Standardized Parts for Activation of Small GTPase Signaling in Living Cells.

Small GTPases comprise a superfamily of over 167 proteins in the human genome and are critical regulators of a variety of pathways including cell migration and proliferation. Despite the importance of these proteins in cell signaling, a standardized approach for controlling small GTPase activation within living cells is lacking. Herein, we report a split-protein-based approach to directly activate small GTPase signaling in living cells. Importantly, our fragmentation site can be applied across the small GTPase superfamily. We highlight the utility of these standardized parts by demonstrating the ability to directly modulate the activity of four different small GTPases with user-defined inputs, providing the first plug and play system for direct activation of small GTPases in living cells.

Scalable decarboxylative trifluoromethylation by ion-shielding heterogeneous photoelectrocatalysis.

Electrochemistry offers a sustainable synthesis route to value-added fine chemicals but is often constrained by competing electron transfer between the electrode and redox-sensitive functionalities distinct from the target site. Here, we describe an ion-shielding heterogeneous photoelectrocatalysis strategy to impose mass-transfer limitations that invert the thermodynamically determined order of electron transfer. This strategy is showcased to enable decarboxylative trifluoromethylation of sensitive (hetero)arenes by using trifluoroacetate, an inexpensive yet relatively inert trifluoromethyl group (CF3) source. An ion-shielding layer, formed by trifluoroacetate anions electrostatically adsorbed on a positive molybdenum-doped tungsten trioxide (WO3) photoanode, prevents undesired electron transfer between substrates and photogenerated holes. The practicality of the developed method was demonstrated with robust photoanode stability (approximately 380 hours), a good substrate scope, and scaling capability to achieve 100-gram synthesis by using photoelectrochemical flow cells.

Targeting lysosomal quality control as a therapeutic strategy against aging and diseases.

Previously, lysosomes were primarily referred to as the digestive organelles and recycling centers within cells. Recent discoveries have expanded the lysosomal functional scope and revealed their critical roles in nutrient sensing, epigenetic regulation, plasma membrane repair, lipid transport, ion homeostasis, and cellular stress response. Lysosomal dysfunction is also found to be associated with aging and several diseases. Therefore, function of macroautophagy, a lysosome-dependent intracellular degradation system, has been identified as one of the updated twelve hallmarks of aging. In this review, we begin by introducing the concept of lysosomal quality control (LQC), which is a cellular machinery that maintains the number, morphology, and function of lysosomes through different processes such as lysosomal biogenesis, reformation, fission, fusion, turnover, lysophagy, exocytosis, and membrane permeabilization and repair. Next, we summarize the results from studies reporting the association between LQC dysregulation and aging/various disorders. Subsequently, we explore the emerging therapeutic strategies that target distinct aspects of LQC for treating diseases and combatting aging. Lastly, we underscore the existing knowledge gap and propose potential avenues for future research.

Declining Chinese attitudes toward the United States amid COVID-19.

In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers.

Insights into the Distinct Behaviors between Bifunctional and Binary Organoborane Catalysts through Terpolymerization of Epoxide, CO2, and Anhydride.

Alkyl borane compounds-mediated polymerizations have expanded to Lewis pair polymerization, free radical polymerization, ionic ring-opening polymerization, and polyhomologation. The bifunctional organoborane catalysts that contain the Lewis acid and ammonium or phosphonium salt in one molecule have demonstrated superior catalytic performance for ring-opening polymerization of epoxides and ring-opening copolymerization of epoxides and CO2 than their two-component analogues, i.e., the blend of organoborane and ammonium or phosphonium salt. To explore the origin of the differences of the one-component and two-component organoborane catalysts, here we conducted a systematic investigation on the catalytic performances of these two kinds of organoborane catalysts via terpolymerization of epoxide, carbon dioxide and anhydride. The resultant terpolymers produced independently by bifunctional and binary organoborane catalyst exhibited distinct microstructures, where a series of gradient polyester-polycarbonate terpolymers with varying polyester content were afforded using the bifunctional catalyst, while tapering diblock terpolymers were obtained using the binary system. The bifunctional catalyst enhances the competitiveness of CO2 insertion than anhydride, which leads to the premature incorporation of CO2 into the polymer chains and ultimately results in the formation of gradient terpolymers. DFT calculations revealed the role of electrostatic interaction and charge distribution caused by intramolecular synergistic effect for bifunctional organoborane catalyst.

LipiDex 2 Integrates MSn Tree-Based Fragmentation Methods and Quality Control Modules to Improve Discovery Lipidomics.

As lipidomics experiments increase in scale and complexity, data processing tools must support workflows for new liquid chromatography-mass spectrometry (LC-MS) methods while simultaneously supporting quality controls to maximize the confidence in lipid identifications. LipiDex 2 improves lipidomics data processing algorithms from LipiDex 1 and introduces new tools for spectral matching and peak annotation functions, with improvements in speed and user-friendliness. In silico spectral library generation now supports tandem mass spectral (MSn) tree-based fragmentation methods, and the LipiDex 2 workflow fully integrates the fragmentation logic into the data processing steps to enable lipid identification at the appropriate level of structural resolution. Finally, LipiDex 2 features new modules for automated quality control checks that also allow users to visualize data quality in a data dashboard user interface.

Lysosomal storage disease proteo/lipidomic profiling using nMOST links ferritinophagy with mitochondrial iron deficiencies in cells lacking NPC2.

Lysosomal storage diseases (LSDs) comprised ~50 monogenic diseases characterized by the accumulation of cellular material in lysosomes and associated defects in lysosomal function, but systematic molecular phenotyping is lacking. Here, we develop a nanoflow-based multi-omic single-shot technology (nMOST) workflow allowing simultaneously quantify HeLa cell proteomes and lipidomes from more than two dozen LSD mutants, revealing diverse molecular phenotypes. Defects in delivery of ferritin and its autophagic receptor NCOA4 to lysosomes (ferritinophagy) were pronounced in NPC2-/- cells, which correlated with increased lyso-phosphatidylcholine species and multi-lamellar membrane structures visualized by cryo-electron-tomography. Ferritinophagy defects correlated with loss of mitochondrial cristae, MICOS-complex components, and electron transport chain complexes rich in iron-sulfur cluster proteins. Strikingly, mitochondrial defects were alleviated when iron was provided through the transferrin system. This resource reveals how defects in lysosomal function can impact mitochondrial homeostasis in trans and highlights nMOST as a discovery tool for illuminating molecular phenotypes across LSDs.

A multi-rate high-order dynamic twin-latent-variable probabilistic modeling and its process monitoring application.

Quality-relevant process monitoring provides important guarantees for the safety of industrial operation, which is based on the assumption that data collection is complete and low-order autocorrelated. However, real industrial processes always exhibit complex characteristics such as multi-rate sampling and high-order dynamic, which pose great challenges for process monitoring. To this end, a multi-rate high-order dynamic twin-latent-variable probabilistic (MHDTVP) model is presented in this paper to extract data correlations among multi-rate measurements from quality-relevant and irrelevant perspectives. Moreover, to reveal the dynamics in the multi-rate sampling process, an autoregressive twin-latent-variable structure is designed to extract both quality-relevant and quality-irrelevant high-order dynamic features. In the MHDTVP model, parameters are trained through an efficient expectation maximization (EM) iteration framework. Finally, the performance conclusions of MHDTVP are validated with the Tennessee Eastman process (TEP) and Thermal Power Plant (TPP). The experimental results demonstrate that the proposed model exhibits superior monitoring efficiency for multi-rate dynamic processes compared to similar approaches.

Comparative efficacy and safety of different surgical approaches for the treatment of anterior cruciate ligament injury: a Bayesian network meta-analysis protocol.

INTRODUCTION: Anterior cruciate ligament (ACL) rupture can be treated surgically or non-surgically, with several surgical interventions available at present. However, the comparatively effective surgical intervention with relatively fewer side effects remains unknown. This study aims to fill in this gap by conducting a Bayesian network meta-analysis (NMA) and provide a theoretical basis for the clinical application. METHOD AND ANALYSIS: We will perform a Bayesian NMA and will include randomised controlled trials (RCTs) published in English or Chinese that compare surgical intervention (ie, standard ACL reconstruction, ACL remnant-preserving reconstruction and ACL repair with suture augmentation to conservative therapy or studies that compare one surgical intervention to another for the symptom relief and function recovery of patients with ACL rupture. Primary outcome will be the proportion of patients with symptomatic and functional improvement measured by the Knee Injury and Osteoarthritis Outcome Score before and 6 months after treatment, with scores ranging from 0 (worst) to 100 (best). Secondary outcomes will be knee-specific quality of life (ACL QoL), return to activity and level of sport participation (Tegner or modified Tegner score), health-related QoL (EuroQol Group 5-Dimension 5-Level, EQ-5D-5L), resource use, intervention-related complications and patient satisfaction. We have developed search strategies for PubMed, Embase, the Cochrane Library and Web of Science, retrieving RCTs that meet the inclusion criteria from database inception to 1 December 2023. The methodological quality of the included RCTs will be assessed based on the Cochrane risk of bias table. The relative ranking probability of the best intervention will be estimated using the surface under the cumulative ranking curve. The Bayesian NMA will be conducted by using WinBUGS V.1.4.3. The Grading of Recommendations Assessment, Development and Evaluation approach will be applied to determine our confidence in an overall treatment ranking from the NMA. ETHICS AND DISSEMINATION: Ethical approval for this study is not required because no private or confidential patient data will be used in this study. Findings of this study would be disseminated through the publication in a peer-reviewed medical journal. PROSPERO REGISTRATION NUMBER: CRD42023437115.

Standardized Parts for Activation of Small GTPase Signaling in Living Cells.

Small GTPases comprise a superfamily of over 167 proteins in the human genome and are critical regulators of a variety of pathways including cell migration and proliferation. Despite the importance of these proteins in cell signaling, a standardized approach for controlling small GTPase activation within living cells is lacking. Herein, we report a split-protein-based approach to directly activate small GTPase signaling in living cells. Importantly, our fragmentation site can be applied across the small GTPase superfamily. We highlight the utility of these standardized parts by demonstrating the ability to directly modulate the activity of four different small GTPases with user-defined inputs, providing a plug and play system for direct activation of small GTPases in living cells.

Azaphosphinate Dyes: A Low Molecular Weight Near-Infrared Scaffold for Development of Photoacoustic or Fluorescence Imaging Probes.

Near-infrared (NIR) dyes are desirable for biological imaging applications including photoacoustic (PA) and fluorescence imaging. Nonetheless, current NIR dyes are often plagued by relatively large molecular weights, poor water solubility, and limited photostability. Herein, we provide the first examples of azaphosphinate dyes which display desirable properties such as low molecular weight, absorption/emission above 750 nm, and remarkable water solubility. In PA imaging, an azaphosphinate dye exhibited a 4.1-fold enhancement in intensity compared to commonly used standards, the ability to multiplex with existing dyes in whole blood, imaging depths of 2.75 cm in a tissue model, and contrast in mice. An improved derivative for fluorescence imaging displayed a >10-fold reduction in photobleaching in water compared to the FDA-approved indocyanine green dye and could be visualized in mice. This new dye class provides a robust scaffold for the development of photoacoustic or NIR fluorescence imaging agents.

A deep unrolled neural network for real-time MRI-guided brain intervention.

Accurate navigation and targeting are critical for neurological interventions including biopsy and deep brain stimulation. Real-time image guidance further improves surgical planning and MRI is ideally suited for both pre- and intra-operative imaging. However, balancing spatial and temporal resolution is a major challenge for real-time interventional MRI (i-MRI). Here, we proposed a deep unrolled neural network, dubbed as LSFP-Net, for real-time i-MRI reconstruction. By integrating LSFP-Net and a custom-designed, MR-compatible interventional device into a 3 T MRI scanner, a real-time MRI-guided brain intervention system is proposed. The performance of the system was evaluated using phantom and cadaver studies. 2D/3D real-time i-MRI was achieved with temporal resolutions of 80/732.8 ms, latencies of 0.4/3.66 s including data communication, processing and reconstruction time, and in-plane spatial resolution of 1 × 1 mm2. The results demonstrated that the proposed method enables real-time monitoring of the remote-controlled brain intervention, and showed the potential to be readily integrated into diagnostic scanners for image-guided neurosurgery.

Gamma Rhythm and Theta-gamma Coupling Alternation in Chronic Unpredictable Stress (CUS)-induced Depression Rats.

Depression is a debilitating disease, which, in severe cases, can lead to suicide. However, objective and reliable biomarker for the diagnosis of depression is lack. In this preclinical study, we recorded resting local field potentials (LFPs) from chronic unpredictable stress (CUS)-induced depressed (n =20) and control (n = 20) rats and then compared their gamma activities in terms of single-band and cross-frequency coupling patterns. Both theta-gamma coupling and relative power in total gamma band revealed significant abnormalities in gamma rhythm in the right auditory cortex of depressed rats. These findings implied that resting-state gamma rhythms may be a promising objective diagnostic biomarker for depression. Furthermore, our research provided direct evidence from the perspective of source signals in deep brain sites, which might be useful for clinical applications.Clinical Relevance- This research showed that resting gamma in the auditory cortex is a promising biomarker for depression diagnosis.

Transcranial alternating current stimulation ameliorates emotional attention through neural oscillations modulation.

Background: Numerous clinical reports have suggested that psychopathy like schizophrenia, anxiety and depression is accompanied by early attentional abnormalities in emotional processing. Recently, the efficacy of transcranial alternating current stimulation (tACS) in changing emotional functioning has been repeatedly observed and demonstrated a causal relationship between endogenous oscillations and emotional processing. Aims: Up to now, tACS effects on emotional attention have not yet been tested. To assess such ability, we delivered active-tACS at individual alpha frequency (IAF), 10 Hz or sham-tACS for 7 consecutive days in the bilaterally dorsolateral prefrontal cortex (dlPFC) to totally 79 healthy participants. Results: IAF-tACS group showed significant alpha entrainment at-rest, especially in open state around stimulation area and showed an obvious advantage compared to 10 Hz-tACS. Event-related potential revealed a significant larger P200 amplitude after active-tACS and IAF group showed wider range of emotions than 10 Hz-tACS, indicating the attentional improvement in facial emotion processing. A notable positive correlation between alpha power and P200 amplitude provided an electrophysiological interpretation regarding the role of tACS in emotional attention modulation instead of somatosensory effects. Conclusion: These results support a seminal outcome for the effect of IAF-tACS on emotional attention modulation, demonstrating a feasible and individual-specific therapy for neuropsychiatric disorders related to emotion processing, especially regarding oscillatory disturbances.

A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s.

A forest wildlife detection algorithm based on an improved YOLOv5s network model is proposed to advance forest wildlife monitoring and improve detection accuracy in complex forest environments. This research utilizes a data set from the Hunan Hupingshan National Nature Reserve in China, to which data augmentation and expansion methods are applied to extensively train the proposed model. To enhance the feature extraction ability of the proposed model, a weighted channel stitching method based on channel attention is introduced. The Swin Transformer module is combined with a CNN network to add a Self-Attention mechanism, thus improving the perceptual field for feature extraction. Furthermore, a new loss function (DIOU_Loss) and an adaptive class suppression loss (L_BCE) are adopted to accelerate the model's convergence speed, reduce false detections in confusing categories, and increase its accuracy. When comparing our improved algorithm with the original YOLOv5s network model under the same experimental conditions and data set, significant improvements are observed, in particular, the mean average precision (mAP) is increased from 72.6% to 89.4%, comprising an accuracy improvement of 16.8%. Our improved algorithm also outperforms popular target detection algorithms, including YOLOv5s, YOLOv3, RetinaNet, and Faster-RCNN. Our proposed improvement measures can well address the challenges posed by the low contrast between background and targets, as well as occlusion and overlap, in forest wildlife images captured by trap cameras. These measures provide practical solutions for enhanced forest wildlife protection and facilitate efficient data acquisition.

Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism.

Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors. Our model adopts the convolution neural network to extract the data feature and optimize the pre-trained model. In particular, the model obtains faster training and prediction due to the embedded attention mechanism. For datasets, we take ATAC-seq, ChIP-seq, and DNA sequences data for the pre-trained model, and single-cell ATAC-seq data is used to predict the TF binding graph in the given cell. We verify the interpretability of the model through ablation experiments and sensitivity analysis. IscPAM can efficiently predict the combination of whole genome transcription factors in single cells and study cellular heterogeneity through chromatin accessibility of related diseases.

Force field-inspired transformer network assisted crystal density prediction for energetic materials.

Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they can automatically learn the features of the molecule from the graph, significantly reducing the time needed to find and build molecular descriptors. However, the application of machine learning to energetic materials property prediction is still in the initial stage due to insufficient data. In this work, we first curated a dataset of 12,072 compounds containing CHON elements, which are traditionally regarded as main composition elements of energetic materials, from the Cambridge Structural Database, then we implemented a refinement to our force field-inspired neural network (FFiNet), through the adoption of a Transformer encoder, resulting in force field-inspired Transformer network (FFiTrNet). After the improvement, our model outperforms other machine learning-based and GNNs-based models and shows its powerful predictive capabilities especially for high-density materials. Our model also shows its capability in predicting the crystal density of potential energetic materials dataset (i.e. Huang & Massa dataset), which will be helpful in practical high-throughput screening of energetic materials.

High-quality and high-diversity conditionally generative ghost imaging based on denoising diffusion probabilistic model.

Deep-learning (DL) methods have gained significant attention in ghost imaging (GI) as promising approaches to attain high-quality reconstructions with limited sampling rates. However, existing DL-based GI methods primarily emphasize pixel-level loss and one-to-one mapping from bucket signals or low-quality GI images to high-quality images, tending to overlook the diversity in image reconstruction. Interpreting image reconstruction from the perspective of conditional probability, we propose the utilization of the denoising diffusion probabilistic model (DDPM) framework to address this challenge. Our designed method, known as DDPMGI, can not only achieve better quality but also generate reconstruction results with high diversity. At a sampling rate of 10%, our method achieves an average PSNR of 21.19 dB and an SSIM of 0.64, surpassing the performance of other comparison methods. The results of physical experiments further validate the effectiveness of our approach in real-world scenarios. Furthermore, we explore the potential application of our method in color GI reconstruction, where the average PSNR and SSIM reach 20.055 dB and 0.723, respectively. These results highlight the significant advancements and potential of our method in achieving high-quality image reconstructions in GI, including color image reconstruction.

Continuous enrichment and trace analysis of tetracyclines in bovine milk using dual-functionalized aqueous biphasic system combined with high-performance liquid chromatography.

Two PPG1000 based temperature-sensitive magnetic ionic liquid were synthesized and characterized. The temperature-sensitive magnetic ionic liquid aqueous biphasic system combined with HPLC was applied for the continuous enrichment and trace analysis of tetracycline antibiotics (TC) in bovine milk for the first time. High enrichment factors were achieved and the detection was highly sensitive. The trace analysis of TC was rapid, free of organic solvent, recyclable and magnetically assisted for phase separation. Under optimum conditions, wide linear ranges of 0.25-300 ng/mL for all TC, high enrichment factors of 217.7-231.4, good precisions with relative standard deviation in the range of 0.74-3.97%, very low limits of detection of 0.031-0.067 ng/mL, limits of quantification of 0.103-0.223 ng/mL, and good recoveries of 94.28-99.76% were acquired for the proposed analytical method. Real milk analysis was satisfactory. This developed analytical method is showing great potential for trace analysis of targeted analytes in foods and drinks.