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Lymphocyte subsets are the most intuitive expression of the body's immune ability, and the lymphocyte-to-monocyte ratio (LMR) also clearly reflect the degree of chronic inflammation activity. The purpose of this study is to investigate their predictive value of lymphocyte subsets and LMR to neoadjuvant therapy (NAT) efficacy in breast cancer patients. In this study, lymphocyte subsets and LMR were compared between breast cancer patients (n = 70) and benign breast tumor female populations (n = 48). Breast cancer patients were treated with NAT, and the chemotherapy response of the breast was evaluated using established criteria. The differences in lymphocyte subsets and LMR were also compared between pathological complete response (pCR) and non-pCR patients before and after NAT. Finally, data were analyzed using SPSS. The analytical results demonstrated that breast cancer patients showed significantly lower levels of CD3 + T cells, CD4 + T cells, CD4 + /CD8 + ratio, NK cells, and LMR compared to benign breast tumor women (P < 0.05). Among breast cancer patients, those who achieved pCR had higher levels of CD4 + T cells, NK cells, and LMR before NAT (P < 0.05). NAT increased CD4 + /CD8 + ratio and decreased CD8 + T cells in pCR patients (P < 0.05). Additionally, both pCR and non-pCR patients exhibited an increase in CD3 + T cells and CD4 + T cells after treatment, but the increase was significantly higher in pCR patients (P < 0.05). Conversely, both pCR and non-pCR patients experienced a decrease in LMR after treatment. However, this decrease was significantly lower in pCR patients (P < 0.05). These indicators demonstrated their predictive value for therapeutic efficacy. In conclusion, breast cancer patients experience tumor-related immunosuppression and high chronic inflammation response. But this phenomenon can be reversed to varying degrees by NAT. It has been found that lymphocyte subsets and LMR have good predictive value for pCR. Therefore, these markers can be utilized to identify individuals who are insensitive to NAT early on, enabling the adjustment of treatment plans and achieving precise breast cancer treatment. [ABSTRACT FROM AUTHOR]

Different from the Qaidam basin with about 320 billion m3 microbial gas, only limited microbial gases were found from the Junggar basin with similarly abundant type III kerogen. To determine whether microbial gases have not yet identified, natural gas samples from the Carboniferous to Cretaceous in the Junggar basin have been analyzed for chemical and stable isotope compositions. The results reveal some of the gases from the Mahu sag, Zhongguai, Luliang and Wu-Xia areas in the basin may have mixed with microbial gas leading to straight ethane to butane trends with a "dogleg" light methane in the Chung's plot. Primary microbial gas from degradation of immature sedimentary organic matter is found to occur in the Mahu sag and secondary microbial gas from biodegradation of oils and propane occurred in the Zhongguai, Luliang and Beisantai areas where the associated oils were biodegraded to produce calcites with δ13C values from + 22.10‰ to + 22.16‰ or propane was biodegraded leading to its 13C enrichment. Microbial CH4 in the Mahu sag is most likely to have migrated up from the Lower Wuerhe Formation coal-bearing strata by the end of the Triassic, and secondary microbial gas in Zhongguai and Beisantan uplifts may have generated after the reservoirs were uplifted during the period of the Middle and Late Jurassic. This study suggests widespread distribution of microbial gas and shows the potential to find large microbial gas accumulation in the basin. [ABSTRACT FROM AUTHOR]

Chinese Painting and Calligraphy (ChP&C) holds significant cultural value, representing integral aspects of both Chinese culture and global art. A considerable amount of ChP&C works are dispersed worldwide. With the emergence of digital humanities, a vast collection of cultural artifacts data is now available online. However, the online databases of these artifacts remain decentralized and diverse, posing significant challenges to their effective organization and utilization. Addressing this, our paper focuses on the Wu Men School of Painting and proposes a framework for constructing a multimodal knowledge graph for the ChP&C domain. We construct the domain ontology by analyzing the ChP&C knowledge schema. Then, we acquire knowledge from diverse data sources, including textual and visual information. To enhance data collection around collecting historical context and subject matter, we propose methods for seal extraction and subject extraction specific to ChP&C. We validate the effectiveness of these methods on the constructed dataset. Finally, we construct the Wu Men Multimodal Knowledge Graph (WuMKG) and implement applications such as cross-modal retrieval, knowledge-based question-answering and visualization. [ABSTRACT FROM AUTHOR]

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species’ potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen’s kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species. [ABSTRACT FROM AUTHOR]

Research into new composites utilizing cubic boron nitride (PcBN) shows promise for enhancing cutting tool performance. The unique properties of these materials stem from the addition of microfibers made of refractory compounds to their structure. This study looks at developing two-layer composites based on cBN group BL, reinforced with SiCw and Al2O3w microfibers. The goal is to improve tool stability when cutting hardened steels with impact loads. PcBN composite samples were made by sintering a mixture of cBN powder with bundles and microfibers under 7.7 GPa pressure. Bond material selection was based on analyzing the relationship between Poisson's ratio (η) and plasticity parameter (G/B). The density, Young's modulus, Poisson's ratio, and hardness of the composites were determined, and the microstructure of samples with TiCN bond was studied. Tool-life tests were conducted on two-layer cutting inserts made of PcBN reinforced with SiCw and Al2O3w microfibers during the machining of hardened KhVG steel (HRC 55) under impact loads at cutting speeds of 100 and 200 m/min. [ABSTRACT FROM AUTHOR]

This paper proposes a deep reinforcement learning-based framework for robot autonomous grasping and assembly skill learning. Meanwhile, a deep Q-learning-based robot grasping skill learning algorithm and a PPO-based robot assembly skill learning algorithm are presented, where a priori knowledge information is introduced to optimize the grasping action and reduce the training time and interaction data needed by the assembly strategy learning algorithm. Besides, a grasping constraint reward function and an assembly constraint reward function are designed to evaluate the robot grasping and assembly quality effectively. Finally, the effectiveness of the proposed framework and algorithms was verified in both simulated and real environments, and the average success rate of grasping in both environments was up to 90%. Under a peg-in-hole assembly tolerance of 3 mm, the assembly success rate was 86.7% and 73.3% in the simulated environment and the physical environment, respectively. [ABSTRACT FROM AUTHOR]

Butterfly valves are widely used in water supply systems, sewage treatment plants, chemical processes, and the natural gas industry to control and regulate the flow of fluids. When the liquid medium flows through a butterfly valve, the local pressure decreases rapidly because of the sudden change in the flow channel area, which leads to the cavitation generated at the downstream of butterfly valve if the local pressure is lower than saturated vapor pressure. This cavitation negatively affects the performance of the valve, causing severe vibration and noise. In some cases, this may affect the sealing performance and lifetime of the valve and could even lead to accidents. This paper mainly carried out experimental studies on the cavitation phenomenon inside butterfly valves. The cavitation evolution was recorded inside the butterfly valve at different opening degrees and pressure conditions by a high-speed camera, and the pressure was monitored at upstream and downstream of valve plate. The influences of valve opening degree and total pressure on dynamic evolution and distribution of cavitation was revealed. By comparing the distribution of vortices and cavitation bubbles in the flow field, it was found that vortices led to the aggregation of cavitation bubbles and changed the distribution of these bubbles in the flow field. We also analyzed the interaction between the cavitation and the surface of the butterfly plate. The shedding of the attached cavitation was mainly caused by a reentrant jet. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology, the Internet has brought significant convenience to various sectors of society, holding a prominent position. Due to the unpredictable and severe consequences resulting from malicious attacks, the detection of anomalous network traffic has garnered considerable attention from researchers over the past few decades. Accurately labeling a sufficient amount of network traffic data as a training dataset within a short period of time is a challenging task, given the rapid and massive generation of network traffic data. Furthermore, the proportion of malicious attack traffic is relatively small compared to the overall traffic data, and the distribution of traffic data across different types of malicious attacks also varies significantly. To address the aforementioned challenges, this paper presents a novel network anomaly detection algorithm based on semi-supervised learning and adaptive multiclass balancing. Building upon the assumption of consistent distribution between labeled and unlabeled data, this paper introduces the multiclass split balancing strategy and the adaptive confidence threshold function. These innovative approaches aim to tackle the issue of the multiclass imbalanced in traffic data. By leveraging the mutually beneficial relationship between semi-supervised learning and ensemble learning, this paper presents the collaborative rotation forest algorithm. This algorithm is specifically designed to enhance performance of anomaly detection in an environment with label inadequacy. Several comparative experiments conducted on the NSL-KDD, UNSW-NB15, and ToN-IoT demonstrate that the proposed algorithm achieves significant improvements in performance. Specifically, it enhances precision by 1.5–5.7%, recall by 1.5−5.7%, and F-Measure by 1.4−4.3% compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Pansharpening strives to improve the spatial resolution of multi-spectral images while maintaining spectral fidelity. However, existing methods usually cannot guarantee a balance between spatial and spectral quality, and degrade when handling the desirable scale of full resolution. To address these challenges, this prospective study proposes a scale-transfer learning framework via estimating spectral observation model. Specifically, we design a cross-spectral transfer network to learn an expected spectral observation model by the cycle adversarial between spectral degradation and interpolation, which describes the accurate nonlinear mapping process from multi-spectral to panchromatic images. Having the favorable spectral observation model established, a scale-transfer pansharpening paradigm with co-learning of full and reduced resolutions can be constructed. First, we develop a spectral observation model-based spatial fidelity term at the reduced-resolution scale, which can alleviate the imbalance problem of spectral and spatial information widespread in current supervised paradigms. Second, we explore the reprojection regularization from full to reduced resolution based on the spectral observation model, which facilitates the ability of the pansharpening model to be extended to the scale of full resolution. Extensive experiments demonstrate the advantage of our method over the current state-of-the-arts in terms of information balance and scale transformation. We further apply our method to produce the high-resolution normalized difference vegetation index and achieve vegetation enhancement with competitive performance. Moreover, our method is lightweight and faster than other comparative methods. [ABSTRACT FROM AUTHOR]

Near-surface seismic shear wave is a basic tool for seismic investigations. However, its frequency-dependent property is not fully investigated, especially by the in situ observation method. Here, we develop the seismic interferometry with a moving frequency window to process the natural seismic signals recorded by the KiK-net network. It is observed that the phase velocity of the shear wave decreases sharply as the frequency increases in the low-frequency range, and remains constant in the high-frequency range. The observed dispersion phenomenon presents a challenge to existing site effect prediction theories, while also providing an observational reference for understanding how the shear wave propagates in near-surface sediment. Key points: Moving-frequency-window seismic interferometry is developed to extract the frequency-varying information of the ground motion. It is firstly observed that the shear wave velocity in near-surface sediment is strongly frequency-dependent. The phase velocity of the shear wave decreases sharply in the low-frequency range as the frequency increases. [ABSTRACT FROM AUTHOR]

In order to investigate the strategy choice of each player in capacity sharing, the article constructs a tripartite game model based on capacity provider-capacity demander-government, introduces the prospect theory and conducts numerical simulation analysis using MATLAB. The results show that capacity sharing in the manufacturing industry is related to three parties: capacity providers, capacity demanders and the government, and their strategies in the game process influence each other; the sensitivity of capacity providers and capacity demanders is higher than that of the government; the increase of risk-return coefficient and loss-avoidance coefficient is conducive to the evolution of subjects to the ideal state. [ABSTRACT FROM AUTHOR]

Network intrusion detection system plays a crucial role in protecting the integrity and availability of sensitive assets, where the detected traffic data contain a large amount of time, space, and statistical information. However, existing research lacks the utilization of spatial-temporal multi-granularity data features and the mutual support among different data features, thus making it difficult to specifically and accurately identify anomalies. Considering the distinctions among different granularities, we propose a framework called tri-broad learning system (TBLS), which can learn and integrate the three granular features. To explore the spatial-temporal connotation of the traffic information accurately, a feature dataset containing three granularities is constructed according to the characteristics of time, space, and data content. In this way, we use broad learning basic units to extract abstract features of different granularities and then express these features in different feature spaces to enhance them separately. We use a normal distribution initialization method in BLS to optimize the weights of feature nodes and enhancement nodes for better detection accuracy. The merits of our proposed model are exhibited on the UNSW-NB15, CIC-IDS-2017, CIC-DDoS-2019, and mixed traffic datasets. Experimental results show that TBLS outperforms the typical BLS in terms of various evaluation metrics and time consumption. Compared with other machine learning methods, TBLS achieves better performance metrics. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) seriously threatens human health and has been disseminated worldwide. Although there are several treatments for COVID-19, its control is currently suboptimal. Therefore, the development of novel strategies to treat COVID-19 is necessary. Ion channels are located on the membranes of all excitable cells and many intracellular organelles and are key components involved in various biological processes. They are a target of interest when searching for drug targets. This study aimed to reveal the relevant molecular features of ion channel genes in COVID-19 based on bioinformatic analyses. The RNA-sequencing data of patients with COVID-19 and healthy subjects (GSE152418 and GSE171110 datasets) were obtained from the Gene Expression Omnibus (GEO) database. Ion channel genes were selected from the Hugo Gene Nomenclature Committee (HGNC) database. The RStudio software was used to process the data based on the corresponding R language package to identify ion channel-associated differentially expressed genes (DEGs). Based on the DEGs, Gene Ontology (GO) functional and pathway enrichment analyses were performed using the Enrichr web tool. The STRING database was used to generate a protein–protein interaction (PPI) network, and the Cytoscape software was used to screen for hub genes in the PPI network based on the cytoHubba plug-in. Transcription factors (TF)–DEG, DEG–microRNA (miRNA) and DEG–disease association networks were constructed using the NetworkAnalyst web tool. Finally, the screened hub genes as drug targets were subjected to enrichment analysis based on the DSigDB using the Enrichr web tool to identify potential therapeutic agents for COVID-19. A total of 29 ion channel-associated DEGs were identified. GO functional analysis showed that the DEGs were integral components of the plasma membrane and were mainly involved in inorganic cation transmembrane transport and ion channel activity functions. Pathway analysis showed that the DEGs were mainly involved in nicotine addiction, calcium regulation in the cardiac cell and neuronal system pathways. The top 10 hub genes screened based on the PPI network included KCNA2, KCNJ4, CACNA1A, CACNA1E, NALCN, KCNA5, CACNA2D1, TRPC1, TRPM3 and KCNN3. The TF–DEG and DEG–miRNA networks revealed significant TFs (FOXC1, GATA2, HINFP, USF2, JUN and NFKB1) and miRNAs (hsa-mir-146a-5p, hsa-mir-27a-3p, hsa-mir-335-5p, hsa-let-7b-5p and hsa-mir-129–2-3p). Gene-disease association network analysis revealed that the DEGs were closely associated with intellectual disability and cerebellar ataxia. Drug-target enrichment analysis showed that the relevant drugs targeting the hub genes CACNA2D1, CACNA1A, CACNA1E, KCNA2 and KCNA5 were gabapentin, gabapentin enacarbil, pregabalin, guanidine hydrochloride and 4-aminopyridine. The results of this study provide a valuable basis for exploring the mechanisms of ion channel genes in COVID-19 and clues for developing therapeutic strategies for COVID-19. [ABSTRACT FROM AUTHOR]

To elevate the accuracy of diagnostic results, CNV-seq is usually performed simultaneously with karyotyping or QF-PCR. Although several studies have investigated the performance of the combined use of CNV-seq with karyotyping or QF-PCR, there have been no reports focusing on the comparison of these 2 diagnostic strategies. In our study, 2507 pregnant women were included to investigate these 2 strategies. The detection rates of foetal genetic abnormalities and turnaround time were compared between these 2 groups. Moreover, the detection rates of foetal genetic abnormalities in different indications were analyzed. Our results unveiled that the detection rates of numerical chromosomal abnormalities were nearly the same in these 2 groups. In addition to numerical chromosomal abnormalities, 39 balanced karyotypic changes and chromosome polymorphisms were detected via the combined use of karyotyping and CNV-seq. Further investigation revealed that the vast majority of these karyotypic changes were inherited from parents. Compared with the karyotyping group, the combination of QF-PCR and CNV-seq reduced the reporting time from 31.593 ± 4.944 days to 11.460 ± 4.894 days. Meanwhile, NIPT, maternal serum screening and ultrasound scan significantly improved the detection of foetal genetic abnormalities. In conclusion, our results revealed that parental karyotyping is a useful supplementary method for CNV-seq and systematic prenatal examinations improved the detection of foetal genetic defects. [ABSTRACT FROM AUTHOR]

Overly restrictive eligibility criteria for clinical trials may limit the generalizability of the trial results to their target real-world patient populations. We developed a novel machine learning approach using large collections of real-world data (RWD) to better inform clinical trial eligibility criteria design. We extracted patients' clinical events from electronic health records (EHRs), which include demographics, diagnoses, and drugs, and assumed certain compositions of these clinical events within an individual's EHRs can determine the subphenotypes—homogeneous clusters of patients, where patients within each subgroup share similar clinical characteristics. We introduced an outcome-guided probabilistic model to identify those subphenotypes, such that the patients within the same subgroup not only share similar clinical characteristics but also at similar risk levels of encountering severe adverse events (SAEs). We evaluated our algorithm on two previously conducted clinical trials with EHRs from the OneFlorida+ Clinical Research Consortium. Our model can clearly identify the patient subgroups who are more likely to suffer or not suffer from SAEs as subphenotypes in a transparent and interpretable way. Our approach identified a set of clinical topics and derived novel patient representations based on them. Each clinical topic represents a certain clinical event composition pattern learned from the patient EHRs. Tested on both trials, patient subgroup (#SAE=0) and patient subgroup (#SAE>0) can be well-separated by k-means clustering using the inferred topics. The inferred topics characterized as likely to align with the patient subgroup (#SAE>0) revealed meaningful combinations of clinical features and can provide data-driven recommendations for refining the exclusion criteria of clinical trials. The proposed supervised topic modeling approach can infer the clinical topics from the subphenotypes with or without SAEs. The potential rules for describing the patient subgroups with SAEs can be further derived to inform the design of clinical trial eligibility criteria. [ABSTRACT FROM AUTHOR]

Hemin with functions such as oxygen carrying, oxygen storing, promoting redox, and performing electron transfer is important for the health of organisms. In this paper, green synthetic silicon nanoparticles (Si-NPs) were synthesized and used for free hemin detection in serum (a low limit of detection (LOD), 29.5 nM). The quenching mechanism was investigated by UV–vis absorption spectra, time-resolved luminescence decay curve, and circular dichroism (CD) spectra. It was confirmed that multiple redox centers of hemin led to intensified effective collision and increased the electron transfer rate, therefore enhancing the dynamic quenching, and it was undeniable that the inner filter effect (IFE) also played a role in the quenching. [ABSTRACT FROM AUTHOR]

TGF‐β1 is the strongest cytokine known to promote liver fibrosis. It has been previously demonstrated that the activation of TGF‐β1 initiates a temporary collagen accumulation program, which is important for wound repair in several organs. Furthermore, temporary extracellular matrix enhancement often leads to progressive fibrosis, which is accountable for cases of severe morbidity and mortality worldwide. However, its action mechanism has not been fully explored. It was previously reported that UCA1 could promote its occurrence and development in various tumors. Importantly, it was reported that TGF‐β1 could activate the expression of UCA1 in liver cancer, gastric cancer, and breast cancer. However, the role of UCA1 in organ fibrosis, including liver fibrosis, remains unreported. The present study reported for the first time that TGF‐β1/Smad3 could promote liver fibrosis by upregulating UCA1, which further affected DKK1 and collagen, such as COL1A1, COL1A2, and COL3A1. Meanwhile, UCA1 could competitively bind with miR18a to stabilize Smad3 to constitute a positive feedback pathway, which played a significant role in the promotion of liver fibrosis. Altogether, the present study provides a theoretical basis for devising promising treatment strategies for liver fibrosis. Key messages: UCA1 was found to promote the progression of liver fibrosis in vitro. UCA1 is regulated by TGF-β1 and promotes liver fibrosis through the canonical Smad pathway. UCA1 can competitively bind with miR18a, promote liver fibrosis by stabilizing Smad3, and form a UCA1-miR18a/Smad3 positive feedback. UCA1 binds EZH2 to inhibit the DKK1 expression and promote liver fibrosis. [ABSTRACT FROM AUTHOR]

Emerging evidence suggests that pyroptosis is involved in sepsis. However, the role of neutrophil pyroptosis in sepsis and the mechanisms remains elusive. We find that N-acetyltransferase 10 (NAT10), an acetyltransferase responsible for the N4-acetylation of Cytidine (ac4C) in mRNA, is significantly downregulated in neutrophils from septic mice. Neutrophil-specific over-expression of NAT10 improves the survival and ameliorates lung injury in septic mice by inhibiting neutrophil pyroptosis. Notably, UNC-52-like kinase 1 (ULK1) is identified as the target of NAT10 in neutrophils. The decreased expression of NAT10 resultes in the decay of ULK1 transcripts and therefore the reduced expression of ULK1. As a regulator of STING phosphorylation, the loss of ULK1 enhances the activation of STING-IRF3 signaling and subsequently the elevated pyroptosis-inducing NLRP3 inflammasome in neutrophils. While over-expression of NAT10 restrains pyroptosis in neutrophils as well as septic lethality in mice by reversing the ULK1-STING-NLRP3 axis. The decreased expression of NAT10 are also observed in sepsis patients and its correlation with clinical severity is found. Collectively, our findings disclose that NAT10 is a negative regulator of neutrophil pyroptosis and its downregulation contributes to the progress of sepsis by exacerbating pyroptosis via the ULK1-STING-NLRP3 axis, therefore revealing a potential therapeutic target for sepsis. The enzyme N-acetyltransferase NAT10 is a negative regulator of neutrophil pyroptosis and its downregulation contributes to the progress of sepsis by exacerbating pyroptosis via the ULK1-STING-NLRP3 pathway. [ABSTRACT FROM AUTHOR]

Current image-based keypoint detection methods for animal (including human) bodies and faces are generally divided into fully supervised and few-shot class-agnostic approaches. The former typically relies on laborious and time-consuming manual annotations, posing considerable challenges in expanding keypoint detection to a broader range of keypoint categories and animal species. The latter, though less dependent on extensive manual input, still requires necessary support images with annotation for reference during testing. To realize zero-shot keypoint detection without any prior annotation, we introduce the Open-Vocabulary Keypoint Detection (OVKD) task, which is innovatively designed to use text prompts for identifying arbitrary keypoints across any species. In pursuit of this goal, we have developed a novel framework named Open-Vocabulary Keypoint Detection with Semantic-feature Matching (KDSM). This framework synergistically combines vision and language models, creating an interplay between language features and local keypoint visual features. KDSM enhances its capabilities by integrating Domain Distribution Matrix Matching (DDMM) and other special modules, such as the Vision-Keypoint Relational Awareness (VKRA) module, improving the framework’s generalizability and overall performance. Our comprehensive experiments demonstrate that KDSM significantly outperforms the baseline in terms of performance and achieves remarkable success in the OVKD task. Impressively, our method, operating in a zero-shot fashion, still yields results comparable to state-of-the-art few-shot species class-agnostic keypoint detection methods. Codes and data are available at https://github.com/zhanghao5201/KDSM. [ABSTRACT FROM AUTHOR]

We also found that patients treated by primary care and advanced practice were more likely to be Black and Hispanic and have injection drug use diagnoses, suggesting greater treatment uptake by non-specialists to these most vulnerable populations. Patients treated by gastroenterologists were significantly less likely to be Black or Hispanic than those treated by primary care physicians, and less likely to have an injection drug use diagnosis. Our findings demonstrate substantial state variation in the specialties providing HCV treatment, implying that policy or healthcare supply may play important roles for the HCV treatment landscape. [Extracted from the article]

Network intrusion detection plays an important role as tools for managing and identifying potential threats, which presents various challenges. Redundant features and difficult marking in data cause a long-term problem in network traffic detection. In this paper, we propose a semi-supervised machine learning framework based on multi-strategy feature filtering, principal component analysis (PCA), and an improved Tri-Light Gradient Boosting Machine (Tri-LightGBM) based on stratified sampling. This multi-strategy feature filtering method employing Fisher score and Information gain can select features that have good category discrimination and are more relevant to category labels. After that, we combine PCA to convert multiple features into comprehensive features, which are used as the input of the Tri-LightGBM model. Tri-LightGBM can exploit unlabeled data cooperatively and maintain a large disagreement among the base learners. Moreover, we propose a stratified sampling based on labeled categories to reduce the probability of being selected as the same category during the model update process. Thus, the Tri-LightGBM based on stratified sampling can compensate for the classification error rate caused by the imbalance of the dataset. The semi-supervised machine learning framework is evaluated on two intrusion detection evaluation datasets, namely UNSW-NB15 and CIC-IDS-2017. The evaluation results show that the multi-strategy feature filtering method can increase the accuracy, recall, precision, and F-measure by up to 0.5%, and reduce the false-positive rate by up to 0.5%. Furthermore, the precision rate of minority categories can be increased by about 1–2%. [ABSTRACT FROM AUTHOR]

Mechanical exfoliation is a facile way for the preparation of two-dimensional (2D) nanoflakes. However, the quality of obtained flakes still needs to be further improved, e.g., the exfoliated size and production yield, which plays a crucial role in practical applications. Here, solvent-assisted method is proposed by introducing chemical solvents in the exfoliations. The impacts of wrinkles and defects existed in the outmost layer of bulk MoS2 crystal are overcome due to the capillary and diffusion forces driven by solvent molecules underlying flakes. Hence, the size and yield of nanoflakes are promoted manifestly. The MoS2 nanoflakes are obtained with the largest area of ~ 1.5 mm2 that is three orders of magnitude bigger than conventional mechanical exfoliation. In addition, the characterizations verify the existence of monolayer which may exhibit intrinsic properties of the MoS2 flakes. Subsequently, MoS2 field-effect transistors are conveniently fabricated by one-step mechanical shadow-mask process. The field-effect mobility of ~ 26 cm2V−1 s−1 and on/off ratio of ~ 106 are achieved. All results indicate that the solvent-assisted exfoliations provide a universal path in the preparation of 2D nanoflakes, which is a fundamental support for the studies of 2D materials and devices. [ABSTRACT FROM AUTHOR]

Mo2FeB2 coatings were fabricated by laser cladding with different pre-placed powder sizes. In the present research, a coaxial nozzle head coupled with a continuous fiber laser with a maximum power of 3 kW was used. The influence of powder size (~ 6 μm, ~ 13 μm, ~ 65 μm) on the morphology, microstructure, microhardness, wear resistance, and fracture toughness of the coating were discussed in this research. The morphology showed that coating obtained with ~ 6-μm powder size had excellent wettability and avoided obvious defects; microstructure revealed that the coating appeared particle agglomeration phenomenon with the increase of powder size. The coating obtained with ~ 6 μm had herringbone and feature structures to improve the density, and the bonding zone had typical columnar crystal and no cracks. The microhardness, wear resistance, and fracture toughness decreased as the increase of powder size. The optimal microhardness, COF, and fracture toughness of coating reached 1138.56 HV0.5, 0.59, 14.65 MPa·m1/2, respectively, when powder size was ~ 6 μm. The wear form of coatings was typical abrasive wear and adhesive wear; the fracture mechanism was transgranular fracture. The research can provide a reference for the preparation of Mo2FeB2 ternary boride coating. [ABSTRACT FROM AUTHOR]

WCoB-TiC composite coatings with in-situ formation of NbC were fabricated on the surface of an AISI 1045 substrate by laser cladding. The geometric characteristics, microstructure, microhardness, and fracture toughness of coatings were investigated by means of an optical microscope (OM), a scanning electron microscope (SEM), energy-dispersive spectroscopy (EDS), X-ray diffraction (XRD), an electron probe microanalyzer (EPMA), and a microhardness tester. In addition, the elastic constants and bulk property of the reinforced phases were investigated by first principles calculation. The results showed that a reliable metallurgical bonding was formed between the coating and the substrate and in-situ synthesized reinforcement phases of the coating consisted of WCoB, W2CoB2, TiC, NbC, (Nb,Ti,W)C, and traces of Nb2C. The dilution rate and porosity had a negative effect with the addition of Nb. According to the results of SEM, EDS, and EPMA, Nb was diffused uniformly in the TiC structure. The NbC phase had the highest hardness among all in-situ synthesized reinforcement phases, which reached 24.525 GPa, while Nb2C reflected the strongest metallicity. The microhardness and fracture toughness of the coating were increased firstly and then decreased following the increase of Nb content, and when the Nb addition was 4 wt%, the coating had the highest average microhardness and fracture toughness (1755.42 HV0.5, 8.23 MPa·m1/2, respectively). The microhardness and fracture toughness was 24% and 30% higher than that of the coating without Nb addition, respectively. From the crack propagation morphology of coatings, all coatings had fine transgranular fracture. [ABSTRACT FROM AUTHOR]

Exploring a modified stage (mStage) for pN0 colon cancer patients. 39,637 pN0 colon cancer patients were collected from the SEER database (2010–2015) (development cohort) and 455 pN0 colon cancer patients from the Second Affiliated Hospital of Harbin Medical University (2011–2015) (validation cohort). The optimal lymph nodes examined (LNE) stratification for cancer-specific survival (CSS) was obtained by X-tile software in the development cohort. LNE is combined with conventional T stage to form the mStage. The novel N stage was built based on the LNE (N0a: LNE ≥ 26, N0b: LNE = 11–25 and N0c: LNE ≤ 10). The mStage include mStageA (T1N0a, T1N0b, T1N0c and T2N0a), mStageB (T2N0b, T2N0c and T3N0a), mStageC (T3N0b), mStageD (T3N0c, T4aN0a and T4bN0a), mStageE (T4aN0b and T4bN0b) and mStageF (T4aN0c and T4bN0c). Cox regression model showed that mStage was an independent prognostic factor. AUC showed that the predictive accuracy of mStage was better than the conventional T stage for 5-year CSS in the development (0.700 vs. 0.678, P < 0.001) and validation cohort (0.649 vs. 0.603, P = 0.018). The C-index also showed that mStage had a superior model-fitting. Besides, calibration curves for 3-year and 5-year CSS revealed good consistencies between observed and predicted survival rates. For pN0 colon cancer patients, mStage might be superior to conventional T stage in predicting the prognosis. [ABSTRACT FROM AUTHOR]

To study the influence of the three soluble salt ions of Mg2+, Cl−, and SO42− on the strength of salt-rich cement soil, an unconfined compression test was conducted on cement soil with different curing times to study the effect of the content of soluble salt ions on the strength of cement soil. X-ray diffraction and environmental scanning electron microscopy were used to study the influence of soluble salt ions on the microstructure and chemical composition of salt-rich cement soil and to reveal the mechanism of interaction between soluble salt ions and cement soil from macro and micro perspectives. The results show that the Mg2+, Cl−, and SO42− soluble salt ions have distinct adverse effects on the strength of cement soil samples. Moreover, with the increase in the content of soluble salt ions, the adverse effects become more apparent. The main reason for this phenomenon is that the high content of soluble salt ions consumes C–S–H and C–A–H gels and generates M–S–H, M–A–H, and a large number of crystals. M–S–H and M–A–H dispersed in C–S–H and C–A–H gels can reduce the cementing force of cement soil and the strength of salt-rich cement soil. [ABSTRACT FROM AUTHOR]

Low-light environments introduce various complex degradations into captured images. Retinex-based methods have demonstrated effective enhancement performance by decomposing an image into illumination and reflectance, allowing for selective adjustment and removal of degradations. However, different types of pollutions in reflectance are often treated together. The absence of explicit distinction and definition of various pollution types results in residual pollutions in the results. Typically, the color shift, which is generally spatially invariant, differs from other spatially variant pollution and proves challenging to eliminate with denoising methods. The remaining color shift compromises color constancy both theoretically and in practice. In this paper, we consider different manifestations of degradations and further decompose them. We propose a color-shift aware Retinex model, termed as CRetinex, which decomposes an image into reflectance, color shift, and illumination. Specific networks are designed to remove spatially variant pollution, correct color shift, and adjust illumination separately. Comparative experiments with the state-of-the-art demonstrate the qualitative and quantitative superiority of our approach. Furthermore, extensive experiments on multiple datasets, including real and synthetic images, along with extended validation, confirm the effectiveness of color-shift aware decomposition and the generalization of CRetinex over a wide range of low-light levels. [ABSTRACT FROM AUTHOR]

In this research, the TiC–Ni35A composite coating was fabricated on the AISI 1045 steel substrate by laser cladding process. The cross-sectional morphology, microstructure, microhardness, and wear resistance of coatings obtained under different laser energy densities (E) and TiC powder ratios (PR) were analyzed. According to the results, all the coating had a reliable metallurgical bonding with the AISI 1045 steel substrate. The X-ray diffraction (XRD) analysis revealed that the coating phases were Ni and TiC. The average microhardness of the Ni35A-80wt.% TiC coating reached up to 75.1 HRC. The minimum coefficient of friction of the composite coating was only about 30% of the AISI 1045 steel substrate. The wear form was mainly adhesive wear when altering the TiC powder ratios, while the wear form also contained abrasion wear under different energy densities. The ability of decomposition and re-nucleation of TiC was significantly improved with the increase of laser energy densities and the decrease of TiC powder ratios. The microhardness, wear resistance, and coefficient of friction of the composite coating were improved because of the TiC strengthening phase particles. Compared with the AISI 1045 steel substrate, the microhardness and wear resistance of the composite coating were increased by 5.3 times and 6.3 times, respectively. [ABSTRACT FROM AUTHOR]

A coupled multiphase model based on computational fluid dynamics (CFD) and discrete element method (DEM) is developed to numerically investigate the extrusion-based 3D printing process of discontinuous carbon fibre-reinforced polymer composites. Short carbon fibres are modelled as rigid bodies by clumping discrete spheres in DEM, while polymer matrix is treated as an incompressible Newtonian fluid in CFD. A fluid-particle interaction model is adopted to couple DEM and CFD and represent the dynamic fibre/matrix interaction. Collisions between fibres are considered naturally in DEM by using the Hertz-Mindlin contact law. The coupled CFD-DEM is validated, both qualitatively and quantitatively, against X-ray microtomography (μCT) experimental results for the T300/PA6 composite. Parametric study on various fibre lengths, fibre volume fraction and resin viscosity using the CFD-DEM model shows that the nozzle clogging tends to occur when the fibre length and/or the fibre volume fraction are increased. Use of a polymer matrix with a lower viscosity can be effective to eliminate the clogging issue when printing composites with relatively short fibres. The fibre length is dominating when long fibres are used and the clogging is largely independent on the viscosity of the polymer matrix. Finally, a potential solution of using a cone sleeve insert located above the shrinking region to address the nozzle clogging issue is proposed and numerically assessed. [ABSTRACT FROM AUTHOR]

Cycloid gear is an important transmission part of RV reducer of robot. Its surface mechanical properties significantly affect the precision and life of robot. During the forming and grinding process of the cycloid, the temperature gradient of the tooth surface changes greatly, resulting in residual stress, which is very important to the yield limit and service life of the cycloid. The thermal-mechanical coupling model of cycloid wheel grinding is established by using moving heat source method and load step loading method. The magnitude and direction of the first, second, and third principal stresses are obtained by calculating the Mises equivalent stress in the three-dimensional space of the grinding area. The temperature field and thermal stress of multi-tooth form grinding are simulated by finite element method, and the influence of grinding parameters on stress distribution is analyzed. The X-ray method is used to detect the residual stress of cycloid gear in form grinding, and the variation law of residual stress on the tooth surface under different process parameters is analyzed, and the change trend is basically consistent with the simulation results. The results show that the grinding process parameters can be optimized by theoretical calculation and finite element simulation, which can effectively reduce the residual tensile stress and increase the residual compressive stress. A method for predicting the residual stress of the cycloid gear form grinding is proposed, which provides a basis for the selection of the process parameters of the cycloid gear form grinding [ABSTRACT FROM AUTHOR]

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6–7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

Soil salinization has become one of the important factors affecting the sustainable development of agriculture. Among them, the soda-alkali soil composed of sodium carbonate (Na2CO3) has a more serious impact on crops. In this study, 175 Brassica napus accessions from different sources were used as materials, combined with Brassica 60 K SNP chip data, and the root length traits of Brassica napus during germination under Na2CO3 stress (0.15%) and control conditions were analyzed for genome-wide association (GWAS). GWAS analysis detected that 5 SNPs were significantly related to root length traits under stress conditions; a single SNP could explain 10.22–12.01% of the phenotypic variation. A total of 15 candidate genes related to Na2CO3 stress resistance were identified upstream and downstream of significant SNPs, including cation exchange protein genes (CAX1), members of the zinc finger protein family (ZFHD1), peroxidase family proteins (POD), and transcription factors (MYB family and WRKY family), etc. The expression analysis of 5 candidate genes in extreme phenotypic materials showed that BnaA04g21850D (CAX1) and BnaA06g24040D (ACX5) were induced by Na2CO3 stress in both materials; BnaA06g31200D (ZFHD1) and BnaC02g37590D (MYB60) were up-regulated expression in sensitive materials; BnaA04g21990D (POD) was up-regulated expression in alkali-tolerant materials, indicating that these candidate genes may be involved in the process of rapeseed response to Na2CO3 stress. This study can provide a reference for in-depth analysis of the molecular mechanism of Na2CO3 stress resistance in rapeseed. [ABSTRACT FROM AUTHOR]

In this paper, a squeeze-and-decomposition network (SDNet) is proposed to realize multi-modal and digital photography image fusion in real time. Firstly, we generally transform multiple fusion problems into the extraction and reconstruction of gradient and intensity information, and design a universal form of loss function accordingly, which is composed of intensity term and gradient term. For the gradient term, we introduce an adaptive decision block to decide the optimization target of the gradient distribution according to the texture richness at the pixel scale, so as to guide the fused image to contain richer texture details. For the intensity term, we adjust the weight of each intensity loss term to change the proportion of intensity information from different images, so that it can be adapted to multiple image fusion tasks. Secondly, we introduce the idea of squeeze and decomposition into image fusion. Specifically, we consider not only the squeeze process from source images to the fused result, but also the decomposition process from the fused result to source images. Because the quality of decomposed images directly depends on the fused result, it can force the fused result to contain more scene details. Experimental results demonstrate the superiority of our method over the state-of-the-arts in terms of subjective visual effect and quantitative metrics in a variety of fusion tasks. Moreover, our method is much faster than the state-of-the-arts, which can deal with real-time fusion tasks. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and wearable devices, flexible magnetic sensors have attracted much attention in recent years. In this work, large self-biased 0–3 type poly(vinylidene fluoride-trifluoroethylene)/cobalt ferrite (P(VDF-TrFE)/CoFe2O4) flexible magnetoelectric (ME) composite films are proposed for the development of AC magnetic sensors. P(VDF-TrFE)/CoFe2O4 composite films with CoFe2O4 nanoparticle mass contents from 5 to 30% are successfully fabricated through the solution casting method. The results show that the nanoparticles are homogeneously dispersed in the P(VDF-TrFE) matrix, and the composite films have both excellent ferroelectric and ferromagnetic properties. The P(VDF-TrFE)/CoFe2O4 composite films all exhibit a large ME effect. The ME coefficient reaches 47.1 mV·cm−1·Oe−1 for the film with a CoFe2O4 nanoparticle mass content of 20% at resonance frequency. Moreover, the composite films have a large self-biased ME effect with a maximum value of 20.4 mV·cm−1·Oe−1, which is mainly due to the high remnant magnetization of CoFe2O4 nanoparticles. To evaluate the composite films for application in magnetic sensors, the response of the ME output voltage to an AC magnetic field without a bias DC magnetic field was measured. The good linear correlation coefficient, sensitivity and repeatability indicate that the proposed 0–3 type P(VDF-TrFE)/CoFe2O4 composite film is a promising material for flexible AC magnetic sensors. [ABSTRACT FROM AUTHOR]

Various pollutants in wastewater including water-insoluble oils and water-soluble toxic organic pollutants, have been threatening ecosystems and human health, making wastewater purification difficult. Inspired by the hierarchical structures and chemical compositions of lotus leaf, we combined the usage of polydopamine (PDA) chemistry and photocatalytically active TiO2 to develop a facile approach toward composite PDA-based TiO2 coated mesh with restorable superhydrophobicity. In this procedure, PDA assisted loading of TiO2 nanoparticles onto stainless steel mesh with a result of micro/nanoscale hierarchical surface was achieved via a single-step solvothermal method. After modified with 1H, 1H, 2H, 2H-perfluorodecyltriethoxysilane, this mesh became superhydrophobic. The TiO2 coated mesh exhibited enhanced superhydrophobicity, excellent environmental stability, robust chemical resistance and high mechanical durability. Moreover, it has been demonstrated the high separation capacity and extraordinary recyclability for various oils collection from water due to its special surface superhydrophobicity. Importantly, such a mesh possessed photocatalytic activity due to the embedded TiO2, which allowed for the effective degradation of organic pollutants under UV irradiation, representing a plausible way to restore superhydrophobic surface by photocatalytically decomposing the attached contaminants. This investigation suggests a green and facile way to prepare superhydrophobic materials with restored surface wetting property, which will be useful for wastewater purification. [ABSTRACT FROM AUTHOR]

In this study, an effective and portable method for enzyme activity detection and inhibitory activity evaluation was developed based on the alkaline phosphatase (ALP)-mediated reaction in a personal glucose meter (PGM). In this method, ALP catalyzes the hydrolysis of substrate amifostine (WR-2721) to produce ethanethiol (WR-1065), which can trigger the reduction of ferricyanide (K3[Fe(CN)6]), an electron transfer mediator in glucose test strips, to ferrocyanide ([K4Fe(CN)6]) and generate a PGM-detectable signal. Thus, WR-1065 can be directly quantified by a PGM as simply as detecting glucose in blood. After being systematically optimized, the method was applied to evaluate the inhibitory activity of ten small-molecule compounds and six Cordyceps sinensis (CS) extracts on ALP. The results showed that adenosine-5-monophosphate and theophylline had high inhibitory activity, but two CS extracts have promotion potency on ALP with the values of –20.7 ± 1.3% and –46.6 ± 2.1%, respectively. Moreover, the binding sites and modes of small-molecule compounds to ALP were investigated by molecular docking, while a new substrate competitor with theoretically good inhibitory activity against ALP was designed by scaffold hopping. Finally, the accuracy of the PGM method for enzyme activity detection was assessed by detecting ALP from milk samples, and the recovery ranged from 87.7% to 116.9%. These results indicate that it is feasible to evaluate enzyme activity and the inhibitory activity of small-molecule compounds and CS extracts on ALP using a PGM based on ALP-mediated reaction. [ABSTRACT FROM AUTHOR]

Because of the limited treatment strategy of gliomas, the key of diagnosis and treatment is finding new molecular biomarkers. Here, we explored the potential of β2-microglobulin (B2M) to serve as a hopeful candidate for immunotherapy or diagnostic biomarker in gliomas. The genomic profiles, clinical characteristics, and immune signatures were analyzed based on TCGA and CGGA databases. We carried out the whole statistical analyses using R project. High B2M expression correlated with worse prognosis. Somatic mutations of gliomas with high B2M expression are associated with PTEN deletion and EGFR amplification. Isocitrate dehydrogenase (IDH) mutations accounted for 82% in gliomas with low B2M expression. In addition, B2M positively correlated with ESTIMATE scores, interacted with infiltrating immune and stromal cell types. B2M also suppressed anti-tumor immunity through immune related processes. Meanwhile, B2M was associated with immune checkpoint molecules and inflammatory activities. Finally, functional annotation of the identified B2M related genes verified that B2M was a potential candidate for immunotherapy. We confirmed that B2M played a critical role in tumor progression, patient prognosis and immunotherapy of gliomas. [ABSTRACT FROM AUTHOR]

In this paper, we explore the zoo of 5d superconformal field theories (SCFTs) constructed from M-theory on Isolated Complete Intersection Singularities (ICIS). We systematically investigate the crepant resolution of such singularities, and obtain a classification of rank ⩽ 10 models with a smooth crepant resolution and smooth exceptional divisors, as well as a number of infinite sequences with the same smoothness properties. For these models, we study their Coulomb branch properties and compute the flavor symmetry algebra from the resolved CY3 and/or the magnetic quiver. We check the validity of the conjectures relating the properties of the 5d SCFT and the 4d N = 2 SCFT from IIB superstring on the same singularity. When the 4d N = 2 SCFT has a Lagrangian quiver gauge theory description, one can obtain the magnetic quiver of the 5d theory by gauging flavor symmetry, which encodes the 5d Higgs branch information. Regarding the smoothness of the crepant resolution and integrality of 4d Coulomb branch spectrum, we find examples with a smooth resolved CY3 and smooth exceptional divisors, but fractional 4d Coulomb branch spectrum. Moreover, we compute the discrete (higher)-symmetries of the 5d/4d SCFTs from the link topology for a few examples. [ABSTRACT FROM AUTHOR]

Inspired by the Mordell–Tornheim double zeta function, in this paper, we introduce the Eisenstein series of Mordell–Tornheim type. Based on the theory of Cohen series and partial fraction decomposition, we explicitly compute these series. We end by discussing several identities about products of Eisenstein series and their Fourier expansions. [ABSTRACT FROM AUTHOR]

Percutaneous puncture ethanol sclerotherapy is a simple, effective, minimally invasive, and inexpensive procedure to manage symptomatic simple renal cysts. We modified specific technical aspects to balance certain potential intraprocedural factors for ensuring minimal procedural pain and duration as well as maximal clinical therapeutic effects and evaluated the safety and efficacy of this modified technique. A total of 84 eligible patients underwent computed tomography-guided single-session ethanol sclerotherapy using the conventional (group A) or modified (group B) technique. In group B, the puncture route was modified from tansparenchymal to direct for reducing renal injury, and preinjection of low-dose intracystic lidocaine was used to control distending pain caused by ethanol injections; therefore, greater ethanol volumes could be injected for improving the resistance and contact of ethanol with the cyst wall, precluding the need for patient repositioning multiple times to decrease procedural duration. Visual analog scale score for pain and procedural time were significantly higher in group A than in group B. The complication rate was slightly higher in group A than in group B, but the success rate was comparable between the two groups. These results suggest that the modified technique is painless, time-saving, and injury-reducing and can thus improve medical care. [ABSTRACT FROM AUTHOR]

This research investigated the influence of different processing parameters in curved surface multi-track laser cladding with curve paths. Mathematical models of flatness ratio, incomplete fusion, and pore area in the clad were developed by central composite design with altering the input laser power, scanning speed, gas flow, and overlapping rate. Response surface methodology was used to analyze the correlation of different processing parameters affecting the selected responses. A clad with better flatness ratio was achieved by properly increasing the laser power and gas flow while reducing the overlapping rate. Appropriately increasing the laser power and overlapping rate while reducing the scanning speed and gas flow effectively diminished the incomplete fusion. Less pore area in the clad was obtained by appropriately increasing the laser power and overlapping rate while reducing the scanning speed and gas flow. Afterwards, desired processing parameters set was obtained by the optimization with the target of maximizing the flatness ratio and also minimizing the incomplete fusion and pore area. Experimental validation with this processing parameter setup provided satisfactory clad, and the error rate for the flatness ratio, incomplete fusion, and pore area was 1.708%, 5.714%, and 6.522%, respectively. This paper provides the theoretical guidance for the prediction and control of the flatness ratio, incomplete fusion, and pore area in curved surface multi-track laser cladding with curve paths. [ABSTRACT FROM AUTHOR]

Based on a standard 0.18μm BiCMOS process, a 12 bit 2GSps ADC is achieved using time-interleaved pipelined architecture in this work. The DC offset caused by the mismatch of ADC channels is removed due to the application of digital calibration technology, which improves the performance of the ADC. The power supply voltage is 1.8 V and the power consumption is 100 mW for each lane. The measurement results indicated that the circuit in this paper can be used in multi-channel time-domain interleaved pipelined ADC architecture to achieve a 2GSps ultra high speed ADC. [ABSTRACT FROM AUTHOR]

We aimed to detect whether the effect of apigenin (Apig) on the myocardial infarction-induced cardiomyocyte injury of mouse myocardial cells and acute myocardial infarction (AMI) mice was through regulating Parkin expression via miR-103-1-5p. The myocardial infarction cardiomyocyte model (Hypoxia/reoxygenation) was first constructed, then the mouse myocardial cells were treated with Apig, and the expression of miR-103-1-5p was decreased and the expression of Parkin was increased by qRT-PCR and Western blot. It was confirmed by miRNA pulldown and luciferase reporter system that miR-103-1-5p in mouse myocardial cells can bind to Parkin mRNA and inhibit Parkin expression. Next, a lentiviral vector silenced Parkin and overexpressing miR-103-1-5p was constructed and transfected into Apig-treated cells. Autophagy was detected by mitochondrial autophagy marker proteins [atypical protein kinase C (aPKC)-interacting protein (p62) and bcl-2/Adenovirus E1B 19-kd interacting protein 3 (BNIP3)] via Western blot, mitochondrial function was detected by JC-1 probe, and apoptosis was detected by flow cytometry. It was confirmed that Apig regulated mitochondria autophagy through miR-103-1-5p and Parkin, which ultimately affected cardiomyocyte death. Finally, an AMI mouse model was constructed, and then the mice were treated with Apig. The infarct size was detected by triphenyl tetrazolium chloride (TTC) staining, and the Apig relieved the myocardial infarction. The expression of miR-103-1-5p was decreased and the expression of Parkin was increased by qRT-PCR and Western blot. The above results simplified that the cardio protection of Apig and miR-103-1-5p against injury of myocardial infarction cardiomyocyte by targeting Parkin. These results provided a novel treatment against myocardial infarction cardiomyocyte. [ABSTRACT FROM AUTHOR]

Setting carbon abatement quota is a strategy to abate carbon emissions. Practically, the allocation mechanism of carbon abatement quota is always formulated and implemented as a dynamic process, affected by the outside production environment. Such formulations have seldom been addressed in the literature. Unlike existing studies, this study proposes a novel DEA allocation model to measure centralized resource allocation in a dynamic process, and the model is in a multiplier form, which reflects the impact of allocation on the technology frontier. Meanwhile, contextual impacts are also incorporated into the dynamic allocation model. Empirically, the carbon allocation of China’s industrial systems is examined at macro and micro levels. From a dynamic perspective, diverse carbon abatement amounts and carbon intensities are identified for the industrial producers in allocation. We find that the dynamic modeling has affected the annual carbon allocation results, and observe that contextual impacts smooth the annual changes in carbon abatement amounts. Additionally, contextual factors adjust the industrial carbon abatement tasks by transferring some abatement burden from the eastern and western areas to the central area, and from small and micro firms to large and medium firms. Besides, contextual factors affect the robustness of the allocation measurement, by reducing the gaps of allocation results among DMUs, making the allocation results less stable corresponding to upper bound changes. [ABSTRACT FROM AUTHOR]

Abstract: NLRP3, a member of the nucleotide-binding oligomerization domain (NOD)-like receptor family, is involved in cardiac inflammation. However, the functional role of NLRP3 in cardiac remodeling is not clear. To investigate the roles of NLRP3 in pressure overload-induced cardiac remodeling, NLRP3 knockout and wild-type mice were subjected to aortic banding to induce cardiac remodeling. The data showed that NLRP3 expression was downregulated in the remodeling process. NLRP3 deficiency accelerated cardiac hypertrophy, fibrosis, and inflammation responses with deteriorating cardiac dysfunction in the pressure overload-induced cardiac remodeling mouse model. Neonatal rat cardiomyocytes were isolated and stimulated with phenylephrine (PE). We identified NLRP3 as a negative regulator of cardiomyocyte remodeling in PE-stimulated cardiomyocyte remodeling using adenovirus-NLRP3 and NLRP3 siRNA. Mechanistically, we found that the expression of Toll-like receptor (TLR) 4 was upregulated in NLRP3-deficient mouse hearts and PE-stimulated cardiomyocytes. NLRP3 knockout mice subjected to a TLR4 inhibitor revealed a relieved cardiac remodeling response with improved cardiac dysfunction. Our data suggested that NLRP3 could be a therapeutic target for cardiac remodeling and heart failure.Key messages: NLRP3 expression was downregulated in the remodeling process.NLRP3 deficiency accelerated pressure overload-induced cardiac remodeling.NLRP3 acted as a negative regulator of cardiomyocyte remodeling via downregulating TLR4. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of the vehicle-to-infrastructure (V2I) fair access with a finite retry limit through constructing an analytical model. Firstly, the process of the distributed coordination function (DCF) mechanism with a finite retry limit is described through developing a 2-D Markov chain; based on the Markov chain, the transmission probability with the access parameters including the minimum window and the retry limit is derived. Then, considering the V2I unfair access problem, the relationship between the velocity and the transmission probability of a vehicle is derived. Based on the proposed model, given a finite retry limit, the minimum window of a vehicle can be determined according to its velocity. In the simulation, the proposed model is justified to be effective and the V2I fair access performance with a finite retry limit is evaluated through comparison. [ABSTRACT FROM AUTHOR]

The Harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed Harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. Firstly, the pinhole imaging strategy is used to enable the Harris' hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the Harris' hawks more diverse and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy. [ABSTRACT FROM AUTHOR]

Polyaniline (PANI) multidimensional (MD) structures (i.e., flowers, sheets, and plates) are successfully synthesized by adjusting the dosage of cationic surfactant cetyltrimethylammonium chloride (CTAC) in the range of 0.050-0.50 g in static HCl solution (0.010 M) at room temperature without stirring. CTAC micellar templates consisting of quaternary ammonium cations and binary persulfate anions direct the fabrication of PANI MD structures. On the basis of the employment of CTAC, the effect of pH value on PANI morphologies is investigated in detail, and followed by the obtainment of other PANI structures, including nanobelts, net-like structures, and particulates. Among the PANI MD structures, PANI sheets in micron size have good dispersibility and excellent processability, which are due to the synergistic effect of PANI's suitable size and appropriate CTAC molecules. The results and strategies presented here provide opportunities to obtain good water dispersibility to conduct polymer structures with big size. [ABSTRACT FROM AUTHOR]