Characterizing heterogeneity of non-small cell lung tumour microenvironment to identify signature prognostic genes.

Growing evidence has highlighted the immune response as an important feature of carcinogenesis and therapeutic efficacy in non-small cell lung cancer (NSCLC). This study focused on the characterization of immune infiltration profiling in patients with NSCLC and its correlation with survival outcome. All TCGA samples were divided into three heterogeneous clusters based on immune cell profiles: cluster 1 ('low infiltration' cluster), cluster 2 ('heterogeneous infiltration' cluster) and cluster 3 ('high infiltration' cluster). The immune cells were responsible for a significantly favourable prognosis for the 'high infiltration' community. Cluster 1 had the lowest cytotoxic activity, tumour-infiltrating lymphocytes and interferon-gamma (IFN-γ), as well as immune checkpoint molecules expressions. In addition, MHC-I and immune co-stimulator were also found to have lower cluster 1 expressions, indicating a possible immune escape mechanism. A total of 43 differentially expressed genes (DEGs) that overlapped among the groups were determined based on three clusters. Finally, based on a univariate Cox regression model, prognostic immune-related genes were identified and combined to construct a risk score model able to predict overall survival (OS) rates in the validation datasets.

Modulation of the plasticity of an all-metal oxide synaptic transistor via laser irradiation.

Artificial intelligence devices that can mimic human brains are the foundation for building future artificial neural networks. A key step in mimicking biological neural systems is the modulation of synaptic weight, which is mainly achieved by various engineering approaches using material design, or modification of the device structure. Here, we realize the modulation of the synaptic weight of a Ta2O5/ITO-based all-metal oxide synaptic transistor via laser irradiation. Prior to the deposition of the active layer and electrodes, a femtosecond laser was used to irradiate the surface of the insulator layer. Typical synaptic characteristics such as excitatory postsynaptic current, paired pulse facilitation and long-term potentiation were successfully simulated under different laser intensities and scanning rates. In particular, we demonstrate for the first time that laser irradiation could control the quantity of oxygen vacancies in the Ta2O5 thin film, leading to precise modulation of the synaptic weight. Our research provides an instantaneous (<1 s), convenient and low-temperature approach to improving synaptic behaviors, which could be promising for neuromorphic computing hardware design.

All-metal oxide synaptic transistor with modulatable plasticity.

The artificial neural system has attracted tremendous attention in the field of artificial intelligence due to operate mode of parallel computation which is superior to traditional Von Neumann computers in processing complex sensory data and real-time situations with extremely low power dissipation. Remarkable progress has been made in the hardware-based electric-double-layer synaptic transistors as its modulation by ion movement is similar to biological synapse for the past few years. Unfortunately, long-term potentiation (LTP) timescale is still a big challenge in hardware-based electric-double-layer synaptic transistors which is essential to processing capacity and memory formation. Meanwhile, the effect of ion concentration on the synaptic plasticity has rarely been reported. Here, a solid state electrolyte-gated transistor using Ta2O5 as dielectric layer with unique ionic composition was demonstrated and the regulation of synaptic weight was realized by changing ion concentration. Both the potentiation and depression of synaptic weight such as excitatory post-synaptic current, inhibitory response (IPSC), paired pulse facilitation as well as LTP were successfully simulated. More importantly, oxygen vacancy content was tuned for the first time to modulate synaptic plasticity by varying film thickness and gas ratio, through which the intensity and duration of memory were enhanced with appropriate vacancy concentration. It indicated that appropriate vacancy concentration avoided the effects of internal electric field induced by ion excess, leading to a long-term memory. These results reveal a promising path to improve memory capacity of artificial synapse via ion modulation.

Characterization of the metabolic alteration-modulated tumor microenvironment mediated by TP53 mutation and hypoxia.

BACKGROUND: TP53 mutation and hypoxia play an essential role in cancer progression. However, the metabolic reprogramming and tumor microenvironment (TME) heterogeneity mediated by them are still not fully understood. METHODS: The multi-omics data of 32 cancer types and immunotherapy cohorts were acquired to comprehensively characterize the metabolic reprogramming pattern and the TME across cancer types and explore immunotherapy candidates. An assessment model for metabolic reprogramming was established by integration of multiple machine learning methods, including lasso regression, neural network, elastic network, and survival support vector machine (SVM). Pharmacogenomics analysis and in vitro assay were conducted to identify potential therapeutic drugs. RESULTS: First, we identified metabolic subtype A (hypoxia-TP53 mutation subtype) and metabolic subtype B (non-hypoxia-TP53 wildtype subtype) in hepatocellular carcinoma (HCC) and showed that metabolic subtype A had an "immune inflamed" microenvironment. Next, we established an assessment model for metabolic reprogramming, which was more effective compared to the traditional prognostic indicators. Then, we identified a potential targeting drug, teniposide. Finally, we performed the pan-cancer analysis to illustrate the role of metabolic reprogramming in cancer and found that the metabolic alteration (MA) score was positively correlated with tumor mutational burden (TMB), neoantigen load, and homologous recombination deficiency (HRD) across cancer types. Meanwhile, we demonstrated that metabolic reprogramming mediated a potential immunotherapy-sensitive microenvironment in bladder cancer and validated it in an immunotherapy cohort. CONCLUSION: Metabolic alteration mediated by hypoxia and TP53 mutation is associated with TME modulation and tumor progression across cancer types. In this study, we analyzed the role of metabolic alteration in cancer and propose a predictive model for cancer prognosis and immunotherapy responsiveness. We also explored a potential therapeutic drug, teniposide.

Immune Cell Infiltration-Based Characterization of Triple-Negative Breast Cancer Predicts Prognosis and Chemotherapy Response Markers.

Breast cancer represents the number one cause of cancer-associated mortality globally. The most aggressive molecular subtype is triple negative breast cancer (TNBC), of which limited therapeutic options are available. It is well known that breast cancer prognosis and tumor sensitivity toward immunotherapy are dictated by the tumor microenvironment. Breast cancer gene expression profiles were extracted from the METABRIC dataset and two TNBC clusters displaying unique immune features were identified. Activated immune cells formed a large proportion of cells in the high infiltration cluster, which correlated to a good prognosis. Differentially expressed genes (DEGs) extracted between two heterogeneous subtypes were used to further explore the underlying immune mechanism and to identify prognostic biomarkers. Functional enrichment analysis revealed that the DEGs were predominately related to some processes involved in activation and regulation of innate immune signaling. Using network analysis, we identified two modules in which genes were selected for further prognostic investigation. Validation by independent datasets revealed that CXCL9 and CXCL13 were good prognostic biomarkers for TNBC. We also performed comparisons between the above two genes and immune markers (CYT, APM, TILs, and TIS), as well as cell checkpoint marker expressions, and found a statistically significant correlation between them in both METABRIC and TCGA datasets. The potential of CXCL9 and CXCL13 to predict chemotherapy sensitivity was also evaluated. We found that the CXCL9 and CXCL13 were good predictors for chemotherapy and their expressions were higher in chemotherapy-responsive patients in contrast to those who were not responsive. In brief, immune infiltrate characterization on TNBC revealed heterogeneous subtypes with unique immune features allowed for the identification of informative and reliable characteristics representative of the local immune tumor microenvironment and were potential candidates to guide the management of TNBC patients.

High-Performance Organic Synaptic Transistors with an Ultrathin Active Layer for Neuromorphic Computing.

In recent years, much attention has been focused on two-dimensional (2D) material-based synaptic transistor devices because of their inherent advantages of low dimension, simultaneous read-write operation and high efficiency. However, process compatibility and repeatability of these materials are still a big challenge, as well as other issues such as complex transfer process and material selectivity. In this work, synaptic transistors with an ultrathin organic semiconductor layer (down to 7 nm) were obtained by the simple dip-coating process, which exhibited a high current switch ratio up to 106, well off state as low as nearly 10-12 A, and low operation voltage of -3 V. Moreover, various synaptic behaviors were successfully simulated including excitatory postsynaptic current, paired pulse facilitation, long-term potentiation, and long-term depression. More importantly, under ultrathin conditions, excellent memory preservation, and linearity of weight update were obtained because of the enhanced effect of defects and improved controllability of the gate voltage on the ultrathin active layer, which led to a pattern recognition rate up to 85%. This is the first work to demonstrate that the pattern recognition rate, a crucial parameter for neuromorphic computing can be significantly improved by reducing the thickness of the channel layer. Hence, these results not only reveal a simple and effective way to improve plasticity and memory retention of the artificial synapse via thickness modulation but also expand the material selection for the 2D artificial synaptic devices.

Construction and Analysis of Human Diseases and Metabolites Network.

The relationship between aberrant metabolism and the initiation and progression of diseases has gained considerable attention in recent years. To gain insights into the global relationship between diseases and metabolites, here we constructed a human diseases-metabolites network (HDMN). Through analyses based on network biology, the metabolites associated with the same disorder tend to participate in the same metabolic pathway or cascade. In addition, the shortest distance between disease-related metabolites was shorter than that of all metabolites in the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic network. Both disease and metabolite nodes in the HDMN displayed slight clustering phenomenon, resulting in functional modules. Furthermore, a significant positive correlation was observed between the degree of metabolites and the proportion of disease-related metabolites in the KEGG metabolic network. We also found that the average degree of disease metabolites is larger than that of all metabolites. Depicting a comprehensive characteristic of HDMN could provide great insights into understanding the global relationship between disease and metabolites.

Synaptic Transistor Capable of Accelerated Learning Induced by Temperature-Facilitated Modulation of Synaptic Plasticity.

Neuromorphic computation, which emulates the signal process of the human brain, is considered to be a feasible way for future computation. Realization of dynamic modulation of synaptic plasticity and accelerated learning, which could improve the processing capacity and learning ability of artificial synaptic devices, is considered to further improve energy efficiency of neuromorphic computation. Nevertheless, realization of dynamic regulation of synaptic weight without an external regular terminal and the method that could endow artificial synaptic devices with the ability to modulate learning speed have rarely been reported. Furthermore, finding suitable materials to fully mimic the response of photoelectric stimulation is still challenging for photoelectric synapses. Here, a floating gate synaptic transistor based on an L-type ligand-modified all-inorganic CsPbBr3 perovskite quantum dots is demonstrated. This work provides first clear experimental evidence that the synaptic plasticity can be dynamically regulated by changing the waveforms of action potential and the environment temperature and both of these parameters originate from and are crucial in higher organisms. Moreover, benefiting from the excellent photoelectric properties and stability of quantum dots, a temperature-facilitated learning process is illustrated by the classical conditioning experiment with and without illumination, and the mechanism of synaptic plasticity is also demonstrated. This work offers a feasible way to realize dynamic modulation of synaptic weight and accelerating the learning process of artificial synapses, which showed great potential in the reduction of energy consumption and improvement of efficiency of future neuromorphic computing.

High-Performance Quantum-Dot Light-Emitting Transistors Based on Vertical Organic Thin-Film Transistors.

In this work, a novel vertical quantum-dot light-emitting transistor (VQLET) based on a vertical organic thin-film transistor is successfully fabricated. Benefiting from the new vertical architecture, the VQLET is able to afford an extremely high current density, which allows most of the organic thin film transistors (OTFT) even with low mobility (for instance, poly(3-hexylthiophene)) to drive a quantum-dot light-emitting diode (QLED), which was previously unavailable. Moreover, the hole injection barrier could be modulated by the additional gate electrode, which precisely optimizes the charge balance in the device, a critical issue in QLED, resulting in the precise control of current density and brightness of the VQLET. The VQLET shows a high performance with a maximum current efficiency of 37 cd/A. Furthermore, integrating OTFT and QLED into a single device, the VQLET features drastic advantages by realizing active matrix quantum-dot light-emitting diodes (AMQLEDs), which significantly reduces the number of transistors and frees the large area fraction occupied by transistors. Hence, these results indicate that the VQLET provides a new strategy for realizing a low-cost, solution-processed, high-performance OTFT-AMQLED for the flat panel display technology. Moreover, the novel design offers a unique method to exquisitely control the charge balance and maximize the efficiency the QLED.