Explore the vast range of titles available.

The reprogramming of somatic cells with defined factors, which converts cells from one lineage into cells of another, has greatly reshaped our traditional views on cell identity and cell fate determination. Direct reprogramming (also known as transdifferentiation) refers to cell fate conversion without transitioning through an intermediary pluripotent state. Given that the number of cell types that can be generated by direct reprogramming is rapidly increasing, it has become a promising strategy to produce functional cells for therapeutic purposes. This Review discusses the evolution of direct reprogramming from a transcription factor-based method to a small-molecule-driven approach, the recent progress in enhancing reprogrammed cell maturation, and the challenges associated with in vivo direct reprogramming for translational applications. It also describes our current understanding of the molecular mechanisms underlying direct reprogramming, including the role of transcription factors, epigenetic modifications, non-coding RNAs, and the function of metabolic reprogramming, and highlights novel insights gained from single-cell omics studies.Direct reprogramming converts cells from one lineage into cells of another without going through an intermediary pluripotent state. This Review describes our current understanding of the molecular mechanisms underlying direct reprogramming as well as the progress in improving its efficiency and the maturation of reprogrammed cells, and the challenges associated with its translational applications.

Background The aim of this study was to investigate the biological functions and underlying mechanisms of SIRT5 in clear cell renal cell carcinoma (ccRCC). Methods SIRT5 expression data in The Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) were selected, and the correlations between SIRT5 expression and various clinicopathological parameters were analysed. SIRT5 expression in ccRCC tissues was examined using immunohistochemistry. Stable cell lines with SIRT5 knockdown were established. In vitro and in vivo experiments were conducted to investigate the functional roles of SIRT5 in the cellular biology of ccRCC, including cell viability assays, wound healing assays, soft agar colony formation assays, Transwell invasion assays, qRT–PCR, and Western blotting. In addition, microarrays, rescue experiments and Western blotting were used to investigate the molecular mechanisms underlying SIRT5 functions. Results SIRT5 expression was downregulated in ccRCC compared with normal tissues, which correlated with a poor prognosis of ccRCC. SIRT5 knockdown significantly increased cell proliferation, migration and invasion in vitro. In vivo experiments revealed that SIRT5 knockdown promoted ccRCC tumorigenesis and metastasis. Mechanistically, SIRT5 deglycosylated PDHA1 at K351 and increased PDC activity, thereby altering the metabolic crosstalk with the TCA cycle and inhibiting the Warburg effect. SIRT5 overexpression was related to low succinylation of PDHA1. Conclusions Downregulated SIRT5 expression in ccRCC accelerated the Warburg effect through PDHA1 hypersuccinylation and induced tumorigenesis and progression, indicating that SIRT5 may become a potential target for ccRCC therapy.

Video anomaly detection has an important application value in the field of intelligent surveillance; however, due to the problems of sparse anomaly events and expensive labeling, it has made weakly supervised methods a research hotspot. Most of the current methods still adopt the strategy of processing temporal and spatial features independently, which makes it difficult to fully capture their temporal and spatial complex dependencies, affecting the accuracy and robustness of detection. Existing studies predominantly process temporal and spatial information independently, which limits the ability to effectively capture their interdependencies. To address this, we propose the Local Temporal and Global Spatial Network (LTGS) for weakly supervised video anomaly detection. The LTGS architecture incorporates a clip-level temporal feature relation module and a video-level spatial feature module, which collaboratively enhance discriminative representations. Through joint training of these modules, we develop a feature encoder specifically tailored for video anomaly detection. To further refine clip-level annotations and better align them with actual events, we employ a dynamic label updating strategy. These updated labels are utilized to optimize the model and enhance its robustness. Extensive experiments on two widely used public datasets, ShanghaiTech and UCF-Crime, validate the effectiveness of the proposed LTGS method. Experimental results demonstrate that the LTGS achieves an AUC of 96.69% on the ShanghaiTech dataset and 82.33% on the UCF dataset, outperforming various state-of-the-art algorithms in anomaly detection tasks.

Glioblastoma (GBM) is the most lethal primary tumor of the central nervous system, with its resistance to treatment posing significant challenges. This study aims to develop a comprehensive prognostic model to identify biomarkers associated with temozolomide (TMZ) resistance. We employed a multifaceted approach, combining differential expression and univariate Cox regression analyses to screen for TMZ resistance-related differentially expressed genes (TMZR-RDEGs) in GBM. Using LASSO Cox analysis, we selected 12 TMZR-RDEGs to construct a risk score model, which was evaluated for performance through survival analysis, time-dependent ROC, and stratified analyses. Functional enrichment and mutation analyses were conducted to explore the underlying mechanisms of the risk score and its relationship with immune cell infiltration levels in GBM. The prognostic risk score model, based on the 12 TMZR-RDEGs, demonstrated high efficacy in predicting GBM patient outcomes and emerged as an independent predictive factor. Additionally, we focused on the molecule TSPAN13, whose role in GBM is not well understood. We assessed cell proliferation, migration, and invasion capabilities through in vitro assays (including CCK-8, Edu, wound healing, and transwell assays) and quantitatively analyzed TSPAN13 expression levels in clinical glioma samples using tissue microarray immunohistochemistry. The impact of TSPAN13 on TMZ resistance in GBM cells was validated through in vitro experiments and a mouse orthotopic xenograft model. Notably, TSPAN13 was upregulated in GBM and correlated with poorer patient prognosis. Knockdown of TSPAN13 inhibited GBM cell proliferation, migration, and invasion, and enhanced sensitivity to TMZ treatment. This study provides a valuable prognostic tool for GBM and identifies TSPAN13 as a critical target for therapeutic intervention.

With rising global temperatures, wildfires frequently occur worldwide during the summer season. The timely detection of these fires, based on unmanned aerial vehicle (UAV) images, can significantly reduce the damage they cause. Existing Convolutional Neural Network (CNN)-based fire detection methods usually use multiple convolutional layers to enhance the receptive fields, but this compromises real-time performance. This paper proposes a novel real-time semantic segmentation network called FireFormer, combining the strengths of CNNs and Transformers to detect fires. An agile ResNet18 as the encoding component tailored to fulfill the efficient fire segmentation is adopted here, and a Forest Fire Transformer Block (FFTB) rooted in the Transformer architecture is proposed as the decoding mechanism. Additionally, to accurately detect and segment small fire spots, we have developed a novel Feature Refinement Network (FRN) to enhance fire segmentation accuracy. The experimental results demonstrate that our proposed FireFormer achieves state-of-the-art performance on the publicly available forest fire dataset FLAME—specifically, with an impressive 73.13% IoU and 84.48% F1 Score.

This study investigates the impact of the aerosol hygroscopic growth effect on haze events in Xingtai, a heavily polluted city in the central part of the North China Plain (NCP), using a large array of instruments measuring aerosol optical, physical, and chemical properties. Key instruments used and measurements made include the Raman lidar for atmospheric water vapor content and aerosol optical profiles, the PC-3016A GrayWolf six-channel handheld particle and mass meter for atmospheric total particulate matter (PM) that has diameters less than 1 and 2.5 µm (PM1 and PM2.5, respectively), the aerosol chemical speciation monitor (ACSM) for chemical components in PM1, and the hygroscopic tandem differential mobility analyzer (H-TDMA) for aerosol hygroscopicity. The changes in PM1 and PM2.5 agreed well with that of the water vapor content due to the aerosol hygroscopic growth effect. Two cases were selected to further analyze the effects of aerosol hygroscopic growth on haze events. The lidar-estimated hygroscopic enhancement factor for the aerosol backscattering coefficient during a relatively clean period (Case I) was lower than that during a pollution event (Case II) with similar relative humidity (RH) levels of 80 %–91 %. The Kasten model was used to fit the aerosol optical hygroscopic growth factor (GF) whose parameter b differed considerably between the two cases, i.e., 0.1000 (Case I) versus 0.9346 (Case II). The aerosol acidity value calculated from ACSM data for Case I (1.35) was less than that for Case II (1.50) due to different amounts of inorganics such as NH4NO3, NH4HSO4, and (NH4)2SO4. Model results based on H-TDMA data showed that aerosol hygroscopic growth factors in each size category (40, 80, 110, 150, and 200 nm) at different RH levels (80 %–91 %) for Case I were lower than those for Case II. For similar ambient RH levels, the high content of nitrate facilitates the hygroscopic growth of aerosols, which may be a major factor contributing to heavy haze episodes in Xingtai.

•A simple yet effective framework decomposes whole brain dynamics into four fundamental states.•State sequences derive multi-mode representations of brain function at regional and network levels.•State-based representations serve as discriminative ‘brain fingerprints’ for identifying individuals.•State-based representations enhance the power of brain-phenotype modeling. Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels. Briefly, brain dynamics are captured by temporal first-order derivatives and spatially divided into ‘state sets’ at each time point based on the velocity and direction of change. This approach transforms the original signals into discrete series consisting of four fundamental states, which efficiently encode individual-specific information. Subsequently, we designed a suite of state-based metrics to quantify regional activities and network interactions. Compared with conventional representations such as resting-state fluctuation amplitude and Pearson’s functional connectivity, the state-based representations serve as more discriminative ‘brain fingerprints’ for individuals and produce reproducible spatial patterns across heterogeneous cohorts (n = 1015). Regarding functional organization, our proposed profiles extend previous representations into nonlinear domains, revealing not only the canonical default-mode dominant pattern but also patterns dominated by the attention network and basal ganglia. Moreover, we demonstrate that personal phenotypes (such as age and gender) can be decoded from regional representations with high accuracy. The equivalence between state series outperforms other existing network representations in predicting individual fluid intelligence. Overall, this framework establishes a foundation for enriching the repertoire of brain functional representations and enhancing the power of brain-phenotype modeling.

The Taklamakan desert is an important dust source for the global atmospheric dust budget and a cause of the dust weather in East Asia. The characterization of Taklamakan dust in the source region is still very limited. To fill this gap, the DAO (dust aerosol observation) was conducted in April 2019 in Kashi, China. The Kashi site is about 150 km from the western rim of the Taklamakan desert and is strongly impacted by desert dust aerosols, especially in spring time, i.e., April and May. According to sun–sky photometer measurements, the aerosol optical depth (at 500 nm) varied in the range of 0.07–4.70, and the Ångström exponent (between 440 and 870 nm) in the range of 0.0–0.8 in April 2019. In this study, we provide the first profiling of the 2α+3β+3δ parameters of Taklamakan dust based on a multiwavelength Mie–Raman polarization lidar. For Taklamakan dust, the Ångström exponent related to the extinction coefficient (EAE, between 355 and 532 nm) is about 0.01 ± 0.30, and the lidar ratio is found to be 45 ± 7 sr (51 ± 8–56 ± 8 sr) at 532 (355) nm. The particle linear depolarization ratios (PLDRs) are about 0.28–0.32 ± 0.07 at 355 nm, 0.36 ± 0.05 at 532 nm and 0.31 ± 0.05 at 1064 nm. Both lidar ratios and depolarization ratios are higher than the typical values of Central Asian dust in the literature. The difference is probably linked to the fact that observations in the DAO campaign were collected close to the dust source; therefore, there is a large fraction of coarse-mode and giant particles (radius >20 µm) in the Taklamakan dust. Apart from dust, fine particles coming from local anthropogenic emissions and long-range transported aerosols are also non-negligible aerosol components. The signatures of pollution emerge when dust concentration decreases. The polluted dust (defined by PLDR532≤0.30 and EAE355-532≥0.20) is featured with reduced PLDRs and enhanced EAE355−532 compared to Taklamakan dust. The mean PLDRs of polluted dust generally distributed in the range of 0.20–0.30. Due to the complexity of the nature of the involved pollutants and their mixing state with dust, the lidar ratios exhibit larger variabilities compared to those of dust. The study provides the first reference of novel characteristics of Taklamakan dust measured by Mie–Raman polarization lidar. The data could contribute to complementing the dust model and improving the accuracy of climate modeling.

Various factors play key roles in maintaining intestine homeostasis. Disruption of the balance may lead to inflammatory bowel diseases and even colorectal cancer (CRC). Loss or gain of function of many key proteins can result in dysregulated intestinal homeostasis. Our research demonstrated that neural precursor cells expressed developmentally downregulated 4-like protein (NEDD4L, or NEDD4-2), a type of HECT family E3 ubiquitin ligase, played an important role in maintaining intestinal homeostasis. NEDD4L expression was significantly inhibited in intestinal epithelial cells (IECs) of patients with Crohn's disease, ulcerative colitis, and CRC. Global KO of NEDD4L or its deficiency in IECs exacerbated colitis induced by dextran sulfate sodium (DSS) and 2,4,6-trinitrobenzene sulfonic acid (TNBS) and CRC induced by azoxymethane and DSS. Mechanistically, NEDD4L deficiency in IECs inhibited expression of the key ferroptosis regulator glutathione peroxidase 4 (GPX4) by reducing the protein expression of solute carrier family 3 member 2 (SLC3A2) without affecting its gene expression, ultimately promoting DSS-induced IEC ferroptosis. Importantly, ferroptosis inhibitors reduced the susceptibility of NEDD4L-deficient mice to colitis and colitis-associated CRC. Thus, NEDD4L is an important regulator in IEC ferroptosis, maintaining intestinal homeostasis, making it a potential clinical target for diagnosing and treating IBDs.

Background Albumin infusion is the primary therapeutic strategy for septic patients with liver cirrhosis. Although recent studies have investigated the efficacy of albumin in the resuscitation stage of septic patients with liver cirrhosis, it remains unclear whether daily albumin administration can improve outcomes. Furthermore, the indications for initiating albumin therapy are not well defined. Methods Septic patients with liver cirrhosis were obtained from the Medical Information Mart for Intensive Care (MIMIC-IV 2.0) database. Marginal structural Cox models were employed to investigate the association between daily albumin infusion and 28-day mortality. We also aimed to explore under what circumstances enrolled patients could benefit most from albumin administration, based on the clinical parameters collected on the day of albumin infusion, including serum albumin concentration, serum lactate concentration, mean arterial pressure (MAP), and vasopressor dosage. Results A total of 2265 patients were included in the final analysis, of whom 1093 (48.3%) had received albumin treatment at least once. The overall 28-day mortality was 29.6%. After marginal structural modeling, daily albumin infusion was associated with a reduced risk of 28-day death (hazard ratio, 0.76; 95% CI 0.61–0.94). We found that patients benefit most from albumin infusion when initiated on the day of serum albumin concentration between 2.5 and 3.0 g/dL, serum lactate concentration greater than or equal to 2 mmol/L, MAP less than 60 mmHg, or vasopressor dosage between 0.2 and 0.3 mcg/kg/min (norepinephrine equivalent, NEE). Conclusions Albumin infusion is associated with a reduction in mortality in septic patients with liver cirrhosis under specific circumstances. Serum albumin concentration, serum lactate, MAP, and vasopressor dosage were found to be modifiers of treatment effectiveness and should be considered when deciding to initial albumin infusion.