Janus Binder Chemistry for Synchronous Enhancement of Iodine Species Adsorption and Redox Kinetics toward Sustainable Aqueous Zn-I2 Batteries.

Currently, the desired research focus in energy storage technique innovation has been gradually shifted to next-generation aqueous batteries holding both high performance and sustainability. However, aqueous Zn-I2 batteries have been deemed to have great sustainable potential, owing to the merits of cost-effective and eco-friendly nature. However, their commercial application is hindered by the serious shuttle effect of polyiodides during reversible operations. In this work, a Janus functional binder based on chitosan (CTS) molecules was designed and prepared; the polar terminational groups impart excellent mechanical robustness to hybrid binders; meanwhile, it can also deliver isochronous enhancement on physical adsorption and redox kinetics toward I2 species. By feat of highly effective remission to shuttle effect, the CTS cell exhibits superb electrochemical storage capacities with long-term robustness, specifically, 144.1 mAh g-1, at a current density of 0.2 mA g-1 after 1500 cycles. Simultaneously, the undesired self-discharging issue could be also well-addressed; the Coulombic efficiency could remain at 98.8 % after resting for 24 h. More importantly, CTS molecules endow good biodegradability and reusable properties; after iodine species were reloaded, the recycled devices could also deliver specific capacities of 73.3 mAh g-1, over 1000 cycles. This Janus binder provides a potential synchronous solution to realize high comprehensive performance with high iodine utilization and further make it possible for sustainable Zn-I2 batteries.

Hybrid Binder Chemistry with Hydrogen-Bond Helix for High-Voltage Cathode of Sodium-Ion Batteries.

Since sodium-ion batteries (SIBs) have become increasingly commercialized in recent years, Na3V2(PO4)2O2F (NVPOF) offers promising economic potential as a cathode for SIBs because of its high operating voltage and energy density. According to reports, NVPOF performs poorly in normal commercial poly(vinylidene fluoride) (PVDF) binder systems and performs best in combination with aqueous binder. Although in line with the concept of green and sustainable development for future electrode preparation, aqueous binders are challenging to achieve high active material loadings at the electrode level, and their relatively high surface tension tends to cause the active material on the electrode sheet to crack or even peel off from the collector. Herein, a cross-linkable and easily commercial hybrid binder constructed by intermolecular hydrogen bonding (named HPP) has been developed and utilized in an NVPOF system, which enables the generation of a stable cathode electrolyte interphase on the surface of active materials. According to theoretical simulations, the HPP binder enhances electronic/ionic conductivity, which greatly lowers the energy barrier for Na+ migration. Additionally, the strong hydrogen-bond interactions between the HPP binder and NVPOF effectively prevent electrolyte corrosion and transition-metal dissolution, lessen the lattice volume effect, and ensure structural stability during cycling. The HPP-based NVPOF offers considerably improved rate capability and cycling performance, benefiting from these benefits. This comprehensive binder can be extended to the development of next-generation energy storage technologies with superior performance.

Interfacial-Confined Isochronous Conversion to Biphasic Selenide Heterostructure with Enhanced Adsorption Behaviors for Robust High-Rate Na-Ion Storage.

Sodium-ion batteries (SIBs) have gradually become one of the most promising energy storage techniques in the current era of post-lithium-ion batteries. For anodes, transitional metal selenides (TMSe) based materials are welcomed choices , owing to relatively higher specific capacities and enriched redox active sites. Nevertheless, current bottlenecks are blamed for their poor intrinsic electronic conductivities, and uncontrollable volume expansion during redox reactions. Given that, an interfacial-confined isochronous conversion strategy is proposed, to prepare orthorhombic/cubic biphasic TMSe heterostructure, namely CuSe/Cu3 VSe4 , through using MXene as the precursor, followed by Cu/Se dual anchorage. As-designed biphasic TMSe heterostructure endows unique hierarchical structure, which contains adequate insertion sites and diffusion spacing for Na ions, besides, the surficial pseudocapacitive storage behaviors can be also proceeded like 2D MXene. By further investigation on electronic structure, the theoretical calculations indicate that biphasic CuSe/Cu3 VSe4 anode exhibits well-enhanced properties, with smaller bandgap and thus greatly improves intrinsic poor conductivities. In addition, the dual redox centers can enhance the electrochemical Na ions storage abilities. As a result, the as-designed biphasic TMSe anode can deliver a reversible specific capacity of 576.8 mAh g-1 at 0.1 A g-1 , favorable Na affinity, and reduced diffusion barriers. This work discloses a synchronous solution toward demerits in conductivities and lifespan, which is inspiring for TMSe-based anode development in SIBs systems.

A Green Environmental Protection Photocatalytic Molecular Reactor for Aerobic Oxidation of Sulfide to Sulfoxide.

The design and synthesis of metal-organic frameworks (MOFs) as photocatalytic molecular reactors for varied reactions have drawn great attention. In this work, we designed a novel photoactive perylenediimides-based (PDI) carboxylate ligand N,N'-di(3',3",5',5"-tetrakis(4-carboxyphenyl))-1,2,6,7-tetrachloroperylene-3,4,9,10-tetracarboxylic acid diimide (Cl-PDI-TA) and use it to successfully synthesize a novel Zr(IV)-based MOF 1 constructed from [Zr6 O8 (H2 O)8 ]8+ clusters bridged by Cl-PDI-TA ligands. Structural analysis revealed that Zr-MOF 1 manifests a 3D framework with (4,8)-connected csq topology and possesses triangular channels of ~17 Šand mesoporous hexagonal channels of ~26 Šalong c-axis. Moreover, the synthesized Zr-MOF 1 exhibits visible-light absorption and efficient photoinduced free radical generation property, making it a promising photocatalytic molecular reactor. When Zr-MOF 1 was used as a photocatalyst for the aerobic oxidation of sulfides under irradiation of visible light, it could afford the corresponding sulfoxides with high yield and selectivity. Experimental results demonstrated that the substrate sulfides could be fixed in the pores of 1 and directly transformed to the products sulfoxides in the solid state. Furthermore, the mechanism for the photocatalytic transformation was also investigated and the results revealed that the singlet oxygen (1 O2 ) and superoxide radical (O2 ⋅- ) generated by the energy transfer and electron transfer from the photoexcited Zr-MOF to oxidants were the main active species for the catalytic reactions. This work offers a perceptive comprehension of the mechanism in PDI-based MOFs for further study on photocatalytic reactions.

Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Each patient's cancer consists of multiple cell subpopulations that are inherently heterogeneous and may develop differing phenotypes such as drug sensitivity or resistance. A personalized treatment regimen should therefore target multiple oncoproteins in the cancer cell populations that are driving the treatment resistance or disease progression in a given patient to provide maximal therapeutic effect, while avoiding severe co-inhibition of non-malignant cells that would lead to toxic side effects. To address the intra- and inter-tumoral heterogeneity when designing combinatorial treatment regimens for cancer patients, we have implemented a machine learning-based platform to guide identification of safe and effective combinatorial treatments that selectively inhibit cancer-related dysfunctions or resistance mechanisms in individual patients. In this case study, we show how the platform enables prediction of cancer-selective drug combinations for patients with high-grade serous ovarian cancer using single-cell imaging cytometry drug response assay, combined with genome-wide transcriptomic and genetic profiles. The platform makes use of drug-target interaction networks to prioritize those combinations that warrant further preclinical testing in scarce patient-derived primary cells. During the case study in ovarian cancer patients, we investigated (i) the relative performance of various ensemble learning algorithms for drug response prediction, (ii) the use of matched single-cell RNA-sequencing data to deconvolute cell population-specific transcriptome profiles from bulk RNA-seq data, (iii) and whether multi-patient or patient-specific predictive models lead to better predictive accuracy. The general platform and the comparison results are expected to become useful for future studies that use similar predictive approaches also in other cancer types.

Advanced Covalent Organic Frameworks for Multi-Valent Metal Ion Batteries.

Covalent organic frameworks (COFs) have received increased interest in recent years as an advanced class of materials. By virtue of the available monomers, multiple conformations and various linkages, COFs offer a wide range of opportunities for complex structural design and specific functional development of materials, which has facilitated the widespread application in many fields, including multi-valent metal ion batteries (MVMIBs), described as the attractive candidate replacing lithium-ion batteries (LIBs). With their robust skeletons, diverse pores, flexible structures and abundant functional groups, COFs are expected to help realize a high performance MVMIBs. In this review, we present an overview of COFs, describe advances in topology design and synthetic reactions, and study the application of COFs in MVMIBs, as well as discuss challenges and solutions in the preparation of COFs electrodes, in the hope of providing constructive insights into the future direction of COFs.

PRISM: recovering cell-type-specific expression profiles from individual composite RNA-seq samples.

MOTIVATION: A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. RESULTS: To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. AVAILABILITYAND IMPLEMENTATION: https://bitbucket.org/anthakki/prism. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Advanced Lithium Primary Batteries: Key Materials, Research Progresses and Challenges.

In recent years, with the vigorous development and gradual deployment of new energy vehicles, more attention has been paid to the research on lithium-ion batteries (LIBs). Compared with the booming LIBs, lithium primary batteries (LPBs) own superiority in specific energy and self-discharge rate and are usually applied in special fields such as medical implantation, aerospace, and military. Widespread application in special fields also means more stringent requirements for LPBs in terms of energy density, working temperature range and shelf life. Therefore, how to obtain LPBs with high energy density, wide operational temperature range and long storage life is of great importance in future development. In view of the above, this paper reviews the latest research on LPBs in cathode, anode and electrolyte over the years, and puts forward relevant insights for LPBs, along with the intention to explore avenues for the design of LPBs components in the coming decades and promote further development in this field.

miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a 'low-quality' cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC.

Development of Magnetically Levitated Rotary Table for Repetitive Trajectory Tracking.

The magnetic levitation system has been considered as a promising actuator in micromachining areas of study. In order to improve the tracking performance and disturbance rejection of the magnetically levitated rotary table, an iterative learning PID control strategy with disturbance compensation is proposed. The estimated disturbance compensates for the control signals to enhance the active disturbance rejection ability. The iterative learning control is used as a feed-forward unit to further reduce the trajectory tracking error. The convergence and stability of the iterative learning PID with disturbance compensation are analysed. A series of comparative experiments are carried out on the in-house, custom-made, magnetically levitated rotary table, and the experimental results highlight the superiority of the proposed control strategy. The iterative learning PID with disturbance compensation enables the magnetically levitated rotary table to realize good tracking performance with complex external disturbance. The proposed control strategy strengthens the applicability of magnetically levitated systems in the mechanism manufacturing area.

Anduril 2: upgraded large-scale data integration framework.

SUMMARY: Anduril is an analysis and integration framework that facilitates the design, use, parallelization and reproducibility of bioinformatics workflows. Anduril has been upgraded to use Scala for pipeline construction, which simplifies software maintenance, and facilitates design of complex pipelines. Additionally, Anduril's bioinformatics repository has been expanded with multiple components, and tutorial pipelines, for next-generation sequencing data analysis. AVAILABILITYAND IMPLEMENTATION: Freely available at http://anduril.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Ink4a/Arf locus operates as a regulator of the circadian clock modulating RAS activity.

The mammalian circadian clock and the cell cycle are two major biological oscillators whose coupling influences cell fate decisions. In the present study, we use a model-driven experimental approach to investigate the interplay between clock and cell cycle components and the dysregulatory effects of RAS on this coupled system. In particular, we focus on the Ink4a/Arf locus as one of the bridging clock-cell cycle elements. Upon perturbations by the rat sarcoma viral oncogene (RAS), differential effects on the circadian phenotype were observed in wild-type and Ink4a/Arf knock-out mouse embryonic fibroblasts (MEFs), which could be reproduced by our modelling simulations and correlated with opposing cell cycle fate decisions. Interestingly, the observed changes can be attributed to in silico phase shifts in the expression of core-clock elements. A genome-wide analysis revealed a set of differentially expressed genes that form an intricate network with the circadian system with enriched pathways involved in opposing cell cycle phenotypes. In addition, a machine learning approach complemented by cell cycle analysis classified the observed cell cycle fate decisions as dependent on Ink4a/Arf and the oncogene RAS and highlighted a putative fine-tuning role of Bmal1 as an elicitor of such processes, ultimately resulting in increased cell proliferation in the Ink4a/Arf knock-out scenario. This indicates that the dysregulation of the core-clock might work as an enhancer of RAS-mediated regulation of the cell cycle. Our combined in silico and in vitro approach highlights the important role of the circadian clock as an Ink4a/Arf-dependent modulator of oncogene-induced cell fate decisions, reinforcing its function as a tumour-suppressor and the close interplay between the clock and the cell cycle network.

Critical questions in ovarian cancer research and treatment: Report of an American Association for Cancer Research Special Conference.

Substantial progress has been made in understanding ovarian cancer at the molecular and cellular level. Significant improvement in 5-year survival has been achieved through cytoreductive surgery, combination platinum-based chemotherapy, and more effective treatment of recurrent cancer, and there are now more than 280,000 ovarian cancer survivors in the United States. Despite these advances, long-term survival in late-stage disease has improved little over the last 4 decades. Poor outcomes relate, in part, to late stage at initial diagnosis, intrinsic drug resistance, and the persistence of dormant drug-resistant cancer cells after primary surgery and chemotherapy. Our ability to accelerate progress in the clinic will depend on the ability to answer several critical questions regarding this disease. To assess current answers, an American Association for Cancer Research Special Conference on "Critical Questions in Ovarian Cancer Research and Treatment" was held in Pittsburgh, Pennsylvania, on October 1-3, 2017. Although clinical, translational, and basic investigators conducted much of the discussion, advocates participated in the meeting, and many presentations were directly relevant to patient care, including treatment with poly adenosine diphosphate ribose polymerase (PARP) inhibitors, attempts to improve immunotherapy by overcoming the immune suppressive effects of the microenvironment, and a better understanding of the heterogeneity of the disease.

Identifying differentially methylated sites in samples with varying tumor purity.

Motivation: DNA methylation aberrations are common in many cancer types. A major challenge hindering comparison of patient-derived samples is that they comprise of heterogeneous collection of cancer and microenvironment cells. We present a computational method that allows comparing cancer methylomes in two or more heterogeneous tumor samples featuring differing, unknown fraction of cancer cells. The method is unique in that it allows comparison also in the absence of normal cell control samples and without prior tumor purity estimates, as these are often unavailable or unreliable in clinical samples. Results: We use simulations and next-generation methylome, RNA and whole-genome sequencing data from two cancer types to demonstrate that the method is accurate and outperforms alternatives. The results show that our method adapts well to various cancer types and to a wide range of tumor content, and works robustly without a control or with controls derived from various sources. Availability and implementation: The method is freely available at https://bitbucket.org/anthakki/dmml. Supplementary information: Supplementary data are available at Bioinformatics online.

Landscape of RNA polyadenylation in E. coli.

Polyadenylation is thought to be involved in the degradation and quality control of bacterial RNAs but relatively few examples have been investigated. We used a combination of 5΄-tagRACE and RNA-seq to analyze the total RNA content from a wild-type strain and from a poly(A)polymerase deleted mutant. A total of 178 transcripts were either up- or down-regulated in the mutant when compared to the wild-type strain. Poly(A)polymerase up-regulates the expression of all genes related to the FliA regulon and several previously unknown transcripts, including numerous transporters. Notable down-regulation of genes in the expression of antigen 43 and components of the type 1 fimbriae was detected. The major consequence of the absence of poly(A)polymerase was the accumulation of numerous sRNAs, antisense transcripts, REP sequences and RNA fragments resulting from the processing of entire transcripts. A new algorithm to analyze the position and composition of post-transcriptional modifications based on the sequence of unencoded 3΄-ends, was developed to identify polyadenylated molecules. Overall our results shed new light on the broad spectrum of action of polyadenylation on gene expression and demonstrate the importance of poly(A) dependent degradation to remove structured RNA fragments.

An energy landscape approach reveals the potential key bacteria contributing to the development of inflammatory bowel disease.

The dysbiosis of microbiota has been reported to be associated with numerous human pathophysiological processes, including inflammatory bowel disease (IBD). With advancements in high-throughput sequencing, various methods have been developed to study the alteration of microbiota in the development and progression of diseases. However, a suitable approach to assess the global stability of the microbiota in disease states through time-series microbiome data is yet to be established. In this study, we have introduced a novel Energy Landscape construction method, which incorporates the Latent Dirichlet Allocation (LDA) model and the pairwise Maximum Entropy (MaxEnt) model for their complementary advantages, and demonstrate its utility by applying it to an IBD time-series dataset. Through this approach, we obtained the microbial assemblages' energy profile of the whole microbiota under the IBD condition and uncovered the hidden stable stages of microbiota structure during the disease development with time-series microbiome data. The Bacteroides-dominated assemblages presenting in multiple stable states suggest the potential contribution of Bacteroides and interactions with other microbial genera, like Alistipes, and Faecalibacterium, to the development of IBD. Our proposed method provides a novel and insightful tool for understanding the alteration and stability of the microbiota under disease states and offers a more holistic view of the complex dynamics at play in microbiota-mediated diseases.

Protocol to identify defined reprogramming factor expression using a factor-indexing single-nuclei multiome sequencing approach.

Ectopic expression of lineage-specific transcription factors (TFs) of another cell type can induce cell fate reprogramming. However, the heterogeneity of reprogramming cells has been a challenge for data interpretation and model evaluation. Here, we present a protocol to characterize cells expressing defined factors during direct cell reprogramming using a factor-indexing approach based on single-nuclei multiome sequencing (FI-snMultiome-seq). We describe the steps for barcoding TFs, converting human fibroblasts to pancreatic ductal-like cells using defined TFs, and preparing library for FI-snMultiome-seq analysis. For complete details on the use and execution of this protocol, please refer to Fei et al.1.

Stable hydrogen evolution reaction at high current densities via designing the Ni single atoms and Ru nanoparticles linked by carbon bridges.

Continuous and effective hydrogen evolution under high current densities remains a challenge for water electrolysis owing to the rapid performance degradation under continuous large-current operation. In this study, theoretical calculations, operando Raman spectroscopy, and CO stripping experiments confirm that Ru nanocrystals have a high resistance against deactivation because of the synergistic adsorption of OH intermediates (OHad) on the Ru and single atoms. Based on this conceptual model, we design the Ni single atoms modifying ultra-small Ru nanoparticle with defect carbon bridging structure (UP-RuNiSAs/C) via a unique unipolar pulse electrodeposition (UPED) strategy. As a result, the UP-RuNiSAs/C is found capable of running steadily for 100 h at 3 A cm-2, and shows a low overpotential of 9 mV at a current density of 10 mA cm-2 under alkaline conditions. Moreover, the UP-RuNiSAs/C allows an anion exchange membrane (AEM) electrolyzer to operate stably at 1.95 Vcell for 250 h at 1 A cm-2.

Supramolecular assemblies of Cu(II) with a tetraphenylethene-imidazole ligand for tuning photocatalytic CO2 reduction.

Cu(II) supramolecular assemblies [Cu2(tipe)2(H2O)2](NO3)4·2.5H2O (CuN4) and [Cu2Cl4(tipe)(CH3CN)]·H2O (CuN2Cl2) (tipe = 1,1,2,2-tetrakis(4-(imidazole-1-yl)phenyl)ethene) were synthesized and utilized for photocatalytic CO2 reduction. CuN4 exhibits CO production of up to 891 µmol gcat-1 with a selectivity of 79.9%, while CuN2Cl2 gives low CO production of 206 µmol gcat-1 but with a high selectivity of >99.9% in 5 h. The experimental and DFT calculation results indicate that the coordination environment and non-covalent interactions within the assemblies have a great impact on the photocatalytic CO2 reduction behavior. This work provides useful insights on Cu(II) assembly catalyzed CO2 photoreduction.

Defect engineering unveiled: Enhancing potassium storage in expanded graphite anode.

Expanded graphite (EG) stands out as a promising material for the negative electrode in potassium-ion batteries. However, its full potential is hindered by the limited diffusion pathway and storage sites for potassium ions, restricting the improvement of its electrochemical performance. To overcome this challenge, defect engineering emerges as a highly effective strategy to enhance the adsorption and reaction kinetics of potassium ions on electrode materials. This study delves into the specific effectiveness of defects in facilitating potassium storage, exploring the impact of defect-rich structures on dynamic processes. Employing ball milling, we introduce surface defects in EG, uncovering unique effects on its electrochemical behavior. These defects exhibit a remarkable ability to adsorb a significant quantity of potassium ions, facilitating the subsequent intercalation of potassium ions into the graphite structure. Consequently, this process leads to a higher potassium voltage. Furthermore, the generation of a diluted stage compound is more pronounced under high voltage conditions, promoting the progression of multiple stage reactions. Consequently, the EG sample post-ball milling demonstrates a notable capacity of 286.2 mAh g-1 at a current density of 25 mA g-1, showcasing an outstanding rate capability that surpasses that of pristine EG. This research not only highlights the efficacy of defect engineering in carbon materials but also provides unique insights into the specific manifestations of defects on dynamic processes, contributing to the advancement of potassium-ion battery technology.