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Abstract Background Astragalus cicer L. is a perennial rhizomatous legume forage known for its quality, high biomass yield, and strong tolerance to saline-alkaline soils. Soil salinization is a widespread environmental pressure. To use A. cicer L. more scientifically and environmentally in agriculture and ecosystems, it is highly important to study the molecular response mechanism of A. cicer L. to salt stress. Results In this study, we used RNA-seq technology and weighted gene coexpression network analysis (WGCNA) were performed. The results showed 4 key modules were closely related to the physiological response of A. cicer. L. to salt stress. The differentially expressed genes (DEGs) of key modules were mapped into the KEGG database, and found that the most abundant pathways were the plant hormone signal transduction pathway and carbon metabolism pathway. The potential regulatory networks of the cytokinin signal transduction pathway, the ethylene signal transduction pathway, and carbon metabolism related pathways were constructed according to the expression pathways of the DEGs. Seven hub genes in the key modules were selected and distributed among these pathways. They may involved in the positive regulation of cytokinin signaling and carbon metabolism in plant leaves, but limited the positive expression of ethylene signaling. Thus endowing the plant with salt tolerance in the early stage of salt stress. Conclusions Based on the phenotypic and physiological responses of A. cicer L. to salt stress, this study constructed the gene coexpression network of potential regulation to salt stress in key modules, which provided a new reference for exploring the response mechanism of legumes to abiotic stress.

Cellular heterogeneity plays a significant role in understanding biological processes, such as cell cycle and disease progression. Microfluidics has emerged as a versatile tool for manipulating single cells and analyzing their heterogeneity with the merits of precise fluid control, small sample consumption, easy integration, and high throughput. Specifically, integrating microfluidics with electrical techniques provides a rapid, label-free, and non-invasive way to investigate cellular heterogeneity at the single-cell level. Here, we review the recent development of microfluidic-based electrical strategies for single-cell manipulation and analysis, including dielectrophoresis- and electroporation-based single-cell manipulation, impedance- and AC electrokinetic-based methods, and electrochemical-based single-cell detection methods. Finally, the challenges and future perspectives of the microfluidic-based electrical techniques for single-cell analysis are proposed.

Abstract Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.

With the development of big data and cloud computing, data management is breaking down ＆quot;data silos＆quot; and developing from isolated services for single domain to cross-domain data sharing and collaborative services.Based on the public data authorization and operation framework, this paper presented the full-link structure of cross-domain data authorization and operation, and discussed the challenges of data privacy and efficiency in the cross-domain data processing.In response to these challenges, two data processing models, centralized and privacy computing, were proposed, which could improve data processing efficiency while protecting data privacy.Finally, an application case of a cross-domain data authorization operation platform in a practical scenario was given.

Hydrogen peroxide production from water electrochemical oxidation is a challenging process. Here the authors report discovery of LaAlO3 after screening a series of perovskites as active, stable, and selective catalyst for electrochemical H2O2 production from water oxidation.

BackgroundCoronary artery calcification (CAC) is associated with high rates of restenosis and adverse clinical events after percutaneous coronary intervention (PCI) with drug-eluting stents (DES).ObjectivesThe aim of this study was to evaluate the long-term clinical outcomes of drug-coated balloon (DCB)-only treatment for de novo lesions with and without CAC.MethodsPatients with de novo coronary disease treated with the DCB-only strategy were retrospectively enrolled from three centers and categorized into a CAC group and a non-CAC group. The primary endpoint was the target lesion failure (TLF) rate during the 3-year follow-up. Secondary endpoints included the occurrence of major adverse cardiac events (MACEs), target lesion revascularization (TLR), cardiac death, myocardial infarction (MI) and any revascularization. Propensity score matching (PSM) was conducted to assemble a cohort of patients with similar baseline characteristics.ResultsA total of 1,263 patients with 1,392 lesions were included, and 243 patients were included in each group after PSM. Compared with the non-CAC group, the incidence rates of TLF (9.52% vs. 4.94%, odds ratio [OR]: 2.080; 95% confidence interval [CI]: 1.083–3.998, P = 0.034) and TLR (7.41% vs. 2.88%, OR: 2.642; 95% CI: 1.206–5.787, P = 0.020) in the CAC group were higher. The incidence rates of MACE (12.35% vs. 7.82%, OR: 1.665; 95% CI: 0.951–2.916, P = 0.079), cardiac death (2.06% vs. 2.06%, OR: 0.995; 95% CI: 0.288–3.436, P = 0.993), MI (1.23% vs. 0.82%, OR: 2.505; 95% CI: 0.261–8.689, P = 0.652) and any revascularization (12.76% vs. 9.67%, OR: 1.256; 95% CI: 0.747–2.111, P = 0.738) were similar between groups.ConclusionsCAC increased the incidence of TLF and TLR without a substantial increase in the risk of MACE, cardiac death, MI, or any revascularization in patients treated with DCB-only angioplasty during the 3-year follow-up.

Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analyzing and predicting the association between the data and particular credit values based on similar data. Recently, ACS has been handled as a machine learning problem, and numerous models were developed to address it. In this paper, we address ACS issues concerning credit scoring in a batch of machine learning problems, namely, feature irregularities due to empty features in many records, class imbalance due to non-uniform statistical distributions of the records between classes, and concept drift due to changing statistical characteristics concerning certain classes and features with time. Considering the limited credit scoring data volume, we propose to address the challenge using the Transfer Learning with Lag (TLL) algorithm based on embedded shallow neural networks that enable knowledge transfer when the number of active features changes. Knowledge transfer is based on lags having an adaptive length that is changed based on performance change feedback. Furthermore, the framework proposes classifier aggregation and the chunk balancing mechanism for handling class imbalance. An evaluation was conducted using the Lending club, German, Default, and PPDai datasets. The results show the superiority of the proposed algorithm over the benchmarks in terms of the majority of classification metrics concerning both time series and overall results.

BackgroundAs a device for percutaneous coronary intervention, drug-coated balloon (DCB) is widely used to treat in-stent restenosis. However, data regarding the use of DCB in treating de novo saphenous vein graft (SVG) lesions are limited. This study aimed to explore the outcomes of using the DCB in the treatment of de novo SVG lesions of coronary heart disease (CHD).MethodsThis retrospective and observational study analyzed CHD patients with de novo SVG lesions treated with DCB or the new-generation drug-eluting stent (DES) between January 2018 and December 2020. Restenosis was the primary endpoint, whereas target lesion revascularization (TLR), major adverse cardiac events, restenosis, cardiac death, target vessel revascularization, and myocardial infarction were the secondary outcomes.ResultsWe enrolled 31 and 23 patients treated with DCB and DES, respectively. The baseline clinical data, lesion characteristics, and procedural characteristics were similar between the two groups. Twenty-eight (90.3%) patients in the DCB group and 21 (91.3%) in the DES group completed follow-up angiography after 1 year. The quantitative coronary angiography measurements at angiographic follow-up showing late lumen loss were −0.07 ± 0.95 mm for the DCB group and 0.86 ± 0.71 mm for the DES group (P = 0.039), and the rates of restenosis were 13.3% and 21.7% for the DCB and DES groups, respectively (P = 0.470). No significant differences were observed in the rates of MACE (16.7% vs. 26.1%, P = 0.402) and TLR (13.3% vs. 4.3%, P = 0.374) during clinical follow-up.ConclusionOur findings suggest that when pre-dilatation was successful, DCB might be safe and effective in treating de novo SVG lesions.

BackgroundData on drug-coated balloons (DCB) for de novo coronary chronic total occlusion (CTO) are limited. We aimed to investigate the long-term outcomes of substitution of drug-eluting stents (DES) by DCB.MethodsWe compared the outcomes of less DES strategy (DCB alone or combined with DES) and DES-only strategy in treating de novo coronary CTO in this prospective, observational, multicenter study. The primary endpoints were major adverse cardiovascular events (MACE), target vessel revascularization, myocardial infarction, and death during 3-year follow-up. The secondary endpoints were late lumen loss (LLL) and restenosis until 1-year after operation.ResultsOf the 591 eligible patients consecutively enrolled between January 2015 and December 2019, 281 (290 lesions) were treated with DCB (DCB-only or combined with DES) and 310 (319 lesions) with DES only. In the DCB group, 147 (50.7%) lesions were treated using DCB-only, and the bailout stenting rate was relatively low (3.1%). The average stent length per lesion in the DCB group was significantly shorter compared with the DES-only group (21.5 ± 25.5 mm vs. 54.5 ± 26.0 mm, p 0.999). The Kaplan–Meier estimates of MACE at 3-year (11.8% vs. 12.0%, log-rank p = 0.688) was similar between the groups.ConclusionPercutaneous coronary intervention with DCB is a potential “stent-less” therapy for de novo CTO lesions with satisfactory long-term clinical results compared to the DES-only approach.

The three-level active neutral-point-clamped (3L-ANPC) inverters have been widely used in medium-voltage high-power electrical drives. The purpose of this paper is to achieve the reliable operation for 3L-ANPC inverters by reliability analysis and optimal switching and control strategies, while the performance of output waveforms of inverters is maintained. This paper starts by analyzing the power loss and the reliability of power semiconductor devices. On this basis, a mathematical model is derived for online condition monitoring of semiconductor devices. Then, the modulation strategies of the passive commutation mode and the active commutation mode are designed for the 3L-ANPC inverter, and the power loss distributions are analyzed among different semiconductor devices accordingly. Finally, three optimized strategies are designed to improve the reliability of the 3L-ANPC inverter under different load conditions based on different commutation modes and the developed reliability model. Both the simulation using MATLAB Simulink as well as PLECS have been conducted to verify the validity of analysis and control for improving reliability of 3L-ANPC inverters in this paper.

ObjectivesIf hilar and mediastinal lymph node metastases occur in solid nodule lung cancer is critical for tumor staging, which determines the treatment strategy and prognosis of patients. We aimed to develop an effective model to predict hilar and mediastinal lymph node metastases by using texture features of solid nodule lung cancer.MethodsTwo hundred eighteen patients with solid nodules on CT images were analyzed retrospectively. The 3D tumors were delineated using ITK-SNAP software. Radiomics features were extracted from unenhanced and enhanced CT images based on AK software. Correlations between radiomics features of unenhanced and enhanced CT images were analyzed with Spearman rank correlation analysis. According to pathological findings, the patients were divided into no lymph node metastasis group and lymph node metastasis group. All patients were randomly divided into training group and test group at a ratio of 7:3. Valuable features were selected. Multivariate logistic regression was used to build predictive models. Two predictive models were established with unenhanced and enhanced CT images. ROC analysis was used to estimate the predictive efficiency of the models.ResultsA total of 7 categories of features, including 107 features, were extracted. There was a high correlation between the 7 categories of features from unenhanced CT images and enhanced CT images (all r > 0.7, p

Performance of Si nanowires as thermoelectrics are evaluated only from cryogenic to ambient temperatures and ZT has remained low. Here, the authors systematically optimized the synthesis method and improved the suspended microdevice platform to achieve high-performance thermoelectrics up to 700 K.

Peach fruits are known to be highly susceptible to chilling injury (CI) during low-temperature storage, which has been linked to the level of sugar concentration in the fruit. In order to better understand the relationship between sugar metabolism and CI, we conducted a study examining the concentration of sucrose, fructose, and glucose in peach fruit with different sugar concentrations and examined their relationship with CI. Through transcriptome sequencing, we screened the functional genes and transcription factors (TFs) involved in the sugar metabolism pathway that may cause CI in peach fruit. Our results identified five key functional genes (PpSS, PpINV, PpMGAM, PpFRK, and PpHXK) and eight TFs (PpMYB1/3, PpMYB-related1, PpWRKY4, PpbZIP1/2/3, and PpbHLH2) that are associated with sugar metabolism and CI development. The analysis of co-expression network mapping and binding site prediction identified the most likely associations between these TFs and functional genes. This study provides insights into the metabolic and molecular mechanisms regulating sugar changes in peach fruit with different sugar concentrations and presents potential targets for breeding high-sugar and cold-tolerant peach varieties.

Kiwifruit (Actinidia deliciosa) ‘Bruno’ is prone to accumulate ethanol rapidly after respiratory climacteric during storage at ambient conditions without stresses, which causes quality deterioration of the fruit associated with alcohol off-flavor. For maintaining the postharvest quality of kiwifruit ‘Bruno’, the effects of 1.0 µL • L−1 1-methylcyclopropene (1-MCP) treatment on regulating the ethanol fermentation and γ-aminobutyric acid (GABA) shunt pathway associated with control of alcohol off-flavor were investigated during storage at room temperature (24 ± 1) °C for 27 days. The results showed that 1-MCP treatment significantly reduced the respiration rate, ethylene production, decay rate, ascorbic acid (AsA) loss, and delayed the decline in the firmness and titratable acidity (TA), and the increase in total soluble solid (TSS) in kiwifruit. Furthermore, 1-MCP treatment effectively inhibited the increases in contents of acetaldehyde, ethanol, and GABA along with the suppressed activities of key enzymes involved in ethanol fermentation and GABA shunt pathway, such as pyruvate decarboxylase (PDC), alcohol dehydrogenase (ADH), glutamate decarboxylase (GAD), and GABA-transaminase (GABA-T) in kiwifruit during storage. In conclusion, 1-MCP treatment efficiently regulated the ethanol fermentation and GABA shunt pathway by delaying the ripening process to avoid the alcohol off-flavor development, thereby contributed to maintaining the quality of the kiwifruit.

Abstract Background Porcine circovirus type 2 (PCV2) is the causative agent of porcine circovirus-associated disease (PCVAD). Its prevalence in swine herds was first reported in China in 2000. PCV2 infection causes immunosuppression that leads to multiple diseases, causing serious economic problems for the swine industry in China. Since information on the genetic variation of PCV2 in Yunnan province is limited, this study aims to investigate the molecular epidemiological and evolutionary characteristics of PCV2 from 2016 to 2019. Methods A total of 279 clinical samples were collected from different regions of Yunnan between 2016 to 2019, and PCV2 was detected by PCR. We then amplified full genomes from the positive samples, and the sequences were analysed for homology and genetic evolution. Results Overall, 60.93% (170/279) of the screened swine herd samples were positive for PCV2. We sequenced 15 Yunnan province PCV2 strains from positive samples. Analyses of the complete genomes and Cap genes led to the classification of the 15 Yunnan PCV2 strains into PCV2a (2 of 15), PCV2b (1of 15) and PCV2d (12 of 15). All strains shared 94.3–99.9% of their identities with the nucleotide sequences of complete genomes in this study and shared 94.2–99.9% identity with the reference sequences. All strains share 89.4–100% and 86.8–100% of their identities with the nucleotide and amino acid (aa) sequences of Cap, respectively. Conclusions The results of this study provide evidence that PCV2a, PCV2b and PCV2d genotypes coexisted in Yunnan Province from 2016 to 2019, and the priority prevalence genotype was PCV2d. The data provide evidence for the increased genetic diversity and insights into the molecular epidemiology of PCV2. This study also provides basic data for the Yunnan province PCV2 molecular epidemiological survey and accumulates effective materials for the development of PCV2 vaccines.

Lysosomal-associated protein transmembrane 5 (LAPTM5) is mainly expressed in immune cells and has been reported to regulate inflammation, apoptosis and autophagy. Although LAPTM5 is expressed in the heart, whether LAPTM5 plays a role in regulating cardiac function remains unknown. Here, we show that the expression of LAPTM5 is dramatically decreased in murine hypertrophic hearts and isolated hypertrophic cardiomyocytes. In this study, we investigated the role of LAPTM5 in pathological cardiac hypertrophy and its possible mechanism. Our results show that LAPTM5 gene deletion significantly exacerbates cardiac remodeling, which can be demonstrated by reduced myocardial hypertrophy, fibrosis, ventricular dilation and preserved ejection function, whereas the opposite phenotype was observed in LAPTM5 overexpression mice. In line with the in vivo results, knockdown of LAPTM5 exaggerated angiotensin II-induced cardiomyocyte hypertrophy in neonatal rat ventricular myocytes, whereas overexpression of LAPTM5 protected against angiotensin II-induced cardiomyocyte hypertrophy in vitro. Mechanistically, LAPTM5 directly bound to Rac1 and further inhibited MEK-ERK1/2 signaling, which ultimately regulated the development of cardiac hypertrophy. In addition, the antihypertrophic effect of LAPTM5 was largely blocked by constitutively active mutant Rac1 (G12V). In conclusion, our results suggest that LAPTM5 is involved in pathological cardiac hypertrophy and that targeting LAPTM5 has great therapeutic potential in the treatment of pathological cardiac hypertrophy.

The plant hormone ABA regulates various physiological processes, such as promoting stomatal closure and inhibiting hypocotyl elongation by mediating de-phosphorylation of H+-ATPase. However, the mechanism acting on ABA-induced de-phosphorylation of H+-ATPase remains largely unknown. SMALL AUXIN UP RNAs (SAURs), the largest family of early auxin-response genes, were well-reported to bind to and inhibit PP2C.D phosphatases to maintain plasma membrane H+-ATPase activity. In this study, we aimed to investigate whether SAUR-PP2C.D functional modules were involved in ABA-mediated inhibition of hypocotyl elongation. Here, we show that ABA suppresses hypocotyl elongation in both light-grown and dark-grown tomato seedlings in a dose-dependent manner. Hypocotyl elongation of dark-grown seedlings was more sensitive to ABA compared to that of light-grown seedlings. ABA upregulates seven SlPP2C.D genes. SlPP2C.D1 was highly expressed in hypocotyl and upregulated by light. Y2H data showed SlPP2C.D1 interacted with SlSAUR2, 35, 40, 55, 57, 59, 65, and 70. The other four SlPP2C.Ds were also associated with a subset of SAUR proteins. Our findings have provided new insights for further examination on the SAUR-PP2C.D modules that regulate outputs of ABA and other phytohormones controlling plant growth and development.

The traditional quantitative analysis methods of ascorbic acid (AA), which require expensive equipment, a large amount of samples and professional technicians, are usually complex and time-consuming. A low-cost and high-efficiency AA detection device is reported in this work. It integrates a three-electrode sensor module prepared by screen printing technology, and a microfluidic chip with a finger-actuated micropump peeled from the liquid-crystal display (LCD) 3D printing resin molds. The AA detection process on this device is easy to operate. On-chip detection has been demonstrated to be 2.48 times more sensitive than off-chip detection and requires only a microliter-scale sample volume, which is much smaller than that required in traditional electrochemical methods. Experiments show that the sample and buffer can be fully mixed in the microchannel, which is consistent with the numerical simulation results wherein the mixing efficiency is greater than 90%. Commercially available tablets and beverages are also tested, and the result shows the reliability and accuracy of the device, demonstrating its broad application prospects in the field of point-of-care testing (POCT).

Abstract Monolayer MoS2 is a promising semiconductor to overcome the physical dimension limits of microelectronic devices. Understanding the thermochemical stability of MoS2 is essential since these devices generate heat and are susceptible to oxidative environments. Herein, the promoting effect of molybdenum oxides (MoOx) particles on the thermal oxidation of MoS2 monolayers is shown by employing operando X‐ray absorption spectroscopy, ex situ scanning electron microscopy and X‐ray photoelectron spectroscopy. The study demonstrates that chemical vapor deposition‐grown MoS2 monolayers contain intrinsic MoOx and are quickly oxidized at 100 °C (3 vol% O2/He), in contrast to previously reported oxidation thresholds (e.g., 250 °C, t ≤ 1 h in the air). Otherwise, removing MoOx increases the thermal oxidation onset temperature of monolayer MoS2 to 300 °C. These results indicate that MoOx promote oxidation. An oxide‐free lattice is critical to the long‐term stability of monolayer MoS2 in state‐of‐the‐art 2D electronic, optical, and catalytic applications.

Deep brain stimulation (DBS) is a neurosurgical technique that regulates neuron activity by using internal pulse generators to electrodes in specific target areas of the brain. As a blind treatment, DBS is widely used in the field of mental and neurological diseases, although its mechanism of action is still unclear. In the past 10 years, DBS has shown a certain positive effect in animal models and patients with Alzheimer's disease (AD), but there are also different results that may be related to the stimulation parameters of DBS. Based on this, determining the optimal stimulation parameters for DBS in AD and understanding its mechanism of action are essential to promote the clinical application of DBS in AD. This review aims to explore the therapeutic effect of DBS in AD, and to analyze its stimulation parameters and potential mechanism of action. The keywords “Deep brain stimulation” and “Alzheimer's Disease” were used for systematic searches in the literature databases of Web of Science and PubMed (from 1900 to September 29, 2020). All human clinical studies and animal studies were reported in English, including individual case studies and long-term follow-up studies, were included. These studies described the therapeutic effects of DBS in AD. The results included 16 human clinical studies and 14 animal studies, of which 28 studies clearly demonstrated the positive effect of DBS in AD. We analyzed the current stimulation parameters of DBS in AD from stimulation target, stimulation frequency, stimulation start time, stimulation duration, unilateral/bilateral treatment and current intensity, etc., and we also discussed its potential mechanism of action from multiple aspects, including regulating related neural networks, promoting nerve oscillation, reducing β-amyloid and tau levels, reducing neuroinflammation, regulating the cholinergic system, inducing the synthesis of nerve growth factor.

Background. The revascularization of small vessels using drug-eluting stents remains challenging. The use of the drug-coated balloon is an attractive therapeutic strategy in de novo lesions in small coronary vessels, particularly in the diabetic group. This study aimed to assess the outcomes of DCB-only angioplasty in small vessel disease. Methods. A total of 1198 patients with small vessel disease treated with DCB-only strategy were followed. Patients were divided into the diabetic and nondiabetic groups. Clinical and angiographical follow-up were organized at 12 months. The primary endpoints were target lesion failure and secondary major adverse cardiac events. Results. There was a significantly higher rate of target lesion failure among diabetic patients compared to nondiabetic [17 (3.9%) vs. 11 (1.4%), P=0.006], taken separately, the rate of target lesion revascularization significantly differed between groups with a higher rate observed in the diabetic group [9 (2%) vs. 4 (0.5%), P=0.014]. Diabetes mellitus remained an independent predictor for TLF (HR: 2.712, CI: 1.254–5.864, P=0.011) and target lesion revascularization (HR: 3.698, CI: 1.112–12.298, P=0.033) after adjustment. However, no significant differences were observed between groups regarding the target vessel myocardial infarction (0.6% vs. 0.1%, P=0.110) and MACE [19 (4.4%) vs. 21 (2.7%), P=0.120]. Conclusion. Drug-coated balloon-only treatment achieved lower incidence rates of TLF and MACE. Diabetes is an independent predictor for target lesion failure and target lesion revascularization at one year following DCB treatment in small coronary vessels. We observed no significant differences between groups regarding MACE in one year.

Background. Relative to nondiabetic patients, percutaneous coronary intervention (PCI) in patients with diabetes mellitus (DM) is associated with inferior clinical outcomes. We aimed to evaluate the outcomes of drug-coated balloon (DCB) in diabetic versus nondiabetic patients. Methods and Results. In this observational, prospective, multicenter study, we compared the outcomes of patients with and without DM after undergoing PCI with DCBs. Target lesion failure (TLF) was analyzed as primary endpoint. Secondary endpoints were the rates of target lesion revascularization (TLR), major adverse cardiovascular events (MACE), cardiac death, myocardial infarction (MI), and any revascularization. Propensity score matching was used to assemble a cohort of patients with similar baseline characteristics. Among 2,306 eligible patients, 578 with DM and 578 without DM had similar propensity scores and were included in the analyses. During follow-up (366±46 days), compared with DM patients, patients without DM were associated with a lower yearly incidence of TLF (2.77% vs. 5.36%; OR, 1.991; 95% CI, 1.077 to 3.681; P=0.025) and TLR (1.90% vs. 4.15%; OR, 2.233; 95% CI, 1.083 to 4.602; P=0.026). No significant differences were observed with regards to rates of MACE (OR: 1.580, 95% CI: 0.912-2.735; P=0.100), cardiac death (OR: 1.608, 95% CI: 0.523-4.946; P=0.403), MI (OR: 4.042, 95% CI: 0.855-19.117; P=0.057), and any revascularization (OR: 1.534, 95% CI: 0.983-2.393; P=0.058). Conclusions. Diabetic patients experience higher TLF and TLR rates following DCB angioplasty without substantial increase in the risk of MACE, cardiac death, MI, or revascularization.

Boron (B), with high energy density and low cost, is regarded as a promising fuel for energetic applications such as propellants. However, achieving efficient combustion of B is challenging due to its delayed ignition and slow combustion. Additives are needed to promote the ignition and combustion of B particles by providing heat, gases, and/or fluorine-containing species. In this study, we demonstrated that a mixture of graphene oxide (GO) and graphite fluoride (GtF) is a new and effective additive for B particles. We compared the energetic performance of B/GO/GtF mixtures with B/GO, B/GtF, and B/PTFE in a laser ignition experiment. B/GO/GtF has the shortest ignition delay time, longest burning duration, and strongest BO2 emission among all four samples. This enhancement is caused by the synergistic properties of GO and GtF, which releases heat, gases, and fluorocarbon radicals to facilitate B ignition and combustion. Our study suggests that the mixture of functionalized graphene and graphite materials can be a new class of promising additive for metal combustion and other energetic materials.

Cross-domain recommendation has become increasingly popular because it can determine dependencies and correlations among different domains. However, this type of recommendation still suffers from data sparsity limitations. Social trust relationship helps alleviate this problem. Existing cross-domain recommendation algorithms focus on modeling user behavior in different domains but disregard the social trust relationship among users. In this paper, we propose a transitive trust-aware cross-domain recommendation model that incorporates the context dependence and transitivity of social trust relations. First, we construct a different transitive trust network for each single domain. Then, we develop a novel probabilistic matrix factorization model for each domain that utilizes the transitive trust-aware model to mine social trust. Finally, we present a nonlinear user-vector mapping algorithm to bridge the feedback of different domains. Experimental results indicate that our method significantly outperforms several state-of-the-art methods, produces higher rating prediction accuracy, and exhibits better recommendation performance on several real-world recommendation tasks.

Producing hydrogen peroxide via electrochemical oxidation of water is an attractive route to this valuable product. Here the authors theoretically and experimentally investigate hydrogen peroxide production activity trends for a range of metal oxides and identify the optimal bias ranges for high Faraday efficiencies.

In order to fully utilize sulfur vacancies in MoS2 catalysts for industrial applications, a facile and general route for making sulfur vacancies in MoS2 is needed. Here, the authors introduce a scalable route towards generating sulfur vacancies on the MoS2basal plane using electrochemical desulfurization.

In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is typically biased and results in difficulty obtaining a comprehensive view of urban land use. It remains challenging to fuse high-dimensional and noisy multi-source urban mobility data to infer urban land use. This study aimed to infer urban land use from multi-source urban mobility data using latent multi-view subspace clustering. The variation in the number of origin/destination points over time was initially used to characterize land use types. Then, a latent multi-view representation was applied to construct the common underlying structure shared by multi-source urban mobility data and effectively deal with noise. Finally, based on the latent multi-view representation, the subspace clustering method was used to infer the land use types. Experiments on taxi trajectory data and bus smart card data in Beijing reveal that, compared with the method using a single type of urban mobility dataset and the weighted fusion method, the approach presented in this study obtains the highest detection rate of land use. The urban land use inferred in this study provides calibration and reference for urban planning.

Integrating three-dimensional (3D) microelectrodes on microfluidic chips based on polydimethylsiloxane (PDMS) has been a challenge. This paper introduces a composite 3D electrode composed of Ag powder (particle size of 10 nm) and PDMS. Ethyl acetate is added as an auxiliary dispersant during the compounding process. A micromachining technique for processing 3D microelectrodes of any shape and size was developed to allow the electrodes to be firmly bonded to the PDMS chip. Through theoretical calculations, numerical simulations, and experimental verification, the role of the composite 3D microelectrodes in separating polystyrene particles of three different sizes via dielectrophoresis was systematically studied. This microfluidic device separated 20-, 10-, and 5-μm polystyrene particles nondestructively, efficiently, and accurately.

This work presents a magnetic-driven deterministic lateral displacement (m-DLD) microfluidic device. A permanent magnet located at the outlet of the microchannel was used to generate the driving force. Two stages of mirrored round micropillar array were designed for the separation of magnetic beads with three different sizes in turn. The effects of the forcing angle and the inlet width of the micropillar array on the separating efficiency were studied. The m-DLD device with optimal structure parameters shows that the separating efficiencies for the 10 μm, 20 μm and 40 μm magnetic beads are 87%, 89% and 94%, respectively. Furthermore, this m-DLD device was used for antibody recognition and separation among a mixture solution of antibodies. The trajectories of different kinds of magnetic beads coupled with different antigens showed that the m-DLD device could realize a simple and low-cost diagnostic test.

Purpose: Amyotrophic lateral sclerosis (ALS) is a motor neuro-degenerative disorder that also damages extra-motor neural pathways. A significant proportion of existing evidence describe alterations in the strengths of functional connectivity, whereas the changes in the density of these functional connections have not been explored. Therefore, our study seeks to identify ALS-induced alternations in the resting-state functional connectivity density (FCD).Methods: Two groups comprising of 38 ALS patients and 35 healthy participants (age and gender matched) were subjected to the resting-state functional magnetic resonance imaging (MRI) scanning. An ultra-fast graph theory method known as FCD mapping was utilized to calculate the voxel-wise short- and long-range FCD values of the brain for each participant. FCD values of patients and controls were compared based on voxels in order to discern cerebral regions that possessed significant FCD alterations. For areas demonstrating a group effect of atypical FCD in ALS, seed-based functional connectivity analysis was then investigated. Partial correlation analyses were carried out between aberrant FCDs and several clinical variables, controlling for age, gender, and total intracranial volume.Results: Patients with ALS were found to have decreased short-range FCD in the primary motor cortex and increased long-range FCD in the premotor cortex. Extra-motor areas that also displayed extensive FCD alterations encompassed the temporal cortex, insula, cingulate gyrus, occipital cortex, and inferior parietal lobule. Seed-based correlation analysis further demonstrated that these regions also possessed disrupted functional connectivity. However, no significant correlations were identified between aberrant FCDs and clinical variables.Conclusion: FCD changes in the regions identified represent communication deficits and impaired functional brain dynamics, which might underlie the motor, motor control, language, visuoperceptual and high-order cognitive deficits in ALS. These findings support the fact that ALS is a disorder affecting multiple systems. We gain a deeper insight of the neural mechanisms underlying ALS.

The United States Food and Drug Administration (FDA) Center for Devices and Radiological Health (CDRH) is charged with ensuring patients in the US have timely access to high-quality, safe, and effective medical devices of public health importance. Within CDRH, the Division of Neurological and Physical Medicine Devices reviews medical technologies that interface with the central and peripheral nervous system (neurotechnologies), including neurointerventional medical devices that are used in the treatment of stroke. Endovascular treatments have demonstrated recent advances in reaching the marketplace and providing more options for patients with acute ischemic stroke and intracranial aneurysms specifically. Depending upon the pathway chosen for regulatory approval, and the evidentiary standard for different regulatory pathways, neurotechnologies can have well-established safety and effectiveness profiles, varying degrees of scientific and clinical uncertainty regarding safety and effectiveness, or when a humanitarian use exists, need only demonstrate a probable benefit and safety to the patient so potentially life-saving treatments can reach the marketplace. Reperfusion therapies have had specific advances in the treatment of stroke patients that originally had limited or no treatment options and for preventative treatments in providing care to patients with intracranial aneurysms to avoid potentially more catastrophic outcomes. Collaboration in multiple forums and environments will be important to continue to foster the neurointerventional technology sector and positively impact clinical medicine, from diagnosing and treating a neurological disorder, to potentially altering the progression of disease, and in many ways, contemporary approved devices have brought a new sense of hope and optimism that serious and otherwise disabling neurological diseases can be treated and in many cases cured with modern therapy. We present here the scope of FDA’s regulatory landscape for neurological devices and neurointerventional endovascular approaches for acute ischemic stroke; this is essential information for those seeking to successfully translate medical device neurotechnologies for patient and consumer use.

So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gestures dominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 ± 1.2 years) were recruited to perform similar grasping tasks (fist, spherical, and cylindrical grasping) and similar pinch tasks (finger, key, and tape pinch). Each task was sustained for 4 s and followed by a 5-s rest interval to avoid fatigue, and the procedure was repeated 60 times for every task. sEMG were recorded from six forearm hand muscles during grasping or pinch tasks, and 4-s sEMG from each channel was segmented for empirical mode decomposition analysis trial by trial. The muscle activity was quantified with zero crossing (ZC) and Wilson amplitude (WAMP) of the first four resulting intrinsic mode function. Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with support vector machine and genetic algorithm was used for hand gesture recognition. The sample number for each hand gesture was designed to be rearranged according to different sample proportion in training set, and corresponding recognition rate was calculated to evaluate the effect of sample assignment change on gesture classification. Either for similar grasping or pinch tasks, the sample assignment change in training set affected the overall recognition rate of candidate hand gesture. Compare to conventional results with uniformly assigned training samples, the recognition rate of similar pinch gestures was significantly improved when the sample of finger-, key-, and tape-pinch gesture were assigned as 60, 20, and 20%, respectively. Similarly, the recognition rate of similar grasping gestures also rose when the sample proportion of fist, spherical, and cylindrical grasping was 40, 30, and 30%, respectively. Our results suggested that the recognition rate of hand gestures can be regulated by change sample arrangement in training set, which can be potentially used to improve fine-gesture recognition for myoelectric robotic hand exoskeleton control.

The standard matrix factorization methods for recommender systems suffer from data sparsity and cold-start problems. Thus, in real-world scenarios where items are commonly associated with textual data such as reviews, it becomes necessary to build a hybrid recommendation model that can fully utilize the text features. However, existing methods in this area either cannot extract good features from the texts due to their order−insensitive document modeling approaches or fail to learn the hybrid model in an effective way due to their complexity of inferring the latent vectors. To this end, we propose a deep hybrid recommendation model which seamlessly integrates matrix factorization with a Convolutional Neural Network (CNN), a powerful text feature extraction tool with the capability of detecting the information of word orders. Unlike previous works which use content features as prior knowledge to regularize the latent vectors, we combine CNN into MF in an additive manner to allow training CNN with direct learning signals. Furthermore, we propose an adversarial training framework to learn the hybrid recommendation model, where a generator model is built to learn the distribution over the pairwise ranking pairs while training a discriminator to distinguish generated (fake) and real item pairs. We conduct extensive experiments on three real-world datasets to demonstrate the effectiveness of our proposed model against state-of-the-art methods in various recommendation settings.

Surface plasmon resonance (SPR) sensor is widely used for its high precision and real-time analysis. Fiber-optic SPR sensor is easy for miniaturization, so it is commonly used in the development of portable detection equipment. It can also be used for remote, real-time, and online detection. In this study, a wavelength modulation fiber-optic SPR sensor is designed, and theoretical analysis of optical propagation in the optical fiber is also done. Compared with existing methods, both the transmission of a skew ray and the influence of the chromatic dispersion are discussed. The resonance wavelength is calculated at two different cases, in which the chromatic dispersion in the fiber core is considered. According to the simulation results, a novel multi-channel fiber-optic SPR sensor is likewise designed to avoid defaults aroused by the complicated computation of the skew ray as well as the chromatic dispersion. Avoiding the impact of skew ray can do much to improve the precision of this kind of sensor.

Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting.A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data.Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner.http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

This paper aims to investigate the effect of light emitting diode therapy (LEDT) on exercise-induced hand muscle fatigue by measuring the surface electromyography (sEMG) of flexor digitorum superficialis. Ten healthy volunteers were randomly placed in the equal sized LEDT group and control group. All subjects performed a sustained fatiguing isometric contraction with the combination of four fingertips except thumb at 30% of maximal voluntary contraction (MVC) until exhaustion. The active LEDT or an identical passive rest therapy was then applied to flexor digitorum superficialis. Each subject was required to perform a re-fatigue task immediately after therapy which was the same as the pre-fatigue task. Average rectified value (ARV) and fractal dimension (FD) of sEMG were calculated. ARV and FD were significantly different between active LEDT and passive rest groups at 20%–50%, 70%–80%, and 100% of normalized contraction time (P