Impact of vaccination on the COVID-19 pandemic in U.S. states

Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies

Endogenous cross-region human mobility and pandemics

We study infectious diseases using a Susceptible-Infected-Recovered-Deceased model with endogenous cross-region human mobility. Individuals weigh the risk of infection against economic opportunities when moving across regions. The model predicts that the mobility rate of susceptible individuals declines with a higher infection rate at the destination. With cross-region mobility, a decrease in the transmission rate or an increase in the removal rate of the virus in any region reduces the global basic reproduction number (R0). Global R0 falls between the minimum and maximum of local R0s. A new method of Normalized Hat Algebra is developed to solve the model dynamics. Simulations indicate that a decrease in global R0 does not always imply a lower cumulative infection rate. Local and central governments may prefer different mobility control policies

Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities

Key Aquatic Environmental Factors Affecting Ecosystem Health of Streams in the Dianchi Lake Watershed, China

AbstractStreams in a lake watershed are important landscape corridors which link the lake and terrestrial ecosystems. Therefore, the ecosystem health of streams is usually used to indicate aquatic biodiversity of the lake ecosystem, as well as being affected by aquatic environmental factors in response to changes in land use cover of the terrestrial ecosystem due to natural geographic characteristics of the watershed with the closure of ridge lines. This study was carried out at a shallow freshwater lake watershed in the Yunnan-Guizhou Plateau of China, the Dianchi Lake watershed (DLW). Field survey of periphytic algal and macrozoobenthic biodiversity during July and August of 2009, as well as monthly monitoring of water temperature, pH, TSS, DO, TN, TP, NH3N, NO3N, CODMn, BOD, TOC, and the heavy metals Zn (II), Cd (II), Pb (II), Cu (II), and Cr (VI) from January to December 2009 was carried out in 29 streams flowing into Dianchi lake. Multivariate statistical techniques such as factor analysis and canonical correspondence analysis were applied to analyze the structure of the aquatic community in relation to aquatic environmental factors in order to provide controlling objectives for integrated watershed management and improvement of stream rehabilitation in the DLW. The results showed that the structure of the periphytic algal and macrozoobenthic communities were dominated by pollution-tolerant genera, namely the bacillariophytes Navicula and the annelids Tubificidae respectively, and TN, NH3N and TP were key aquatic environmental factors affecting the ecosystem health of streams in the DLW

DDI-CPI, a server that predicts drug–drug interactions through implementing the chemical–protein interactome

Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug–human protein interactions, it is reasonable to analyze the chemical–protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/

Evaluating diabetes and hypertension disease causality using mouse phenotypes

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used.</p> <p>Results</p> <p>Here we tapped the rich resource of mouse phenotype data and developed a method to quantify the probability that a gene perturbation causes the phenotypes of a disease. Using type II diabetes (T2D) and hypertension (HT) as study cases, we found that the genes, when perturbed, having high probability to cause T2D and HT phenotypes tend to be hubs in the interactome networks and are enriched for signaling pathways regulating metabolism but not metabolic pathways, even though the genes in these metabolic pathways are often the most significantly changed in expression levels in these diseases.</p> <p>Conclusions</p> <p>Compared to human genetic disease-based predictions, our mouse phenotype based predictors greatly increased the coverage while keeping a similarly high specificity. The disease phenotype probabilities given by our approach can be used to evaluate the likelihood of disease causality of disease-associated genes and genes surrounding disease-associated SNPs.</p

Mitochondria-Targeted Nanomedicine for Enhanced Efficacy of Cancer Therapy

Nanomedicines have been designed and developed to deliver anticancer drugs or exert anticancer therapy more selectively to tumor sites. Recent investigations have gone beyond delivering drugs to tumor tissues or cells, but to intracellular compartments for amplifying therapy efficacy. Mitochondria are attractive targets for cancer treatment due to their important functions for cells and close relationships to tumor occurrence and metastasis. Accordingly, multifunctional nanoplatforms have been constructed for cancer therapy with the modification of a variety of mitochondriotropic ligands, to trigger the mitochondria-mediated apoptosis of tumor cells. On this basis, various cancer therapeutic modalities based on mitochondria-targeted nanomedicines are developed by strategies of damaging mitochondria DNA (mtDNA), increasing reactive oxygen species (ROS), disturbing respiratory chain and redox balance. Herein, in this review, we highlight mitochondria-targeted cancer therapies enabled by nanoplatforms including chemotherapy, photothermal therapy (PTT), photodynamic therapy (PDT), chemodynamic therapy (CDT), sonodynamic therapy (SDT), radiodynamic therapy (RDT) and combined immunotherapy, and discussed the ongoing challenges.Peer reviewe

A single-step preparation of carbohydrate functionalized monoliths for separation and trapping of polar compounds

A single-step copolymerization strategy was developed for the preparation of carbohydrate (glucose and maltose) functionalized monoliths using click reaction. Firstly, novel carbohydrate-functionalized methacrylate monomers were synthesized through Cu(I)-catalyzed 1,3-dipolar cycloaddition (alkyne-azide reaction) of terminal alkyne with azide of carbohydrate derivatives. The corresponding carbohydrate functionalized monolithic columns were then prepared through a single-step in-situ copolymerization. The physicochemical properties and performance of the fabricated monolithic columns were evaluated using scanning electron microscopy, Fourier-transform infrared spectroscopy, and nano-liquid chromatography. For the optimized monolithic column, satisfactory column permeability and good separation performance were demonstrated for polar compounds including nucleoside, phenolic compounds and benzoic acid derivatives. The monolithic column is also highly useful for selective and efficient enrichment of glycopeptides from human IgG tryptic digests. This study not only provided a novel hydrophilic column for separation and selective trapping of polar compounds, but also proposed a facile and efficient approach for preparing carbohydrate functionalized monoliths

Optimizing Methanol Blending Performance of Electronically Controlled Diesel Engines through Fuzzy Analysis

This paper presents a comprehensive optimization approach for enhancing the performance of a methanol/diesel Exhaust Gas Recirculation (EGR) engine. Initially, a hybrid fuel engine combustion chamber model was developed using AVL-FIRE software, and the simulated results were compared with the values obtained from bench tests. An orthogonal experimental design was employed to optimize five key factors, namely methanol blending ratio, EGR rate, injection advance angle, intake pressure, and intake temperature. Evaluation indexes were established, with indicated power and NO emissions assigned weights of 0.35 and 0.65, respectively. The optimal parameter combinations were determined as follows: methanol blending ratio (a1=20%), EGR rate (a2=12.5%), injection advance angle (a3=16.6°CA), intake temperature (a4 = 315.15 K), and intake pressure (a5=0.173 MPa). The indicated power of the optimized configuration reached 47.8 kW, slightly lower than the original 55 kW, while the NO emission mass fraction decreased to 1.9×10-4%, representing a significant reduction of 77.6% compared to the original value of 8.5×10-4%. This optimization methodology demonstrates the effective reduction of NO emissions without compromising power performance in methanol/diesel EGR engines