Accumulating evidence shows that cellular and acellular components in tumor microenvironment (TME) can reprogram tumor initiation, growth, invasion, metastasis, and response to therapies. Cancer research and treatment have switched from a cancer-centric model to a TME-centric one, considering the increasing significance of TME in cancer biology. Nonetheless, the clinical efficacy of therapeutic strategies targeting TME, especially the specific cells or pathways of TME, remains unsatisfactory. Classifying the chemopathological characteristics of TME and crosstalk among one another can greatly benefit further studies exploring effective treating methods. Herein, we present an updated image of TME with emphasis on hypoxic niche, immune microenvironment, metabolism microenvironment, acidic niche, innervated niche, and mechanical microenvironment. We then summarize conventional drugs including aspirin, celecoxib, β-adrenergic antagonist, metformin, and statin in new antitumor application. These drugs are considered as viable candidates for combination therapy due to their antitumor activity and extensive use in clinical practice. We also provide our outlook on directions and potential applications of TME theory. This review depicts a comprehensive and vivid landscape of TME from biology to treatment.Membraneless organelles are sites for RNA biology including small non-coding RNA (ncRNA) mediated gene silencing. How small ncRNAs utilise phase separated environments for their function is unclear. We investigated how the PIWI-interacting RNA (piRNA) pathway engages with the membraneless organelle P granule in Caenorhabditis elegans. Proteomic analysis of the PIWI protein PRG-1 reveals an interaction with the constitutive P granule protein DEPS-1. DEPS-1 is not required for piRNA biogenesis but piRNA-dependent silencing deps-1 mutants fail to produce the secondary endo-siRNAs required for the silencing of piRNA targets. We identify a motif on DEPS-1 which mediates a direct interaction with PRG-1. find protocol DEPS-1 and PRG-1 form intertwining clusters to build elongated condensates in vivo which are dependent on the Piwi-interacting motif of DEPS-1. Additionally, we identify EDG-1 as an interactor of DEPS-1 and PRG-1. Our study reveals how specific protein-protein interactions drive the spatial organisation and piRNA-dependent silencing within membraneless organelles.As a promising hydrogen carrier, formic acid (HCOOH) is renewable, safe and nontoxic. Although noble-metal-based catalysts have exhibited excellent activity in HCOOH dehydrogenation, developing non-noble-metal heterogeneous catalysts with high efficiency remains a great challenge. Here, we modulate oxygen coverage on the surface of Ti3C2Tx MXenes to boost the catalytic activity toward HCOOH dehydrogenation. Impressively, Ti3C2Tx MXenes after treating with air at 250 °C (Ti3C2Tx-250) significantly increase the amount of surface oxygen atoms without the change of crystalline structure, exhibiting a mass activity of 365 mmol·g-1·h-1 with 100% of selectivity for H2 at 80 °C, which is 2.2 and 2.0 times that of commercial Pd/C and Pt/C, respectively. Further mechanistic studies demonstrate that HCOO* is the intermediate in HCOOH dehydrogenation over Ti3C2Tx MXenes with different coverages of surface oxygen atoms. Increasing the oxygen coverage on the surface of Ti3C2Tx MXenes not only promotes the conversion from HCOO* to CO2* by lowering the energy barrier, but also weakens the adsorption energy of CO2 and H2, thus accelerating the dehydrogenation of HCOOH.A mechanistic understanding of core cognitive processes, such as working memory, is crucial to addressing psychiatric symptoms in brain disorders. We propose a combined psychophysical and biophysical account of two symptomatologically related diseases, both linked to hypofunctional NMDARs schizophrenia and autoimmune anti-NMDAR encephalitis. We first quantified shared working memory alterations in a delayed-response task. In both patient groups, we report a markedly reduced influence of previous stimuli on working memory contents, despite preserved memory precision. We then simulated this finding with NMDAR-dependent synaptic alterations in a microcircuit model of prefrontal cortex. Changes in cortical excitation destabilized within-trial memory maintenance and could not account for disrupted serial dependence in working memory. Rather, a quantitative fit between data and simulations supports alterations of an NMDAR-dependent memory mechanism operating on longer timescales, such as short-term potentiation.The genome sequences of many microbial species from the phytobiomes of several leafy Asian greens remain unknown. Here, we address this gap by reconstructing 910 prokaryotic draft genomes from 24 leaf, 65 root, 12 soil, and 6 compost metagenomes from the seedling and adult developmental stages of three leafy Asian greens - Brassica rapa var. parachinensis, Brassica oleracea var. alboglabra and Amaranthus spp. - grown in a commercial, soil-based urban farm. Of these, 128 are near-complete (>90% completeness, less then 5% redundancy), 540 are substantially complete (≥70% completeness, less then 10%, redundancy), while the rest have a completeness ≥50% and redundancy less then 10%. The draft genomes together span 292 bacterial and 3 archaeal species, a subset of which are from underrepresented genus-level lineages in public databases. We expect our dataset to facilitate a wide range of comparative studies that seek to understand the different functional aspects of vegetable crop phytobiomes and for devising new strategies for microbial cultivation in the future.Recently, deep learning has unlocked unprecedented success in various domains, especially using images, text, and speech. However, deep learning is only beneficial if the data have nonlinear relationships and if they are exploitable at available sample sizes. We systematically profiled the performance of deep, kernel, and linear models as a function of sample size on UKBiobank brain images against established machine learning references. On MNIST and Zalando Fashion, prediction accuracy consistently improves when escalating from linear models to shallow-nonlinear models, and further improves with deep-nonlinear models. In contrast, using structural or functional brain scans, simple linear models perform on par with more complex, highly parameterized models in age/sex prediction across increasing sample sizes. In sum, linear models keep improving as the sample size approaches ~10,000 subjects. Yet, nonlinearities for predicting common phenotypes from typical brain scans remain largely inaccessible to the examined kernel and deep learning methods.