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The charge transport in quasi‐2D perovskites limits their applications despite the superior stability and optoelectronic properties. Herein, a novel strategy is proposed to enhance the charge transport by regulating 3D perovskite phase in quasi‐2D perovskite films. The carbohydrazide (CBH) as an additive is introduced into (PEA)2MA3Pb4I13 precursors, which slows down the crystallization process and improves the phase ratio and crystal quality of the 3D phase. This structure change results in a significant improvement in charge transport and extraction, leading to the device demonstrating an almost 100% internal quantum efficiency, a peak responsivity of 0.41 A W−1, and a detectivity of 1.31 × 1012 Jones at 570 nm under 0 V bias. Furthermore, the air and moisture stability of (PEA)2MA3Pb4I13 films is not deteriorated but gets significantly improved due to the better crystal quality and the passivation of defects by the residual CBH molecule. This work demonstrates a strategy for improving the charge transport properties of quasi‐2D perovskites and also sheds light on solving the stability issue of 3D perovskite films via the proper passivation or additives, which will inspire the fast development of the perovskite community. [ABSTRACT FROM AUTHOR]

Single‐cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data‐independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single‐cell samples, without losing proteomic depth. Lys‐N digestion enables five‐plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven‐fold for microdissection and four‐fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology. Synopsis: A robust and automated multiplexed DIA (mDIA) workflow is presented, using complete dimethyl labeling for bulk or single‐cell proteomics. Accurate quantification with a reference channel, combined with the RefQuant algorithm, confirms the hypothesis of a stable single‐cell proteome. Five‐plex quantification at MS1 and MS2 level for multiplexed DIA is enabled by the Lys‐N enzyme.A reference channel in mDIA doubles proteomic depth in single cells at 80 single cells per day.mDIA is combined with Deep Visual Proteomics (DVP) for precision oncology. [ABSTRACT FROM AUTHOR]

SARS‐CoV‐2 may directly and indirectly damage lung tissue and other host organs, but there are few system‐wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus‐infected FFPE tissues. The first layer of response to SARS‐CoV‐2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue‐specific effects to reflect distinct COVID‐19‐associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non‐COVID‐19 patients. Extensive organ‐specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS‐proteomics‐derived results contribute substantially to our understanding of COVID‐19 pathomechanisms and suggest strategies for organ‐specific therapeutic interventions. Synopsis: This study reports a proteomic investigation of fatal COVID‐19 across organs using mass spectrometry, highlighting the central role of circulating inflammatory molecules and uncovering tissue‐specific alterations beyond inflammation. Parallelized sonification and streamlined MS‐based proteomics for FFPE tissue.Circulating inflammatory effectors dominate tissue responses to SARS‐CoV‐2.Deconvolution of the inflammatory response unmasks organ‐specific effects.Distinct remodeling of lung tissue compared to other destructive lung diseases. [ABSTRACT FROM AUTHOR]

The realization that mobile phones can detect rice diseases and insect pests not only solves the problems of low efficiency and poor accuracy from manually detection and reporting, but it also helps farmers detect and control them in the field in a timely fashion, thereby ensuring the quality of rice grains. This study examined two Improved detection models for the detection of six high-frequency diseases and insect pests. These models were the Improved You Only Look Once (YOLO)v5s and YOLOv7-tiny based on their lightweight object detection networks. The Improved YOLOv5s was introduced with the Ghost module to reduce computation and optimize the model structure, and the Improved YOLOv7-tiny was introduced with the Convolutional Block Attention Module (CBAM) and SIoU to improve model learning ability and accuracy. First, we evaluated and analyzed the detection accuracy and operational efficiency of the models. Then we deployed two proposed methods to a mobile phone. We also designed an application to further verify their practicality for detecting rice diseases and insect pests. The results showed that Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9). It also reduced network parameters, model size, and the floating point operations per second (FLOPs) by 47.5, 45.7, and 48.7%, respectively. Furthermore, it increased the model inference speed by 38.6% compared with the original YOLOv5s model. Improved YOLOv7-tiny outperformed the original YOLOv7-tiny in detection accuracy, which was second only to Improved YOLOv5s. The probability heat maps of the detection results showed that Improved YOLOv5s performed better in detecting large target areas of rice diseases and insect pests, while Improved YOLOv7-tiny was more accurate in small target areas. On the mobile phone platform, the precision and recall of Improved YOLOv5s under FP16 accuracy were 0.925 and 0.939, and the inference speed was 374 ms/frame, which was superior to Improved YOLOv7-tiny. Both of the proposed improved models realized accurate identification of rice diseases and insect pests. Moreover, the constructed mobile phone application based on the improved detection models provided a reference for realizing fast and efficient field diagnoses. [ABSTRACT FROM AUTHOR]

Adult-onset acquired immunodeficiency caused by anti-IFN-γ autoantibodies is associated with severe opportunistic infection. Due to lack of specific symptoms and different manifestations, this form of infection can be easily misdiagnosed or overlooked. Herein, we present a case of Nocardia farcinica and Talaromyces marneffei co-infection in a patient with anti-IFN-γ autoantibodies (AIGAs). The patient, a 54-year-old man, presented with a 1-month history of fever, coughing and expectoration, dizziness, headache and gait imbalance. Laboratory workup revealed increased inflammatory markers, negative anti-HIV antibody and a high positive titer of AIGAs. Chest computed tomography (CT) showed multiple patches of high-density shadows in both lungs, and brain enhanced magnetic resonance imaging (MRI) showed an irregular lesion. The patient underwent a craniotomy for resection of the lesion. Pulmonary T. marneffei infection was diagnosed through sputum and bronchoalveolar lavage fluid culture, and brain nocardiosis was confirmed via purulent fluid culture of brain tissue. With regular antibiotic therapy, his symptoms improved and there was no recurrence during 18-month follow-up. This may be the first detailed case report detailing infection with these two distinct pathogens in disparate anatomical locations. [ABSTRACT FROM AUTHOR]

In recent years, substantial advancements have been made in the development of enzyme-free glucose sensors utilizing pristine metal-organic frameworks (MOFs) and their combinations. This paper provides a comprehensive exploration of various MOF-based glucose sensors, encompassing monometallic MOF sensors as well as multi-metal MOF combinations. These approaches demonstrate improved glucose detection capabilities, facilitated by the augmented surface area and availability of active sites within the MOF structures. Furthermore, the paper delves into the application of MOF complexes and derivatives in enzyme-free glucose sensing. Derivatives incorporating carbon or metal components, such as carbon cloth synthesis, rGO-MOF composites, and core–shell structures incorporating noble metals, exhibit enhanced electrochemical performance. Additionally, the integration of MOFs with foams or biomolecules, such as porphyrins, enhances the electrocatalytic properties for glucose detection. Finally, this paper concludes with an outlook on the future development prospects of enzyme-free glucose MOF sensors. [ABSTRACT FROM AUTHOR]

While exploring the process of CO/CO2 electroreduction (COxRR) is of great significance to achieve carbon recycling, deciphering reaction mechanisms so as to further design catalytic systems able to overcome sluggish kinetics remains challenging. In this work, a model single-Co-atom catalyst with well-defined coordination structure is developed and employed as a platform to unravel the underlying reaction mechanism of COxRR. The as-prepared single-Co-atom catalyst exhibits a maximum methanol Faradaic efficiency as high as 65% at 30 mA/cm2 in a membrane electrode assembly electrolyzer, while on the contrary, the reduction pathway of CO2 to methanol is strongly decreased in CO2RR. In-situ X-ray absorption and Fourier-transform infrared spectroscopies point to a different adsorption configuration of *CO intermediate in CORR as compared to that in CO2RR, with a weaker stretching vibration of the C–O bond in the former case. Theoretical calculations further evidence the low energy barrier for the formation of a H-CoPc-CO– species, which is a critical factor in promoting the electrochemical reduction of CO to methanol. Deciphering the reaction mechanisms of CO/CO2 electroreduction to methanol remains challenging. Here the authors report the higher electron density of single-Co-atom center, along with a different adsorption configuration of *CO, is crucial for promoting the CO electroreduction to methanol. [ABSTRACT FROM AUTHOR]

The segment‐valve graph model is widely used in the reliable evaluation of water distribution networks (WDNs). However, as an undirected graph, a segment‐valve graph does not express the upstream‐downstream relationships between segments, which hinders finding valves that need to be closed when a pipe bursts and the water outage region. Therefore, in this paper, directed edges applied to a segment‐valve graph are introduced to demonstrate the downstream segments of critical segments, and an improved segment‐valve graph with flow directions is proposed. The improved model is used to solve the valve isolation problem and evaluate the reliability of WDNs. The results show that the improved model can determine that the valve needs to be closed more quickly when a pipe burst occurs. In addition, we improve the traditional classical indicators based on the segment‐valve graph model. The new indicators based on the improved model can reflect the actual impact and the disposal difficulty of pipe network accidents more accurately and clearly. The proposed method can play a critical role in accident processing and reliability analysis of WDNs. Key Points: A novel analysis model of the water distribution networks is proposed to solve the problem of valve isolation and reliability evaluationThe flow direction of critical valves is marked with directed edges in the segment‐valve modelThe improved model could be useful to solve the problem of valve isolation and reliability evaluation [ABSTRACT FROM AUTHOR]

The study of wearable systems based on surface electromyography (sEMG) signals has attracted widespread attention and plays an important role in human–computer interaction, physiological state monitoring, and other fields. Traditional sEMG signal acquisition systems are primarily targeted at body parts that are not in line with daily wearing habits, such as the arms, legs, and face. In addition, some systems rely on wired connections, which impacts their flexibility and user-friendliness. This paper presents a novel wrist-worn system with four sEMG acquisition channels and a high common-mode rejection ratio (CMRR) greater than 120 dB. The circuit has an overall gain of 2492 V/V and a bandwidth of 15~500 Hz. It is fabricated using flexible circuit technologies and is encapsulated in a soft skin-friendly silicone gel. The system acquires sEMG signals at a sampling rate of over 2000 Hz with a 16-bit resolution and transmits data to a smart device via low-power Bluetooth. Muscle fatigue detection and four-class gesture recognition experiments (accuracy greater than 95%) were conducted to validate its practicality. The system has potential applications in natural and intuitive human–computer interaction and physiological state monitoring. [ABSTRACT FROM AUTHOR]