Integrative Proteomics and Phosphoproteomics Profiling Reveals Dynamic Signaling Networks and Bioenergetics Pathways Underlying T Cell Activation.

The molecular circuits by which antigens activate quiescent T cells remain poorly understood. We combined temporal profiling of the whole proteome and phosphoproteome via multiplexed isobaric labeling proteomics technology, computational pipelines for integrating multi-omics datasets, and functional perturbation to systemically reconstruct regulatory networks underlying T cell activation. T cell receptors activated the T cell proteome and phosphoproteome with discrete kinetics, marked by early dynamics of phosphorylation and delayed ribosome biogenesis and mitochondrial activation. Systems biology analyses identified multiple functional modules, active kinases, transcription factors and connectivity between them, and mitochondrial pathways including mitoribosomes and complex IV. Genetic perturbation revealed physiological roles for mitochondrial enzyme COX10-mediated oxidative phosphorylation in T cell quiescence exit. Our multi-layer proteomics profiling, integrative network analysis, and functional studies define landscapes of the T cell proteome and phosphoproteome and reveal signaling and bioenergetics pathways that mediate lymphocyte exit from quiescence.

Combinatorial expression of GPCR isoforms affects signalling and drug responses.

G-protein-coupled receptors (GPCRs) are membrane proteins that modulate physiology across human tissues in response to extracellular signals. GPCR-mediated signalling can differ because of changes in the sequence1,2 or expression3 of the receptors, leading to signalling bias when comparing diverse physiological systems4. An underexplored source of such bias is the generation of functionally diverse GPCR isoforms with different patterns of expression across different tissues. Here we integrate data from human tissue-level transcriptomes, GPCR sequences and structures, proteomics, single-cell transcriptomics, population-wide genetic association studies and pharmacological experiments. We show how a single GPCR gene can diversify into several isoforms with distinct signalling properties, and how unique isoform combinations expressed in different tissues can generate distinct signalling states. Depending on their structural changes and expression patterns, some of the detected isoforms may influence cellular responses to drugs and represent new targets for developing drugs with improved tissue selectivity. Our findings highlight the need to move from a canonical to a context-specific view of GPCR signalling that considers how combinatorial expression of isoforms in a particular cell type, tissue or organism collectively influences receptor signalling and drug responses.

Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

Dissecting Detergent-Insoluble Proteome in Alzheimer's Disease by TMTc-Corrected Quantitative Mass Spectrometry.

Protein aggregation of amyloid-ß peptides and tau are pathological hallmarks of Alzheimer's disease (AD), which are often resistant to detergent extraction and thus enriched in the insoluble proteome. However, additional proteins that coaccumulate in the detergent-insoluble AD brain proteome remain understudied. Here, we comprehensively characterized key proteins and pathways in the detergent-insoluble proteome from human AD brain samples using differential extraction, tandem mass tag (TMT) labeling, and two-dimensional LC-tandem mass spectrometry. To improve quantification accuracy of the TMT method, we developed a complement TMT-based strategy to correct for ratio compression. Through the meta-analysis of two independent detergent-insoluble AD proteome datasets (8914 and 8917 proteins), we identified 190 differentially expressed proteins in AD compared with control brains, highlighting the pathways of amyloid cascade, RNA splicing, endocytosis/exocytosis, protein degradation, and synaptic activity. To differentiate the truly detergent-insoluble proteins from copurified background during protein extraction, we analyzed the fold of enrichment for each protein by comparing the detergent-insoluble proteome with the whole proteome from the same AD samples. Among the 190 differentially expressed proteins, 84 (51%) proteins of the upregulated proteins (n = 165) were enriched in the insoluble proteome, whereas all downregulated proteins (n = 25) were not enriched, indicating that they were copurified components. The vast majority of these enriched 84 proteins harbor low-complexity regions in their sequences, including amyloid-ß, Tau, TARDBP/TAR DNA-binding protein 43, SNRNP70/U1-70K, MDK, PTN, NTN1, NTN3, and SMOC1. Moreover, many of the enriched proteins in AD were validated in the detergent-insoluble proteome by five steps of differential extraction, proteomic analysis, or immunoblotting. Our study reveals a resource list of proteins and pathways that are exclusively present in the detergent-insoluble proteome, providing novel molecular insights to the formation of protein pathology in AD.

Reciprocal interplay between OTULIN-LUBAC determines genotoxic and inflammatory NF-κB signal responses.

Targeting nuclear factor-kappa B (NF-κB) represents a highly viable strategy against chemoresistance in cancers as well as cell death. Ubiquitination, including linear ubiquitination mediated by the linear ubiquitin chain assembly complex (LUBAC), is emerging as a crucial mechanism of overactivated NF-κB signaling. Ovarian tumor family deubiquitinase OTULIN is the only linear linkage-specific deubiquitinase; however, the molecular mechanisms of how it counteracts LUBAC-mediated NF-κB activation have been largely unknown. Here, we identify Lys64/66 of OTULIN for linear ubiquitination facilitated in a LUBAC-dependent manner as a necessary event required for OTULIN-LUBAC interaction under unstressed conditions, which becomes deubiquitinated by OTULIN itself in response to genotoxic stress. Furthermore, this self-deubiquitination of OTULIN occurs intermolecularly, mediated by OTULIN dimerization, resulting in the subsequent dissociation of OTULIN from the LUBAC complex and NF-κB overactivation. Oxidative stress induces OTULIN dimerization via cysteine-mediated covalent disulfide bonds. Our study reveals that the status of the physical interaction between OTULIN and LUBAC is a crucial determining factor for the genotoxic NF-κB signaling, as measured by cell survival and proliferation, while OTULIN loss of function resulting from its dimerization and deubiquitination leads to a dissociation of OTULIN from the LUBAC complex. Of note, similar molecular mechanisms apply to the inflammatory NF-κB signaling in response to tumor necrosis factor α. Hence, a fuller understanding of the detailed molecular mechanisms underlying the disruption of the OTULIN-LUBAC interaction will be instrumental for developing future therapeutic strategies against cancer chemoresistance and necroptotic processes pertinent to numerous human diseases.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Photothermal Conversion of the Oleophilic PVDF/Ti3C2Tx Porous Foam Enables Non-Aqueous Liquid System Applicable Actuator.

Various physical and chemical reaction processes occur in non-aqueous liquid systems, particularly in oil phase systems. Therefore, achieving efficient, accurate, controllable, and cost-effective movement and transfer of substances in the oil phase is crucial. Liquid-phase photothermal actuators (LPAs) are commonly used for material transport in liquid-phase systems due to their remote operability and precise control. However, existing LPAs typically rely on materials like hydrogels and flexible polymers, commonly unsuitable for non-aqueous liquids. Herein, a 3D porous poly(vinylidene fluoride) (PVDF)/Ti3C2Tx actuator is developed using a solvent displacement method. It demonstrates directional movement and controlled material transport in non-aqueous liquid systems. When subject to infrared light irradiation (2.0 W cm-2), the actuator achieves motion velocities of 7.3 and 6 mm s-1 vertically and horizontally, respectively. The actuator's controllable motion capability is primarily attributed to the foam's oil-wettable properties, 3D porous oil transport network, and the excellent photothermal conversion performance of Ti3C2Tx, facilitating thermal diffusion and the Marangoni effect. Apart from multidimensional directions, the actuator enables material delivery and obstacle avoidance by transporting and releasing target objects to a predetermined position. Hence, the developed controllable actuator offers a viable solution for effective motion control and material handling in non-aqueous liquid environments.

Precisely Controlled Polymerization of Two-Dimensional Conducting Polymers in Quasi-Liquid Layer Enables Ultrahigh Sensing Performance.

Gas sensors based on conducting polymers offer great potential for high-performance room temperature applications due to their cost-effectiveness, high-sensitivity, and operational advantage. However, their current performance is limited by the deficiency of control in conventional polymerization methods, leading to poor crystallinity and inconsistent material properties. Here, the quasi-liquid layer (QLL) on the ice surface acts as a self-regulating nano-reactor for precise control of thermodynamics and kinetics in the polymerization, resulting in a 7.62 nm thick two-dimensional (2D) polyaniline (PANI) film matching the QLL thickness. The ultra-thin film optimizes the exposure of active sites, enhancing the detection of analyte gases at low concentrations. It is validated by fabricating a chemiresistive gas sensor with the 2D PANI film, demonstrating stable room-temperature detection of ammonia down to 10 ppt in ambient air with an impressive 10% response. This achievement represents the highest sensitivity among sensors of this kind while maintaining excellent selectivity and repeatability. Moreover, the QLL-controlled polymerization strategy offers an alternative route for precise control of the polymerization process for conducting polymers, enabling the creation of advanced materials with enhanced properties.

A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.

Tetraspanin CD82 Correlates with and May Regulate S100A7 Expression in Oral Cancer.

Many metastatic cancers with poor prognoses correlate to downregulated CD82, but exceptions exist. Understanding the context of this correlation is essential to CD82 as a prognostic biomarker and therapeutic target. Oral squamous cell carcinoma (OSCC) constitutes over 90% of oral cancer. We aimed to uncover the function and mechanism of CD82 in OSCC. We investigated CD82 in human OSCC cell lines, tissues, and healthy controls using the CRISPR-Cas9 gene knockout, transcriptomics, proteomics, etc. CD82 expression is elevated in CAL 27 cells. Knockout CD82 altered over 300 genes and proteins and inhibited cell migration. Furthermore, CD82 expression correlates with S100 proteins in CAL 27, CD82KO, SCC-25, and S-G cells and some OSCC tissues. The 37-50 kDa CD82 protein in CAL 27 cells is upregulated, glycosylated, and truncated. CD82 correlates with S100 proteins and may regulate their expression and cell migration. The truncated CD82 explains the invasive metastasis and poor outcome of the CAL 27 donor. OSCC with upregulated truncated CD82 and S100A7 may represent a distinct subtype with a poor prognosis. Differing alternatives from wild-type CD82 may elucidate the contradictory functions and pave the way for CD82 as a prognostic biomarker and therapeutic target.

Identifying Sex-Specific Serum Patterns of Alzheimer's Mice through Deep TMT Profiling and a Concentration-Dependent Concatenation Strategy.

Alzheimer's disease (AD) is the most prevalent form of dementia, disproportionately affecting women in disease prevalence and progression. Comprehensive analysis of the serum proteome in a common AD mouse model offers potential in identifying possible AD pathology- and gender-associated biomarkers. Here, we introduce a multiplexed, nondepleted mouse serum proteome profiling via tandem mass-tag (TMTpro) labeling. The labeled sample was separated into 475 fractions using basic reversed-phase liquid chromatography (RPLC), which were categorized into low-, medium-, and high-concentration fractions for concatenation. This concentration-dependent concatenation strategy resulted in 128 fractions for acidic RPLC-tandem mass spectrometry (MS/MS) analysis, collecting ∼5 million MS/MS scans and identifying 3972 unique proteins (3413 genes) that cover a dynamic range spanning at least 6 orders of magnitude. The differential expression analysis between wild type and the commonly used AD model (5xFAD) mice exhibited minimal significant protein alterations. However, we detected 60 statistically significant (FDR < 0.05), sex-specific proteins, including complement components, serpins, carboxylesterases, major urinary proteins, cysteine-rich secretory protein 1, pregnancy-associated murine protein 1, prolactin, amyloid P component, epidermal growth factor receptor, fibrinogen-like protein 1, and hepcidin. The results suggest that our platform possesses the sensitivity and reproducibility required to detect sex-specific differentially expressed proteins in mouse serum samples.

Proteomics Profiling Reveals Regulation of Immune Response to Salmonella enterica Serovar Typhimurium Infection in Mice.

Regulation of the immune response to Salmonella enterica serovar Typhimurium (S. Typhimurium) infection is a complex process, influenced by the interaction between genetic and environmental factors. Different inbred strains of mice exhibit distinct levels of resistance to S. Typhimurium infection, ranging from susceptible (e.g., C57BL/6J) to resistant (e.g., DBA/2J) strains. However, the underlying molecular mechanisms contributing to the host response remain elusive. In this study, we present a comprehensive proteomics profiling of spleen tissue from C57BL/6J and DBA/2J strains with different doses of S. Typhimurium infection by tandem mass tag labeling coupled with two-dimensional liquid chromatography-tandem mass spectrometry (TMT-LC/LC-MS/MS). We identified and quantified 3,986 proteins, resulting in 475 differentially expressed proteins (DEPs) between C57BL/6J and DBA/2J strains. Functional enrichment analysis unveiled that the mechanisms of innate immune responses to S. Typhimurium infection could be associated with several signaling pathways, including the interferon (IFN) signaling pathway. We experimentally validated the roles of the IFN signaling pathway in the innate immune response to S. Typhimurium infection using an IFN-γ neutralization assay. We further illustrated the importance of macrophage and proinflammatory cytokines in the mechanisms underlying the resistance to S. Typhimurium using quantitative reverse transcription-PCR (qRT-PCR). Taken together, our results provided new insights into the genetic regulation of the immune response to S. Typhimurium infection in mice and might lead to the discovery of potential protein targets for controlling salmonellosis.

Tandem Photocatalysis of CO2 to C2H4 via a Synergistic Rhenium-(I) Bipyridine/Copper-Porphyrinic Triazine Framework.

The photocatalytic conversion of CO2 into C2+ products such as ethylene is a promising path toward the carbon neutral goal but remains a big challenge due to the high activation barrier for CO2 and similar reduction potentials of many possible multi-electron-transfer products. Herein, an effective tandem photocatalysis strategy has been developed to support conversion of CO2 to ethylene by construction of the synergistic dual sites in rhenium-(I) bipyridine fac-[ReI(bpy)(CO)3Cl] (Re-bpy) and copper-porphyrinic triazine framework [PTF(Cu)]. With these two catalysts, a large amount of ethylene can be produced at a rate of 73.2 µmol g-1 h-1 under visible light irradiation. However, ethylene cannot be obtained from CO2 by use of either component of the Re-bpy or PTF(Cu) catalysts alone; with a single catalyst, only monocarbon product CO is produced under similar conditions. In the tandem photocatalytic system, the CO generated at the Re-bpy sites is adsorbed by the nearby Cu single sites in PTF(Cu), and this is followed by a synergistic C-C coupling process which ultimately produces ethylene. Density functional theory calculations demonstrate that the coupling process between PTF(Cu)-*CO and Re-bpy-*CO to form the key intermediate Re-bpy-*CO-*CO-PTF(Cu) is vital to the C2H4 production. This work provides a new pathway for the design of efficient photocatalysts for photoconversion of CO2 to C2 products via a tandem process driven by visible light under mild conditions.

Single-cell analysis reveals distinct functional heterogeneity of CD34+ cells in anagen wound and diabetic wound.

Wound healing is a complex biological process involving multiple cell types with their critical functions. The diabetic wounds show delayed wound healing, while the anagen wounds display accelerated wound closure. However, the mechanisms underlying the effect of cellular heterogeneity on wound healing are still unclear. CD34+ cells exhibit high heterogeneity in wound skins and improve wound healing. Herein, we investigated the phenotypic and functional heterogeneity of CD34+ cells in normal, anagen, and diabetic wounds. We obtained CD34 lineage tracing mice, constructed distinct wound models, collected CD34+ cells from wound edges, and performed single-cell RNA sequencing. We identified 10 cell clusters and 6 cell types of CD34+ cells, including endothelial cells, fibroblasts, keratinocytes, neutrophils, macrophages, and T cells. 5 subclusters were defined as fibroblasts. The CD34+ fibroblasts C2 highly expressed papillary fibroblastic markers took up the largest proportion in anagen wounds and were associated with inflammation and extracellular matrix. Increased CD34+ endothelial cells, fibroblasts C4, and neutrophils as well as decreased fibroblasts C1 were discovered in diabetic wounds. We also filtered out differentially expressed genes (DEGs) of each cell cluster in anagen wounds and diabetic wounds. Functional enrichment analysis was performed on these DEGs to figure out the enriched pathways and items for each cell cluster. Pseudotime analysis of CD34+ fibroblasts was next carried out indicating fibroblast C4 mainly with low differentiation. Our results have important implications for understanding CD34+ cell type-specific roles in anagen and diabetic wounds, provide the possible mechanisms of wound healing from a new perspective, and uncover potential therapeutic approaches to treating wounds.

Metal Halides for High-Capacity Energy Storage.

High-capacity electrochemical energy storage systems are more urgently needed than ever before with the rapid development of electric vehicles and the smart grid. The most efficient way to increase capacity is to develop electrode materials with low molecular weights. The low-cost metal halides are theoretically ideal cathode materials due to their advantages of high capacity and redox potential. However, their cubic structure and large energy barrier for deionization impede their rechargeability. Here, the reversibility of potassium halides, lithium halides, sodium halides, and zinc halides is achieved through decreasing their dimensionality by the strong π-cation interactions between metal cations and reduced graphene oxide (rGO). Especially, the energy densities of KI-, KBr-, and KCl-based materials are 722.2, 635.0, and 739.4 Wh kg-1 , respectively, which are higher than those of other cathode materials for potassium-ion batteries. In addition, the full-cell with 2D KI/rGO as cathode and graphite as anode demonstrates a lifespan of over 150 cycles with a considerable capacity retention of 57.5%. The metal halides-based electrode materials possess promising application prospects and are worthy of more in-depth researches.

Hybrid mixed-dimensional WTe2/CsPbI3perovskite heterojunction for high-performance photodetectors.

Perovskites have showed significant potential for the application in photodetectors due to their outstanding electrical and optical properties. Integrating two-dimensional (2D) materials with perovskites can make full use of the high carrier mobility of 2D materials and strong light absorption of perovskite to realize excellent optoelectrical properties. Here, we demonstrate a photodetector based on the WTe2/CsPbI3heterostructure. The quenching and the shortened lifetime of photoluminescence (PL) for CsPbI3perovskite confirms the efficient charge transfer at the WTe2/CsPbI3heterojunction. After coupled with WTe2, the photoresponsivity of the CsPbI3photodetector is improved by almost two orders of magnitude due to the high-gain photogating effect. The WTe2/CsPbI3heterojunction photodetector reveals a large responsivity of 1157 A W-1and a high detectivity of 2.1 × 1013Jones. The results pave the way for the development of high-performance optoelectronic devices based on 2D materials/perovskite heterojunctions.

In Situ Encapsulation of Graphene Quantum Dots in Highly Stable Porphyrin Metal-Organic Frameworks for Efficient Photocatalytic CO2 Reduction.

Photocatalytic CO2 reduction to valuable hydrocarbon solar fuel is of great significance but still challenging. Strong CO2 enrichment ability and easily adjustable structures make metal-organic frameworks (MOFs) potential photocatalysts for CO2 conversion. Even though pure MOFs have the potential for photoreduction of CO2, the efficiency is still quite low due to rapid photogenerated electron-hole recombination and other drawbacks. In this work, graphene quantum dots (GQDs) were in situ encapsulated into highly stable MOFs via a solvothermal method for this challenging task. The GQDs@PCN-222 with encapsulated GQDs showed similar Powder X-ray Diffraction (PXRD) patterns to PCN-222, indicating the retained structure. The porous structure was also retained with a Brunauer-Emmett-Teller (BET) surface area of 2066 m2/g. After incorporation of GQDs, the shape of GQDs@PCN-222 particles remained, as revealed by the scanning electron microscope (SEM). As most of the GQDs were covered by thick PCN-222, it was hard to observe those GQDs using a transmission electron microscope (TEM) and a high-resolution transmission electron microscope (HRTEM) directly, the treatment of digested GQDs@PCN-222 particles by immersion in a 1 mM aqueous KOH solution can make the incorporated GQDs visible in TEM and HRTEM. The linker, deep purple porphyrins, make MOFs a highly visible light harvester up to 800 nm. The introduction of GQDs inside PCN-222 can effectively promote the spatial separation of the photogenerated electron-hole pairs during the photocatalytic process, which was proved by the transient photocurrent plot and photoluminescence emission spectra. Compared with pure PCN-222, the obtained GQDs@PCN-222 displayed dramatically enhanced CO production derived from CO2 photoreduction with 147.8 µmol/g/h in a 10 h period under visible light irradiation with triethanolamine (TEOA) as a sacrificial agent. This study demonstrated that the combination of GQDs and high light absorption MOFs provides a new platform for photocatalytic CO2 reduction.

An Extended NIR-II Superior Imaging Window from 1500 to 1900†nm for High-Resolution In Vivo Multiplexed Imaging Based on Lanthanide Nanocrystals.

High-resolution in vivo optical multiplexing in second near-infrared window (NIR-II, 1000-1700 nm) is vital to biomedical research. Presently, limited by bio-tissue scattering, only luminescent probes located at NIR-IIb (1500-1700 nm) window can provide high-resolution in vivo multiplexed imaging. However, the number of available luminescent probes in this narrow NIR-IIb region is limited, which hampers the available multiplexed channels of in vivo imaging. To overcome the above challenges, through theoretical simulation we expanded the conventional NIR-IIb window to NIR-II long-wavelength (NIR-II-L, 1500-1900 nm) window on the basis of photon-scattering and water-absorption. We developed a series of novel lanthanide luminescent nanoprobes with emission wavelengths from 1852 nm to 2842 nm. NIR-II-L nanoprobes enabled high-resolution in vivo dynamic multiplexed imaging on blood vessels and intestines, and provided multi-channels imaging on lymph tubes, tumors and intestines. The proposed NIR-II-L probes without mutual interference are powerful tools for high-contrast in vivo multiplexed detection, which holds promise for revealing physiological process in living body.

Multi-omics analysis reveals expression complexity and functional diversity of mouse kinome.

Protein kinases are a crucial component of signaling pathways involved in a wide range of cellular responses, including growth, proliferation, differentiation, and migration. Systematic investigation of protein kinases is critical to better understand phosphorylation-mediated signaling pathways and may provide insights into the development of potential therapeutic drug targets. Here we perform a systems-level analysis of the mouse kinome by analyzing multi-omics data. We used bulk and single-cell transcriptomic data from the C57BL/6J mouse strain to define tissue- and cell-type-specific expression of protein kinases, followed by investigating variations in sequence and expression between C57BL/6J and DBA/2J strains. We then profiled a deep brain phosphoproteome from C57BL/6J and DBA/2J strains as well as their reciprocal hybrids to infer the activity of the mouse kinome. Finally, we performed phenome-wide association analysis using the BXD recombinant inbred (RI) mice (a cross between C57BL/6J and DBA/2J strains) to identify any associations between variants in protein kinases and phenotypes. Collectively, our study provides a comprehensive analysis of the mouse kinome by investigating genetic sequence variation, tissue-specific expression patterns, and associations with downstream phenotypes.

MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection.

As Android is a popular a mobile operating system, Android malware is on the rise, which poses a great threat to user privacy and security. Considering the poor detection effects of the single feature selection algorithm and the low detection efficiency of traditional machine learning methods, we propose an Android malware detection framework based on stacking ensemble learning-MFDroid-to identify Android malware. In this paper, we used seven feature selection algorithms to select permissions, API calls, and opcodes, and then merged the results of each feature selection algorithm to obtain a new feature set. Subsequently, we used this to train the base learner, and set the logical regression as a meta-classifier, to learn the implicit information from the output of base learners and obtain the classification results. After the evaluation, the F1-score of MFDroid reached 96.0%. Finally, we analyzed each type of feature to identify the differences between malicious and benign applications. At the end of this paper, we present some general conclusions. In recent years, malicious applications and benign applications have been similar in terms of permission requests. In other words, the model of training, only with permission, can no longer effectively or efficiently distinguish malicious applications from benign applications.