Real-time tracking of electron transfer at catalytically active interfaces in lithium-ion batteries.

Transition metals and related compounds are known to exhibit high catalytic activities in various electrochemical reactions thanks to their intriguing electronic structures. What is lesser known is their unique role in storing and transferring electrons in battery electrodes which undergo additional solid-state conversion reactions and exhibit substantially large extra capacities. Here, a full dynamic picture depicting the generation and evolution of electrochemical interfaces in the presence of metallic nanoparticles is revealed in a model CoCO3/Li battery via an in situ magnetometry technique. Beyond the conventional reduction to a Li2CO3/Co mixture under battery operation, further decomposition of Li2CO3 is realized by releasing interfacially stored electrons from its adjacent Co nanoparticles, whose subtle variation in the electronic structure during this charge transfer process has been monitored in real time. The findings in this work may not only inspire future development of advanced electrode materials for next-generation energy storage devices but also open up opportunities in achieving in situ monitoring of important electrocatalytic processes in many energy conversion and storage systems.

Electrochemical interfacial catalysis in Co-based battery electrodes involving spin-polarized electron transfer.

Interfacial catalysis occurs ubiquitously in electrochemical systems, such as batteries, fuel cells, and photocatalytic devices. Frequently, in such a system, the electrode material evolves dynamically at different operating voltages, and this electrochemically driven transformation usually dictates the catalytic reactivity of the material and ultimately the electrochemical performance of the device. Despite the importance of the process, comprehension of the underlying structural and compositional evolutions of the electrode material with direct visualization and quantification is still a significant challenge. In this work, we demonstrate a protocol for studying the dynamic evolution of the electrode material under electrochemical processes by integrating microscopic and spectroscopic analyses, operando magnetometry techniques, and density functional theory calculations. The presented methodology provides a real-time picture of the chemical, physical, and electronic structures of the material and its link to the electrochemical performance. Using Co(OH)2 as a prototype battery electrode and by monitoring the Co metal center under different applied voltages, we show that before a well-known catalytic reaction proceeds, an interfacial storage process occurs at the metallic Co nanoparticles/LiOH interface due to injection of spin-polarized electrons. Subsequently, the metallic Co nanoparticles act as catalytic activation centers and promote LiOH decomposition by transferring these interfacially residing electrons. Most intriguingly, at the LiOH decomposition potential, electronic structure of the metallic Co nanoparticles involving spin-polarized electrons transfer has been shown to exhibit a dynamic variation. This work illustrates a viable approach to access key information inside interfacial catalytic processes and provides useful insights in controlling complex interfaces for wide-ranging electrochemical systems.

The usage and costs of national drug price-negotiated anticancer medicines in a first-tier city in Northeast China: a study based on health insurance data.

BACKGROUND: The National Drug Price Negotiation (NDPN) policy has entered a normalisation stage, aiming to alleviate, to some extent, the disease-related and economic burdens experienced by cancer patients. This study analysed the use and subsequent burden of anticancer medicines among cancer patients in a first-tier city in northeast China. METHODS: We assessed the usage of 64 negotiated anticancer medicines using the data on the actual drug deployment situation, the frequency of medical insurance claims and actual medication costs. The affordability of these medicines was measured using the catastrophic health expenditure (CHE) incidence and intensity of occurrence. Finally, we used the defined daily doses (DDDs) and defined daily doses cost (DDDc) as indicators to evaluate the actual use of these medicines in the region. RESULTS: During the study period, 63 of the 64 medicines were readily available. From the perspective of drug usage, the frequency of medical insurance claims for negotiated anticancer medicines and medication costs showed an increasing trend from 2018 to 2021. Cancer patients typically sought medical treatment at tertiary hospitals and purchased medicines at community pharmacies. The overall quantity and cost of medications for patients covered by the Urban Employee Basic Medical Insurance (UEBMI) were five times higher than those covered by the Urban and Rural Resident Medical Insurance (URRMI). The frequency of medical insurance claims and medication costs were highest for lung and breast cancer patients. Furthermore, from 2018 to 2021, CHE incidence showed a decreasing trend (2.85-1.60%) under urban patients' payment capability level, but an increasing trend (11.94%-18.42) under rural patients' payment capability level. The average occurrence intensities for urban (0.55-1.26 times) and rural (1.27-1.74 times) patients showed an increasing trend. From the perspective of drug utilisation, the overall DDD of negotiated anticancer medicines showed an increasing trend, while the DDDc exhibited a decreasing trend. CONCLUSION: This study demonstrates that access to drugs for urban cancer patients has improved. However, patients' medical behaviours are affected by some factors such as hospital level and type of medical insurance. In the future, the Chinese Department of Health Insurance Management should further improve its work in promoting the fairness of medical resource distribution and strengthen its supervision of the nation's health insurance funds.

Highly N-Doped Fe/Co Phosphide Superstructures for Efficient Water Splitting.

Developing an inexpensive bifunctional electrocatalyst for overall water splitting is critical for acquiring scalable green hydrogen and thereby realizing carbon neutralization. Herein, an "all-in-one" method is developed for the fabrication of highly N-doped binary FeCo-phosphides (N-FeCoP) with hierarchical superstructure, this delicately designed synthesis route allows the following merits for benefiting water splitting electrocatalysis in alkaline, including high N/defect-doping for mediating the surface property of the as-made N-FeCoP, binary Fe and Co components exhibiting strong coupling interaction, and 3D hierarchical superstructure for shortening diffusion length and thereby improving reaction kinetics. Electrochemical measurements reveal that the N-FeCoP sample exhibits very low overpotentials for initiating the hydrogen and oxygen evolution reactions. Remarkably, overall water splitting can be promoted on N-FeCoP using a commercial primary Zn-MnO2 battery. The developed synthesis strategy may potentially inspire the preparation of other N-doped metal-based nanostructures for broad electrocatalysis.

Production and waste treatment of polyesters: application of bioresources and biotechniques.

Chemical resources and techniques have long been used in the history of bulk polyester production and still dominate today's chemical industry. The sustainable development of the polyester industry demands more renewable resources and environmentally benign polyester products. Accordingly, the rapid development of biotechnology has enabled the production of an extensive range of aliphatic and aromatic polyesters from renewable bio-feedstocks. This review addresses the production of representative commercial polyesters (polyhydroxyalkanoates, polylactic acid, poly ε-caprolactone, polybutylene succinate, polyethylene terephthalate, polybutylene terephthalate, polypropylene terephthalate, polyethylene furandicarboxylate, polypropylene furandicarboxylate, and polybutylene furandicarboxylate) or their monomers (lactic acid, succinic acid, 1,4-butanediol, ethylene glycol, terephthalic acid, 1,3-propanediol, and 2,5-furandicarboxylic acid) from renewable bioresources. In addition, this review summarizes advanced biotechniques in the treatment of polyester wastes, representing the near-term trends and future opportunities for waste-to-value recycling and the remediation of polyester wastes under sustainable models. For future prospects, it is essential to further expand: non-food bioresources, optimize bioprocesses and biotechniques in the preparation of bioderived or biodegradable polyesters with promising: material performance, biodegradability, and low production cost.

Genetic polymorphisms of purinergic P2Y2 receptor were associated with the susceptibility to essential hypertension in Chinese postmenopausal women.

Hypertension has become a prominent public health concern. Essential hypertension (EH) is a polygenic disorder caused by multiple susceptibility genes. It has been previously shown that the purinergic P2Y2 receptor (P2Y2R) regulates blood pressure; however, whether P2Y2R genetic polymorphisms correlate with EH has not been investigated in Chinese. Our study included 500 EH cases and 504 controls who are Chinese postmenopausal women. We used allele-specific polymerase chain reaction (ASPCR) to genotype five single-nucleotide polymorphism (SNPs) in the P2Y2R gene, i.e., rs4944831, rs12366239, rs1783596, rs4382936, and rs10898909. We assessed the association of P2Y2R genetic polymorphisms with EH susceptibility. The results demonstrated that P2Y2R rs4382936A was correlated with a high risk of EH; particularly, the participants with the rs4382936A allele and CA/AA/(CA+AA) genotypes were at higher risks to EH compared to the subjects with the rs4382936C allele and CC genotype. Moreover, haplotype CAG combined by rs1783596-rs4382936-rs10898909 was a susceptible haplotype for EH, whereas haplotype CCG was a protective haplotype for EH. These results may provide new evidence for applying P2Y2R genetic polymorphisms as useful markers in clinic screening or monitoring potential EH cases in a population of Chinese postmenopausal women.

HiGwas: how to compute longitudinal GWAS data in population designs.

SUMMARY: Genome-wide association studies (GWAS), particularly designed with thousands and thousands of single-nucleotide polymorphisms (SNPs) (big p) genotyped on tens of thousands of subjects (small n), are encountered by a major challenge of p ≪ n. Although the integration of longitudinal information can significantly enhance a GWAS's power to comprehend the genetic architecture of complex traits and diseases, an additional challenge is generated by an autocorrelative process. We have developed several statistical models for addressing these two challenges by implementing dimension reduction methods and longitudinal data analysis. To make these models computationally accessible to applied geneticists, we wrote an R package of computer software, HiGwas, designed to analyze longitudinal GWAS datasets. Functions in the package encompass single SNP analyses, significance-level adjustment, preconditioning and model selection for a high-dimensional set of SNPs. HiGwas provides the estimates of genetic parameters and the confidence intervals of these estimates. We demonstrate the features of HiGwas through real data analysis and vignette document in the package. AVAILABILITY AND IMPLEMENTATION: https://github.com/wzhy2000/higwas. CONTACT: rwu@phs.psu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning.

MOTIVATION: Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype-phenotype association. RESULTS: In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. AVAILABILITY AND IMPLEMENTATION: R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Co-Production of Isoprene and Lactate by Engineered Escherichia coli in Microaerobic Conditions.

Lactate and isoprene are two common monomers for the industrial production of polyesters and synthetic rubbers. The present study tested the co-production of D-lactate and isoprene by engineered Escherichia coli in microaerobic conditions. The deletion of alcohol dehydrogenase (adhE) and acetate kinase (ackA) genes, along with the supplementation with betaine, improved the co-production of lactate and isoprene from the substrates of glucose and mevalonate. In fed-batch studies, microaerobic fermentation significantly improved the isoprene concentration in fermentation outlet gas (average 0.021 g/L), compared with fermentation under aerobic conditions (average 0.0009 g/L). The final production of D-lactate and isoprene can reach 44.0 g/L and 3.2 g/L, respectively, through fed-batch microaerobic fermentation. Our study demonstrated a dual-phase production strategy in the co-production of isoprene (gas phase) and lactate (liquid phase). The increased concentration of gas-phase isoprene could benefit the downstream process and decrease the production cost to collect and purify the bio-isoprene from the fermentation outlet gas. The proposed microaerobic process can potentially be applied in the production of other volatile bioproducts to benefit the downstream purification process.

Functional mapping of N deficiency-induced response in wheat yield-component traits by implementing high-throughput phenotyping.

As overfertilization leads to environmental concerns and the cost of N fertilizer increases, the issue of how to select crop cultivars that can produce high yields on N-deficient soils has become crucially important. However, little information is known about the genetic mechanisms by which crops respond to environmental changes induced by N signaling. Here, we dissected the genetic architecture of N-induced phenotypic plasticity in bread wheat (Triticum aestivum L.) by integrating functional mapping and semiautomatic high-throughput phenotyping data of yield-related canopy architecture. We identified a set of quantitative trait loci (QTLs) that determined the pattern and magnitude of how wheat cultivars responded to low N stress from normal N supply throughout the wheat life cycle. This analysis highlighted the phenological landscape of genetic effects exerted by individual QTLs, as well as their interactions with N-induced signals and with canopy measurement angles. This information may shed light on our mechanistic understanding of plant adaptation and provide valuable information for the breeding of N-deficiency tolerant wheat varieties.

[Application of in situ amniocyte culture for prenatal diagnosis].

OBJECTIVE: To explore the value of in situ amniocyte culture for prenatal diagnosis. METHODS: 2716 amniotic fluid samples were cultured in situ on slides. After the culture, the slides were stained, photographed and analyzed. RESULTS: All samples were successfully analyzed, with the success rates for primary culture and subculture being 98.42% and 1.58%, respectively. 224 samples (8.25%) were detected with chromosomal aberrations, which included 125 cases with trisomy 21, 31 with trisomy 18, 3 with trisomy 13, 4 with 45,X, 17 with 47,XXY, 5 with 47,XYY, 1 with 48,XXY,+18, 1 with 48,XXYY, 26 with structural chromosomal aberrations, and 11 with mosaicisms for aneuploidies. CONCLUSION: In situ amniocyte culture is stable and has a high success rate, and is capable of identifying true and false mosaicisms, which can improve the accuracy of prenatal diagnosis.

Poly I:C-based rHBVvac therapeutic vaccine eliminates HBV via generation of HBV-specific CD8+ effector memory T cells.

OBJECTIVE: Chronic hepatitis B (CHB) virus infection is a global health problem. Finding a cure for CHB remains a challenging task. DESIGN: In this study, poly I:C was employed as an adjuvant for HBV therapeutic vaccine (referred to as pHBV-vaccine) and the feasibility and efficiency of pHBV-vaccine in CHB treatment were evaluated in HBV-carrier mice. RESULTS: We found that pHBV-vaccine decreased HBsAg and HBV DNA efficiently and safely in HBV-carrier mice. Further investigation showed that pHBV-vaccine promoted maturation and antigen presentation ability of dendritic cells in vivo and in vitro. This vaccine successfully restored the exhaustion of antigen-specific CD8+ T cells and partly broke the immune tolerance established in HBV-carrier mice. pHBV-vaccine also enhanced the proliferation and polyfunctionality of HBV-specific CD11ahi CD8αlo cells. Importantly, we observed that T cell activation molecule KLRG1 was only expressed on HBV specific CD11ahi CD8αlo cells. Furthermore, pHBV-vaccine reduced the expression of Eomes and increased the serum IL-12 levels, which in turn promoted the generation of effector memory short-lived effector cells (SLECs) to exhibit a critical role in HBV clearance. SLECs induced by pHBV-vaccine might play a crucial role in protecting from HBV reinfection. CONCLUSIONS: Findings from this study provide a new basis for the development of therapeutic pHBV-vaccine, which might be a potential candidate for clinical CHB therapy.

A Carbapenem-Based Off-On Fluorescent Probe for Specific Detection of Metallo-ß-Lactamase Activities.

Metallo-ß-lactamase is one of the major clinical threats because this ß-lactam-hydrolyzing enzyme confers significant resistance to most ß-lactam antibiotics, including carbapenems, among bacterial pathogens. Reported herein is a novel fluorogenic sensor for the specific detection of metallo-ß-lactamase activities. This carbapenem-based reagent exhibits excellent selectivity to metallo-ß-lactamase over other serine-ß-lactamases, including serine carbapenemases. The usefulness of this probe was further demonstrated in the rapid identification of metallo-ß-lactamase-expressing pathogenic bacteria.

A multi-Poisson dynamic mixture model to cluster developmental patterns of gene expression by RNA-seq.

Dynamic changes of gene expression reflect an intrinsic mechanism of how an organism responds to developmental and environmental signals. With the increasing availability of expression data across a time-space scale by RNA-seq, the classification of genes as per their biological function using RNA-seq data has become one of the most significant challenges in contemporary biology. Here we develop a clustering mixture model to discover distinct groups of genes expressed during a period of organ development. By integrating the density function of multivariate Poisson distribution, the model accommodates the discrete property of read counts characteristic of RNA-seq data. The temporal dependence of gene expression is modeled by the first-order autoregressive process. The model is implemented with the Expectation-Maximization algorithm and model selection to determine the optimal number of gene clusters and obtain the estimates of Poisson parameters that describe the pattern of time-dependent expression of genes from each cluster. The model has been demonstrated by analyzing a real data from an experiment aimed to link the pattern of gene expression to catkin development in white poplar. The usefulness of the model has been validated through computer simulation. The model provides a valuable tool for clustering RNA-seq data, facilitating our global view of expression dynamics and understanding of gene regulation mechanisms.

Functional mapping of seasonal transition in perennial plants.

Unlike annuals, all perennial plants undergo seasonal transitions during ontogeny. As an adaptive response to seasonal changes in climate, the seasonal pattern of growth is likely to be under genetic control, although its underlying genetic basis remains unknown. Here, we develop a computational model that can map specific quantitative trait loci (QTLs) responsible for seasonal transitions of growth in perennials. The model is founded on functional mapping, a statistical framework to map developmental dynamics, which is reformed to integrate a seasonally adjusted growth function. The new model is equipped with a capacity to characterize the genetic effects of QTLs on seasonal alternation at different ages and then to better elucidate the genetic architecture of development. The model is implemented with a series of testing procedures, including (i) how a QTL controls an overall ontogenetic growth curve, (ii) how the QTL determines seasonal trajectories of growth within years and (iii) how it determines the dynamic nature of age-specific season response. The model was validated through computer simulation. The extension of season adjustment to other types of biological curves is statistically straightforward, facilitating a wider variety of genetic studies into ontogenetic growth and development in perennial plants.

Understanding the Size-Dependent Sodium Storage Properties of Na2C6O6-Based Organic Electrodes for Sodium-Ion Batteries.

Organic electroactive materials represent a new generation of sustainable energy storage technology due to their unique features including environmental benignity, material sustainability, and highly tailorable properties. Here a carbonyl-based organic salt Na2C6O6, sodium rhodizonate (SR) dibasic, is systematically investigated for high-performance sodium-ion batteries. A combination of structural control, electrochemical analysis, and computational simulation show that rational morphological control can lead to significantly improved sodium storage performance. A facile antisolvent method was developed to synthesize microbulk, microrod, and nanorod structured SRs, which exhibit strong size-dependent sodium ion storage properties. The SR nanorod exhibited the best performance to deliver a reversible capacity of ∼190 mA h g(-1) at 0.1 C with over 90% retention after 100 cycles. At a high rate of 10 C, 50% of the capacity can be obtained due to enhanced reaction kinetics, and such high electrochemical activity maintains even at 80 °C. These results demonstrate a generic design route toward high-performance organic-based electrode materials for beyond Li-ion batteries. Using such a biomass-derived organic electrode material enables access to sustainable energy storage devices with low cost, high electrochemical performance and thermal stability.

A bi-Poisson model for clustering gene expression profiles by RNA-seq.

With the availability of gene expression data by RNA-seq, powerful statistical approaches for grouping similar gene expression profiles across different environments have become increasingly important. We describe and assess a computational model for clustering genes into distinct groups based on the pattern of gene expression in response to changing environment. The model capitalizes on the Poisson distribution to capture the count property of RNA-seq data. A two-stage hierarchical expectation­maximization (EM) algorithm is implemented to estimate an optimal number of groups and mean expression amounts of each group across two environments. A procedure is formulated to test whether and how a given group shows a plastic response to environmental changes. The impact of gene­environment interactions on the phenotypic plasticity of the organism can also be visualized and characterized. The model was used to analyse an RNA-seq dataset measured from two cell lines of breast cancer that respond differently to an anti-cancer drug, from which genes associated with the resistance and sensitivity of the cell lines are identified. We performed simulation studies to validate the statistical behaviour of the model. The model provides a useful tool for clustering gene expression data by RNA-seq, facilitating our understanding of gene functions and networks.

Towards a comprehensive picture of the genetic landscape of complex traits.

The formation of phenotypic traits, such as biomass production, tumor volume and viral abundance, undergoes a complex process in which interactions between genes and developmental stimuli take place at each level of biological organization from cells to organisms. Traditional studies emphasize the impact of genes by directly linking DNA-based markers with static phenotypic values. Functional mapping, derived to detect genes that control developmental processes using growth equations, has proven powerful for addressing questions about the roles of genes in development. By treating phenotypic formation as a cohesive system using differential equations, a different approach-systems mapping-dissects the system into interconnected elements and then map genes that determine a web of interactions among these elements, facilitating our understanding of the genetic machineries for phenotypic development. Here, we argue that genetic mapping can play a more important role in studying the genotype-phenotype relationship by filling the gaps in the biochemical and regulatory process from DNA to end-point phenotype. We describe a new framework, named network mapping, to study the genetic architecture of complex traits by integrating the regulatory networks that cause a high-order phenotype. Network mapping makes use of a system of differential equations to quantify the rule by which transcriptional, proteomic and metabolomic components interact with each other to organize into a functional whole. The synthesis of functional mapping, systems mapping and network mapping provides a novel avenue to decipher a comprehensive picture of the genetic landscape of complex phenotypes that underlie economically and biomedically important traits.

A Conductive Self-Healing Hybrid Gel Enabled by Metal-Ligand Supramolecule and Nanostructured Conductive Polymer.

Self-healing materials emerge as a fascinating class of materials important for various technological applications. However, achieving the synergistic characteristics of high conductivity, room-temperature self-healing ability, and decent mechanical properties still remains a critical challenge. Here we develop for the first time a hybrid gel based on self-assembled supramolecular gel and nanostructured polypyrrole that synergizes the dynamic assembly/disassembly nature of metal-ligand supramolecule and the conductive nanostructure of polypyrrole hydrogel and exhibits features of high conductivity (12 S m(-1)), appealing mechanical and electrical self-healing property without any external stimuli, and enhanced mechanical strength and flexibility. The attractive characteristics of the hybrid gel are further demonstrated by a flexible yet self-healable electrical circuit. Our work shows the great potential of self-healing hybrid gel system in flexible electronics and provides a useful strategy to design multifunctional self-healing materials.

Dopant-Enabled Supramolecular Approach for Controlled Synthesis of Nanostructured Conductive Polymer Hydrogels.

Conducting polymer hydrogels emerge as a novel class of polymeric materials that show great potential in many energy, environmental, and biomedical devices. We describe here for the first time a general supramolecular approach toward controlled in situ synthesis of one-dimensional nanostructured conductive hydrogels (polypyrrole (PPy) as a model system) using a rational dopant counterion, which is a disc-shaped liquid crystal molecular copper phthalocyanine-3,4',4″,4‴-tetrasulfonic acid tetrasodium salt (CuPcTs). The dopant molecule CuPcTs cross-linked the PPy chains to form a three-dimensional network that gelated into a hydrogel. The PPy hydrogel could be synthesized in bulk quantities with uniform morphology of self-assembled interconnected nanofibers. The tetra-functional dopant favors a supramolecular self-assembly mechanism to form one-dimensional PPy nanostructures. Furthermore, the enhanced interchain charge transport of CuPcTs doped PPy resulted in greatly enhanced conductivity and pseudocapacitance compared with pristine PPy.