Comprehensive Clustering Analysis and Profiling of COVID-19 Vaccine Hesitancy and Related Factors across U.S. Counties: Insights for Future Pandemic Responses.

This study employs comprehensive clustering analysis to examine COVID-19 vaccine hesitancy and related socio-demographic factors across U.S. counties, using the collected and curated data from Johns Hopkins University. Utilizing K-Means and hierarchical clustering, we identify five distinct clusters characterized by varying levels of vaccine hesitancy, MMR vaccination coverage, population demographics, and political affiliations. Principal Component Analysis (PCA) was conducted to reduce dimensionality, and key variables were selected based on their contribution to cumulative explained variance. Our analysis reveals significant geographic and demographic patterns in vaccine hesitancy, providing valuable insights for public health strategies and future pandemic responses. Geospatial analysis highlights the distribution of clusters across the United States, indicating areas with high and low vaccine hesitancy. In addition, multiple regression analyses within each cluster identify key predictors of vaccine hesitancy in corresponding U.S. county clusters, emphasizing the importance of socio-economic and demographic factors. The findings underscore the need for targeted public health interventions and tailored communication strategies to address vaccine hesitancy across the United States and, potentially, across the globe.

Social Behavior and COVID-19: Analysis of the Social Factors behind Compliance with Interventions across the United States.

Since its emergence, COVID-19 has caused a great impact in health and social terms. Governments and health authorities have attempted to minimize this impact by enforcing different mandates. Recent studies have addressed the relationship between various socioeconomic variables and compliance level to these interventions. However, little attention has been paid to what constitutes people's response and whether people behave differently when faced with different interventions. Data collected from different sources show very significant regional differences across the United States. In this paper, we attempt to shed light on the fact that a response may be different depending on the health system capacity and each individuals' social status. For that, we analyze the correlation between different societal (i.e., education, income levels, population density, etc.) and healthcare capacity-related variables (i.e., hospital occupancy rates, percentage of essential workers, etc.) in relation to people's level of compliance with three main governmental mandates in the United States: mobility restrictions, mask adoption, and vaccine participation. Our aim was to isolate the most influential variables impacting behavior in response to these policies. We found that there was a significant relationship between individuals' educational levels and political preferences with respect to compliance with each of these mandates.

A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: A case study of lipase identification.

The growing adoption of enzymes as biocatalysts in various industries has accentuated the demand for acquiring access to the great natural diversity and, in the meantime, the advent and advancements of metagenomics and high-throughput sequencing technologies have offered an unprecedented opportunity to explore this extensive resource. Lipases, enzymes responsible for the biological turnover of lipids, are among the most commercialized biocatalysts with numerous applications in different domains and therefore are of high industrial value. The relatively costly and time-consuming wet-lab experimental pipelines commonly used for novel enzyme discovery, highlight the necessity of agile in silico approaches to keep pace with the exponential growth of available sequencing data. In the present study, an in-depth analysis of a tannery wastewater metagenome, including taxonomic and enzymatic profiling, was performed. Using sequence homology-based screening methods and supervised machine learning-based regression models aimed at prediction of lipases' pH and temperature optima, the metagenomic data set was screened for lipolytic enzymes, which led to the isolation of alkaline and highly thermophilic novel lipase. Moreover, MeTarEnz (metagenomic targeted enzyme miner) software was developed and made freely accessible (at https://cbb.ut.ac.ir/MeTarEnz) as a part of this study. MeTarEnz offers several functions to automate the process of targeted enzyme mining from high-throughput sequencing data. This study highlights the competence of computational approaches in exploring vast biodiversity within environmental niches, while providing a set of practical in silico tools as well as a generalized methodology to facilitate the sequence-based mining of biocatalysts.

Efficient saccharification of ionic liquid-pretreated rice straw in a one-pot system using novel metagenomics derived cellulases.

Ionic liquids (ILs)-resistant cellulase enzymes can facilitate the saccharification of IL- pretreated biomass in a one-pot wash-free method. Using a bioinformatics approach, two cellulases, Persicel7 and Persicel8, with convincing evidence for ionic liquid tolerance were identified. Subsequently, these enzymes were heterologously expressed and biochemically characterized. Persicel7 and Persicel8 exhibited endo-ß-1, 4-glucanase activity and were resistant to inhibitors and several organic solvents. Their activity in 10% (v/v) 1-ethyl-3-methylimidazolium chloride and 1-butyl-3-methylimidazolium chloride were 130% higher compared with IL-free control. The half-life of cellulases was improved up to 11-fold when incubated with 20% (v/v) solution of ion liquids. In addition, a one-pot IL-pretreatment and enzymatic saccharification of rice straw enhanced the saccharification rate by 33% compared to the untreated reaction. The Persicel7 and Persicel8 unique properties make them attractive candidates for industrial applications, particularly hydrolyzing ion liquid activated biomass in a one-pot system.

Zeolite-based nanocomposite as a smart pH-sensitive nanovehicle for release of xylanase as poultry feed supplement.

Xylanase improves poultry nutrition by degrading xylan in the cell walls of feed grains and release the entrapped nutrients. However, the application of xylanase as a feed supplement is restricted to its low stability in the environment and gastrointestinal (GI) tract of poultry. To overcome these obstacles, Zeozyme NPs as a smart pH-responsive nanosystem was designed based on xylanase immobilization on zeolitic nanoporous as the major cornerstone that was modified with L-lysine. The immobilized xylanase was followed by encapsulating with a cross-linked CMC-based polymer. Zeozyme NPs was structurally characterized using TEM, SEM, AFM, DLS, TGA and nitrogen adsorption/desorption isotherms at liquid nitrogen temperature. The stability of Zeozyme NPs was evaluated at different temperatures, pH, and in the presence of proteases. Additionally, the release pattern of xylanase was investigated at a digestion model mimicking the GI tract. Xylanase was released selectively at the duodenum and ileum (pH 6-7.1) and remarkably preserved at pH ≤ 6 including proventriculus, gizzard, and crop (pH 1.6-5). The results confirmed that the zeolite equipped with the CMC matrix could enhance the xylanase thermal and pH stability and preserve its activity in the presence of proteases. Moreover, Zeozyme NPs exhibited a smart pH-dependent release of xylanase in an in vitro simulated GI tract.

Highly Efficient Computationally Derived Novel Metagenome α-Amylase With Robust Stability Under Extreme Denaturing Conditions.

α-Amylases are among the very critical enzymes used for different industrial purposes. Most α-amylases cannot accomplish the requirement of industrial conditions and easily lose their activity in harsh environments. In this study, a novel α-amylase named PersiAmy1 has been identified through the multistage in silico screening pipeline from the rumen metagenomic data. The long-term storage of PersiAmy1 in low and high temperatures demonstrated 82.13 and 71.01% activities after 36 days of incubation at 4 and 50°C, respectively. The stable α-amylase retained 61.09% of its activity after 180 min of incubation at 90°C and was highly stable in a broad pH range, showing 60.48 and 86.05% activities at pH 4.0 and pH 9.0 after 180 min of incubation, respectively. Also, the enzyme could resist the high-salinity condition and demonstrated 88.81% activity in the presence of 5 M NaCl. PersiAmy1 showed more than 74% activity in the presence of various metal ions. The addition of the detergents, surfactants, and organic solvents did not affect the α-amylase activity considerably. Substrate spectrum analysis showed that PersiAmy1 could act on a wide array of substrates. PersiAmy1 showed high stability in inhibitors and superb activity in downstream conditions, thus useful in detergent and baking industries. Investigating the applicability in detergent formulation, PersiAmy1 showed more than 69% activity after incubation with commercial detergents at different temperatures (30-50°C) and retained more than 56% activity after incubation with commercial detergents for 3 h at 10°C. Furthermore, the results of the wash performance analysis exhibited a good stain removal at 10°C. The power of PersiAmy1 in the bread industry revealed soft, chewable crumbs with improved volume and porosity compared with control. This study highlights the intense power of robust novel PersiAmy1 as a functional bio-additive in many industrial applications.

Application of free and immobilized novel bifunctional biocatalyst in biotransformation of recalcitrant lignocellulosic biomass.

Herein, an innovative, green, and practical biocatalyst was developed using conjugation of a novel bifunctional mannanase/xylanase biocatalyst (PersiManXyn1) to the modified cellulose nanocrystals (CNCs). Firstly, PersiManXyn1 was multi-stage in-silico screened from rumen macrobiota, and then cloned, expressed, and purified. Next, CNCs were synthesized from sugar beet pulp using enzymatic and acid hydrolysis processes, and then Fe3O4 NPs were anchored on their surface to produce magnetic CNCs (MCNCs). This hybrid was modified by dopamine providing DA/MCNCs nano-carrier. The bifunctional PersiManXyn1 demonstrated the superior hydrolysis activity on corn cob compared with the monofunctional xylanase enzyme (PersiXyn2). Moreover, the immobilization of PersiManXyn1 on the nano-carrier resulted in an improvement of the thermal stability, kinetic parameters (Kcat), and storage stability of the enzyme. Incorporation of the Fe3O4 NPs on the CNCs made magnetic nano-carrier with high magnetization value (25.8 emu/g) which exhibited rapid response toward the external magnetic fields. Hence, the immobilized biocatalyst could be easily separated from the products by a magnet, and reused up to 8 cycles with maintaining more than 50% of its original activity. The immobilized PersiManXyn1 generated 22.2%, 38.7%, and 35.1% more reducing sugars after 168 h hydrolysis of the sugar beet pulp, coffee waste, and rice straw, respectively, compared to the free enzyme. Based on the results, immobilization of the bifunctional PersiManXyn1 exhibited the superb performance of the enzyme to improve the conversion of the lignocellulosic wastes into high value products and develop the cost-competition biomass operations.

A novel high performance in-silico screened metagenome-derived alkali-thermostable endo-ß-1,4-glucanase for lignocellulosic biomass hydrolysis in the harsh conditions.

BACKGROUND: Lignocellulosic biomass, is a great resource for the production of bio-energy and bio-based material since it is largely abundant, inexpensive and renewable. The requirement of new energy sources has led to a wide search for novel effective enzymes to improve the exploitation of lignocellulose, among which the importance of thermostable and halotolerant cellulase enzymes with high pH performance is significant. RESULTS: The primary aim of this study was to discover a novel alkali-thermostable endo-ß-1,4-glucanase from the sheep rumen metagenome. At first, the multi-step in-silico screening approach was utilized to find primary candidate enzymes with superior properties. Among the computationally selected candidates, PersiCel4 was found and subjected to cloning, expression, and purification followed by functional and structural characterization. The enzymes' kinetic parameters, including Vmax, Km, and specific activity, were calculated. The PersiCel4 demonstrated its optimum activity at pH 8.5 and a temperature of 85 °C and was able to retain more than 70% of its activity after 150 h of storage at 85 °C. Furthermore, this enzyme was able to maintain its catalytic activity in the presence of different concentrations of NaCl and several metal ions contains Mg2+, Mn2+, Cu2+, Fe2+ and Ca2+. Our results showed that treatment with MnCl2 could enhance the enzyme's activity by 78%. PersiCel4 was ultimately used for enzymatic hydrolysis of autoclave pretreated rice straw, the most abundant agricultural waste with rich cellulose content. In autoclave treated rice straw, enzymatic hydrolysis with the PersiCel4 increased the release of reducing sugar up to 260% after 72 h in the harsh condition (T = 85 °C, pH = 8.5). CONCLUSION: Considering the urgent demand for stable cellulases that are operational on extreme temperature and pH conditions and due to several proposed distinctive characteristics of PersiCel4, it can be used in the harsh condition for bioconversion of lignocellulosic biomass.

A novel thermostable cellulase cocktail enhances lignocellulosic bioconversion and biorefining in a broad range of pH.

Lignocellulose is the most abundant biomass in nature, and the effective biorefining of them is dependent upon enzymes with high catalytic activity and stability in extreme pH and high temperatures. Due to the molecular constraints for a single enzyme, obtaining a more excellent active pH range can be more easily achievable through the simultaneous activity of two or more enzymes in a cocktail. To address this, we attempted to develop a cocktail of novel thermostable cellulases with high hydrolytic ability and stability. Two cellulases were mined, identified, cloned, and expressed from the camel rumen microbiota. The PersiCel1 demonstrated its maximum relative activity at the pH of 8, and the temperature of 60 °C and the PersiCel2 was optimally active at the pH of 5 and the temperature of 50 °C. Furthermore, utilization of the enzyme cocktail implies the synergistic relationship and significantly increased the saccharification yield of lignocellulosic substrates up to 71.7% for sugar-beet pulp (active pH range of 4-9) and 138.7% for rice-straw (active pH range of 5-8), compared to maximum hydrolysis of Persicel1 or PersiCel2 separately at 55 °C. Our results indicate the probable applicability of PersiCel1, PersiCel2, and their cocktail in numerous industries, specifically biorefineries and lignocellulose bioconversion based technologies.

Mining of camel rumen metagenome to identify novel alkali-thermostable xylanase capable of enhancing the recalcitrant lignocellulosic biomass conversion.

The aim of this study was to isolate and characterize novel alkali-thermostable xylanase genes from the mixed genome DNA of camel rumen metagenome. In this study, a five-stage computational screening procedure was utilized to find the primary candidate enzyme with superior properties from the camel rumen metagenome. This enzyme was subjected to cloning, purification, and structural and functional characterization. It showed high thermal stability, high activity in a broad range of pH (6-11) and temperature (30-90 °C) and effectivity in recalcitrant lignocellulosic biomass degradation. Our results demonstrated the power of in silico analysis to discover novel alkali-thermostable xylanases, effective for the bioconversion of lignocellulosic biomass.

Identification and characterization of a novel thermostable xylanase from camel rumen metagenome.

Metagenomics has emerged to isolate novel enzymes from the uncultured microbiota in the environment. In this study, the metagenomic data obtained from camel rumen was considered as the potential source of microbial xylanase enzymes with proper activity in extreme conditions. The metagenomic data were assembled and contigs were used for in-silico identification of candidate thermostable enzyme. A novel thermostable xylanase enzyme, named PersiXyn1, with 1146 bp full-length gene which encodes a 381 amino acid protein was identified. Using the DNA template extracted from camel rumen metagenomic samples, the candidate enzyme genes were cloned and expressed in proper E. coli strains. The phylogenetic analysis showed the evolutionary position of PersiXyn1 among the known thermostable xylanases. The results of the CD analysis and determining the secondary structure of the enzyme, confirmed the presence of a high percentage of ß-sheets as an important characteristic of thermophilic xylanases. The PersiXyn1 was active at a broad range of pH (6-11) and temperature (25-90 °C). The optimum pH and temperature were 8 and 40 °C respectively, and the enzyme maintained 80% of its maximum activity in the pH 8 and temperature 40 °C for 1 h. The Scanning electron microscope (SEM) micrograph of enzyme treated pulp clearly showed that the effective use of enzymes in fiber separation may reduce the cost of carton paper production. The novelty of this enzyme lies in the fact that it is highly active and stable in a broad range of pH and temperature. This study highlights the potential importance of camel microbiome for discovering novel thermostable enzymes with applications in agriculture and industries.

Cross State-dependent Learning Interaction Between Scopolamine and Morphine in Mice: The Role of Dorsal Hippocampus.

INTRODUCTION: The current study aimed at investigating the existence of the cross state-dependent learning between morphine and scopolamine (SCO) in mice by passive avoidance method, pointing to the role of CA1 area. METHODS: The effects of pre-training SCO (0.75, 1.5, and 3 µg, Intra-CA1), or morphine (1, 3, and 6 mg/kg, intraperitoneal (i.p.) was evaluated on the retrieval of passive avoidance learning using step-down task in mice (n=10). Then, the effect of pretest administration of morphine (1.5, 3, and 6 mg/kg, i.p.) was examined on passive avoidance retrieval impairment induced by pre-training SCO (3 µg/mice, Intra-CA1). Next, the effect of pretest Intra-CA1 injection of scopolamine (0.75, 1.5, and 3 µg/mice) was evaluated on morphine (6 mg/kg, i.p.) pre-training deficits in this task in mice. RESULTS: The pre-training Intra-CA1 injection of scopolamine (1.5 and 3 µg/mouse), or morphine (3 and 6 mg/kg, i.p.) impaired the avoidance memory retrieval when it was tested 24 hours later. Pretest injection of both drugs improved its pre-training impairing effects on mice memory. Moreover, the amnesia induced by the pre-training injections of scopolamine (3 µg/mice) was restored significantly (P<0.01) by pretest injections of morphine (3 and 6 mg/kg, i.p.). Similarly, pretest injection of scopolamine (3 µg/mice) restored amnesia induced by the pre-training injections of morphine (6 mg/kg, i.p.), significantly (P<0.01). CONCLUSION: The current study findings indicated a cross state-dependent learning between SCO and morphine at CA1 level. Therefore, it seems that muscarinic and opioid receptors may act reciprocally on modulation of passive avoidance memory retrieval, at the level of dorsal hippocampus, in mice.