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Background: Ventriculoperitoneal (VP) shunts are commonly used for managing hydrocephalus, with mechanical dysfunction being the most common cause of complications that require revision. A VP shunt placed using a real-time three-dimensional (3D) robotic C-arm navigation system may have better outcomes and fewer complications. Methods: In this technical note, we introduced the workflow of the use of the real-time 3D robotic C-arm navigation system for ventriculoperitoneal shunting. Conclusion: The real-time 3D robotic C-arm can provide a more precise approach to the target. Furthermore, this technique may lower the risk of complications and increase the success rate of shunt placements. [ABSTRACT FROM AUTHOR]

This study aims to assess the association between nicotine replacement therapy (NRT), varenicline, and untreated smoking with the risk of developing eye disorders. We employed a new-user design to investigate the association between NRT use and the incidence of eye disorders by the Taiwan National Health Insurance program. This study included 8416 smokers who received NRT and 8416 smokers who did not receive NRT (control group) matched using propensity scores between 2007 and 2018. After adjustment for relevant factors, a multivariable Cox regression analysis revealed that compared with untreated smokers, NRT use was associated with a significantly reduced risk of macular degeneration (hazard ratio [HR]: 0.34; 95% confidence interval [CI]: 0.13–0.87, P = 0.024). When stratified by dose, short-term NRT use (8–28 defined daily doses) was associated with significantly lower risk of glaucoma (HR: 0.35; 95% CI: 0.16–0.80, P = 0.012) and a trend toward reduced risk of cataract (HR: 0.60; 95% CI: 0.36–1.01, P = 0.053) compared to no treatment. However, these associations were not observed with long-term NRT use. The results of this real-world observational study indicate that NRT use, particularly short-term use, was associated with a lower risk of certain eye disorders compared to no treatment for smoking cessation. Long-term NRT use did not demonstrate the same benefits. Thus, short-term NRT may be a beneficial treatment strategy for reducing the risk of eye disorders in smokers attempting to quit. However, further evidence is required to verify these findings and determine the optimal duration of NRT use. [ABSTRACT FROM AUTHOR]

Chemoresistance is a main reason for treatment failure in patients with nasopharyngeal carcinoma, but the exact regulatory mechanism underlying chemoresistance in nasopharyngeal carcinoma remains to be elucidated. Here, we identify PJA1 as a key E3 ubiquitin ligase involved in nasopharyngeal carcinoma chemoresistance that is highly expressed in nasopharyngeal carcinoma patients with nonresponse to docetaxel-cisplatin-5-fluorouracil induction chemotherapy. We find that PJA1 facilitates docetaxel resistance by inhibiting GSDME-mediated pyroptosis in nasopharyngeal carcinoma cells. Mechanistically, PJA1 promotes the degradation of the mitochondrial protein PGAM5 by increasing its K48-linked ubiquitination at K88, which further facilitates DRP1 phosphorylation at S637 and reduced mitochondrial reactive oxygen species production, resulting in suppression of GSDME-mediated pyroptosis and the antitumour immune response. PGAM5 knockdown fully restores the docetaxel sensitization effect of PJA1 knockdown. Moreover, pharmacological targeting of PJA1 with the small molecule inhibitor RTA402 enhances the docetaxel sensitivity of nasopharyngeal carcinoma in vitro and in vivo. Clinically, high PJA1 expression indicates inferior survival and poor clinical efficacy of TPF IC in nasopharyngeal carcinoma patients. Our study emphasizes the essential role of E3 ligases in regulating chemoresistance and provides therapeutic strategies for nasopharyngeal carcinoma based on targeting the ubiquitin-proteasome system. While chemotherapy may initially be effective in patient with nasopharyngeal carcinoma (NPC), resistance often develops. Here, the authors identify PJA1 as a driver of resistance to docetaxel via inhibition of GSDME-mediated proptosis and target this using a PJA1 inhibitor to restore sensitivity in preclinical models of NPC. [ABSTRACT FROM AUTHOR]

Surface treatment is one of the most effective ways to impart flame retardancy to fabrics, woods, etc. Especially, water-soluble polyelectrolyte complex (PEC) coating deposition is one of the most environmentally benign and facile technologies. However, durability, especially washing durability, remains a challenge for almost all PEC coated materials. In an effort to keep the desirable flame retardancy of cotton despite multiple washings, in this work, perfluorodecyltrichlorosilane modified polyethylenimine (PEI) was prepared to coat onto the ammonium polyphosphate (APP) based PEC coating. The extra 2 wt% fluorinated PEI has not only retained the high LOI value (28.5%) of 10 wt% PEC-coated cotton, but also endowed this coated cotton with high water contact angle of 117°. Also, the treated cotton displayed excellent antifouling performance to milk, coffee, honey, cola and ink. Notably, it still preserved self-extinguishing behavior on a vertical flame test and high LOI value of 26.5%, after vigorously stirring in water at 40 °C for 20 times. This facile and efficient method could provide a new strategy for durable flame-retardant cotton fabric against humid and pollutant conditions. [ABSTRACT FROM AUTHOR]

Current knowledge distillation (KD) methods primarily focus on transferring various structured knowledge and designing corresponding optimization goals to encourage the student network to imitate the output of the teacher network. However, introducing too many additional optimization objectives may lead to unstable training, such as gradient conflicts. Moreover, these methods ignored the guidelines of relative learning difficulty between the teacher and student networks. Inspired by human cognitive science, in this paper, we redefine knowledge from a new perspective — the student and teacher networks’ relative difficulty of samples, and propose a pixel-level KD paradigm for semantic segmentation named relative difficulty distillation (RDD). We propose a two-stage RDD framework: teacher-full evaluated RDD (TFE-RDD) and teacher-student evaluated RDD (TSE-RDD). RDD allows the teacher network to provide effective guidance on learning focus without additional optimization goals, thus avoiding adjusting learning weights for multiple losses. Extensive experimental evaluations using a general distillation loss function on popular datasets such as Cityscapes, CamVid, Pascal VOC, and ADE20k demonstrate the effectiveness of RDD against state-of-the-art KD methods. Additionally, our research showcases that RDD can integrate with existing KD methods to improve their upper performance bound. Codes are available at . [ABSTRACT FROM AUTHOR]

For a long time, the low-voltage distribution network has the problems of untimely management and complex and frequently changing lines, which makes the problem of missing grid topology information increasingly serious. This study proposes an automatic grid topology detection model based on lasso algorithm and t-distributed random neighbor embedding algorithm. The model identifies the household-variable relationship through the lasso algorithm, and then identifies the grid topology of the station area through the t-distributed random neighbor embedding algorithm model. The experimental results indicated that the lasso algorithm, the constant least squares algorithm and the ridge regression algorithm had accuracies of 0.88, 0.80, and 0.71 and loss function values of 0.14, 0.20, and 0.25 for dataset sizes up to 500. Comparing the time spent on identifying household changes in different regions, in Region 1, the training time for the Lasso algorithm, the Constant Least Squares algorithm, and the Ridge Regression algorithm is 2.8 s, 3.0 s, and 3.1 s, respectively. The training time in region 2 is 2.4s, 3.6s, and 3.4s, respectively. The training time in region 3 is 7.7 s, 1.9 s, and 2.8 s, respectively. The training time in region 4 is 3.1 s, 3.6 s, and 3.3 s, respectively. The findings demonstrate that the suggested algorithmic model performs better than the other and can identify the structure of LV distribution networks. [ABSTRACT FROM AUTHOR]

Cell migration, a hallmark of cancer malignancy, plays a critical role in cancers. Improperly initiated or misdirected cell migration can lead to invasive metastatic cancer. Migrasomes are newly discovered vesicular cellular organelles produced by migrating cells and depending on cell migration. Four marker proteins [NDST1 (bifunctionalheparan sulfate N-deacetylase/N-sulfotransferase 1), EOGT (Epidermal growth factor domains pecific O-linked N-acetylglucosaminetransferase), CPQ (carboxypeptidase Q), and PIGK (phosphatidylinositol glycan anchor biosynthesis, class K)] of migrasomes were successfully identified. There are three marker proteins (NDST1, PIGK, and EOGT) of migrasome expressed in cancer. In this review, we will discuss the process of migrasome discovery, the formation of migrasome, the possible functions of migrasome, and the differences between migrasomes and exosomes, especially, the biological functions of migrasome marker proteins in cancer, and discuss some possible roles of migrasomes in cancer. We speculate that migrasomes and migracytosis can play key roles in regulating the development of cancer. [ABSTRACT FROM AUTHOR]

Key summary points: Aim: To analyze the combined effect of falling difficulty and CVD on the risk of all-cause mortality and CVD mortality in older adults. Findings: The combined effect of falling difficulty and CVD was positively associated with the risk of all-cause mortality and CVD mortality in older adults. Message: Clinicians should pay more attention to the older adults at risk of falls and CVD and take appropriate preventive measures timely to decrease the risk of death if necessary. Purpose: Older adults with cardiovascular diseases (CVD) are prone to falls. This study aimed to analyze the combined effect of falling difficulty and CVD on the risk of all-cause- and CVD mortality in older adults. Methods: In this retrospective cohort study, people aged ≥60 years with information on falling difficulty and CVD from the 1999–2004 National Health and Nutrition Examination Survey (NHANES) were selected. Multivariable Cox proportional hazards regression model was used to assess the associations of falling difficulty and CVD with all-cause- and CVD mortality. Results: A total of 1409 participants were included, of whom 868 (58.1%) participants died, and 237 (15.0%) died of CVD. The mean age of participants was 72.1 (0.3) years and 825 (64.7%) were female. Older adults with falling difficulty or CVD were associated with an increased risk of all-cause- and CVD mortality. Older adults in the no falling difficulty & CVD group [hazard ratio (HR) = 1.45, 95% confidence interval (CI) 1.19–1.78], the falling difficulty & no CVD group (HR = 1.45, 95%CI 1.12–1.89), and the falling difficulty & CVD group (HR = 2.13, 95%CI 1.77–2.56) were related to a higher risk of all-cause mortality compared to those in the no falling difficulty & no CVD group. The combined effect of falling difficulty and CVD was positively correlated with the risk of all-cause mortality (HR = 1.26, 95%CI: 1.18–1.34; P-trend <0.001) and CVD mortality (HR = 1.36, 95%CI: 1.18–1.56; P-trend <0.001). Conclusion: The combined effect of falling difficulty and CVD was positively associated with the risk of all-cause- and CVD mortality in older adults. [ABSTRACT FROM AUTHOR]

Whether the application of exergames in physical education (PE) courses can significantly improve student performance in PE learning is still controversial. This review explores the promoting effect of exergames on student PE learning and the conditions in which the effect of exergames can be maximized. Based on the PICOS method, two researchers independently searched the ProQuest database, EBSCO database, Web of Science (WoS) database, PubMed database, Chinese National Knowledge Infrastructure (CNKI) database, Wanfang database, and VIP database, evaluated the literature quality using the Cochrane system evaluation manual, and performed a meta-analysis of the included literature. A total of 16 randomized controlled trials involving 2962 subjects were included in this study. The meta-analysis showed that exergames effectively improved student performance in PE learning (SMD = 0.45, 95% CI: 0.27–0.63, P < 0.00001). Subgroup analysis indicated that better results could be achieved when exergames were introduced in small kindergarten classes and continued for 1–2 months. [ABSTRACT FROM AUTHOR]

One key challenge in the field of topological superconductivity (Tsc) has been the rareness of material realization. This is true not only for the first-order Tsc featuring Majorana surface modes, but also for the higher-order Tsc, which host Majorana hinge and corner modes. Here, we propose a four-step strategy that mathematically derives comprehensive guiding principles for the search and design for materials of general higher-order Tsc phases. Specifically, such recipes consist of conditions on the normal state and pairing symmetry that can lead to a given higher-order Tsc state. We demonstrate this strategy by obtaining recipes for achieving three-dimensional higher-order Tsc phases protected by the inversion symmetry. Following our recipe, we predict that the observed superconductivity in centrosymmetric MoTe2 is a hyrbid-order Tsc candidate, which features both surface and corner modes. Our proposed strategy enables systematic materials search and design for higher-order Tsc, which can mobilize the experimental efforts and accelerate the material discovery for higher-order Tsc phases. [ABSTRACT FROM AUTHOR]

Recently, denoising diffusion probabilistic models (DDPMs) have been effective in speech enhancement. However, existing models largely follow the original diffusion training method, ignoring the trade-off between optimization goals in different training phases. This is not only responsible for the slow training of diffusion models, but also limits their performance. And most current research based on diffusion models predicts Gaussian noise added during the training process or clean data, without considering their relationship to the model. Moreover, the reverse process still does not fully exploit the predictive power of the model. In this work, we integrate the Min-SNR weighting strategy into the training process of the existing probabilistic conditional diffusion model to efficiently exploit different time steps. Based on this strategy, we propose a joint training algorithm and objective to use intermediate data generated during the training phase to assist model learning. Furthermore, we adopt a deterministic reverse process, which is quite different from the existing one, to speed up the inference and improve the quality of the enhanced speech. Experiments show that our proposed method has faster convergence and better performance than related diffusion models and other generative models. [ABSTRACT FROM AUTHOR]

Anisodus tanguticus is a medicinal herb that belongs to the Anisodus genus of the Solanaceae family. This endangered herb is mainly distributed in Qinghai–Tibet Plateau. In this study, we combined the Illumina short-read, Nanopore long-read and high-throughput chromosome conformation capture (Hi-C) sequencing technologies to de novo assemble the A. tanguticus genome. A high-quality chromosomal-level genome assembly was obtained with a genome size of 1.26 Gb and a contig N50 of 25.07 Mb. Of the draft genome sequences, 97.47% were anchored to 24 pseudochromosomes with a scaffold N50 of 51.28 Mb. In addition, 842.14 Mb of transposable elements occupying 66.70% of the genome assembly were identified and 44,252 protein-coding genes were predicted. The genome assembly of A. tanguticus will provide genetic repertoire to understand the adaptation strategy of Anisodus species in the plateau, which will further promote the conservation of endangered A. tanguticus resources. [ABSTRACT FROM AUTHOR]

Identifying subcortical ischemic vascular disease (SIVD) in older adults is important but challenging. Growing evidence suggests that diffusional kurtosis imaging (DKI) can detect SIVD-relevant microstructural pathology, and a systematic assessment of the discriminant power of DKI metrics in various brain tissue microstructures is urgently needed. Therefore, the current study aimed to explore the value of DKI and diffusion tensor imaging (DTI) metrics in detecting early-stage SIVD by combining multiple diffusion metrics, analysis strategies, and clinical-radiological constraints. This prospective study compared DKI with diffusivity and macroscopic imaging evaluations across the aging spectrum including SIVD, Alzheimer's disease (AD), and cognitively normal (NC) groups. Using a white matter atlas and segregated thalamus analysis with considerations of the pre-identified macroscopic pathology, the most effective diffusion metrics were selected and then examined using multiple clinical-radiological constraints in a two-group or three-group paradigm. A total of 122 participants (mean age, 74.6 ± 7.38 years, 72 women) including 42 with SIVD, 50 with AD, and 30 NC were evaluated. Fractional anisotropy, mean kurtosis, and radial kurtosis were critical metrics in detecting early-stage SIVD. The optimal selection of diffusion metrics showed 84.4–100% correct classification of the results in a three-group paradigm, with an area under the curve of.909–.987 in a two-group paradigm related to SIVD detection (all P <.001). We therefore concluded that greatly resilient to the effect of pre-identified macroscopic pathology, the combination of DKI/DTI metrics showed preferable performance in identifying early-stage SIVD among adults across the aging spectrum. [ABSTRACT FROM AUTHOR]

The objective of this study was aimed to develop and validate an instrument for post-COVID-19 symptoms in adults. Data were collected from adults with a previous COVID-19 diagnosis in Taiwan. We developed the initial instrument through systematic review and expert feedback. Its validity was tested using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and criterion-related validity, while its reliability was tested using Cronbach's alpha. In total, 310 adults participated in this study. Examination of the EFA clearly classified a five-factor model with 24 items (Kaiser–Meyer–Olkin = 0.903; Bartlett's test of sphericity: X2 = 5242.956, df = 276, p < 0.01). The goodness of fit indices of the CFA were as follows: chi-square = 635.172 (p < 0.01), normed chi-square = 2.669, standardized root mean square residual = 0.077, root mean square error of approximation = 0.073, comparative fit index = 0.922, and Tuker and Lewis index = 0.910. The value of Cronbach's alpha coefficient for the total items was 0.941, and the values for the subscales ranged from 0.813 to 0.924. The instrument exhibited acceptable psychometric properties, proving it to be a valuable tool for evaluating post-COVID-19 symptoms in patients at hospitals. [ABSTRACT FROM AUTHOR]

Replacing NH3 in NH3-SCR with VOCs provides a new idea for the simultaneous removal of VOCs and NOx, but the technology still has urgent problems such as high cost of catalyst preparation and unsatisfactory catalytic effect in the low-temperature region. In this study, biochar obtained from sewage sludge calcined at different temperatures was used as a carrier, and different Co and Mn injection ratios were selected. Then, a series of sludge-based biochar (SBC) catalysts were prepared by a one-step hydrothermal synthesis method for the simultaneous removal of acetone and NO in a low-temperature photothermal co-catalytic system with acetone replacing NH3. The characterization results show that heat is the main driving force of the reaction system, and the abundance of Co and Mn atoms in high valence states, surface-adsorbed oxygen, and oxygen lattice defects in the catalyst are the most important factors affecting the performance of the catalyst. The performance test results showed that the optimal pyrolysis temperature of sludge was 400 °C, the optimal dosing ratio of Co and Mn was 4:1, and the catalyst achieved 42.98% and 52.41% conversion of acetone and NO, respectively, at 240 °C with UV irradiation. Compared with the pure SBC without catalytic effect, the SBC loaded with Co and Mn gained the ability of simultaneous removal of acetone and NO through the combined effect of multiple factors. The key reaction steps for the catalytic conversion of acetone and NO on the catalyst surface were investigated according to the Mars-van Krevelen (MvK) mechanism, and a possible mechanism was proposed. This study provides a new strategy for the resource utilization of sewage sludge and the preparation of photothermal catalysts for the simultaneous removal of acetone and NO at low cost. [ABSTRACT FROM AUTHOR]

Soft robots capable of efficiently implementing tasks in fluid-immersed environments hold great promise for diverse applications. However, it remains challenging to achieve robotization that relies on dynamic underwater adhesion and morphing capability. Here we propose the construction of such robots with designer protein materials. Firstly, a resilin-like protein is complexed with polyoxometalate anions to form hydrogels that can rapidly switch between soft adhesive and stiff non-adhesive states in aqueous environments in response to small temperature variation. To realize remote control over dynamic adhesion and morphing, Fe3O4 nanoparticles are then integrated into the hydrogels to form soft robots with photothermal and magnetic responsiveness. These robots are demonstrated to undertake complex tasks including repairing artificial blood vessel, capturing and delivering multiple cargoes in water under cooperative control of infrared light and magnetic field. These findings pave an avenue for the creation of protein-based underwater robots with on-demand functionalities. Soft robots have potential in carrying out underwater tasks, but achieving the right level of adhesion and shape-changing ability is challenging. Here, the authors report the development of protein-based hydrogels with iron oxide nanoparticles with photothermal and magnetic responsiveness, capable of carrying out complex tasks. [ABSTRACT FROM AUTHOR]

The global pandemic of coronavirus disease 2019 (COVID-19) has come to a different stage worldwide. Until now, the common flu-like outbreaks have led to increasing demand for screening tests with high sensitivity and specificity. Among biosensors, the noble metal nano-optical sensor based on localized surface plasmon resonance (LSPR) has great potential due to its simple design, feasible manufacturing, and fast response. To develop an efficient and economic examination, this study utilizes high power impulse magnetron sputtering (HiPIMS) to prepare ultrathin gold film (UTGF) on glass substrate. The experimental results show that with an increase in the deposition time from 3 s to 144 s, the UTGF forms from an island-like morphology, a network structure, to ultimately a smooth UTGF layer on glass. When the UTGF sample is conjugated with human serum albumin (HSA) at 5 × 10−4 M as a pretest analyte, a significant peak shift of 25.6 nm was detected for the UTGF deposited at 12 s. Based on the UV-Vis measurement, the plasmonic loss peak of the UTGF sample with deposition times of 6 s, 12 s, and 24 s are 537.1 nm, 601.9 nm, and 665.8 nm, respectively, whereas the deposition time of 12 s prepared UTGF sample revealed the strongest LSPR effect. With a prolonged deposition time over the percolation time (48 s), those UTGF samples gave no LSPR response. To further detect viral antigen, recombinant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein, the UTGF were functionalized with mouse anti-human immunoglobulin G (IgG). The HiPIMS prepared UTGF sample feasible for SARS-CoV-2 detection is demonstrated, giving potential application on rapid and ultrasensitive biomolecules sensor. [ABSTRACT FROM AUTHOR]

Drawing on the international relations literature, the purpose of this paper is to investigate whether and when diplomatic relations between countries have an influence on firm internationalization speed. We discover that the friendlier bilateral diplomatic relations between home and host countries, the more beneficial it is to enhance firm internationalization speed. In addition, the higher trade openness of the host country, the smaller contribution of bilateral diplomatic relations for firm internationalization speed in the host country and friendly bilateral diplomatic relations have a stronger positive impact on internationalization speed when the internationalizing home enterprise is a state-owned enterprise (SOE). Our findings not only contribute to both the firm internationalization speed literature and the international relations literature, but also have managerial significance for how enterprises can use the friendly diplomatic relations between countries to improve firm internationalization speed. [ABSTRACT FROM AUTHOR]

For decades, the slurry coating has been used to manufacture lithium-ion battery electrodes. The process involves toxic and expensive organic solvents, followed by time- and energy-consuming vacuum drying. Herein, a solvent-free method for LiFePO4 cathode preparation using lithium-ion conductive and biodegradable poly(propylene carbonate) (PPC) as a binder is proposed. The cathode is prepared by simple ball milling and hot-pressing method. PPC with a glass transition temperature of 36.5 ℃ is processed at a rubbery state and PPC work as a binder at glassy state while battery running. Microscopically, amorphous PPC contains more polar carbonate groups that coordinate lithium ion at a medium strength to absorb and desorb lithium-ion, which is good for lithium-ion migration. PPC with good mechanical strength as the binder and artificial cathode electrolyte interface in LiFePO4 cathode reduces the internal resistance of the battery effectively and protects LiFePO4 from deformation, realizing the remarkable improvement of electrochemical performance of battery. The mass loading for the LiFePO4 cathode with PPC binder is up to 20 mg/cm2 and the initial discharge capacity of LiFePO4|Li batteries reach 141.9 mAh/g at 0.5 C, and the capacity retention is 79.1% after 800 cycles. Replacing high-cost and environment-polluting PVDF with low-cost and environment-friendly PPC, this solvent-free method will largely reduce the cost and emission of the lithium-ion battery industry. [ABSTRACT FROM AUTHOR]

Background: Vesicoureteral junction (VUJ) obstruction after Deflux® subureteral injection for vesicoureteral reflux (VUR) is rare and minimally invasive management has not been reported. This work investigated the patients who underwent Deflux® injection for VUR and identified those with subsequent VUJ obstruction. Methods: Medical records of matched patients from October 2003 to March 2022 were reviewed, and parameters were retrospectively studied. All patients underwent Deflux® injection. The injection was performed under general anesthesia using the same manner. For patients complicated with VUJ obstruction, the symptoms, signs, management, images, renal ultrasounds, Tc-99m dimercaptosuccinic acid renal scintigraphy, histology of VUJ region, and outcomes were documented and reported. VUJ stenosis was diagnosed by performing renal ultrasound and magnetic resonance imaging. Results: Totally 407 patients (554 ureterorenal units) received Dx/HA injections for VUR. VUJ obstruction was found in three patients (four ureterorenal units). Originally, three were grade V VUR, and one was grade IV. The repeated injection was not a risk factor for VUJ obstruction. The overall incidence of VUJ obstruction post-Dx/HA injection was 0.7% by ureter. The incidences were 0%, 0.75%, and 2.25% for grade I–III, IV, and V VUR, respectively. After the initial conversion case of pneumovesicoscopic ureteral reimplantation, the procedure was performed smoothly and successfully in the two following cases. Conclusions: Pneumovesicoscopic ureteral reimplantation offers an alternative for VUJ obstruction following Dx/HA injection for VUR. Fibrosis and foreign-body reaction may influence the feasibility. High-grade VUR and young age of injection were related to VUJ obstruction. [ABSTRACT FROM AUTHOR]

Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness. Membrane proteins are encoded by approximately a quarter of human genes. Here, the authors propose a deep transfer learning method for predicting inter-chain residue-residue contacts of transmembrane protein complexes. [ABSTRACT FROM AUTHOR]

Objective: Diffusion tensor imaging (DTI) is a useful approach for studying neuronal integrity in animals. However, the test–retest reproducibility of DTI techniques in animals has not been discussed. Therefore, the first part of this work was to systematically elucidate the reliability of DTI-derived parameters in an animal study. Subsequently, we applied the DTI approach to an animal model of diabetes in a longitudinal manner. Materials and methods: In Study 1, nine rats underwent two DTI sessions using the same scanner and protocols, with a gap of 4 weeks. The reliability of the DTI-derived parameters was evaluated in terms of sessions and raters. In Study 2, nine rats received a single intraperitoneal injection of 70 mg/kg streptozotocin (STZ) to develop diabetes. Longitudinal DTI scans were used to assess brain alterations before and 4 weeks after STZ administration. Results: In the test–retest evaluation, the inter-scan coefficient of variation (CoV) ranged from 3.04 to 3.73% and 2.12–2.59% for fractional anisotropy (FA) and mean diffusivity (MD), respectively, in different brain regions, suggesting excellent reproducibility. Moreover, rater-dependence had minimal effects on FA and MD quantification, with all inter-rater CoV values less than 4%. Following the onset of diabetes, FA in striatum and cortex were noted to be significantly lower relative to the period where they had not developed diabetes (both P < 0.05). However, when compared to the control group, a significant change in FA caused by diabetes was detected only in the striatum (P < 0.05), but not in the cortex. Conclusion: These results demonstrate good inter-rater and inter-scan reliability of DTI in animal studies, and the longitudinal setting has a beneficial effect on detecting small changes in the brain due to diseases. [ABSTRACT FROM AUTHOR]

The problem of fault estimation and fault-tolerant control for Lipschitz nonlinear systems subject to actuator and sensor faults is investigated in this paper. Different from the lower triangular matrix linear transformation method in the literature, a fault decomposition technique is proposed to design a set of relaxed iterative observers, so as to derive the iterative estimates for the state and multi-fault. It can be proved that in certain condition, the obtained mean sequence of estimates converge to the true values of state and multi-faults as the number of iterations increases. A perturbation coefficient matrix-dependent LMI condition that guarantees the states of the obtained error dynamics to be uniformly ultimately bounded is proposed, which can degenerate into the traditional ones in the literature by tuning the perturbation coefficient matrix. Based on the obtained final estimation of multi-faults, an output feedback FTC is designed to stabilize the Lipschitz nonlinear system. The longitudinal dynamics of an aircraft is applied to test the proposed method. [ABSTRACT FROM AUTHOR]

Sanhua plum (Prunus salicina), native to Guangdong Province, China, is a highly valued fruit tree because of the fruit's characteristic taste and excellent nutritional properties. A severe blighting of shoots of Sanhua plum was observed at local Sanhua orchards in Guangdong Province from 2019 to 2021, which weakens the tree vigour and reduces the fruit yield. This study aimed to characterize the symptoms of Sanhua plum leaf blight and shoot dieback, evaluate the incidence and impact, and identify the causal agent and evaluate its pathogenicity. The disease was present in all three Sanhua orchards surveyed (a total of 29 hectares), but incidence varied with locations and years, ranging from 0.4% to 3.7%. An oomycete species was isolated from symptomatic shoots collected from the surveyed fields. Based on morphological and cultural characteristics and combined phylogenetic analysis of two molecular markers, including the internal transcribed spacer regions (ITS) of rDNA and Ras-related Ypt1 gene, 15 isolates were identified as belonging to the species Phytophthora nicotianae. The isolates were pathogenic to Sanhua plum plants in nursery experiments, and symptoms identical to that observed in the fields were reproduced. The pathogen was isolated consistently from the inoculated plants, confirming Koch's postulates. Shoot blight caused by P. nicotianae represents a new threat to Sanhua plum productivity and longevity in southern China. [ABSTRACT FROM AUTHOR]

Delivering genes to and across the brain vasculature efficiently and specifically across species remains a critical challenge for addressing neurological diseases. We have evolved adeno-associated virus (AAV9) capsids into vectors that transduce brain endothelial cells specifically and efficiently following systemic administration in wild-type mice with diverse genetic backgrounds, and in rats. These AAVs also exhibit superior transduction of the CNS across non-human primates (marmosets and rhesus macaques), and in ex vivo human brain slices, although the endothelial tropism is not conserved across species. The capsid modifications translate from AAV9 to other serotypes such as AAV1 and AAV-DJ, enabling serotype switching for sequential AAV administration in mice. We demonstrate that the endothelial-specific mouse capsids can be used to genetically engineer the blood-brain barrier by transforming the mouse brain vasculature into a functional biofactory. We apply this approach to Hevin knockout mice, where AAV-X1-mediated ectopic expression of the synaptogenic protein Sparcl1/Hevin in brain endothelial cells rescued synaptic deficits. Delivering genes to and across the brain vasculature efficiently and specifically across species remains challenging. Here, the authors show that endothelial-specific AAVs with serotype flexibility enable redosing and transform the brain vasculature into an in vivo biofactory in genetically diverse rodents. In primates, these vectors cross the blood-brain-barrier and show broad tropism. [ABSTRACT FROM AUTHOR]

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English. [ABSTRACT FROM AUTHOR]

Traditional supervised learning requires ground truth labels for training, whose collection however is difficult in many cases. Recently, crowdsourcing has established itself as an efficient labeling solution by resorting to non-expert crowds. To reduce the labeling error effects, one common practice is to distribute each instance to multiple workers, whereas each worker only annotates a subset of data, resulting in the sparse annotation phenomenon. In this paper, we show that when meeting with class-imbalance, i.e., even when the groundtruth labels are slightly imbalanced, the sparse annotations are prone to be skewly distributed and would bias the learning algorithm severely. To combat this issue, we propose one Distribution Aware Self-training based Crowdsourcing learning (DASC) approach, which supplements the sparse annotations by adding confident pseudo-annotations and at the same time re-balancing the annotation distribution. Specifically, we propose one distribution aware confidence measure to select the most confident pseudo-annotations, with minority/majority classes selected more/less frequently. As a universal framework, DASC is applicable to various crowdsourcing methods for consistent performance gains. We conduct extensive experiments over real-world crowdsourcing benchmarks, from slight to heavy imbalance ratio, with various annotation sparsity levels, and show that DASC substantially improves previous crowdsourcing models by 2 % - 20 % absolute test accuracy, and yields much more balanced annotations. [ABSTRACT FROM AUTHOR]

Cryo-EM has emerged as the most important technique for structure determination of macromolecular complexes. However, raw cryo-EM maps often exhibit loss of contrast at high resolution and heterogeneity over the entire map. As such, various post-processing methods have been proposed to improve cryo-EM maps. Nevertheless, it is still challenging to improve both the quality and interpretability of EM maps. Addressing the challenge, we present a three-dimensional Swin-Conv-UNet-based deep learning framework to improve cryo-EM maps, named EMReady, by not only implementing both local and non-local modeling modules in a multiscale UNet architecture but also simultaneously minimizing the local smooth L1 distance and maximizing the non-local structural similarity between processed experimental and simulated target maps in the loss function. EMReady was extensively evaluated on diverse test sets of 110 primary cryo-EM maps and 25 pairs of half-maps at 3.0–6.0 Å resolutions, and compared with five state-of-the-art map post-processing methods. It is shown that EMReady can not only robustly enhance the quality of cryo-EM maps in terms of map-model correlations, but also improve the interpretability of the maps in automatic de novo model building. Map post-processing is crucial for cryo-EM modeling building. Here, the authors present a deep learning approach to improve both the quality and interpretability of cryo-EM maps by simultaneously considering local and non-local effects. [ABSTRACT FROM AUTHOR]

In order to reduce the high complexity of the successive cancellation list (SCL) algorithm for polar codes, a simplified SCL decoding algorithm based on critical sets (CS-SCL decoding algorithm) is proposed. The algorithm firstly constructs the critical sets according to the channel characteristics of the polar codes as well as comprehensively considering both the minimum Hamming weight (MHW) of the information bits and the channel reliability. The information bits within the critical sets and the path splitting are still performed by the SCL decoding algorithm while the information bits outside the critical sets are directly performed by the hard decision. Thus, the number of path ordering, copying, and deleting can be reduced during decoding. Furthermore, the computational complexity of the SCL decoding can also be reduced. Simulation results demonstrate that the decoding complexity of the proposed CS-SCL decoding algorithm, compared with the conventional SCL decoding algorithm, is reduced by at least 70%, while compared with the simplified SCL (PS-SS-SCL) algorithm which constructs the critical set with the first and second information bits of the Rate-1 nodes, its decoding complexity can also be reduced. Moreover, the loss of the error correction performance for the proposed CS-SCL decoding algorithm is minor. Therefore, the proposed CS-SCL algorithm is effective and can provide a reasonable tradeoff between the decoding performance and complexity for the decoding algorithm of polar codes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a brain–computer interface (BCI)-based Human-in-the-Loop (Hit-Loop) agent for human and artificial intelligence (AI) colearning in music listening and appreciation with an Artificial Intelligence of Things (AIoT) application. The novel BCI-based Hit-Loop agent contains human intelligence with BCI-based AIoT-Fuzzy Markup Language (FML) and BCI-FML agents, as well as machine intelligence with AI-FML Hit-Loop and AIoT-FML agents. We used FML to facilitate communication between humans and the AI-FML robots through an AIoT-FML Learning Tool (AIoT-FML-LT), which was the core technology of the AI-FML Hit-Loop agent for the BCI-based music listening and appreciation application. Furthermore, the novel AIoT-FML-LT in conjunction with the BCI-FML and AIoT-FML agents was developed and presented for music listening and student learning. Moreover, the BCI-based AIoT-FML and AI-FML Hit-Loop agents were applied in English language learning, and the AIoT-FML-LT assisted in measuring student learning performance in Taiwan and Japan. The human-like high-level knowledge base and rule base were constructed by various domain experts, as well as the personalized electroencephalography (EEG) and student English learning data sets collected using the BCI device and AIoT-FML-LT, respectively, and were applied to machine learning models such as deep learning and particle swarm optimization. Additionally, the AIoT-FML-LT was connected to the AIoT-FML Hit-Loop agent for human and robot colearning. The relationship between human perceptions and the AIoT-FML Hit-Loop agent in terms of eyes, ears, nose, tongue, body, and brain corresponding to sights, sounds, smells, tastes, objects of touch, and mind are discussed. Finally, the students learned human language and AI language by using the AI-FML robots and AIoT-FML-LT together with the human English learning and AI-FML machine learning models, respectively. The experimental results reveal that the BCI-based Hit-Loop agent for human and AI-FML robot colearning in conjunction with AIoT applications can effectively facilitate music listening and appreciation application as well as English listening in Taiwan and Japan. The learning behavior and performance of the students also improved after incorporation of the human and robot colearning model. [ABSTRACT FROM AUTHOR]

Pd/In2O3 catalysts with highly dispersed Pd species are prepared via a solid-state routine based on room-temperature grinding. Pd species can serve as the active sites for H2 dissociation, thereby providing abundant H adatoms for CO2 hydrogenation. The addition of Pd significantly improves the catalytic activity of In2O3 for CO2 hydrogenation to methanol. In particular, 2 wt% Pd/In2O3 achieves a methanol space–time yield of 331.7 gMeOH kgcat−1 h−1 at 280 ℃, 3 MPa, and 21,000 mL h−1 gcat−1, 3.6 times that of pure In2O3. [ABSTRACT FROM AUTHOR]

The growing interest in the directed energy deposition process to fabricate and repair thin wall structures has warranted a deeper understanding in the properties of the method's basic building block: clad formation. In this study, clads obtained by depositing stainless steel 316L (SS316L) powder with three different process parameters, namely laser power, laser traverse speed, and powder mass flow rate, were investigated. Repeatability was ensured through a wide sample range per parameter. From the data measurement, the clads have an average hardness close to the typical 200 Hv of SS316L materials, indicating that Hall-Petch effect is dominant. The study also shows that: (i) Laser power is the most significant factor to clad depth, but has little influence on clad thickness. (ii) Laser traverse speed is the dominant parameter to clad height. (iii) Powder mass flow rate tends to compensate depth reduction with thickness gain, resulting in no noticeable effect on clad height. Increasing laser power was observed to be the most effective way to prevent clads from forming with zero dilution, an indicator to how well the printed clad is bonded to the substrate. A dimensionless analysis was derived from the set of SS316L clads. Through validation with different stainless steel datasets and extrapolation to a larger parametric range, the analysis was demonstrated to be able to facilitate the selection of process parameters to meet given requirements on the clad dimensions. As its application is intuitive, the analysis has the potential to be adopted as a standard preprinting tool that will increase success rate, thus improving the manufacturing turnaround time. [ABSTRACT FROM AUTHOR]

NiZnCo ferrite has a wide range of applications in high-frequency devices, but its performance can be further improved to meet more requirements. In this work, Sc3+-substituted Ni0.5Zn0.4Co0.1ScxFe2-xO4 (NZCScF, 0 ≤ x ≤ 0.1) ferrite soft magnetic materials were successfully prepared by sol–gel self-propagating approach. After using Sc3+ doping, the dielectric loss and dielectric constant are significantly reduced. The intensity of the coercive field decreases significantly with a rise in the Sc3+ content. It is noteworthy that after adding Sc3+ ions, the saturation magnetization can reach 94.27 emu/g, an increase of about 27%, and the initial permeability is increased by about 73%. In the high-frequency scope, the magnetic losses increase very little and up to 100 MHz the magnetic losses remain low. In conclusion, Sc3+ substitution effectively improves the electromagnetic properties of NiZnCo ferrite and greatly enhances the saturation magnetization and initial permeability. It can serve as a guide for the material application of a series of high-frequency devices such as radio-frequency microwave devices. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal reactive power control method to maximize wind farm revenue and minimize the total electrical losses of a doubly-fed induction generator (DFIG)-based wind farm. Specifically, the split Bregman method is used to solve the optimal control problem in a distributed manner. That is, the optimization problem is decomposed into sub-problems by the optimal distributed control strategy, and each sub-problem is solved independently in each local controller through the parallel method, which reduces the calculating burden and improves the information privacy. Thus, when a fault occurs, the proposed distributed control strategy can overcome the system fault and improve the reliability and security of the system. Furthermore, an economic financial model of annual revenue is contributed to examine the income impact with or without certified emission reduction (CER) by the clean development mechanism (CDM). Compared with the dual ascent (DA) method, sequential quadratic programming (SQP) method and the proportional dispatch method (PDM), the annual revenue (AR) of the wind farm using the proposed split Bregman method is the highest. Simulation results demonstrate that this method has promising performance in both optimization quality and computational efficiency. [ABSTRACT FROM AUTHOR]

An improved successive cancellation list bit-flip based on assigned set (AS-SCLF) decoding algorithm is proposed to solve the problems that the successive decoding of the successive cancellation (SC) decoder has error propagation and the path extension of the successive cancellation list (SCL) decoder has the decision errors in the traditional cyclic redundancy check aided successive cancellation list (CA-SCL) decoding algorithm. The proposed algorithm constructs the AS firstly. The construction criterion is to use the Gaussian approximation principle to estimate the reliabilities of the polar subchannel and the error probabilities of the bits under SC decoding, and the normalized beliefs of the bits in actual decoding are obtained through the path metric under CA-SCL decoding, thus the error bits containing the SC state are identified and sorted in ascending order of the reliability. Then the SCLF decoding is performed. When the CA-SCL decoding fails for the first time, the decision results on the path of the SC state in the AS are exchanged. The simulation results show that compared with the CA-SCL decoding algorithm, the SCLF decoding algorithm based on the critical set and the decision post-processing decoding algorithm, the improved AS-SCLF decoding algorithm can improve the gain of about 0.29 dB, 0.22 dB and 0.1 dB respectively at the block error rate (BLER) of 10−4 and reduce the number of decoding at the low signal-to-noise ratio (SNR), thus the computational complexity is also reduced. [ABSTRACT FROM AUTHOR]

Purpose: This study examined the effects of an oncofertility education program on decisional conflict in nurses and patients with breast cancer.Methods: A cluster randomized controlled trial was conducted with 84 nurses of five breast care units. Three units were randomly selected from the five as the nurse experimental group. Nurses at the experimental group accepted the oncofertility education based on the naturalistic decision-making (NDM) model, while those at the control group accepted the other non-oncofertility education. We also collected data from female patients before and after the nurses' educational training, respectively. The decisional conflict was measured using the Chinese version of the decisional conflict scale.Results: Nurses in the experimental group had less decisional conflict after the oncofertility educational intervention than those in the control group. After the intervention, nurses with higher infertility knowledge scores had significantly lower decisional conflict. Single nurses had significantly higher decisional conflict than married nurses. A higher perceived barrier score was significantly associated with a higher decisional conflict score. Among patients with the same fertility intention scores, those in the experimental group had lower decisional conflict scores than those in the control group.Conclusions: Our work demonstrates that NDM-based oncofertility care education is feasible and acceptable to improve nurse and patient decisional conflict. Educational training based on the NDM model decreased the decisional conflict regarding oncofertility care.Trial Registration: Clinicaltrials: gov Identifier: NCT04600869. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the observers design and fault estimation for nonlinear systems with faults. The considered nonlinear system is assumed to be represented by T-S fuzzy models or from a LPV system, where the coefficient matrix of measured output is time-varying. A lower triangle matrix (LTM)-based observer structure design criterion (OSDC) for the given nonlinear system is proposed. Under the proposed LTM-based OSDC, an estimation observer with generality and relaxed constraints is designed, by which the fault occurring in the system can be well estimated online. Linear matrix inequality (LMI)-based stability conditions for the estimation error dynamics are given. Simulation examples test the proposed OSDC technique. [ABSTRACT FROM AUTHOR]

A transparent perhydropolysilazane-derived-SiOx coated cellulose film with high gas barrier properties was fabricated via a facile solution dip-coating followed by UV curing method. The conversion of perhydropolysilazane to SiOx was proved by ATR-FTIR, XPS and TOF–SIMS characterizations. AFM and SEM images revealed the smoothness of the coating and good adhesion between the cellulose substrate and the coating. The thickness of the SiOx coating (50–450 nm) can be tuned by changing the concentration of the perhydropolysilazane precursor solution. The cellulose film (35 µm in thickness) with 450 nm thick SiOx coating exhibits low oxygen transmission rate (OTR) value of 0.82 cm3/m2·day and water vapor transmission rate (WVTR) value of 1.28 g/m2·day. The excellent oxygen and water vapor barrier properties, ultrahigh transparency and complete biodegradability of the SiOx coated cellulose film endow it competitive potential for packaging applications. A transparent perhydropolysilazane-derived-SiOx layer is tightly coated on a cellulose film via a facile solution dip-coating followed by UV curing method. The sandwich structured cellulose-SiOx films show excellent oxygen and water vapor barrier properties as well as ultrahigh transparency and complete biodegradability, which has a great potential for food, medicine and other packaging applications. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "CircPVT1 promotes the tumorigenesis and metastasis of osteosarcoma via mediation of miR-26b-5p/CCNB1 axis" published in the Journal of Bone and Mineral Metabolism. The correction states that the funding information section was missing from the original article and should have included the "Hunan Provincial Health Commission (No. 202106022364)" as the funding source. The correction notice also provides the contact information for the corresponding author, Wei-Hua Ye. The original article can be accessed online. [Extracted from the article]

Tropane alkaloids such as hyoscyamine and cocaine are of importance in medicinal uses. Only recently has the hyoscyamine biosynthetic machinery become complete. However, the cocaine biosynthesis pathway remains only partially elucidated. Here we characterize polyketide synthases required for generating 3-oxo-glutaric acid from malonyl-CoA in cocaine biosynthetic route. Structural analysis shows that these two polyketide synthases adopt distinctly different active site architecture to catalyze the same reaction as pyrrolidine ketide synthase in hyoscyamine biosynthesis, revealing an unusual parallel/convergent evolution of biochemical function in homologous enzymes. Further phylogenetic analysis suggests lineage-specific acquisition of polyketide synthases required for tropane alkaloid biosynthesis in Erythroxylaceae and Solanaceae species, respectively. Overall, our work elucidates not only a key unknown step in cocaine biosynthesis pathway but also, more importantly, structural and biochemical basis for independent recruitment of polyketide synthases in tropane alkaloid biosynthesis, thus broadening the understanding of conservation and innovation of biosynthetic catalysts. Cocaine is a tropane alkaloid produced by Erythroxylum novogranatense. Here the authors identify two polyketide synthases involved in cocaine biosynthesis and provide insight into the parallel evolution of these enzymes in tropane alkaloids-producing plants. [ABSTRACT FROM AUTHOR]

Background: Rectal cancer is mainly cured by radical resection with neoadjuvant chemoradiation or adjuvant chemotherapy. Pathological T1 lesions can be managed by local treatment and radiotherapy thereafter. Lower morbidity is the key benefit of these local treatments. Since nodal metastasis is important for staging, radical resection (RR) is suggested. Rectal cancer has higher surgical morbidity than colon cancer; local treatment has been the preferred choice by patients. Methods: We retrospectively enrolled data of 244 patients with pT1 rectal adenocarcinoma. A total of 202 patients (82.8%) underwent RR, including low anterior resection (LAR) and abdomino-perineal resection (APR), and 42 patients (17.2%) underwent LT, including transanal excision and colonoscopic polypectomy. Results: In our study, seven patients (16.7%) had loco-regional recurrence and distant metastasis from the LT group while eight patients (4.0%) had distant metastasis without loco-regional recurrence from the RR group. The lymph node metastasis rate in RR group was 8.4%. Forty-seven patients (24.2%) underwent LAR with temporary stoma, and its reversal rate was 100%. In the RR group, postoperative complication rate was 10.4% with a mortality rate of 0.5%. Recurrence-free survival (RFS) was 95.7% for RR and 80.2% for LT (P = 0.001), and overall survival (OS) was 93.7% for RR and 70.0% for LT (P = 0.001). Conclusion: This study found that RFS and OS in patients of pT1 rectal adenocarcinoma that had received RR were better than receiving LT. Further adjuvant chemotherapy was possible for some RR patients. A higher recurrence rate after LT must be balanced against the morbidity and mortality associated with RR. [ABSTRACT FROM AUTHOR]