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Complementary Metal Oxide Semiconductor (CMOS) and Micro-Electro-Mechanical System (MEMS) devices play significant roles in emerging research fields such as artificial intelligence (AI) [...].Complementary Metal Oxide Semiconductor (CMOS) and Micro-Electro-Mechanical System (MEMS) devices play significant roles in emerging research fields such as artificial intelligence (AI) [...].

The c-MET receptor is a receptor tyrosine kinase (RTK) that plays essential roles in normal cell development and motility. Aberrant activation of c-MET can lead to both tumors growth and metastatic progression of cancer cells. C-MET can be activated by either hepatocyte growth factor (HGF), or its natural isoform NK1. Here, we report the cryo-EM structures of c-MET/HGF and c-MET/NK1 complexes in the active state. The c-MET/HGF complex structure reveals that, by utilizing two distinct interfaces, one HGF molecule is sufficient to induce a specific dimerization mode of c-MET for receptor activation. The binding of heparin as well as a second HGF to the 2:1 c-MET:HGF complex further stabilize this active conformation. Distinct to HGF, NK1 forms a stable dimer, and bridges two c-METs in a symmetrical manner for activation. Collectively, our studies provide structural insights into the activation mechanisms of c-MET, and reveal how two isoforms of the same ligand use dramatically different mechanisms to activate the receptor. Activation of c-MET receptor tyrosine kinase involves hepatocyte growth factor (HGF) and glycosaminoglycans, but the molecular mechanism is still under debate. Here, the authors present cryoEM structures of c-MET bound to two HGF splice variants and heparin, revealing the structural basis for c-MET activation.

Background During the COVID-19 epidemic, the prevalence of neck pain among college students has increased due to the shift from offline to online learning and increasing academic and employment pressures. Therefore, this systematic review aimed to identify the personal, occupational, and psychological factors associated with the development of neck pain to promote the development of preventive strategies and early intervention treatment. Methods Seven electronic databases were searched from inception to December 2022 for cross-sectional studies, cohort studies, case­­-­control studies, and randomized controlled trials (RCTs) on neck pain. The quality of the selected studies were assessed by American Agency for Healthcare Research and Quality (AHRQ) or the Newcastle-Ottawa Scale (NOS). Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the effects of the included risk factors on neck pain. Results Thirty studies were included, including 18,395 participants. And a total of 33 potentially associated risk factors were identified. Ultimately, 11 risk factors were included in the meta-analysis after assessing, and all results were statistically significant ( P  < 0.05). The factors supported by strong evidence mainly include the improper use of the pillow (OR = 2.20, 95% CI: 1.39 to 3.48), lack of exercise (OR = 1.88, 95% CI: 1.53 to 2.30), improper sitting posture (OR = 1.97, 95% CI: 1.39 to 2.78), history of neck and shoulder trauma (OR = 2.32, 95% CI: 1.79 to 3.01), senior grade (OR = 2.86, 95% CI: 2.07 to 3.95), staying up late (OR = 1.80, 95% CI: 1.35 to 2.41), long-time electronic product usage daily (OR = 1.53, 95% CI: 1.33 to 1.76), long-time to bow head (OR = 2.04, 95% CI: 1.58 to 2.64), and emotional problems (OR = 2.09; 95% CI: 1.66  to 2.63). Risk factors supported by moderate evidence were high stress (OR = 1.61, 95% CI: 1.02 to 2.52) and female gender (OR = 1.69, 95% CI: 1.52 to 1.87). Conclusion This study obtained 11 main risk factors affecting college students neck pain, including improper use of the pillow, lack of exercise, improper sitting posture, history of neck and shoulder trauma, senior grade, staying up late, long-term electronic product usage daily, long time to bow head, high stress, emotional problems and female gender.

Metastasis is the main cause of death in individuals with cancer. Immune checkpoint blockade (ICB) can potentially reverse CD8+ cytotoxic T lymphocytes (CTLs) dysfunction, leading to significant remission in multiple cancers. However, the mechanism underlying the development of CTL exhaustion during metastatic progression remains unclear. Here, we established an experimental pulmonary metastasis model with melanoma cells and discovered a critical role for melanoma‐released exosomes in metastasis. Using genetic knockdown of nSMase2 and Rab27a, 2 key enzymes for exosome secretion, we showed that high levels of effector‐like tumor‐specific CD8+ T cells with transitory exhaustion, instead of terminal exhaustion, were observed in mice without exosomes; these cells showed limited inhibitory receptors and strong proliferation and cytotoxicity. Mechanistically, the immunosuppression of exosomes depends on exogenous PD‐L1, which can be largely rescued by pretreatment with antibody blockade. Notably, we also found that exosomal PD‐L1 acts as a promising predictive biomarker for ICB therapies during metastasis. Together, our findings suggest that exosomal PD‐L1 may be a potential immunotherapy target, suggesting a new curative therapy for tumor metastasis. We demonstrate a crucial role for tumor‐released exosomes in promoting metastatic progression. We provide extensive evidence that exosomes from tumor cells act alone as contributors to driving tumor‐specific CD8+ T cell exhaustion in vivo. We also find that exosomal PD‐L1 acts as a promising predictive biomarker for ICB therapies during metastasis.

Van der Waals (vdW) heterostructures provide a unique opportunity to develop various electronic and optoelectronic devices with specific functions by designing novel device structures, especially for bioinspired neuromorphic optoelectronic devices, which require the integration of nonvolatile memory and excellent optical responses. Here, we demonstrate a programmable optoelectronic synaptic floating‐gate transistor based on multilayer graphene/h‐BN/MoS2 vdW heterostructures, where both plasticity emulation and modulation were successfully realized in a single device. The dynamic tunneling process of photogenerated carriers through the as‐fabricated vdW heterostructures contributed to a large memory ratio (105) between program and erase states. Our device can work as a functional or silent synapse by applying a program/erase voltage spike as a modulatory signal to determine the response to light stimulation, leading to a programmable operation in optoelectronic synaptic transistors. Moreover, an ultra‐low energy consumption per light spike event (~2.5 fJ) was obtained in the program state owing to a suppressed noise current by program operation in our floating‐gate transistor. This study proposes a feasible strategy to improve the functions of optoelectronic synaptic devices with ultra‐low energy consumption based on vdW heterostructures designed for highly efficient artificial neural networks. A neuromorphic optoelectronic floating‐gate transistor based on multilayer graphene/h‐BN/MoS2 vdW heterostructure exhibits programmable synaptic plasticity due to the unique light‐induced carrier tunneling through vdW heterostructure. Ultra‐low energy consumption for the electrical response to light stimulation is also realized under a low Vds at program state, demonstrating its great potential in building efficient artificial neural networks based on vdW heterostructures.

The structure-performance relationship for single atom catalysts has remained unclear due to the averaged coordination information obtained from most single-atom catalysts. Periodic array of single atoms may provide a platform to tackle this inaccuracy. Here, we develop a data-driven approach by incorporating high-throughput density functional theory computations and machine learning to screen candidates based on a library of 1248 sites from single atoms array anchored on biaxial-strained transition metal dichalcogenides. Our screening results in Au atom anchored on biaxial-strained MoSe 2 surface via Au-Se 3 bonds. Machine learning analysis identifies four key structural features by classifying the ΔG H* data. We show that the average band center of the adsorption sites can be a predictor for hydrogen adsorption energy. This prediction is validated by experiments which show single-atom Au array anchored on biaxial-strained MoSe 2 archives 1000 hour-stability at 800 mA cm -2 towards acidic hydrogen evolution. Moreover, active hotspot consisting of Au atoms array and the neighboring Se atoms is unraveled for enhanced activity. The structure-performance relationship of single-atom catalysts remains unclear. Here a data-driven approach with high-throughput DFT and machine learning is used to screen 1248 single atoms arrays, to provide a better understanding of the hydrogen evolution reaction mechanism.

Recently, the concept of the difference and sum co-array (DSCa) has attracted much attention in array signal processing due to its high degree of freedom (DOF). In this paper, the DSCa of the nested array (NA) is analyzed and then an improved nested configuration known as the diff-sum nested array (DsNA) is proposed. We find and prove that the sum set for the NA contains all the elements in the difference set. Thus, there exists the dual characteristic between the two sets, i.e., for the difference result between any two sensor locations of the NA, one equivalent non-negative/non-positive sum result of two other sensor locations can always be found. In order to reduce the redundancy for further DOF enhancement, we develop a new DsNA configuration by moving nearly half the dense sensors of the NA to the right side of the sparse uniform linear array (ULA) part. These moved sensors together with the original sparse ULA form an extended sparse ULA. For analysis, we provide the closed form expressions of the DsNA locations as well as the DOF. Compared with some novel sparse arrays with large aperture such as the NA, coprime array and augmented nested array, the DsNA can achieve a higher number of DOF. The effectiveness of the proposed array is proved by the simulations.

In this paper, we propose a vectorized noncircular MUSIC (VNCM) algorithm based on the concept of the coarray, which can construct the difference and sum (diff–sum) coarray, for direction finding of the noncircular (NC) quasi-stationary sources. Utilizing both the NC property and the concept of the Khatri–Rao product, the proposed method can be applied to not only the ULA but also sparse arrays. In addition, we utilize the quasi-stationary characteristic instead of the spatial smoothing method to solve the coherent issue generated by the Khatri–Rao product operation so that the available degree of freedom (DOF) of the constructed virtual array will not be reduced by half. Compared with the traditional NC virtual array obtained in the NC MUSIC method, the diff–sum coarray achieves a higher number of DOFs as it comprises both the difference set and the sum set. Due to the complementarity between the difference set and the sum set for the coprime array, we choose the coprime array with multiperiod subarrays (CAMpS) as the array model and summarize the properties of the corresponding diff–sum coarray. Furthermore, we develop a diff–sum coprime array with multiperiod subarrays (DsCAMpS) whose diff–sum coarray has a higher DOF. Simulation results validate the effectiveness of the proposed method and the high DOF of the diff–sum coarray.

Background Glioma is a primary brain tumor that is frequently encountered in humans, with malignant gliomas representing an exceptionally aggressive subtype. The role of AOC1 in the progression of various human tumors has been documented; however, its involvement in gliomas (including distinct WHO grades, namely WHO Grade IV glioblastoma multiforme [GBM] and WHO Grades II–III low-grade gliomas [LGG]), remains largely unexplored. Methods We analyzed RNA sequencing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases to assess AOC1 expression. We performed transcriptomic analysis and differential gene expression studies based on AOC1 levels in TCGA sequencing data. Functional analyses exploring the signaling pathways involved in AOC1. CIBERSORT, single-sample Gene Set Enrichment Analysis (ssGSEA), and ESTIMATE were employed to estimate the tumor microenvironment in gliomas. Analyzing the single-cell sequencing data further revealed the value of AOC1 played in intercellular communication. Finally, the mechanism of AOC1 involvement in gliomas was verified by cellular and molecular experiments. Results High tumor expression of AOC1 is associated with lower patient survival. Our analyses revealed that cytokine signaling pathways are significantly suppressed in AOC1-high GBMLGG tissues. Q-PCR results corroborated that AOC1 inhibits IL17, IL6 and IL1β secretion in GBM cells. The CIBERSORT analysis showed a significant increase in the proportion of M2 macrophages in AOC1-high cancer tissues. Cell-cell communication analysis indicated that the MIF signaling network between AOC1-high GBMLGG tumor cells and macrophages is notably weakened. Additionally, AOC1 silencing directly delays the proliferation and migration of GBMLGG cells in vitro. Conclusions Tumor AOC1 levels can serve as a molecular marker for diagnosis and molecular subtyping. As a secreted protein, AOC1 holds promise as a new target for non-invasive molecular diagnosis and precision therapy in GBMLGG.