[Research Progress in the Anti-Severe Acute Respiratory Syndrome Coronavirus 2 Drugs Targeting Spike Protein].

The coronavirus disease 2019(COVID-19) caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) is spreading around the world,while the specific drugs targeting SARS-CoV-2 are still under development.On the basis of the biological characteristics of SARS-CoV-2 and the key protein(spike protein) for viral replication,this paper introduces the research progress in the action sites of related drugs,providing information for clinical application and ideas for development of anti-SARS-CoV-2 drugs.

Construction of human activity-based phosphorylation networks.

The landscape of human phosphorylation networks has not been systematically explored, representing vast, unchartered territories within cellular signaling networks. Although a large number of in vivo phosphorylated residues have been identified by mass spectrometry (MS)-based approaches, assigning the upstream kinases to these residues requires biochemical analysis of kinase-substrate relationships (KSRs). Here, we developed a new strategy, called CEASAR, based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high-quality KSRs. We then generated consensus phosphorylation motifs for each of the kinases and integrated this information, along with information about in vivo phosphorylation sites determined by MS, to construct a high-resolution map of phosphorylation networks that connects 230 kinases to 2591 in vivo phosphorylation sites in 652 substrates. The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B-cell receptor signaling. Overall, these studies provide global insights into kinase-mediated signaling pathways and promise to advance our understanding of cellular signaling processes in humans.

Regulation of nuclear PKA revealed by spatiotemporal manipulation of cyclic AMP.

Understanding how specific cyclic AMP (cAMP) signals are organized and relayed to their effectors in different compartments of the cell to achieve functional specificity requires molecular tools that allow precise manipulation of cAMP in these compartments. Here we characterize a new method using bicarbonate-activatable and genetically targetable soluble adenylyl cyclase to control the location, kinetics and magnitude of the cAMP signal. Using this live-cell cAMP manipulation in conjunction with fluorescence imaging and mechanistic modeling, we uncovered the activation of a resident pool of protein kinase A (PKA) holoenzyme in the nuclei of HEK-293 cells, modifying the existing dogma of cAMP-PKA signaling in the nucleus. Furthermore, we show that phosphodiesterases and A-kinase anchoring proteins (AKAPs) are critical in shaping nuclear PKA responses. Collectively, our data suggest a new model in which AKAP-localized phosphodiesterases tune an activation threshold for nuclear PKA holoenzyme, thereby converting spatially distinct second messenger signals to temporally controlled nuclear kinase activity.

Enhancing robustness in video recognition models: Sparse adversarial attacks and beyond.

Recent years have witnessed increasing interest in adversarial attacks on images, while adversarial video attacks have seldom been explored. In this paper, we propose a sparse adversarial attack strategy on videos (DeepSAVA). Our model aims to add a small human-imperceptible perturbation to the key frame of the input video to fool the classifiers. To carry out an effective attack that mirrors real-world scenarios, our algorithm integrates spatial transformation perturbations into the frame. Instead of using the lp norm to gauge the disparity between the perturbed frame and the original frame, we employ the structural similarity index (SSIM), which has been established as a more suitable metric for quantifying image alterations resulting from spatial perturbations. We employ a unified optimisation framework to combine spatial transformation with additive perturbation, thereby attaining a more potent attack. We design an effective and novel optimisation scheme that alternatively utilises Bayesian Optimisation (BO) to identify the most critical frame in a video and stochastic gradient descent (SGD) based optimisation to produce both additive and spatial-transformed perturbations. Doing so enables DeepSAVA to perform a very sparse attack on videos for maintaining human imperceptibility while still achieving state-of-the-art performance in terms of both attack success rate and adversarial transferability. Furthermore, built upon the strong perturbations produced by DeepSAVA, we design a novel adversarial training framework to improve the robustness of video classification models. Our intensive experiments on various types of deep neural networks and video datasets confirm the superiority of DeepSAVA in terms of attacking performance and efficiency. When compared to the baseline techniques, DeepSAVA exhibits the highest level of performance in generating adversarial videos for three distinct video classifiers. Remarkably, it achieves an impressive fooling rate ranging from 99.5% to 100% for the I3D model, with the perturbation of just a single frame. Additionally, DeepSAVA demonstrates favourable transferability across various time series models. The proposed adversarial training strategy is also empirically demonstrated with better performance on training robust video classifiers compared with the state-of-the-art adversarial training with projected gradient descent (PGD) adversary.

Signaling diversity of PKA achieved via a Ca2+-cAMP-PKA oscillatory circuit.

Many protein kinases are key nodal signaling molecules that regulate a wide range of cellular functions. These functions may require complex spatiotemporal regulation of kinase activities. Here, we show that protein kinase A (PKA), Ca(2+) and cyclic AMP (cAMP) oscillate in sync in insulin-secreting MIN6 beta cells, forming a highly integrated oscillatory circuit. We found that PKA activity was essential for this oscillatory circuit and was capable of not only initiating the signaling oscillations but also modulating their frequency, thereby diversifying the spatiotemporal control of downstream signaling. Our findings suggest that exquisite temporal control of kinase activity, mediated via signaling circuits resulting from cross-regulation of signaling pathways, can encode diverse inputs into temporal parameters such as oscillation frequency, which in turn contribute to proper regulation of complex cellular functions in a context-dependent manner.

Hyper-Dense_Lung_Seg: Multimodal-Fusion-Based Modified U-Net for Lung Tumour Segmentation Using Multimodality of CT-PET Scans.

The majority of cancer-related deaths globally are due to lung cancer, which also has the second-highest mortality rate. The segmentation of lung tumours, treatment evaluation, and tumour stage classification have become significantly more accessible with the advent of PET/CT scans. With the advent of PET/CT scans, it is possible to obtain both functioning and anatomic data during a single examination. However, integrating images from different modalities can indeed be time-consuming for medical professionals and remains a challenging task. This challenge arises from several factors, including differences in image acquisition techniques, image resolutions, and the inherent variations in the spectral and temporal data captured by different imaging modalities. Artificial Intelligence (AI) methodologies have shown potential in the automation of image integration and segmentation. To address these challenges, multimodal fusion approach-based U-Net architecture (early fusion, late fusion, dense fusion, hyper-dense fusion, and hyper-dense VGG16 U-Net) are proposed for lung tumour segmentation. Dice scores of 73% show that hyper-dense VGG16 U-Net is superior to the other four proposed models. The proposed method can potentially aid medical professionals in detecting lung cancer at an early stage.

Safety and antifatigue effect of Korean Red Ginseng capsule: A randomized, double-blind and placebo-controlled clinical trial.

Background: In physical activity or labor, the human body is in a state of high intensity stress, and all parts or physiological functions of the body respond positively to maintain or balance the need for movement. The human body has many physiological changes in the process of movement, and fatigue is the external manifestation of various complex changes inside the human body. Fatigue is also a physiological mechanism of self-protection after the body reaches a certain level of activity, which can prevent the occurrence of life-threatening excessive functional failure. The generation of fatigue is a very complex process, and its mechanism has not been concluded yet. Therefore, it is an important work to search and screen the effective components of natural plants that have anti-fatigue effect and to explore their mechanism. Methods: This was a 8-week, randomized, double-blind, placebo-controlled clinical trial. A total of 110 subjects who passed physical examination were included according to the scheme design, and randomly divided into a test group which was given KRG and a placebo control group. The calculation is carried out according to the standard of sub-high-intensity exercise test. Results: There was no adverse effect on safety index of subjects after taking red ginseng capsule. After KRG treatment, subjective strength grade is significant lower than placebo treatment. Blood lactic acid content is significantly get lower after trial in KRG group, and significant lower than placebo group. Creatine phosphokinase(CK) content is significantly get lower after trial in KRG group, and significant lower than placebo group. Conclusion: According to the criterion in the test scheme, the result shows that KRG is helpful on relieving physical fatigue.

Intelligent Multisensor Cooperative Localization Under Cooperative Redundancy Validation.

Localization plays a key role in Internet of Things. This paper proposes a novel intelligent cooperative multisensor localization method called the edge cloud cooperative localization (ECCL) which has the range and angle observations from the neighbor nodes along with the location observations from an absolute coordinate localization system like global positioning system. The edge cloud structure is proposed which employs several distributed Kalman filters in sensor nodes edge and a centralized cooperative fusion unit in the cloud. For a robust fusion, a cooperative redundancy validation method is proposed to detect the outliers. The proposed ECCL scheme has the advantages of both the distributed and centralized localization, which satisfies the needs of high reliability and high accuracy, especially when sensor nodes have limited computational resources. The simulation and experimental results show that our proposed ECCL algorithm outperforms the other schemes both in outlier detection and localization accuracy.

Friction reduction mechanism of glycerol monooleate-containing lubricants at elevated temperature - transition from physisorption to chemisorption.

The friction reduction mechanism of glycerol monooleate (GMO) was investigated under boundary lubrication with elevated temperature. Tribological performances were tested using reciprocating test rig by adding 5 wt.% GMO into Poly-alpha Olefin (PAO) base oil. Friction coefficient and wear were recorded during experiments. The used oil was evaluated by infrared detection after experiments. Results show that GMO could reduce friction coefficient at both low and high temperature. At elevated temperature, the friction coefficient of PAO-GMO blend climb up gradually, followed by a decrease tendency, and the wear increase gradually with temperature. The results of Quartz Crystal Microbalance show that the physical adsorption film plays the main role in friction reduction at low temperature. While at high temperature, the Infrared Spectrum and X-Ray Photoelectron Spectrum show that the GMO involves into the chemisorption with friction surface, producing Fe(OH)O and Fe3O4. The friction reduction mechanism of GMO transferred from physisorption to chemisorption, which reduced friction coefficient at both low and high temperature.

Designing a novel material with considerable nonlinear optical responses based on the bicorannulenyl molecule.

In this work, a series of molecules decorated with Li atom and donor/acceptor have been theoretically designed based on bicorannulenyl molecule, where incorporating Li and different substitution are used as an effective strategy for enhancing nonlinear optical response. The mixed method is constructed through incorporating the Li and NO2/NH2 substitution. To ensure accuracy, results were compared with another two functionals. As expected, data from three different functional approximations indicate that these molecules have large first hyperpolarizability. The calculation proves that these molecules exhibit large first hyperpolarizability in the range of 1956-37,758 au. For Li doped systems, by analyzing NBO, charge transfer occurs in studied molecules, which helps to get large nonlinear optical response. It is revealed that when Li atom is introduced into the molecule with only NO2/NH2 substitution, the first hyperpolarizability increases significantly. Compared with Li doped and NO2 substitution, incorporating Li and NH2 substitution can be more powerful in increasing the first hyperpolarizabilities of bicorannulenyl molecule. In addition, the number of NH2 substitutions can more effectively enhance the first hyperpolarizability. We hope that this study could provide a new idea for designing nonlinear optical materials using bicorannulenyl molecule.

On Aggregation of Unsupervised Deep Binary Descriptor with Weak Bits.

Despite the thrilling success achieved by existing binary descriptors, most of them are still in the mire of three limitations: 1) vulnerable to the geometric transformations; 2) incapable of preserving the manifold structure when learning binary codes; 3) NO guarantee to find the true match if multiple candidates happen to have the same Hamming distance to a given query. All these together make the binary descriptor less effective, given large-scale visual recognition tasks. In this paper, we propose a novel learning-based feature descriptor, namely Unsupervised Deep Binary Descriptor (UDBD), which learns transformation invariant binary descriptors via projecting the original data and their transformed sets into a joint binary space. Moreover, we involve a ℓ2,1-norm loss term in the binary embedding process to gain simultaneously the robustness against data noises and less probability of mistakenly flipping bits of the binary descriptor, on top of it, a graph constraint is used to preserve the original manifold structure in the binary space. Furthermore, a weak bit mechanism is adopted to find the real match from candidates sharing the same minimum Hamming distance, thus enhancing matching performance. Extensive experimental results on public datasets show the superiority of UDBD in terms of matching and retrieval accuracy over state-of-the-arts.

Dynamic visualization of signal transduction in living cells: from second messengers to kinases.

The study of signal transduction, or the highly regulated series of biochemical events which allow a cell to convert a given stimulus into a functional response, has seen a paradigm shift with a recent explosion in the number of genetically encoded FRET-based biosensors capable of detecting spatial and temporal regulation of various signaling events in living cells. The two classes of biosensors discussed, namely kinase activity and second messenger biosensors, utilize two fluorescent proteins (FP) suitable for FRET and convert a signaling event of interest into a conformational change in the biosensor that can be measured as a change in FRET between the two FPs. Individually, these biosensors have been used to elucidate many complex signal transduction mechanisms in various biological systems. However, it has become increasingly clear that it is often more desirable to study multiple signaling events simultaneously, allowing for precise correlation of the temporal profiles of multiple signaling molecules without the complication of cell to cell variability. With the design of spectrally distinct biosensors and new coimaging strategies, simultaneous imaging of multiple signaling events is not only possible, but has aided in mapping the intricate network of cellular signal transduction cascades. Furthermore, as aberrant signal transduction involving second messengers and kinases is implicated in numerous disease states, it is hopeful that these FRET-based biosensors and coimaging strategies can help to unravel the molecular links between altered signal transduction and certain disease states.

General anesthetic neurotoxicity in the young: Mechanism and prevention.

General anesthesia (GA) is usually considered to safely induce a reversible unconscious state allowing surgery to be performed without pain. A growing number of studies, in particular pre-clinical studies, however, demonstrate that general anesthetics can cause neuronal death and even long-term neurological deficits. Herein, we report our literature review and meta-analysis data of the neurological outcomes after anesthesia in the young. We also review available mechanistic and epigenetic data of GA exposure related to cognitive impairment per se and the potential preventive strategies including natural herbal compounds to attenuate those side effects. In summary, anesthetic-induced neurotoxicity may be treatable and natural herbal compounds and other medications may have great potential for such use but warrants further study before clinical applications can be initiated.

Diagnostic accuracy of radiology (CT, X-ray, US) for predicting difficult intubation in adults: A meta-analysis.

OBJECTIVE: The aim of this study was to evaluate the overall accuracy of radiological measurements in prediction of difficult airway and compare the diagnostic value between the radiological measurements and the modified Mallampati score through a meta-analysis of published studies. METHODS: A comprehensive electronic search of related literature was performed in PubMed, Embase, Cochrane Library and China National Knowledge Infrastructure. Meta-DiSc 1.4 and STATA 12.0 were selected for data analysis, and QUADAS-2 tool was used to assess the quality of included studies. Difficult airway was defined as Cormack-Lehane III-IV. Data from selected studies were pooled to yield summary sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio, as well as summary receiver operating characteristic curve. RESULTS: A total of 17 studies dating up to November 2017 with 8779 individuals were enrolled in the present study. Heterogeneity existed in the non-threshold effect, but not in the threshold effect. Subgroup analyses based on radiological methods were conducted. The pooled diagnostic characteristics in the computed tomography subgroup were as follows: sensitivity 0.75 (95%CI, 0.64-0.84), specificity 0.75 (95%CI 0.68-0.81), PLR 3.19 (95%CI 1.91-5.32), NLR 0.38 (95%CI 0.23-0.64), DOR 11.74 (95% CI, 4.19-32.86) and AUC 0.8424 with Q* index 0.7741. In the X-ray subgroup, the sensitivity was 0.78 (95%CI, 0.73-0.82), the specificity was 0.88 (95%CI, 0.87-0.89), PLR was 5.03 (95%CI, 2.44-10.37), NLR was 0.27 (95%CI, 0.22-0.33), DOR was 23.18 (95%CI, 8.81-60.95) and AUC was 0.8970 with Q* index 0.8280. The corresponding values for the ultrasound subgroup were 0.69 (95%CI, 0.63-0.74) for sensitivity, 0.84 (95%CI, 0.82-0.85) for specificity, 6.25 (95%CI, 3.81-10.27) for PLR, 0.36 (95%CI, 0.27-0.47) for NLR, 22.26 (95%CI, 10.45-47.41) for DOR, 0.8942 for AUC with Q* index 0.8251. The pooled sensitivity, specificity and PLR of the modified Mallampati score were 0.61 (95%CI 0.56-0.66), 0.63 (95%CI 0.61-0.64) and 2.11 (95%CI 1.71-2.61) which were significantly lower than that of radiographic methods. CONCLUSIONS: The results indicated that the diagnostic value of CT, X-ray and US was much better than that of modified Mallampati score. Ultrasound had diagnostic indices and the area under curve similar to those of CT and X-ray in predicting difficult airway. Considering being easy, readily availability, low cost, and free from radiological hazards, it can be considered as prior diagnostic strategy in this condition.

Live-cell imaging of cell signaling using genetically encoded fluorescent reporters.

Synergistic advances in fluorescent protein engineering and live-cell imaging techniques in recent years have fueled the concurrent development and application of genetically encoded fluorescent reporters that are tailored for tracking signaling dynamics in living systems over multiple length and time scales. These biosensors are uniquely suited for this challenging task, owing to their specificity, sensitivity, and versatility, as well as to the noninvasive and nondestructive nature of fluorescence and the power of genetic encoding. Over the past 10 years, a growing number of fluorescent reporters have been developed for tracking a wide range of biological signals in living cells and animals, including second messenger and metabolite dynamics, enzyme activation and activity, and cell cycle progression and neuronal activity. Many of these biosensors are gaining wide use and are proving to be indispensable for unraveling the complex biological functions of individual signaling molecules in their native environment, the living cell, shedding new light on the structural and molecular underpinnings of cell signaling. In this review, we highlight recent advances in protein engineering that are likely to help expand and improve the design and application of these valuable tools. We then turn our focus to specific examples of live-cell imaging using genetically encoded fluorescent reporters as an important platform for advancing our understanding of G protein-coupled receptor signaling and neuronal activity.

Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval.

This paper proposes a deep hashing framework, namely Unsupervised Deep Video Hashing (UDVH), for largescale video similarity search with the aim to learn compact yet effective binary codes. Our UDVH produces the hash codes in a self-taught manner by jointly integrating discriminative video representation with optimal code learning, where an efficient alternating approach is adopted to optimize the objective function. The key differences from most existing video hashing methods lie in 1) UDVH is an unsupervised hashing method that generates hash codes by cooperatively utilizing feature clustering and a specifically-designed binarization with the original neighborhood structure preserved in the binary space; 2) a specific rotation is developed and applied onto video features such that the variance of each dimension can be balanced, thus facilitating the subsequent quantization step. Extensive experiments performed on three popular video datasets show that UDVH is overwhelmingly better than the state-of-the-arts in terms of various evaluation metrics, which makes it practical in real-world applications.

Detection of Viruses and Mycoplasma pneumoniae in Hospitalized Patients with Severe Acute Respiratory Infection in Northern China, 2015-2016.

Severe acute respiratory infection (SARI) presents a huge disease and economic burden worldwide. The present study described the frequency and types of different infectious etiologies among hospitalized patients with SARI in Tianjin, China, during 2015 and 2016. Basic information, in addition to a throat or serum sample, was collected from SARI patients. Nine viruses were detected using reverse transcription polymerase chain reaction, and Mycoplasma pneumoniae was detected using the Serodia Myco II gelatin particle agglutination test. A total of 585 specimens from 2,290 SARI cases were collected. The most common infection (19.66%, 115/585) was M. pneumoniae, followed by influenza virus A/B (6.15%, 36/585), and respiratory syncytial virus (4.96%, 29/585). Identification of viral or M. pneumoniae infections was the highest in the pediatric medicine ward (74.84%, 119/159), followed by the intensive care unit (37.04%, 80/216) and respiratory medicine ward (34.29%, 72/210). M. pneumoniae was highest (38.71%, 24/62) in the 5-14-year age group. Influenza was the main infection in January 2015 and March 2016. The correlation coefficient for the proportion of hospitalized cases of SARI and the positive detection rate within the same week was 0.25. M. pneumoniae and influenza were the leading pathogens among hospitalized SARI patients. A continued surveillance of hospitalized cases of SARI can detect emerging diseases, such as avian influenza A (H7N9) virus and other respiratory disease outbreaks.

Preparation of novel biodegradable ropivacaine microspheres and evaluation of their efficacy in sciatic nerve block in mice.

In this study, ropivacaine chitosan-loaded microspheres for subcutaneous administration were developed. The systems were characterized in terms of surface morphology, particle size, encapsulation efficiency, and in vitro release behavior. Results showed that the microspheres had drug loading rate of 7.3% and encapsulation efficiency of 91.2%, and their average diameter was 2.62±0.76 µm. The morphology study revealed that the microspheres are uniform monodispersed spheres and did not form aggregates in aqueous solution. It was clearly observed that the release profile of ropivacaine microspheres exhibited a biphasic pattern: the initial burst release within the first 2 hours and a following slower and sustained release over a long time. In vivo, a greater area under the plasma concentration-time curve from 0 to t (AUC0- t ) was obtained from the microspheres (4.27-fold), than from the injection group, which indicated that there was a significantly improved systemic exposure to ropivacaine. Pharmacodynamics result showed that preparing ropivacaine as microsphere preparation could not only extend the drug effect time but also decrease the administration dosage.

Controlling enzymatic action in living cells with a kinase-inducible bimolecular switch.

Molecular probes designed to monitor or perturb signaling events in living cells rely on engineered molecular switches. Here, we show that a kinase-inducible bimolecular switch comprising a kinase-specific substrate and a phosphoamino acid binding domain can be used for acute regulation of cellular events. As a proof of concept, we employed a Protein Kinase A (PKA)-dependent switch and coupled it to a lipid phosphatase to manipulate the level of phosphatidylinositol 4,5-bisphosphate (PI(4,5)P(2)) in living cells. PKA activation results in rapid degradation of PI(4,5)P(2). Conversely, when PKA is inhibited, dephosphorylation of the switch leads to the replenishment of PI(4,5)P(2). Thus, this strategy can be used for reversibly controlling enzymatic action in living cells. Furthermore, its genetic encodability and modular design should facilitate the adaptation of this approach to control different cellular activities as a function of phosphorylation-dependent input signals, thereby providing versatile tools for potentially perturbing or rewiring signaling pathways.

Parallel tracking of cAMP and PKA signaling dynamics in living cells with FRET-based fluorescent biosensors.

Proper regulation of cellular functions relies upon a network of intricately interwoven signaling cascades in which multiple components must be tightly coordinated both spatially and temporally. To better understand how this network operates within the cellular environment, it is important to define the parameters of various signaling activities and to reveal the characteristic activity structure of the signaling cascades. This task calls for molecular tools capable of parallelly tracking multiple activities in cellular time and space with high sensitivity and specificity. Here, we present new biosensors developed based on two conveniently co-imageable FRET pairs consisting of CFP-RFP and YFP-RFP, specifically Cerulean-mCherry and mVenus-mCherry, for parallel monitoring of PKA activity and cAMP dynamics in living cells. These biosensors provide orthogonal readouts in co-imaging experiments and display a comparable dynamic range to their cyan-yellow counterparts. Characterization of signaling responses induced by a panel of pathway activators using this co-imaging approach reveals distinct activity and kinetic patterns of cAMP and PKA dynamics arising from differential signal activation and processing. This technique is therefore useful for parallel monitoring of multiple signaling dynamics in single living cells and represents a promising approach towards a more precise characterization of the activity structure of the dynamic cellular signaling network.