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BackgroundTreatments for patients with early‐stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively.MethodsWe retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan–Meier survival curve, and decision curve analyses (DCAs), respectively.ResultsPrognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS.ConclusionsWe developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.

Angelica dahurica (A. dahurica) root is a famous edible medicinal herb that has been used in China for thousands of years. To date, more than 300 chemical constituents have been discovered from A. dahurica. Among these ingredients, coumarins and volatile oils are the major active compounds. Moreover, a few other compounds have also been isolated from the root of A. dahurica, such as alkaloids, phenols, sterols, benzofurans, polyacetylenes and polysaccharides. Modern pharmacological studies demonstrated that the root of A. dahurica and its active components displayed various bioactivities such as anti-inflammation, anti-tumor, anti-oxidation, analgesic activity, antiviral and anti-microbial effects, effects on the cardiovascular system, neuroprotective function, hepatoprotective activity, effects on skin diseases and so on. Based on these studies, this review focused on the research publications of A. dahurica and aimed to summarize the advances in the traditional uses, phytochemistry and pharmacology which will provide reference for the further studies and applications of A. dahurica.

The spike (S) protein of SARS-CoV-2 has been observed in three distinct pre-fusion conformations: locked, closed and open. Of these, the function of the locked conformation remains poorly understood. Here we engineered a SARS-CoV-2 S protein construct "S-R/x3" to arrest SARS-CoV-2 spikes in the locked conformation by a disulfide bond. Using this construct we determined high-resolution structures confirming that the x3 disulfide bond has the ability to stabilize the otherwise transient locked conformations. Structural analyses reveal that wild-type SARS-CoV-2 spike can adopt two distinct locked-1 and locked-2 conformations. For the D614G spike, based on which all variants of concern were evolved, only the locked-2 conformation was observed. Analysis of the structures suggests that rigidified domain D in the locked conformations interacts with the hinge to domain C and thereby restrains RBD movement. Structural change in domain D correlates with spike conformational change. We propose that the locked-1 and locked-2 conformations of S are present in the acidic high-lipid cellular compartments during virus assembly and egress. In this model, release of the virion into the neutral pH extracellular space would favour transition to the closed or open conformations. The dynamics of this transition can be altered by mutations that modulate domain D structure, as is the case for the D614G mutation, leading to changes in viral fitness. The S-R/x3 construct provides a tool for the further structural and functional characterization of the locked conformations of S, as well as how sequence changes might alter S assembly and regulation of receptor binding domain dynamics.

Bovine serum albumin (BSA), polyethylene glycol (PEG) and lignosulfonate (LS) have been extensively employed as synergistic agents in lignocellulose saccharification. Nevertheless, the promoting mechanisms have not been fully understood and there are a number of controversial opinions existed. All attention has been paid to the interactions between respective additive and substrate. However, rarely attention has been paid to the interactions between additives and enzymes (cellulase from Trichoderma reesei in this investigation). This interaction is actually more important since cellulase interacts with the additives before it contacts with substrate. Therein, Quartz crystal microbalance with dissipation monitoring (QCM-D), surface plasma resonance (SPR) and small angel X-ray scattering (SAXS) were incorporated to study the interaction between enzyme and additives. The results showed synergistic agents have different interaction modes with cellulase. BSA and LS can form complexes with cellulase and the formed complexes prevent them from nonproductive binding by residue lignin; what’s more, the cellulase-BSA complexes improve the hydrolytic capability of pristine enzyme whereas cellulase-LS complexes reduce. PEG prevents the unproductive binding of cellulase to the residual lignin by forming a thin layer that actually acts as a steric hindrance to the residual lignin. This investigation helps us to understand the sophisticated interactions among the components in the complicated enzymatic system, especially the interactions between enzymes and synergistic agents. It will be helpful in the design and utilization of synergistic additives in the lignocellulose biorefinery process as well.

The activity of polysaccharides is usually related to molecular weight. The molecular weight of polysaccharides is critical to their immunological effect in cancer therapy. Herein, the Codonopsis polysaccharides of different molecular weights were isolated using ultrafiltration membranes of 60- and 100-wDa molecular weight cut-off to determine the relationship between molecular weight and antitumor activities. First, three water-soluble polysaccharides CPPS-I (100 wDa) from Codonopsis were isolated and purified using a combination of macroporous adsorption resin chromatography and ultrafiltration. Their structural characteristics were determined through chemical derivatization, GPC, HPLC, FT–IR, and NMR techniques. In vitro experiments indicated that all Codonopsis polysaccharides exhibited significant antitumor activities, with the tumor inhibition rate in the following order: CPPS-II > CPPS-I > CPPS-III. The treatment of CPPS-II exhibited the highest inhibition rate at a high concentration among all groups, which was almost as efficient as that of the DOX·HCL (10 μg/mL) group at 125 μg/mL concentration. Notably, CPPS-II demonstrated the ability to enhance NO secretion and the antitumor ability of macrophages relative to the other two groups of polysaccharides. Finally, in vivo experiments revealed that CPPS-II increased the M1/M2 ratio in immune system regulation and that the tumor inhibition effect of CPPS-II + DOX was superior to that of DOX monotherapy, implying that CPPS-II + DOX played a synergistic role in regulating the immune system function and the direct tumor-killing ability of DOX. Therefore, CPPS-II is expected to be applied as an effective cancer treatment or adjuvant therapy.

Homodimeric prodrug-based self-assembled nanoparticles, with carrier-free structure and ultrahigh drug loading, is drawing more and more attentions. Homodimeric prodrugs are composed of two drug molecules and a pivotal linkage. The influence of the linkages on the self-assembly, in vivo fate and antitumor activity of homodimeric prodrugs is the focus of research. Herein, three docetaxel (DTX) homodimeric prodrugs are developed using different lengths of diselenide bond-containing linkages. Interestingly, compared with the other two linkages, the longest diselenide bond-containing linkage could facilitate the self-delivery of DTX prodrugs, thus improving the stability, circulation time and tumor targeting of prodrug nanoassemblies. Besides, the extension of linkages reduces the redox-triggered drug release and cytotoxicity of prodrug nanoassemblies in tumor cells. Although the longest diselenide bond-containing prodrug nanoassemblies possessed the lowest cytotoxicity to 4T1 cells, their stable nanostructure maintained intact during circulation and achieve the maximum accumulation of DTX in tumor cells, which finally “turned the table”. Our study illustrates the crucial role of linkages in homodimeric prodrugs, and gives valuable proposal for the development of advanced nano-DDS for cancer treatment.

Abstract Background To assess the therapeutic efficacy of transforaminal epidural steroid injection (TFESI) combined with radio frequency (RF) for the treatment of lumbar disc herniation (LDH). Methods A total of 230 patients participated in the study: TFESI (Group T, n = 110), TFESI combined with RF (Group TR, n = 120). Visual analogue scale (VAS), Oswestry disability index (ODI) and Global perceived effect (GPE) scale were measured pre-operation, 1, 3, 6, 12 and 24 months after the operation. Hospitalization time, treatment time, complications, and recurrence were compared between the two groups. Results The VAS and ODI at each observation point of the post-operation were significantly decreased compared with the pre-operation in both groups (P 0.05). However, The VAS and ODI scores in Group TR were significantly lower than that in Group T at 6, 12 and 24 months of the post-operation (P 0.05). Conclusion These findings suggest that TFESI combined with RF could effectively improve the pain and function, and had a long-term satisfactory effect for the treatment of LDH.

The adenylate cyclase (ADCY) superfamily is a group of glycoproteins regulating intracellular signaling. ADCYs act as key regulators in the cyclic adenosine monophosphate (cAMP) signaling pathway and are related to cell sensitivity to chemotherapy and ionizing radiation. Many members of the superfamily are detectable in most chemoresistance cases despite the complexity and unknownness of the specific mechanism underlying the role of ADCYs in the proliferation and invasion of cancer cells. The overactivation of ADCY, as well as its upstream and downstream regulators, is implicated as a major potential target of novel anticancer therapies and markers of exceptional responders to chemotherapy. The present review focuses on the oncogenic functions of the ADCY family and emphasizes the possibility of the mediating roles of deleterious nonsynonymous single nucleotide polymorphisms (nsSNPs) in ADCY as a prognostic therapeutic target in modulating resistance to chemotherapy and immunotherapy. It assesses the mediating roles of ADCY and its counterparts as stress regulators in reprogramming cancer cell metabolism and the tumor microenvironment. Additionally, the well-evaluated inhibitors of ADCY-related signaling, which are under clinical investigation, are highlighted. A better understanding of ADCY-induced signaling and deleterious nsSNPs (p.E1003K and p.R1116C) in ADCY6 provides new opportunities for developing novel therapeutic strategies in personalized oncology and new approaches to enhance chemoimmunotherapy efficacy in treating various cancers.

BackgroundThe delta variant (B.1.617.2) of SARS-CoV-2 was the dominant viral strain causing COVID-19 in China, 2021. We reported a SARS-CoV-2 delta variant outbreak in Jingmen City, Hubei Province, China.MethodsThe data of epidemiological, clinical, laboratorial, and vaccination of COVID-19 cases were collected through field investigation and analyzed.ResultsDuring the outbreak from 4 to 20 August 2021, 58 cases of the SARS-CoV-2 delta variant (B.1.617.2) were identified with 15 (25.9%) asymptomatic and 43 (74.1%) symptomatic (mild and moderate) patients. The mean serial interval was 2.6 days (standard deviation: 2.0, 95% CI: 1.9–3.6). The median age of the patients was 39 years (ranging from 1 to 60 years) with the high proportion in children (19.0%). The secondary attack rate was 9.8% higher from parents to their children ( 0.05). Approximately half (27; 46.6%) of cases were vaccine breakthrough infections. In vaccine breakthrough cases (fully vaccinated), viral loads decreased 1.9–3.4-folds (p < 0.05), duration of viral shedding shortened 5 days (p < 0.05), and the risk of becoming symptomatic from asymptomatic decreased 33% (95% CI: 5–53%) (aged ≥12 years) than those in unvaccinated infections.ConclusionsChildren are highly susceptible to the SARS-CoV-2 delta variant in the COVID-19 outbreak in Jingmen City in 2021. Inactivated vaccine derived from wide-type strain can effectively reduce the viral load, duration of viral shedding, and clinical severity in vaccine breakthrough cases. Our study indicates that protective measures that include full vaccination against COVID-19, especially in children, should be strengthened.

This pilot study implements a hybrid brain computer interface paradigm based on motor imagery (MI) and steady-state visual evoked potential (SSVEP), in order to explore the neural mechanism and clinical effect of MI-SSVEP intervention paradigm on upper limb functional rehabilitation. In this study, EEG data of 12 healthy participants were collected, and the activation regions of MI-SSVEP paradigm were identified by power spectral density (PSD). By analyzing the inter trial phase consistency (ITPC) of characteristic regions and the causal relationship of brain network, the motor cognitive process including high-level somatosensory joint cortex in the intervention process of MI-SSVEP was studied. Subsequently, this study verified the clinical effect of MI-SSVEP intervention paradigm for 61 stroke patients. The results show that the robot assisted therapy using MI-SSVEP intervention paradigm can more effectively improve the rehabilitation effect of patients.

Background: Almost a year after the outbreak of coronavirus disease 2019 (COVID-19), many hospitalized COVID-19 patients have recovered. However, little is known about the long-term follow-up (> 2 months) of discharged patients. Methods: This study enrolled 527 discharged COVID-19 patients from 05 February to 11 March 2020. Basic characteristics, imaging features, nucleic acid detection results, and antibody levels of these patients were retrospectively reviewed. Results: Of the 527 discharged patients, 32 (6.1%) had re-detectable positive (RP) nucleic acid results for SARS-CoV-2 during follow-up examinations, with 11 and four detections entailing stool samples and anal swabs, respectively, rather than respiratory samples. Juveniles were more susceptible to “infection recurrence” than other age groups, with shorter time spans for re-detectable positive (RP) RNA tests (an average of 8.8 days [6.0–9.0 days]), while the reverse was true for the middle-aged group (17.5 days on average [14.0–17.5 days]). Similar improvements in the imaging features of both RP and no RP (NRP) groups were observed. Negative antibody detections in patients at 3 and 6 months after discharge were 14.2% and 25.0%, respectively. Cases evidencing negative antibodies were more common among juvenile patients (40% vs. 15.6%, P = 0.03) 6 months post-discharge. Conclusions: A total of 6.1% of 527 discharged patients showed RP status, which may be easier to be identified from stool samples than from other samples. Given the dropping rate of SARS-CoV-2 antibodies, reinfection may happen, especially in juvenile patients (aged

Purpose: Dual-energy computed tomography (DECT) has been used to derive relative stopping power (RSP) maps by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could potentially be compromised by image noise levels and the severity of artifacts when using physics-based mapping techniques. This work presents a noise-robust learning-based method to predict RSP maps from DECT for proton radiation therapy. Materials and Methods: The proposed method uses a residual attention cycle-consistent generative adversarial network to bring DECT-to-RSP mapping close to a 1-to-1 mapping by introducing an inverse RSP-to-DECT mapping. To evaluate the proposed method, we retrospectively investigated 20 head-and-neck cancer patients with DECT scans in proton radiation therapy simulation. Ground truth RSP values were assigned by calculation based on chemical compositions and acted as learning targets in the training process for DECT datasets; they were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. Results: The predicted RSP maps showed an average normalized mean square error of 2.83% across the whole body volume and an average mean error less than 3% in all volumes of interest. With additional simulated noise added in DECT datasets, the proposed method still maintained a comparable performance, while the physics-based stoichiometric method suffered degraded inaccuracy from increased noise level. The average differences from ground truth in dose volume histogram metrics for clinical target volumes were less than 0.2 Gy for D95% and Dmax with no statistical significance. Maximum difference in dose volume histogram metrics of organs at risk was around 1 Gy on average. Conclusion: These results strongly indicate the high accuracy of RSP maps predicted by our machine-learning–based method and show its potential feasibility for proton treatment planning and dose calculation.

N-Arylation of NH-diaryl sulfoximines faces synthetic challenges among reported methods. Here, the authors present a mild copper-catalyzed photoredox dehydrogenative Chan-Lam coupling of free diaryl sulfoximines and arylboronic acids via an auto-catalytic process.

Abstract Background Cigarette smoke (CS) is a major risk factor for Chronic Obstructive Pulmonary Disease (COPD). Follistatin-like protein 1 (FSTL1), a critical factor during embryogenesis particularly in respiratory lung development, is a novel mediator related to inflammation and tissue remodeling. We tried to investigate the role of FSTL1 in CS-induced autophagy dysregulation, airway inflammation and remodeling. Methods Serum and lung specimens were obtained from COPD patients and controls. Adult female wild-type (WT) mice, FSTL1± mice and FSTL1flox/+ mice were exposed to room air or chronic CS. Additionally, 3-methyladenine (3-MA), an inhibitor of autophagy, was applied in CS-exposed WT mice. The lung tissues and serum from patients and murine models were tested for FSTL1 and autophagy-associated protein expression by ELISA, western blotting and immunohistochemical. Autophagosome were observed using electron microscope technology. LTB4, IL-8 and TNF-α in bronchoalveolar lavage fluid of mice were examined using ELISA. Airway remodeling and lung function were also assessed. Results Both FSTL1 and autophagy biomarkers increased in COPD patients and CS-exposed WT mice. Autophagy activation was upregulated in CS-exposed mice accompanied by airway remodeling and airway inflammation. FSTL1± mice showed a lower level of CS-induced autophagy compared with the control mice. FSTL1± mice can also resist CS-induced inflammatory response, airway remodeling and impaired lung function. CS-exposed WT mice with 3-MA pretreatment have a similar manifestation with CS-exposed FSTL1± mice. Conclusions FSTL1 promotes CS-induced COPD by modulating autophagy, therefore targeting FSTL1 and autophagy may shed light on treating cigarette smoke-induced COPD.

Pheromones are chemical substances essential for communication among social insects. In the application of swarm intelligence to real micro mobile robots, the deployment of a single virtual pheromone has emerged recently as a powerful real-time method for indirect communication. However, these studies usually exploit only one kind of pheromones in their task, neglecting the crucial fact that in the world of real insects, multiple pheromones play important roles in shaping stigmergic behaviors such as foraging or nest building. To explore the multiple pheromones mechanism which enable robots to solve complex collective tasks efficiently, we introduce an artificial multiple pheromone system (ColCOS $\Phi $ ) to support swarm intelligence research by enabling multiple robots to deploy and react to multiple pheromones simultaneously. The proposed system ColCOS $\Phi $ uses optical signals to emulate different evaporating chemical substances i.e. pheromones. These emulated pheromones are represented by trails displayed on a wide LCD display screen positioned horizontally, on which multiple miniature robots can move freely. The color sensors beneath the robots can detect and identify lingering “pheromones” on the screen. Meanwhile, the release of any pheromone from each robot is enabled by monitoring its positional information over time with an overhead camera. No other communication methods apart from virtual pheromones are employed in this system. Two case studies have been carried out which have verified the feasibility and effectiveness of the proposed system in achieving complex swarm tasks as empowered by multiple pheromones. This novel platform is a timely and powerful tool for research into swarm intelligence.

The development of biomass-based composites has greatly reduced the daily consumption of plastics. However, these materials are rarely recyclable, thus, posing a severe threat to the environment. Herein, we designed and prepared novel composite materials with ultra-high biomass (i.e., wood flour) filling capacity and good closed-loop recycling properties. The dynamic polyurethane polymer was polymerized in situ on the surface of wood fiber, and then they were hot-pressed into composites. Fourier-transform infrared (FTIR), scanning electron microscopy (SEM), and dynamic thermomechanical analysis (DMA) measurements reveal good compatibility between the polyurethane and wood flour in the composites when the wood flour content is ≤80 wt%. The maximum tensile and bending strength of the composite are 37 and 33 MPa when the wood flour content is 80%. The higher wood flour content results in higher thermal expansion stability and creep resistance in the composites. Moreover, the thermal debonding of dynamic phenol–carbamate bonds facilitates the composites to undergo physical and chemical cycling. The recycled and remolded composites exhibit good mechanical property recovery rates and retain the chemical structures of the original composites.

To determine the role of glycoprotein I (gI) in duck plague virus (DPV), a gI-deleted mutant (BAC-CHv-ΔgI) and a gI-revertant virus (BAC-CHv-ΔgI Rev) were constructed by using a markerless two-step Red recombination system implemented on the DPV genome cloned into a bacterial artificial chromosome (BAC). Mutants were characterized on duck embryo fibroblast (DEF) cells compared with wild-type virus. BAC-CHv-ΔgI produced viral plaques on DEF cells that were on average approximately 57.2% smaller than those produced by BAC-CHv-ΔgI Rev and wild-type virus. Electron microscopy confirmed that deleting of gI resulted in nucleocapsids accumulated around the cytoplasm vesicles and few of them could complete the final envelopment process. These results clearly indicated that DPV gI plays significant roles in viral cell-cell spread and viral final envelopment process.

Promoted polymerization of Phthalonitrile containing benzoxazine (BA-ph) was designed on basis of the catalysis performance of a type of aniline curing agent (2,6-bis(4-aminophenoxy)benzonitrile (BAB)). The curing processes and curing behaviors of BA-ph with BAB were investigated by differential scanning calorimetry (DSC) and gelatin time. With the assistance of orthogonal test analysis method, main factors that affected the pre-polymerization of BA-ph were studied and the appropriate pre-polymerization conditions were determined. According to the results of dynamic rheological analysis (DRA) and Fourier transform infrared spectroscopy (FTIR), possible curing mechanism of BA-ph with BAB were proposed. The results show that BAB could reduce the prepolymerization temperature of BA-ph resin, and increase its polymerization rate and crosslinking degree, and the BA-ph/BAB polymer mainly formed the aromatic heterocyclic structure of triazine rings, phthalocyanine rings and polyisoindoline rings. Then, thermal stability of BA-ph (BAB15%) polymers pretreated at different temperatures and prepared at 200 °C was analyzed to understand the cross-linked structures. Results indicated that BA-ph (BAB15%) polymers exhibit outstanding thermal properties (T5%>410 °C and integral program decomposition temperature (IPDT) reached 3342 °C). Mechanical properties and thermomechanical properties of glass fiber reinforced BA-ph composites were also investigated and exhibited excellent mechanical properties, to further reveal the effects of BAB on the polymerization of BA-ph resin and expand the application of high-performance resin-based composites.

BackgroundThe association between gut microbiota and microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains unclarified. Hence, the microbiome analysis of patients with HCC might predict MVI development as an accurate, non-invasive, and convenient assessment. The aim of this study was to investigate the characteristics of gut microbiota in patients with HCC-MVI and establish a microbial prediction model of HCC-MVI based on a microbiome study.MethodsFecal samples were collected from 59 patients with HCC (24 of the total with MVI disease and 16 healthy controls) and were further analyzed by 16S rRNA amplicon sequencing followed by a comprehensive bioinformatic analysis. The diagnostic performance of microbiome characteristics in predicting MVI was assessed by receiver operating characteristic (ROC) curves. The correlation between gut microbiota and tumor microenvironment (TME) in the HCC-MVI group was further analyzed by using immunohistochemistry and immunofluorescence assay.ResultsA significant differentiation trend of microbiota composition and structure was observed between the HCC-MVI group and those without vascular invasion (HCC-NVI). Compared with HCC-NVI group and healthy controls, gut bacteria Klebsiella, Proteobacteria, Prevotellaceae, and Enterobacteriaceae were significantly enriched, whereas Firmicutes, Ruminococcus, and Monoglobaceae were significantly decreased in patients with HCC-MVI. Klebsiella was considered to be the key microbiome signature for patients with HCC-MVI. The area under the curve (AUC) of the established HCC-MVI microbial prediction model was 94.81% (95% CI: 87.63–100%). The percentage of M2-type tumor-associated macrophages (TAMs) was increased in the HCC-MVI group compared with the HCC-NVI group (p < 0.001). M2-type TAMs in TME were negatively correlated with Shannon and Simpson index of HCC-MVI gut microbiota (all p < 0.01). In addition, predicted KEGG pathways showed that the functional differences in the metabolic pathways of microbiota varied among the groups.ConclusionThe results indicated that differences existed in the fecal microbiome of patients with HCC-MVI and healthy controls. The prediction model of HCC-MVI established with certain gut bacterial signatures may have the potential to predict HCC-MVI outcome, and the characteristics of the fecal microbiome in patients with HCC may be associated with TME, though future larger-cohort studies are required to validate this supposition.

Molecular targeted therapy has revolutionized the landscape of cancer treatment due to better therapeutic responses and less systemic toxicity. However, therapeutic resistance is a major challenge in clinical settings that hinders continuous clinical benefits for cancer patients. In this regard, unraveling the mechanisms of drug resistance may identify new druggable genetic alterations for molecularly targeted therapies, thus contributing to improved therapeutic efficacies. The recent rapid development of novel methodologies including CRISPR-Cas9 screening technology and patient-derived models provides powerful tools to dissect the underlying mechanisms of resistance to targeted cancer therapies. In this review, we updated therapeutic targets undergoing preclinical and clinical evaluation for various cancer types. More importantly, we provided comprehensive elaboration of high throughput CRISPR-Cas9 screening in deciphering potential mechanisms of unresponsiveness to molecularly targeted therapies, which will shed light on the discovery of novel opportunities for designing next-generation anti-cancer drugs.

ObjectiveThis study aimed to observe vascular and neuroretinal alterations in people with prediabetes [impaired fasting glucose (IFG) and impaired glucose tolerance (IGT)] and normal glucose metabolism.MethodsA total of 21 patients with prediabetes (42 eyes) and 20 healthy controls (40 eyes) participated in our study. All patients underwent a complete eye examination [including fundus fluorescein angiography (FFA) and optical coherence tomography (OCT)] and a related general examination (complete biochemical analysis, routine blood tests, and glycosylated hemoglobin).ResultsOn FFA, no patients in either group showed any microvascular alterations. The total peripapillary retinal nerve fiber layer (pRNFL) in the prediabetic group was significantly thinner than that in the healthy control group (p < 0.0001). Only the temporal pRNFL thickness was significantly less in patients with prediabetes compared to the normal people. There was no significant difference in the thickness of retina in the range of 1 mm diameter of macular fovea (p = 0.286), but in the prediabetic group, the macular retinal thickness within the diameter of 6 mm in nasal side (p < 0.0001), superior side (p < 0.0001), temporal side (p = 0.008), and inferior side (p = 0.001) were lower than that in the control group.ConclusionIn the prediabetic group, there was no microvascular alterations, but the total pRNFL and the temporal pRNFL was significantly thinner, and the macular retinal thickness within the diameter of 6 mm in the nasal, temporal, and inferior side were lower than that in the healthy control group. These data confirm neuroretinal alterations in prediabetes prior to microvascular injury.

Poststroke aphasia is one of the most dramatic functional deficits that results from direct damage of focal brain regions and dysfunction of large-scale brain networks. The reconstruction of language function depends on the hierarchical whole-brain dynamic reorganization. However, investigations into the longitudinal neural changes of large-scale brain networks for poststroke aphasia remain scarce. Here we characterize large-scale brain dynamics in left-frontal-stroke aphasia through energy landscape analysis. Using fMRI during an auditory comprehension task, we find that aphasia patients suffer serious whole-brain dynamics perturbation in the acute and subacute stages after stroke, in which the brains were restricted into two major activity patterns. Following spontaneous recovery process, the brain flexibility improved in the chronic stage. Critically, we demonstrated that the abnormal neural dynamics are correlated with the aberrant brain network coordination. Taken together, the energy landscape analysis exhibited that the acute poststroke aphasia has a constrained, low dimensional brain dynamics, which were replaced by less constrained and high dimensional dynamics at chronic aphasia. Our study provides a new perspective to profoundly understand the pathological mechanisms of poststroke aphasia.

Purpose: While intensity modulated proton therapy can deliver simultaneous integrated boost (SIB) to the dominant intraprostatic lesion (DIL) with high precision, it is sensitive to anatomic changes. We investigated the dosimetric effects from these changes based on pretreatment cone-beam computed tomographic (CBCT) images and identified the most important factors using a multilayer perceptron neural network (MLPNN). Methods and Materials: DILs were contoured based on coregistered multiparametric magnetic resonance images for 25 previously treated prostate cancer patients. SIB plans were created with (1) prostate clinical target volume − V70 Gy = 98%; (2) DIL − V98 Gy > 95%; and (3) all organs at risk within clinical constraints. SIB plans were applied to daily CBCT-based deformed planning computed tomography (CT). DIL − V98 Gy, bladder/rectum maximum dose (Dmax) and volume changes, femur shifts, and the distance from DIL to organs at risk in both planning computed tomograms and CBCT were calculated. Wilcoxon signed-ranks tests were used to compare the changes. MLPNNs were used to model the change in ΔDIL − V98 Gy > 10% and bladder/rectum Dmax > 80 Gy, and the relative importance factors for the model were provided. The performances of the models were evaluated with receiver operating characteristic curves. Results: Comparing initial plan to the average from evaluation plans, respectively, DIL − V98 Gy was 89.3% ± 19.9% versus 86.2% ± 21.3% (P = .151); bladder Dmax 71.9 ± 0.6 Gy versus 74.5 ± 2.9 Gy (P < .001); and rectum Dmax 70.1 ± 2.4 Gy versus 74.9 ± 9.1Gy (P = .007). Bladder and rectal volumes were 99.6% ± 39.5% and 112.8% ± 27.2%, respectively, of their initial volume. The femur shift was 3.16 ± 2.52 mm. In the modeling of ΔDIL V98 Gy > 10%, DIL to rectum distance changes, DIL to bladder distance changes, and rectum volume changes ratio are the 3 most important factors. The areas under the receiver operating characteristic curves were 0.89, 1.00, and 0.99 for the modeling of ΔDIL − V98 Gy > 10%, and bladder and rectum Dmax > 80 Gy, respectively. Conclusions: Dosimetric changes in DIL SIB with intensity modulated proton therapy can be modeled and classified based on anatomic changes on pretreatment images by an MLPNN.

Many marine organisms are generally poikilotherms, making seawater temperature one of the most important environmental factors affecting gonadal sex differentiation. Mollusca is the second-largest animal phylum with diverse reproductive systems, but studies on the impact of temperature on sex differentiation are limited to a few sequential hermaphrodites. By combining morphological and molecular analyses, we investigated the effect of temperature on gonadal sex differentiation of a commercially important gonochoristic scallop Patinopecten yessoensis in the field and under laboratory conditions. Based on the relative expression of FoxL2 and Dmrt1L in the gonads of 6- to 12 month-old scallops, we found the scallops start to differentiate at 7 months old in September when the seawater temperature was 21°C. To eliminate the effect of factors other than temperature on sex differentiation, we compared the gonadal development of juvenile scallops at different temperatures (21, 16 and 11°C) under laboratory conditions. After 50 days of treatment, the 11°C group contain more germ cell types, and have higher sex differentiation rates than the 21°C group. But no obvious sex bias was observed. These results suggest that high temperature (21°C) inhibits sex differentiation, whereas low temperature (11°C) accelerates sex differentiation by 2 months for this cold-water species. It also supports juvenile P. yessoensis is gonochoristic rather than protandrous hermaphroditic. Our study addresses for the first time an environmental influence associated with genetic controls on scallop sex differentiation. It will facilitate a better understanding of how environmental factors affect gonadal development in poikilotherms, especially in the less studied molluscs.

Degradable hemostatic materials have unique advantages in reducing the amount of bleeding, shortening the surgical operation time, and improving patient prognosis. However, none of the current hemostatic materials are ideal and have disadvantages. Therefore, a novel biodegradable cellulose-based composite hemostatic material was prepared by crosslinking sodium carboxymethyl cellulose (CCNa) and hydroxyethyl cellulose (HEC), following an improved vacuum freeze-drying method. The resulting cellulose composite material was neutral in pH and spongy with a density of 0.042 g/cm3, a porosity of 77.68%, and an average pore size of 13.45 μm. The composite’s compressive and tensile strengths were 0.1 MPa and 15.2 MPa, respectively. Under in vitro conditions, the composites were degraded gradually through petite molecule stripping and dissolution, reaching 96.8% after 14 days and 100% degradation rate at 21 days. When implanted into rats, the degradation rate of the composite was slightly faster, reaching 99.7% in 14 days and 100% in 21 days. Histology showed a stable inflammatory response and no evidence of cell degeneration, necrosis, or abnormal hyperplasia in the tissues around the embedded material, indicating good biocompatibility. In the hemorrhagic liver model, the time to hemostasis and the total blood loss in the cellulose composite group was significantly lower than in the medical gauze group and the blank control group (P

Cytochrome P450s are widespread in nature and play key roles in the diversification and functional modification of plant natural products. Over the last few years, there has been remarkable progress in plant P450s identification with the rapid development of sequencing technology, “omics” analysis and synthetic biology. However, challenges still persist in respect of crystal structure, heterologous expression and enzyme engineering. Here, we reviewed several research hotspots of P450 enzymes involved in the biosynthesis of plant natural products, including P450 databases, gene mining, heterologous expression and protein engineering.

Purpose To estimate influenza-associated excess mortality rates (EMRs) in Chongqing from 2012 to 2018. Methods We obtained weekly mortality data for all-cause and four underlying causes of death (circulatory and respiratory disease (CRD), pneumonia and influenza (P&I), chronic obstructive pulmonary disease (COPD) and ischemic heart disease (IDH)), and influenza surveillance data, from 2012 to 2018. A negative-binomial regression model was used to estimate influenza-associated EMRs in two age groups (

The Top Tensioned Riser (TTR) is mainly applied to single column platforms and tension leg platforms in the development of deep water oil and gas. The risks in the operation of the TTR were analyzed to guarantee the safety and reliability of ocean engineering operation. Based on the data processing and statistical analysis results of the riser failure data in the continental shelves of the United States, the risk sources were identified by conducting the HAZOP analysis on the key operating procedures of the riser. The fault tree of TTR failure was established on the basis of the causality of the risk sources, and the minimum cut set was achieved by ascending method. Integrating the analytic hierarchy process of fuzzy mathematics and experts grading method, the fault tree was analyzed quantitatively. The probabilities of occurrence of basic risk events and top events in the fault tree were determined, on the grounds of the experts grading. The problem of deficiency and high uncertainty of data is solved by identifying the risk sources in the operation of TTR and establishing a fault-tree based failure model. This research provides a reference for the risk management of deepwater risers.

Resting-state fMRI studies have suggested that autism spectrum disorder (ASD) is associated with aberrant dynamic changes. However, existing research either has difficulty showing the brain's dynamic characteristics or cannot obtain stable results. We examined the `two-group cross-location hidden Markov model' of each region of interest (ROI) to identify possible pathogenic features of ASD. Specifically, we selected resting-state fMRI data with complete scales and good quality from Autism Brain Imaging Data Exchange (ABIDEI). Eligible data included 145 ASD and 157 control (CON). Two groups of subjects were separated to train Hidden Markov models representing respective populations. Then, we used each model to estimate the likelihood values of all participants. Using the likelihood value as features, we tested the significant differences of 200 ROIs and finally identified ROIs with common significant differences in the two types of models. Additionally, we investigated the relationship between likelihood values of significantly different ROIs and clinical scales. some ROIs were negatively correlated with the Autism Diagnostic Observation Schedule and positively correlated with full IQ. Finally, we constructed a support vector machine to classify ASD and CON. Overall, our findings suggested that the abnormal areas in the frontopolar area, orbitofrontal area, inferior temporal gyrus, middle temporal gyrus and fusiform gyrus are prominent features of ASD and are closely related to clinical functional decline. The average accuracy rate reached 74.9% after ten cross-validations. This `two-group cross-localized Hidden Markov Model' provides a robust and powerful framework for understanding the dysfunctional brain architecture of ASD.

The amount of short interfering RNA (siRNA) escaping from the endosome has a significant impact on the efficiency of RNAi. In general, the initial injected amount of siRNAs during the experiment is known, and also the amount of siRNAs after the experiment can be revealed by the level of mRNA measured. However, it is impossible to measure the amount of siRNAs that escape from the endosome and really take part in the chemical reaction of RNAi by detecting the biological organism and its tissues. Inspired by the bottleneck effect in the virus, we introduce the Bayesian approach to infer the amount of escape based on a single type and multiple types of siRNA, respectively. With the consideration of the large calculation quantity of the accurate posterior distribution and the unavailable analytic expression of the likelihood function, our article proposes to take samples by the improved Markov chain Monte Carlo (MCMC) method. The article takes the silencing gene of the synthesis of chitin and the interfering multiple target oncogene as numerical examples to show that our improved MCMC method has higher operation efficiency compared to the Bayesian approach. Our research models siRNA endosome escape using statistical methods for the first time. It perhaps provides a theoretical basis to decrease the cost of a biotic experiment for the future and the standardized statistical approaches for the amount of escape estimation. Keywords: amount of escape, Bayesian inference, RNA interference, MCMC method, Gillespie algorithm

Abstract Background In 2017, an outbreak of onychomadesis occurred in kindergarten H, Hubei province, China. We investigated the field to learn about the magnitude and reason of the outbreak. Methods The case definition was that a child with onychomadesis or transverse ridging (Beau’s line) in fingernails and toenails without previous traumatic or systemic disease in kindergarten H from Sep. 1st to Nov. 30th, 2017. A retrospective cohort study was carried out to analyze the epidemiological relationship between onychomadesis and the hand-foot-mouth disease (HFMD) in Primary Class #2, kindergarten H. We also performed a serological survey for neutralizing antibodies against coxsackie virus A6 (CVA6), coxsackie virus A10 (CVA10) among 15 cases and six healthy children in the kindergarten. Meanwhile, some children were carried out with routine blood, fungal microscopic and microelement tests. Indoor environment examinations had been done for all classes. Results A total of 20 cases were identified in Kindergarten H. Seventy-five percent (15/20) cases occurred in Primary Class #2. Fifty-five percent of the cases (11/20) had suffered from HFMD within two months. The median time between onychomadesis and HFMD was 45 days (ranging from 31 to 58 days). A retrospective cohort study in Primary Class #2 showed the attack rate was 90.0% among 10 children who suffered from HFMD in the past two months compared to 30.0% among 20 children who didn’t (Rate Ratio [RR] =3.0, 95% Confidence Interval [CI] =1.5–6.0). The positive rates of neutralizing antibodies were 66.7% for CVA6 and 26.7% for CVA10 in tested cases. The result of routine blood, fungal microscopic, microelements tests were normal in cases. The indicators of environment were within the normal range. Conclusion The results of this study suggested that the outbreak of onychomadesis in Hubei province was probably associated with HFMD epidemic within two months.

Pancreatic cancer is associated with highly malignant tumors and poor prognosis due to strong therapeutic resistance. Accumulating evidence shows that activated pancreatic stellate cells (PSC) play an important role in the malignant progression of pancreatic cancer. In recent years, the rapid development of single-cell sequencing technology has facilitated the analysis of PSC population heterogeneity, allowing for the elucidation of the relationship between different subsets of cells with tumor development and therapeutic resistance. Researchers have identified two spatially separated, functionally complementary, and reversible subtypes, namely myofibroblastic and inflammatory PSC. Myofibroblastic PSC produce large amounts of pro-fibroproliferative collagen fibers, whereas inflammatory PSC express large amounts of inflammatory cytokines. These distinct cell subtypes cooperate to create a microenvironment suitable for cancer cell survival. Therefore, further understanding of the differentiation of PSC and their distinct functions will provide insight into more effective treatment options for pancreatic cancer patients.

The shear-compression deformation of GH4169 superalloy at 1000–1200 °C and 0.01–1 s−1 was investigated by physical and numerical simulation based on a specific shear-compression sample (SCS). OM, EBSD, and TEM analysis revealed the three typical regions in SCS. Dynamic recrystallization (DRX) preferred to occur in the slot region due to the strain concentration, where the Mises equivalent strain was about 4–6 times higher than the pre-set ones. Mixed grains were observed in the transition region because of insufficient deformation. In contrast, equiaxial grains were preserved in the cylindrical region with negligible deformation. Experimental stress decreased with the increase of temperature and decrease of strain rate. The maximum stress was 180 MPa, which was only 30% of that under single compression deformation. Furthermore, four DRX mechanisms were observed in GH4169. Discontinuous dynamic recrystallization (DDRX) and continuous dynamic recrystallization (CDRX) were the dominant and auxiliary mechanisms, respectively. Twinning dynamic recrystallization (TDRX) occurred in the annealing twins with a fast nucleation rate, and the intersected nano-twins provided favorable nucleation sites for DRX. Lastly, secondary dynamic recrystallization (SDRX) with unique nucleation mechanism of triangularly intersecting subgrain boundaries emerged in SCS, which nucleated in the DRX grains and further refined the microstructure of GH4169.

BackgroundRecently, more patients who recovered from the novel coronavirus disease 2019 (COVID-19) may later test positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) again using reverse transcription-polymerase chain reaction (RT-PCR) testing. Even though it is still controversial about the possible explanation for clinical cases of long-term viral shedding, it remains unclear whether the persistent viral shedding means re-infection or recurrence.MethodsSpecimens were collected from three COVID-19-confirmed patients, and whole-genome sequencing was performed on these clinical specimens during their first hospital admission with a high viral load of SARS-CoV-2. Laboratory tests were examined and analyzed throughout the whole course of the disease. Phylogenetic analysis was carried out for SARS-CoV-2 haplotypes.ResultsWe found haplotypes of SARS-CoV-2 co-infection in two COVID-19 patients (YW01 and YW03) with a long period of hospitalization. However, only one haplotype was observed in the other patient with chronic lymphocytic leukemia (YW02), which was verified as one kind of viral haplotype. Patients YW01 and YW02 were admitted to the hospital after being infected with COVID-19 as members of a family cluster, but they had different haplotype characteristics in the early stage of infection; YW01 and YW03 were from different infection sources; however, similar haplotypes were found together.ConclusionThese findings show that haplotype diversity of SARS-CoV-2 may result in viral adaptation for persistent shedding in multiple recurrences of COVID-19 patients, who met the discharge requirement. However, the correlation between haplotype diversity of SARS-CoV-2 virus and immune status is not absolute. It showed important implications for the clinical management strategies for COVID-19 patients with long-term hospitalization or cases of recurrence.

Abstract Antimony and tin are promising anode materials for sodium‐ion batteries due to their high theoretical sodium storage capacities. However, significant volume change during cycling limits their long‐term stability and rate performance. Composite engineering can minimize this problem. A versatile method for the synthesis of Sb nanoparticles inside the mesopores of carbon fibers prepared through electrospinning and subsequent carbothermal reduction is presented in this work. The mesopore architecture can host up to 61 wt% of Sb nanoparticles and buffer the volume changes during cycling. Smaller pores in the carbon provide the pathways for reversible insertion/extraction of sodium. This binder‐free material provides high rate capability and a long‐term cycling performance when used as an anode in half‐cells. When cycled at 0.5 A g−1, the composite shows an initial capacity of 520 mA h g−1 with 507 mA h g−1 remaining after 500 cycles. Even at a high current density of 20 A g−1, a capacity of 197 mA h g−1 is still achieved. Sn nanoparticles can be embedded in the mesopores of the carbon fibers by a similar method. These Sn‐based anodes also show remarkable electrochemical performance, indicating that this approach represents a generally applicable strategy for synthesizing advanced battery anodes.

The purpose of this study is to obtain the digital twin parameters of the thyroid gland and to build a constitutional model of the thyroid gland based on continuum mechanics, which will lay the foundation for the establishment of a surgical training system for the thyroid surgery robot and the development of the digital twin of the thyroid gland. First, thyroid parenchyma was obtained from fresh porcine thyroid tissue and subjected to quasi-static unconfined uniaxial compression tests using a biomechanical test platform with two strain rates (0.005 s−1 and 0.05 s−1) and two loading orientations (perpendicular to the thyroid surface and parallel to the thyroid surface). Based on this, a tensile thyroid model was established to simulate the stretching process by using the finite element method. The thyroid stretching test was carried out under the same parameters to verify the validity of the hyperelastic constitutive model. The quasi-static mechanical property parameters of the thyroid tissue were obtained by a quasi-static unconstrained uniaxial compression test, and a constitutional model that can describe the quasi-static mechanical properties of thyroid tissue was proposed based on the principle of continuum media mechanics, which is of great value for the establishment of a surgical training system for the head and neck surgery robot and for the development of the thyroid digital twin.

Background and aims We hypothesised that intracellular homocysteine and homocysteine metabolite levels in patients with SLE are disproportionately elevated compared with the levels seen in healthy subjects and that they are independently associated with coronary plaque in SLE.Methods A liquid chromatography–tandem mass spectrometry absolute quantification assay was used for the determination of six analytes in both plasma and peripheral blood mononuclear cells (PBMCs): homocysteine (Hcy), S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH), methionine (Met), cystathionine (Cysta) and 5-methyltetrahydrofolate (5m-THF). We then compared intracellular (PBMC) and extracellular (plasma) Hcy and Hcy metabolite (SAM, SAH, Met, Cysta and 5m-THF) concentrations in 10 patients with SLE and in 10 age, sex and ethnicity matched controls. Subjects with a history of diabetes mellitus, cardiovascular disease, hypertension, alcohol consumption in excess of 3 units per day, anaemia, renal insufficiency (serum creatinine >1.5 mg/dL) and pregnancy were excluded. All patients with SLE had two coronary CT angiography studies as screening for occult coronary atherosclerotic disease.Results Plasma from patients with SLE had higher levels of Hcy (p

Collision prevention sets a major research and development obstacle for intelligent robots and vehicles. This paper investigates the robustness of two state-of-the-art neural network models inspired by the locust’s LGMD-1 and LGMD-2 visual pathways as fast and low-energy collision alert systems in critical scenarios. Although both the neural circuits have been studied and modelled intensively, their capability and robustness against real-time critical traffic scenarios where real-physical crashes will happen have never been systematically investigated due to difficulty and high price in replicating risky traffic with many crash occurrences. To close this gap, we apply a recently published robotic platform to test the LGMDs inspired visual systems in physical implementation of critical traffic scenarios at low cost and high flexibility. The proposed visual systems are applied as the only collision sensing modality in each micro-mobile robot to conduct avoidance by abrupt braking. The simulated traffic resembles on-road sections including the intersection and highway scenes wherein the roadmaps are rendered by coloured, artificial pheromones upon a wide LCD screen acting as the ground of an arena. The robots with light sensors at bottom can recognise the lanes and signals, tightly follow paths. The emphasis herein is laid on corroborating the robustness of LGMDs neural systems model in different dynamic robot scenes to timely alert potential crashes. This study well complements previous experimentation on such bio-inspired computations for collision prediction in more critical physical scenarios, and for the first time demonstrates the robustness of LGMDs inspired visual systems in critical traffic towards a reliable collision alert system under constrained computation power. This paper also exhibits a novel, tractable, and affordable robotic approach to evaluate online visual systems in dynamic scenes.

Wastewater treatment for heavy metals is currently transitioning from pollution remediation towards resource recovery. As a controllable and environment-friendly method, electrochemical technologies have recently gained significant attention. However, there is a lack of systematic and goal oriented summarize of electrochemical metal recovery techniques, which has inhibited the optimized application of these methods. This review aims at recent advances in electrochemical metal recovery techniques, by comparing different electrochemical recovery methods, attempts to target recycling heavy metal resources with minimize energy consumption, boost recovery efficiency and realize the commercial application. In this review, different electrochemical recovery methods (including E-adsorption recovery, E-oxidation recovery, E-reduction recovery, and E-precipitation recovery) for recovering heavy metals are introduced, followed an analysis of their corresponding mechanisms, influencing factors, and recovery efficiencies. In addition, the mass transfer efficiency can be promoted further through optimizing electrodes and reactors, and multiple technologies (photo-electrochemical and sono-electrochemical) could to be used synergistically improve recovery efficiencies. Finally, the most promising directions for electrochemical recovery of heavy metals are discussed along with the challenges and future opportunities of electrochemical technology in recycling heavy metals from wastewater.

A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTMfusion LAI provided the best performance (R2 = 0.83, RMSE = 0.82) when compared to LSTMGLASS (R2 = 0.79, RMSE = 0.93), LSTMMODIS (R2 = 0.78, RMSE = 1.25), LSTMVIIRS (R2 = 0.70, RMSE = 0.94), GLASS (R2 = 0.68, RMSE = 1.05), MODIS (R2 = 0.26, RMSE = 1.75), VIIRS (R2 = 0.44, RMSE = 1.37) and DLF LAI (R2 = 0.67, RMSE = 0.98). A temporal comparison among LSTMfusion and three LAI products demonstrated that the LSTMfusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTMfusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI.

With the expansion of wind power grid integration, the challenges of sharp fluctuations and high uncertainty in preparing the power grid day-ahead plan and short-term dispatching are magnified. These challenges can be overcome through accurate short-term wind power process prediction based on mining historical operation data and taking full advantage of meteorological forecast information. In this paper, adopting the ERA5 reanalysis dataset as input, a short-term wind power prediction framework is proposed, combining light gradient boosting machine (LightGBM), mutual information coefficient (MIC) and nonparametric regression. Primarily, the reanalysis data of ERA5 provide more meteorological information for the framework, which can help improve the model input features. Furthermore, MIC can identify effective feature subsets from massive feature sets that significantly affect the output, enabling concise understanding of the output. Moreover, LightGBM is a prediction method with a stronger ability of goodness-of-fit, which can fully mine the effective information of wind power historical operation data to improve the prediction accuracy. Eventually, nonparametric regression expands the process prediction to interval prediction, which significantly improves the utility of the prediction results. To quantitatively analyze the prediction results, five evaluation criteria are used, namely, the Pearson correlation coefficient (CORR), the root mean square error (RMSE), the mean absolute error (MAE), the index of agreement (IA) and Kling–Gupta efficiency (KGE). Compared with support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost) models, the present framework can make full use of meteorological information and effectively improve the prediction accuracy, and the generated output prediction interval can also be used to promote the safe operation of power systems.

GoalBrain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI.MethodsA deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features.ResultsWe collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs.ConclusionThe proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD.SignificanceThese findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.

With the development of electrical stimulation technology, traditional transcranial alternating current stimulation (tACS) technology has been found to have the drawback of not targeting a specific area accurately. Studies have shown that optimizing the number and position of electrodes during electrical stimulation has a very good effect on enhancing brain stimulation accuracy. At present, an increasing number of laboratories have begun to optimize tACS. However, there has been no study summarizing the optimization methods of tACS. Determining whether different optimization methods are effective and the optimization approach could provide information that could guide future tACS research. We describe the results of recent research on tACS optimization and integrate the optimization approaches of tACS in recent research. Optimization approaches can be classified into two groups: high-definition electrical stimulation and interference modulation electrical stimulation. The optimization methods can be divided into five categories: high-definition tACS, phase-shifted tACS, amplitude-modulated tACS, the temporally interfering (TI) method, and the intersectional short pulse (ISP) method. Finally, we summarize the latest research on hardware useful for tACS improvement and outline future directions.

Kasugamycin, a well-known aminoglycoside antibiotic, has been used widely in agriculture and medicine to combat microbial pathogens by binding the ribosome to inhibit translation. Here, kasugamycin was discovered to be a competitive inhibitor of glycoside hydrolase family 18 (GH18) chitinases from three different organisms (bacterium, insect and human). Results from tryptophan fluorescence spectroscopy and molecular docking revealed that kasugamycin binds to the substrate-binding clefts in a similar mode as the substrate. An electrostatic interaction between the amino group of kasugamycin and the carboxyl group of a conserved aspartate in GH18 chitinase (one of the catalytic triad residues) was found to be vital for the inhibitory activity. This paper not only reports new molecular targets of kasugamycin, but also expands our thinking about GH inhibitor design by using a scaffold unrelated to the substrate.

Rare mutations in the mitochondrial protein coiled-coil-helix-coiled-coil-helix domain containing 2 (CHCHD2) are associated with Parkinson’s disease (PD) and other Lewy body disorders. CHCHD2 is a bi-organellar mediator of oxidative phosphorylation, playing crucial roles in regulating electron flow in the mitochondrial electron transport chain and acting as a nuclear transcription factor for a cytochrome c oxidase subunit (COX4I2) and itself in response to hypoxic stress. CHCHD2 also regulates cell migration and differentiation, mitochondrial cristae structure, and apoptosis. In this review, we summarize the known disease-associated mutations of CHCHD2 in Asian and Caucasian populations, the physiological functions of CHCHD2, how CHCHD2 mutations contribute to α-synuclein pathology, and current animal models of CHCHD2. Further, we discuss the necessity of continued investigation into the divergent functions of CHCHD2 and CHCHD10 to determine how mutations in these similar mitochondrial proteins contribute to different neurodegenerative diseases.

Summary: Cell-cell communication is necessary for cellular immune response. Hemichannel closure disrupts communication between intracellular and extracellular environments during polydnavirus-induced immunosuppression in invertebrates. However, the effects of hemichannel closure on cellular immune response are unclear. Here, we examined apoptotic body formation triggered by hemichannel closure in hemocytes of Spodoptera litura infected with bracovirus from the parasitic wasp, Microplitis bicoloratus. We showed that Microplitis bicoloratus bracovirus (MbBV) induced apoptotic cell disassembly, accompanied by hemichannel closure. Hemocyte apoptotic body formation was caused by the dysregulation of the innexins (Inxs), Inx1, Inx2, Inx3, and Inx4, during the MbBV-mediated inhibition of pI3K/AKT signaling and activation of caspase-3, which cleaved gap junction Inx proteins. Our results showed that hemichannel opening or closure in response to various stimuli, which induces the modulation of Inx levels, could inhibit or activate apoptotic body formation, respectively. Therefore, the “hemichannel open and close” model may regulate the cellular immune response.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

PURPOSE: Transitioning from two-dimensional to three-dimensional treatment planning requires developing contouring skills. Contouring atlases are excellent resources, but they do not provide users active feedback. Developing countries may not have many radiation oncologists experienced in three-dimensional planning to provide training. We sought to develop a standardized self-guided educational module with integrated feedback to teach contouring skills. METHODS AND MATERIALS: All 18 oncology residents at Black Lion Hospital/Addis Ababa University in Ethiopia were trained to contour the level II lymph node station. Residents took a baseline pretest quiz, survey, and contouring evaluation. Residents then watched an instructional contouring lecture and performed three additional cases with integrated feedback by comparing their contours to gold-standard contours. Residents then took a post-training quiz, survey, and contouring evaluation. Paired t tests and analysis of variance were used for analysis. RESULTS: Before training, the average number of total cases ever contoured was 2.4 and the average number of head and neck cases contoured was 0.5. Comfort with contouring improved from being “not at all comfortable” to “quite comfortable” after the 3-hour training (P < .001). The standard deviation between the resident contours and gold standard improved from 72.6 cm3 (pretest) to 7.4 cm3 (post-test). The average percentage overlap with the gold-standard contours and Dice similarity coefficient improved with each case performed, from 27.7% and 0.26 (pretest) to 80.1% and 0.77 (post-test), respectively (P < .001). After training, 16 of 18 (88.9%) residents produced a Dice similarity coefficient greater than 0.7, the threshold generally accepted for excellent agreement. CONCLUSION: This self-guided teaching module was an effective tool for developing level II lymph node contouring skills by providing active feedback and resulted in improved user confidence and accuracy compared with a gold standard. This module can be expanded to other disease sites and countries to further facilitate transitioning to three-dimensional treatment planning in developing countries.