Superior UVC light-mediated catalytic activity of a novel NiFe₂O₄@ TiO₂ magnetic nanocomposite synthesized with green route using Pulicaria Gnaphalodes plant extract for enhanced photocatalytic degradation of an antibiotic in water solution

In this study, the NiFe₂O₄@TiO₂ magnetic nanocomposite was synthesized by the green synthesis method, which is an efficient and economical method. Pulicaria Gnaphalodes plant extract was used for nanocomposite synthesis because this method is suitable for the biosynthesis of nanocomposites on a large scale, and the nanocomposite produced by plants is more stable. The efficiency of the synthesized nanocomposite was investigated for the photocatalytic degradation of Penicillin G (PNG) under UVC light irradiation in aqueous solutions. The structural characteristics of this nanocomposite were determined by field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, vibrating sample magnetometer, and dynamic light scattering. The effect of different parameters including pH, nanocomposite dose, penicillin G concentration and time were studied to reach optimum conditions. About 71% of PNG in optimal conditions (pH = 9, nanocomposite dose = 0.6 g/L, and penicillin G concentration = 10 mg/L) was decomposed. Generally, the NiFe₂O₄@TiO₂ nanocomposite can be used as an efficient catalyst for the degradation of PNG in aqueous solutions

Pulicaria gnaphalodes-assisted green synthesis of NiFe₂O₄@ZnO nanocomposites for sustainable remediation of an antibiotic from aqueous solution

In this study, the NiFe₂O₄@ZnO nanocomposite was synthesized in a simple, accessible and affordable method using Pulicaria gnaphalodes plant extract as a reducing agent. The structural characteristics of this nanocomposite were determined by transmission electron microscopy (TEM), X-ray diffraction, Fourier transform infrared spectroscopy, vibrating sample magnetometer, X-ray energy diffraction spectroscopy and dynamic light scattering. TEM micrograph confirmed the formation of spherical and cubic spinel ferrite with average dimensions of 75–85 nm. Some parameters such as pH, dose of NiFe₂O₄@ZnO nanocomposite, concentration of penicillin G and reaction time to reach optimal conditions were investigated. According to the results of the present research, the photocatalyst process along with the use of NiFe₂O₄@ZnO nanocomposite as an oxidizing agent is an effective method in degradation of the penicillin G antibiotic from aqueous solutions

Current approaches for combination therapy of cancer: The role of immunogenic cell death

Cell death resistance is a key feature of tumor cells. One of the main anticancer therapies is increasing the susceptibility of cells to death. Cancer cells have developed a capability of tumor immune escape. Hence, restoring the immunogenicity of cancer cells can be suggested as an effective approach against cancer. Accumulating evidence proposes that several anticancer agents provoke the release of danger-associated molecular patterns (DAMPs) that are determinants of immunogenicity and stimulate immunogenic cell death (ICD). It has been suggested that ICD inducers are two different types according to their various activities. Here, we review the well-characterized DAMPs and focus on the different types of ICD inducers and recent combination therapies that can augment the immunogenicity of cancer cells

Bayesian Prompt Learning for Image-Language Model Generalization

Foundational image-language models have generated considerable interest due to their efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of the language model input as trainable while freezing the rest, and optimizes an Empirical Risk Minimization objective. However, Empirical Risk Minimization is known to suffer from distributional shifts which hurt generalizability to prompts unseen during training. By leveraging the regularization ability of Bayesian methods, we frame prompt learning from the Bayesian perspective and formulate it as a variational inference problem. Our approach regularizes the prompt space, reduces overfitting to the seen prompts and improves the prompt generalization on unseen prompts. Our framework is implemented by modeling the input prompt space in a probabilistic manner, as an a priori distribution which makes our proposal compatible with prompt learning approaches that are unconditional or conditional on the image. We demonstrate empirically on 15 benchmarks that Bayesian prompt learning provides an appropriate coverage of the prompt space, prevents learning spurious features, and exploits transferable invariant features. This results in better generalization of unseen prompts, even across different datasets and domains. Code available at: https://github.com/saic-fi/Bayesian-Prompt-Learnin

A systematic mapping review of factors associated with willingness to work under emergency condition

Introduction: An effective response to an emergency situation relies on health care workers� preparedness. The main purpose of this study was to provide a comprehensive overview of relevant studies regarding the willingness to work in emergency and disaster situations, describe and classify the most important challenges and solutions, identifying knowledge gaps in the literature which could inform future research. Methods: In this Systematic Mapping Review required information was searched from PubMed, Scopus, the web of science, Embase databases, and Google scholar search engine in the period 2000�2020. Data were analyzed using a content framework analysis. Results: From 2902 article search results, 26 articles met the inclusion criteria. The studies varied in terms of aim, study design, and detail of reporting. The results showed that nearly three-quarters of studies were conducted in high and middle-income countries. Most of the studies were published in 2020 due to the COVID-19 pandemic. Also, the most common types of crises reported in the included studies were emerging and re-emerging infectious diseases. The results show that most of the problems were in the dimension of mental and psychological issues, personnel health concerns, and management relationship with personnel. Conclusion: This mapping review illustrated a big picture of health workers' resilience in disaster conditions. This review presents an overview of different kinds of strategies that address the challenges. One of the most important challenges in health workforce retention is poor communication between managers and staff. Being away from family, which leads to mental fatigue, puts staff in moral dilemmas. Attracting adequate health professionals, especially volunteers and regulating the shifts of health personnel in crisis time will largely prevent burnout. © 2021, The Author(s)

The expression pattern of VISTA in the PBMCs of relapsing-remitting multiple sclerosis patients: A single-cell RNA sequencing-based study

Multiple sclerosis (MS) is an inflammatory disease of the central nervous system (CNS). Dysregulated immune responses have been implicated in MS development. Growing evidence has indicated that inhibitory immune checkpoint molecules can substantially regulate immune responses and maintain immune tolerance. V-domain Ig suppressor of T cell activation (VISTA) is a novel inhibitory immune checkpoint molecule that can suppress immune responses; however, its expression pattern in the peripheral blood mononuclear cells (PBMCs) of relapsing-remitting multiple sclerosis (RRMS) has not thoroughly been studied. Herein, we evaluated Vsir expression in PBMCs of RRMS patients and characterized the expression pattern of the Vsir in the PBMCs of MS patients. Besides, we investigated the effect of fingolimod, IFNβ-1α, glatiramer acetate (GA), and dimethyl fumarate (DMF) on Vsir expression in PBMCs of RRMS patients. Our results have shown that Vsir expression is significantly downregulated in the PBMCs of patients with RRMS. Besides, the single-cell RNA sequencing results have demonstrated that Vsir expression is downregulated in classical monocyte, intermediate monocytes, non-classical monocytes, myeloid DCs (mDC), Plasmacytoid dendritic cells (pDCs), and naive B-cells of PBMCs of MS patients compared to the control. In addition, DMF, IFNβ-1α, and GA have significantly upregulated Vsir expression in the PBMCs of RRMS patients. Collectively, the current study has shed light on Vsir expression in the PBMCs of MS patients; however, further studies are needed to elucidate the significance of VISTA in the mentioned immune cells

Improved spectrum sensing for OFDM cognitive radio in the presence of timing offset

Spectrum sensing is an important aspect of an (interweave) cognitive radio network. In the particular case of orthogonal frequency division multiplexing (OFDM) transmission, many previous spectrum sensing algorithms have utilized the unique correlation properties provided by the cyclic prefix (CP). However, they have also had to both estimate and compensate for the inherent timing offset of a practical system. This is because the timing offset will affect both the test statistic and the threshold, and the inaccurate estimation of timing offset will lead to poor performance. So in this paper, we propose an improved CP detector by constructing a likelihood ratio test (LRT) based on the multivariate probability density functions (pdf) of a particular auto-correlation vector that is chosen to exploit the existence of the CP. This leads to ‘probability of detection’ (Pd) and ‘probability of false alarm’ (Pf) terms that are actually independent of timing offset, and we can get an accurate threshold without estimating timing offset. Simulation results illustrate that the proposed algorithm outperforms existing methods, even for low SNR values. Finally, we show how the algorithm’s parameters must be carefully chosen in a trade-off between spectrum sensing success and overall system performance