The agreement of low lean mass with obesity using different definitions and its correlation with hyperuricemia.

Background: The agreement on the identification of sarcopenic obesity remains elusive, and its association with hyperuricemia remains unestablished. This study sought to evaluate the agreement of low lean mass (LLM) with obesity and its correlation with hyperuricemia. Methods: A total of 25,252 study participants, comprising 4,597 individuals with hyperuricemia, were obtained from the National Health and Nutrition Examination Survey spanning the years 1999-2006 and 2011-2018. LLM with obesity was characterized by the coexistence of LLM, determined by the ratio of appendicular lean mass to body mass index (BMI), and three categories of obesity including BMI, body fat percentage (BF%), and waist circumference (WC). We employed Cohen's kappa to evaluate the agreement among the different diagnostic criteria and implemented survey multiple logistic regression and stratified analyses to explicate the connection between LLM with obesity and the risk of hyperuricemia. Results: When defining obesity using BF%, BMI, and WC, the prevalence of LLM with obesity varied from 6.6 to 10.1%, with moderate-to-strong agreement. In the fully adjusted model, individuals with LLM or any of the three types of obesity exhibited notably elevated odds of developing hyperuricemia. Likewise, participants with LLM and obesity had 2.70 (LLM + BMI), 2.44 (LLM + BF%), and 3.12 (LLM + WC) times the risk of hyperuricemia, respectively, compared with healthy individuals. The association between LLM with obesity and hyperuricemia remained stable and significant across different age and sex subgroups. Conclusion: When employing the three definitions of obesity, the incidence of LLM with obesity was not high, and the diagnostic agreement was relatively good. The participants with LLM and obesity exhibited an increased risk of hyperuricemia.

Quantum entanglement and interference at 3 µm.

Given the important advantages of the mid-infrared optical range (2.5 to 25 µm) for biomedical sensing, optical communications, and molecular spectroscopy, extending quantum information technology to this region is highly attractive. However, the development of mid-infrared quantum information technology is still in its infancy. Here, we report on the generation of a time-energy entangled photon pair in the mid-infrared wavelength band. By using frequency upconversion detection technology, we observe the two-photon Hong-Ou-Mandel interference and demonstrate the time-energy entanglement between twin photons at 3082 nm via the Franson-type interferometer, verifying the indistinguishability and nonlocality of the photons. This work is very promising for future applications of optical quantum technology in the mid-infrared band, which will bring more opportunities in the fields of quantum communication, precision sensing, and imaging.

Prediction of Aureococcus anophageffens using machine learning and deep learning.

The recurrent brown tide phenomenon, attributed to Aureococcus anophagefferens (A. anophagefferens), constitutes a significant threat to the Qinhuangdao sea area in China, leading to pronounced ecological degradation and substantial economic losses. This study utilized machine learning and deep learning techniques to predict A. anophagefferens population density, aiming to elucidate the occurrence mechanism and influencing factors of brown tide. Specifically, Random Forest (RF) algorithm was utilized to impute missing water quality data, facilitating its direct application in subsequent algal population prediction models. The results revealed that all four models-RF, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-exhibited high accuracy in predicting A. anophagefferens population densities, with R2 values exceeding 0.75. RF, in particular, showed exceptional accuracy and reliability, with an R2 value surpassing 0.8. Additionally, the study ascertained five critical factors influencing A. anophagefferens population density: ammonia nitrogen, pH, total nitrogen, temperature, and silicate.

Immune landscape in molecular subtypes of human papillomavirus-negative head and neck cancer.

Head and neck squamous cell carcinomas (HNSCC) remain a poorly understood disease clinically and immunologically. HPV is a known risk factor of HNSCC associated with better outcome, whereas HPV-negative HNSCC are more heterogeneous in outcome. Gene expression signatures have been developed to classify HNSCC into four molecular subtypes (classical, basal, mesenchymal, and atypical). However, the molecular underpinnings of treatment response and the immune landscape for these molecular subtypes are largely unknown. Herein, we described a comprehensive immune landscape analysis in three independent HNSCC cohorts (>700 patients) using transcriptomics data. We assigned the HPV- HNSCC patients into these four molecular subtypes and characterized the tumor microenvironment using deconvolution method. We determined that atypical and mesenchymal subtypes have greater immune enrichment and exhibit a T-cell exhaustion phenotype, compared to classical and basal subtypes. Further analyses revealed different B cell maturation and antibody isotypes enrichment patterns, and distinct immune microenvironment crosstalk in the atypical and mesenchymal subtypes. Taken together, our study suggests that treatments that enhances B cell activity may benefit patients with HNSCC of the atypical subtypes. The rationale can be utilized in the design of future precision immunotherapy trials based on the molecular subtypes of HPV- HNSCC.

The ASH1L-AS1-ASH1L axis controls NME1-mediated activation of the RAS signaling in gastric cancer.

Gastric cancer (GC) is one of the most leading cause of malignancies. However, the molecular mechanisms underlying stomach carcinogenesis remain incompletely understood. Dysregulated genetic and epigenetic alternations significantly contribute to GC development. Here, we report that ASH1L and its antisense lncRNA ASH1L-AS1, which are transcribed from the most significant GC-risk signal at 1q22, act as novel oncogenes. The high levels of ASH1L or lncRNA ASH1L-AS1 expression in GC specimens are associated with worse prognosis of patients. In line with this, ASH1L and ASH1L-AS1 are functionally important in promoting GC disease progression. LncRNA ASH1L-AS1 up-regulates ASH1L transcription, increases histone methyltransferase ASH1L expression and elevates genome-wide H3K4me3 modification levels in GC cells. Furthermore, ASH1L-AS1 directly interacts with transcription factor NME1 protein to form the ASH1L-AS1-NME1 ribonucleoprotein, which transcriptionally promotes expression of ASH1L, ASH1L-AS1, KRAS and RAF1, and activates the RAS signaling pathway in GC cells. Taken together, our data demonstrated that the ASH1L-AS1-ASH1L regulatory axis controls histone modification reprogram and activation of the RAS signaling in cancers. Thus, ASH1L-AS1 might be a novel targets of GC therapeutics and diagnosis in the clinic.

Chemotherapy and Immune Checkpoint Blockade for Gastric and Gastroesophageal Junction Adenocarcinoma.

Importance: Combining immune checkpoint blockade (ICB) with chemotherapy improves outcomes in patients with metastatic gastric and gastroesophageal junction (G/GEJ) adenocarcinoma; however, whether this combination has activity in the perioperative setting remains unknown. Objective: To evaluate the safety and preliminary activity of perioperative chemotherapy and ICB followed by maintenance ICB in resectable G/GEJ adenocarcinoma. Design, Setting, and Participants: This investigator-initiated, multicenter, open-label, single-stage, phase 2 nonrandomized controlled trial screened 49 patients and enrolled 36 patients with resectable G/GEJ adenocarcinoma from February 10, 2017, to June 17, 2021, with a median (range) follow-up of 35.2 (17.4-73.0) months. Thirty-four patients were deemed evaluable for efficacy analysis, with 28 (82.4%) undergoing curative resection. This study was performed at 4 referral institutions in the US. Interventions: Patients received 3 cycles of capecitabine, 625 mg/m2, orally twice daily for 21 days; oxaliplatin, 130 mg/m2, intravenously and pembrolizumab, 200 mg, intravenously with optional epirubicin, 50 mg/m2, every 3 weeks before and after surgery with an additional cycle of pembrolizumab before surgery. Patients received 14 additional doses of maintenance pembrolizumab. Main Outcomes and Measures: The primary end point was pathologic complete response (pCR) rate. Secondary end points included overall response rate, disease-free survival (DFS), overall survival (OS), and safety. Results: A total of 34 patients (median [range] age, 65.5 [25-90] years; 23 [67.6%] male) were evaluable for efficacy. Of these patients, 28 (82.4%) underwent curative resection, 7 (20.6%; 95% CI, 10.1%-100%) achieved pCR, and 6 (17.6%) achieved a pathologic near-complete response. Of the 28 patients who underwent resection, 4 (14.3%) experienced disease recurrence. The median DFS and OS were not reached. The 2-year DFS was 67.8% (95% CI, 0.53%-0.87%) and the OS was 80.6% (95% CI, 0.68%-0.96%). Treatment-related grade 3 or higher adverse events for evaluable patients occurred in 20 patients (57.1%), and 12 (34.3%) experienced immune-related grade 3 or higher adverse events. Conclusion and Relevance: In this trial of unselected patients with resectable G/GEJ adenocarcinoma, capecitabine, oxaliplatin, and pembrolizumab resulted in a pCR rate of 20.6% and was well tolerated. This trial met its primary end point and supports the development of checkpoint inhibition in combination with perioperative chemotherapy in locally advanced G/GEJ adenocarcinoma. Trial Registration: ClinicalTrials.gov Identifier: NCT02918162.

EGFR Inhibition by Cetuximab Modulates Hypoxia and IFN Response Genes in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) has one of the most hypoxic and immunosuppressive tumor microenvironments (TME) among solid tumors. However, there is no proven therapeutic strategy to remodel the TME to be less hypoxic and proinflammatory. In this study, we classified tumors according to a Hypoxia-Immune signature, characterized the immune cells in each subgroup, and analyzed the signaling pathways to identify a potential therapeutic target that can remodel the TME. We confirmed that hypoxic tumors had significantly higher numbers of immunosuppressive cells, as evidenced by a lower ratio of CD8+ T cells to FOXP3+ regulatory T cells, compared with nonhypoxic tumors. Patients with hypoxic tumors had worse outcomes after treatment with pembrolizumab or nivolumab, anti-programmed cell death-1 inhibitors. Our expression analysis also indicated that hypoxic tumors predominantly increased the expression of the EGFR and TGFß pathway genes. Cetuximab, an anti-EGFR inhibitor, decreased the expression of hypoxia signature genes, suggesting that it may alleviate the effects of hypoxia and remodel the TME to become more proinflammatory. Our study provides a rationale for treatment strategies combining EGFR-targeted agents and immunotherapy in the management of hypoxic HNSCC. Significance: While the hypoxic and immunosuppressive TME of HNSCC has been well described, comprehensive evaluation of the immune cell components and signaling pathways contributing to immunotherapy resistance has been poorly characterized. We further identified additional molecular determinants and potential therapeutic targets of the hypoxic TME to fully leverage currently available targeted therapies that can be administered with immunotherapy.

Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a.

Accurate predictions of coastal ocean chlorophyll-a (Chl-a) concentrations are necessary for dynamic water quality monitoring, with eutrophication as a critical factor. Prior studies that used the driven-data method have typically overlooked the relationship between Chl-a and marine particulate carbon. To address this gap, marine particulate carbon was incorporated into machine learning (ML) and deep learning (DL) models to estimate Chl-a concentrations in the Yang Jiang coastal ocean of China. Incorporating particulate organic carbon (POC) and particulate inorganic carbon (PIC) as predictors can lead to successful Chl-a estimation. The Gaussian process regression (GPR) model significantly outperforming the DL model in terms of stability and robustness. A lower POC/Chl-a ratio was observed in coastal areas, in contrast to the higher ratios detected in the southern regions of the study area. This study highlights the efficacy of the GPR model for estimating Chl-a and the importance of considering POC in modeling Chl-a concentrations.

Combination IFNß and Membrane-Stable CD40L Maximize Tumor Dendritic Cell Activation and Lymph Node Trafficking to Elicit Systemic T-cell Immunity.

Oncolytic virus therapies induce the direct killing of tumor cells and activation of conventional dendritic cells (cDC); however, cDC activation has not been optimized with current therapies. We evaluated the adenoviral delivery of engineered membrane-stable CD40L (MEM40) and IFNß to locally activate cDCs in mouse tumor models. Combined tumor MEM40 and IFNß expression induced the highest cDC activation coupled with increased lymph node migration, increased systemic antitumor CD8+ T-cell responses, and regression of established tumors in a cDC1-dependent manner. MEM40 + IFNß combined with checkpoint inhibitors led to effective control of distant tumors and lung metastases. An oncolytic adenovirus (MEM-288) expressing MEM40 + IFNß  in phase I clinical testing induced cancer cell loss concomitant with enhanced T-cell infiltration and increased systemic presence of tumor T-cell clonotypes in non-small cell lung cancer (NSCLC) patients. This approach to simultaneously target two major DC-activating pathways has the potential to significantly affect the solid tumor immunotherapy landscape.

Systematic evaluation of the predictive gene expression signatures of immune checkpoint inhibitors in metastatic melanoma.

Advances in immunotherapy, including immune checkpoint inhibitors (ICIs), have transformed the standard of care for many types of cancer including melanoma. ICIs have improved the overall outcome of melanoma patients; however, a significant proportion of patients suffer from primary or secondary tumor resistance. Therefore, there is an urgent need to develop predictive biomarkers to better select patients for ICI therapy. Numerous biomarkers that predict the response of melanoma to ICIs have been investigated, including biomarker signatures based on genomics or transcriptomics. Most of these predictive biomarkers have not been systematically evaluated across different cohorts to determine the reproducibility of these signatures in metastatic melanoma. We evaluated 28 previously published predictive biomarkers of ICIs based on gene expression signatures in eight previously published studies with available RNA-sequencing data in public repositories. We found that signatures related to IFN-γ-responsive genes, T and B cell markers, and chemokines in the tumor immune microenvironment are generally predictive of response to ICIs in these patients. In addition, we identified that these predictive biomarkers have higher predictive values in on-treatment samples as compared to pretreatment samples in metastatic melanoma. The most frequently overlapping genes among the top 18 predictive signatures were CXCL10, CXCL9, PRF1, RANTES, IFNG, HLA-DRA, GZMB, and CD8A. From gene set enrichment analysis and cell type deconvolution, we estimated that the tumors of responders were enriched with infiltrating cytotoxic T-cells and other immune cells and the upregulation of genes related to interferon-γ signaling. Conversely, the tumors of non-responders were enriched with stromal-related cell types such as fibroblasts and myofibroblasts, as well as enrichment with T helper 17 cell types across all cohorts. In summary, our approach of validating and integrating multi-omics data can help guide future biomarker development in the field of ICIs and serve the quest for a more personalized therapeutic approach for melanoma patients.

Characterization of light chain c-terminal extension sequence variant in one bispecific antibody.

Protein modifications such as post-translational modifications (PTMs) and sequence variants (SVs) occur frequently during protein biosynthesis and have received great attention by biopharma industry and regulatory agencies. In this study, an aberrant peak near light chain (LC) was observed in the non-reduced capillary electrophoresis sodium dodecyl sulfate (nrCE-SDS) electrophoretogram during cell line development of one bispecific antibody (BsAb) product, and the detected mass was about 944 Da higher than LC. The corresponding peak was then enriched by denaturing size-exclusion chromatography (SEC-HPLC) and further characterized by nrCE-SDS and peptide mapping analyses. De novo mass spectra/mass spectra (MS/MS) analysis revealed that the aberrant peak was LC related sequence variant, with the truncated C-terminal sequence "SFNR" ("GEC"deleted) linked with downstream SV40 promotor sequence "EAEAASASELFQ". The unusual sequence was further confirmed by comparing with the direct synthetic peptide "SFNREAEAASASELFQ". It was demonstrated by mRNA sequencing of the cell pool that the sequence variant was caused by aberrant splicing at the transcription step. The prepared product containing this extension variant maintained well-folded structure and good functional properties though the LC/Heavy chain (HC) inter-chain disulfide was not formed. Several control strategies to mitigate the risk of this LC related sequence variant were also proposed.

LncRNAs LCETRL3 and LCETRL4 at chromosome 4q12 diminish EGFR-TKIs efficiency in NSCLC through stabilizing TDP43 and EIF2S1.

Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are effective targeted therapy drugs for advanced non-small cell lung cancer (NSCLC) patients carrying sensitized EGFR mutations. The rapid development of EGFR-TKIs resistance represents a major clinical challenge for managing NSCLC. The chromosome 4q12 is the first genome-wide association study (GWAS)-reported locus associated with progression-free survival (PFS) of NSCLC patients treated with EGFR-TKIs. However, the biological significance of the noncoding transcripts at 4q12 in NSCLC remains elusive. In the present study, we identified two 4q12 long noncoding RNAs (lncRNAs) LCETRL3 and LCETRL4 which could significantly dimmish EGFR-TKIs efficiency. In line with their oncogenic role, evidently higher LCETRL3 and LCETRL4 levels were observed in NSCLC tissues as compared with normal specimens. Importantly, lncRNA LCETRL3 can interact with oncoprotein TDP43 and inhibit ubiquitination and degradation of TDP43. Similarly, lncRNA LCETRL4 can bind and stabilize oncoprotein EIF2S1 through reducing ubiquitin-proteasome degradation of EIF2S1. In particular, elevated levels of LCETRL3 or LCETRL4 in NSCLC cells resulted in stabilization of TDP43 or EIF2S1, increased levels of NOTCH1 or phosphorylated PDK1, activated AKT signaling and, thus, EGFR-TKIs resistance. Taken together, our data revealed a novel model that integrates two lncRNAs transcribed from the 4q12 locus into the regulation of EGFR-TKIs resistance in NSCLC. These findings shed new light on the importance of functionally annotating lncRNAs in the GWAS loci and provided insights to declare novel druggable targets, i.e., lncRNAs, which may unlock the therapeutic potential of EGFR-TKIs resistant NSCLC in the clinic.

Abortion occurs during double fertilization and ovule development in Paeonia ludlowii.

Paeonia ludlowii (Stern & Taylor) D.Y.Hong, an endangered species, is indigenous to Tibet, China and propagated only by seed under natural conditions. Its natural reproduction is constrained by low fecundity. Excess seed abortion is a key factor restricting its natural reproduction, cultivation, introduction, and protection. Understanding the specific origin and occurrence of aborted ovules is important for the protection of offspring. Using serial sectioning analysis, we studied the process of pollination and fertilization of P. ludlowii and examined the characteristics of aborted ovules, developmental differences after flowering of normal and aborted ovules, and their ratios at different positions in P. ludlowii ovaries. During pollination, fertilization, and seed development, ovule abortion was frequent, with a random abortion position. There were three types of abortion, namely, abnormal pistil, sterile ovules, and embryo and endosperm abortions. Of these, embryo and endosperm abortions could be divided into early abortion and middle abortion. The early aborted ovules stopped growing on day 12, the endoblast and endosperm in the embryo sac aborted gradually. Furthermore, the shape of the embryo sac cavity changed. The volume of aborted ovules was significantly different from that of fertile ovules. At ripening, the external morphology of different types of aborted seeds was significantly different. The possible reasons for the abortion of the ovules are also discussed.

Neoadjuvant endocrine therapy expands stromal populations that predict poor prognosis in estrogen receptor-positive breast cancer.

The tumor microenvironment (TME) is an important modulator of response and resistance to endocrine therapy in estrogen receptor alpha (ER) positive breast cancer. Endocrine therapy is highly effective at reducing tumor burden and preventing recurrence in most estrogen receptor alpha (ER) positive breast cancers. Existing drugs work either directly by targeting tumor-cell ER or indirectly by inhibiting estrogen production in stromal cells with aromatase inhibitors (AI). However, many stromal cells also express ER and the direct impact of endocrine therapies on ER + stromal cells remain unclear. In this study, we investigated how neoadjuvant endocrine therapy (NET) directly effects stromal cells by measuring changes in stomal components of the TME that favor tumor progression. We previously defined two major subsets of tumor-associated stromal cells (TASCs): CD146 positive/CDCP1 negative (TASCCD146 ), CD146 negative/CDCP1 positive (TASCCDCP1 ), and generated a differentially expressed genes list associated with each type. Here, we applied the TASC gene list for classification and an algorithm that estimates immune cell abundance (TIMEx) to METABRIC transcriptomic data for ER + breast cancer patients coupled with multiplex imaging and analysis of paired tissue samples pre- and post- NET with the AI exemestane. TASCCDCP1 composition predicted for decreased patient survival in the METABRIC cohort. Exemestane treatment significantly increased expression of TASCCDCP1 and decreased expression of TASCCD146 . The posttreatment shift toward TASCCDCP1 composition correlated with increased macrophage infiltration and increased CD8+ T-cell, B cell, and general stromal components. The effectiveness of NET is currently based solely on the reduction of ER+ breast cancer cells. Here, we show NET displays clear TME effects that promote the expansion of the less favorable TASCCDCP1 population which are correlated with TME remodeling and reshaping immune infiltration supportive of tumor progression. Our findings highlight the need to further understand the role of endocrine therapy on TME remodeling, tumor progression, and patient outcomes.

Implementation of a twin-beam state-based clock synchronization system with dispersion-free HOM feedback.

In the field of clock synchronization, the application of frequency-entangled source is a promising direction to improve accuracy and security. In this paper, we analyze the performance of the twin-beam state and the difference-beam state using a practical second-order interference-based scheme. The advantages of the twin-beam state are pointed out especially for the dispersion-free property of HOM interference in a long-distance clock transfer. With the introduction of dispersion-compensated material, our experimental system based on a twin-beam state achieves a clock accuracy at 4 ps with a time offset precision of 1.8 ps over 10 s acquisition time while the time deviation is 0.15 ps over an averaging time of 5500 s in a 22 km-long transmission. These properties exhibit a leading position compared with the current clock synchronization system using the same theoretical scheme and also competitive among the implementations using other second-order interference-based schemes.

Roles of fiber birefringence and Raman scattering in the spontaneous four-wave mixing process through birefringent fibers.

We investigate the impact of fiber birefringence and spontaneous Raman scattering on the properties of photon pairs that are generated by the spontaneous four-wave mixing process in birefringent fibers. Starting from the formulation of the theory of four-wave mixing, we show a theoretical model for a generated optical field with the consideration of the Raman scattering and a Gaussian-distributed pump. The theoretical model is then applied for deriving the closed expressions of the photon-pair spectral properties as a function of the fiber birefringence. Also, with the modeled Raman gain, we evaluate the reduction of the pair production rate due to the presence of the Raman effect as well as the contributions of the Raman-scattered photons over a broad wavelength range. The predictions are experimentally verified with a commercial polarization-maintaining fiber.

Deep Learning of Histopathology Images at the Single Cell Level.

The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to "weakly-label" the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.

LncPSCA in the 8q24.3 risk locus drives gastric cancer through destabilizing DDX5.

Genome-wide association studies (GWAS) have identified multiple gastric cancer risk loci and several protein-coding susceptibility genes. However, the role of long-noncoding RNAs (lncRNAs) transcribed from these risk loci in gastric cancer development and progression remains to be explored. Here, we functionally characterize a lncRNA, lncPSCA, as a novel tumor suppressor whose expression is fine-regulated by a gastric cancer risk-associated genetic variant. The rs2978980 T > G change in an intronic enhancer of lncPSCA interrupts binding of transcription factor RORA, which down-regulates lncPSCA expression in an allele-specific manner. LncPSCA interacts with DDX5 and promotes DDX5 degradation through ubiquitination. Increased expression of lncPSCA results in low levels of DDX5, less RNA polymerase II (Pol II) binding with DDX5 in the nucleus, thus activating transcription of multiple p53 signaling genes by Pol II. These findings highlight the importance of functionally annotating lncRNAs in GWAS risk loci and the great potential of modulating lncRNAs as innovative cancer therapy.

Construction of an integrated multi-layer textile for solar-driven steam generation.

Solar steam generation has widespread application in wastewater treatment, seawater desalination, liquid-liquid separation, and other fields, providing potential opportunities for producing fresh water. Up until now, most researchers in this field focused on enhancing the evaporation rate of the solar steam generation device. However, problems in terms of its portability and flexibility still exist when it comes to real application scenarios. Herein, we propose a novel, to the best of our knowledge, integrated multi-layer textile composed of reduced graphene oxide/cotton (RGO/cotton) fabric, cotton yarn, and polypropylene (PP) fabric for solar-driven steam generation. The evaporation rate obtained by the integrated multi-layer textile as prepared is ${0.83}\;{{\rm kg\cdot m}^{- 2}}\cdot{{\rm h}^{- 1}}$ under one sun solar radiation, which is 3.16 times higher than that of blank experiment and is superior to many previously reported works. Its remarkable evaporation performance is mainly attributed to the inherent multi-layer structures, where porous RGO/cotton fabric exhibits ultra-water vapor permeability, hydrophilic cotton yarn supplies water continuously, and low-density hydrophobic PP fabric hinders heat sustainably. Based on the results of application performance evaluation, the integrated multi-layer textile with scalable manufacturability, portability, durability, and flexibility is expected to boost the development of solar-driven steam generation.

Oncogenic SNORD12B activates the AKT-mTOR-4EBP1 signaling in esophageal squamous cell carcinoma via nucleus partitioning of PP-1α.

Esophageal cancer is a complex malignancy and the sixth leading cause of cancer death worldwide. In Eastern Asia including China, about 90% of all incident cases have esophageal squamous cell carcinoma (ESCC). Mounting evidence elucidates that aberrant expression of various non-coding RNAs (ncRNAs) contributes to ESCC progression, but it remains unclear how small nucleolar RNAs (snoRNAs) are involved in ESCC development. We systemically screened clinically relevant snoRNAs in ESCC via integrative analyses of The Cancer Genome Atlas (TCGA) data and validation in ESCC tissues. We found that snoRNA SNORD12B was one of the most evidently upregulated snoRNAs in ESCC specimens and its high expression was significantly associated with poor prognosis of patients. SNORD12B profoundly promoted proliferation, migration, invasion, and metastasis of ESCC cells in vitro and in vivo, indicating its oncogene nature. In particular, SNORD12B could interact with PP-1α, one of the three catalytic subunits of serine/threonine protein phosphatase 1, which is a major phosphatase that directly dephosphorylates AKT to suppress its activation. Interestingly, high levels of SNORD12B in ESCC cells could break interactions between 14-3-3ζ and PP-1α, abolish the retention of PP-1α in the cytosol by 14-3-3ζ and relocate PP-1α from the cytosol to the nucleus. This led to sequestered PP-1α in the nucleus, enhanced phosphorylation of AKT in the cytosol, activated AKT-mTOR-4EBP1 signaling, and, thus, ESCC progression. These insights would improve our understanding of how snoRNAs contribute to tumorigenesis and highlight the potential of snoRNAs as future therapeutic targets against cancers.