. We find evidence that the level of analyst coverage is negatively associated with a firm’s ESG incidents covered by the media. This association is more pronounced for firms with high business risk, high information risk, more intense industrial product market competition, and more severe ESG scandals. We also find a positive association between media-covered ESG incidents and analyst forecast error and dispersion, suggesting that analysts might fail to incorporate the ESG risk exposures into their forecasts in an appropriate manner. The business risk and information risk tend to be higher for firms that are covered by the media for having got involved in ESG incidents, thereby explaining why the analyst coverage and forecasts for these firms are adversely affected. Overall, our results suggest that corporate social irresponsibility undermines the role analysts play as information intermediaries for investors in the stock market.
OCCUPATIONAL STRESS AND DISSATISFACTION WITH QUALITY OF WORK LIFE IN NURSING

Objective: to analyze the association between occupational stress, quality of work life and associated factors among nursing workers. Method: 309 nursing workers of a public general hospital participated in this cross-sectional study. The Total Quality of Work Life and the Job Stress Scale instruments were used to evaluate the quality of work life and occupational stress, respectively. Results: associations were estimated using Poisson regression analysis combined with robust variance. We found association between dissatisfaction with quality of work life and smoking (PR=1.53; 95%CI: 1.02-2.31), work at the care units for highly dependent or critically ill patients (PR=2.47; 95%CI: 1.18-5.19), low social support at work (PR=1.76; 95%CI: 1.29-2.40) and the quadrants of the demand-control model "active job" (PR=1.74; 95%CI: 1.04-2.92) and "high-strain job" (PR=2.54; 95%CI: 1.51-4.27) . Conclusion: these results may help the hospital and nursing managers to develop and implement strategies in order to reduce excessive demands and work overload and increase social support among the nursing staff.
Abnormal thrombosis and neutrophil activation increases the risk of hospital-acquired sacral pressure injuries and morbidity in patients with COVID-19

Hospitalized patients have an increased risk of developing hospital-acquired sacral pressure injury (HASPI). However, it is unknown whether SARS-CoV-2 infection affects HASPI development. To explore the role of SARS-CoV-2 infection in HASPI development, we conducted a single institution, multi-hospital, retrospective study of all patients hospitalized for [≥]5 days from March 1, 2020 to December 31, 2020. Patient demographics, hospitalization information, ulcer characteristics, and 30-day-related morbidity were collected for all patients with HASPIs, and intact skin was collected from HASPI borders in a patient subset. We determined the incidence, disease course, and short-term morbidity of HASPIs in COVID-19(+) patients, and characterized the skin histopathology and tissue gene signatures associated with HASPIs in COVID-19 disease. COVID-19(+) patients had a 63% increased HASPI incidence rate, HASPIs of more severe ulcer stage (OR 2.0, p<0.001), and HASPIs more likely to require debridement (OR 3.1, p=0.04) compared to COVID-19(-) patients. Furthermore, COVID-19(+) patients with HASPIs had 2.2x odds of a more severe hospitalization course compared to COVID-19(+) patients without HASPIs. HASPI skin histology from COVID-19(+) patients predominantly showed thrombotic vasculopathy, with the number of thrombosed vessels being significantly greater than HASPIs from COVID-19(-) patients. Transcriptional signatures of COVID-19(+) samples were enriched for innate immune responses, thrombosis, and neutrophil activation genes. SARS-CoV-2 viral transcripts were detected in skin tissue of COVID-19(+) patients with severe disease. Overall, our results suggest that immunologic dysregulation secondary to SARS-CoV-2 infection, including neutrophil dysfunction and abnormal thrombosis, may play a pathogenic role in development of HASPIs in patients with severe COVID-19.
Modeling the impact of the Omicron infection wave in Germany

Background In November 2021, the first case of SARS-CoV-2 "variant of concern" (VOC) B.1. 1.529 ("Omicron") was reported in Germany, alongside global reports of reduced vaccine efficacy against infections with this variant. The potential threat posed by the rapid spread of this variant in Germany remained, at the time, elusive. Methods We developed a variant-dependent population-averaged susceptible-exposed-infected-recovered (SEIR) infectious disease model. The model was calibrated on the observed fixation dynamics of the Omicron variant in December 2021, and allowed us to estimate potential courses of upcoming infection waves in Germany, focusing on the corresponding burden on intensive care units (ICUs) and the efficacy of contact reduction strategies. Results A maximum median incidence of approximately 300,000 (50% PI in 1000: [181,454], 95% PI in 1000: [55,804]) reported cases per day was expected with the median peak occurring in the mid of February 2022, reaching a cumulative Omicron case count of 16.5 million (50% PI in mio: [11.4, 21.3], 95% PI in mio: [4.1, 27.9]) until Apr 1, 2022. These figures were in line with the actual Omicron waves that were subsequently observed in Germany with respective peaks occurring in mid February (peak: 191k daily new cases) and mid March (peak: 230k daily new cases), cumulatively infecting 14.8 million individuals during the study period. The model peak incidence was observed to be highly sensitive to variations in the assumed generation time and decreased with shorter generation time. Low contact reductions were expected to lead to containment. Early, strict, and short contact reductions could have led to a strong "rebound" effect with high incidences after the end of the respective non-pharmaceutical interventions. Higher vaccine uptake would have led to a lower outbreak size. To ensure that ICU occupancy remained below maximum capacity, a relative risk of requiring ICU care of 10%-20% was necessary (after infection with Omicron vs. infection with Delta). Conclusions We expected a large cumulative number of infections with the VOC Omicron in Germany with ICU occupancy likely remaining below capacity nevertheless, even without additional non-pharmaceutical interventions. Our estimates were in line with the retrospectively observed waves. The results presented here informed legislation in Germany. The methodology developed in this study might be used to estimate the impact of future waves of COVID-19 or other infectious diseases.
Feasibility of and Experience with Telehealth Based Patient Self-referral for COVID-19 Monoclonal Antibody Therapy

Background: Monoclonal antibody (mAb) treatment for COVID-19 has been underutilized due to logistical challenges, lack of access and variable treatment awareness among patients and providers. The use of telehealth during the pandemic provides an opportunity to increase access to COVID care. Methods: This is a single-center descriptive study of telehealth-based patient self-referral for mAb therapy between March 1, 2021 to October 31, 2021 at Baltimore Convention Center Field Hospital (BCCFH). Results: Among the 1001 self-referral patients, the mean age was 47, , and most were female (57%) white (66%), and had a primary care provider (62%). During the study period, self-referrals increased from 14 per month in March to 427 in October resulting in a 30-fold increase. About 57% of self-referred patients received a telehealth visit, and of those 82% of patients received mAb infusion therapy, either onsite or at other infusion sites. The median time from self-referral to onsite infusion was 2 days (1-3 IQR). Discussion: Our study shows the integration of telehealth with a self-referral process improved access to mAb infusion. A high proportion of self-referrals were appropriate and led to timely treatment. Incorporation of self-referral and telehealth for monoclonal antibody therapy led to successful timely infusions. This approach helped those without traditional avenues for care and avoided potential delay for patients seeking referral from their medical providers.
RMHP_A_362420 1315..1323

Objective: To discuss the application value of the plan–do–check–act (PDCA) cycle in shortening the decision to delivery interval (DDI) time. Methods: A total of 106 DDI cases from the Ningbo Women and Children’s Hospital (China) from January 2019 to December 2020 were selected as the subjects of this study. The causes for the prolongation of DDI were analyzed and protocols were developed. Through continuous summaries and improvement, a standardized process was established to direct clinical application, ie, the PDCA cycle. Results: The DDI was shortened from 14.26 min in 2019 to 12.18 min in 2020 and the neonatal asphyxia rate significantly decreased from 34.69% in 2019 to 12.50% in 2020 (P < 0.05). Conclusion: The PDCA cycle management mode effectively shortened the DDI time and reduced the neonatal asphyxia rate, without increasing adverse maternal outcomes.
COSMOLOGICAL EXACT SOLUTIONS OF PETROV TYPE D. A MIXTURE OF TWO FLUIDS: DARK ENERGY AND RADIATION SOLUCIONES COSMOLÓGICAS EXACTAS DEL TIPO PETROV D. UNA MEZCLA DE DOS FLUIDOS: ENERGÍA OSCURA Y RADIACIÓN

In this paper, two exact solutions of the Einstein’s equations are obtained for an anisotropic and homogeneous symmetry of Petrov Type D, the difference between both solutions lies in how relevant is the expansion that is presented initially, either on an axis or on a perpendicular plane. Both solutions represent a mixture of two fluids with minimum interaction: dark energy (P = −μ) and radiation (P = μ/3). The singularities and the influence that these fluids have on this metric are studied; the Hubble parameters, the deceleration parameter and the role that these fluids represent on them are determined and analyzed. Additionally, their temperature and the role that both play on this magnitude are determined.
The welfare effects of nonlinear health dynamics

We generate a continuous measure of health to estimate a non-parametric model of health dynamics, showing that adverse health shocks are highly persistent when suffered by people in poor health. Canonical models cannot account for this pattern. We incorporate this health dynamic into a life-cycle model of consumption, savings, and labor force participation. After estimating the model parameters, we simulate the effects of health shocks on economic outcomes. We find that bad health shocks have long-term adverse economic effects that are more extreme for those in poor health. Furthermore, bad health shocks also increase the disparity of asset accumulation among this group of people. A canonical model of health dynamics would not reveal these effects.
Duality for Distributive Modal Algebras

A set of two cutting units are located on the end of a tool to be rotated. The cutting units are radially aligned and on a bevel. Each blade on each cutting unit has a radial rake of at least 60 degrees, a clearance angle, and serrations cut at an angle normal to the cutting edge to provide a series of points extending along that cutting edge.
Guiding the retraining of convolutional neural networks against adversarial inputs

Background When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions (e.g., incorrect classification of an image) with minor perturbations. To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations. Aim We examined six guidance metrics for retraining deep learning models, specifically with convolutional neural network architecture, and three retraining configurations. Our goal is to improve the convolutional neural networks against the attack of adversarial inputs with regard to the accuracy, resource utilization and execution time from the point of view of a data scientist in the context of image classification. Method We conducted an empirical study using five datasets for image classification. We explore: (a) the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distance-based surprise adequacy, DeepGini, softmax entropy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (one-step adversarial retraining, adversarial retraining and adversarial fine-tuning). Results We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r. t. accuracy, resource utilization, and execution time. Conclusions Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results.
Anthropomorphic Twisted String-Actuated Soft Robotic Gripper With Tendon-Based Stiffening

Realizing high-performance soft robotic grippers is challenging because of the inherent limitations of the soft actuators and artificial muscles that drive them, including low force generation, small actuation range, and poor compactness to name a few. Despite advances in this area, realizing compact soft grippers, which exhibit high dexterity and force output, is still challenging. This article explores using twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been widely used in numerous robotic applications, but their inclusion in soft robots has been limited. The proposed design of the gripper was inspired by the human hand, with four fingers and a thumb. Tunable stiffness was implemented in the fingers by using antagonistic TSAs. The fingers' bending angles, actuation speed, blocked force output, and stiffness tuning are experimentally characterized. The gripper achieves a score of 6 on the Kapandji test and recreate 31 of the 33 grasps of the Feix GRASP taxonomy. It exhibits a maximum grasping force of 72 N, which is almost 13 times its own weight. A comparison study reveals that the proposed gripper exhibits equivalent or superior performance compared to other similar soft grippers.