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Abstract Background Chronic kidney disease (CKD) patients face the risk of rapid kidney function decline leading to adverse outcomes like dialysis and mortality. Lipid metabolism might contribute to acute kidney function decline in CKD patients. Here, we utilized the Mendelian Randomization approach to investigate potential causal relationships between drug target-mediated lipid phenotypes and rapid renal function decline. Methods In this study, we utilized two methodologies: summarized data-based Mendelian randomization (SMR) and inverse variance-weighted Mendelian randomization (IVW-MR), to approximate exposure to lipid-lowering drugs. This entailed leveraging expression quantitative trait loci (eQTL) for drug target genes and genetic variants proximal to drug target gene regions, which encode proteins associated with low-density lipoprotein (LDL) cholesterol, as identified in genome-wide association studies. The objective was to investigate causal associations with the progression of rapid kidney function decline. Results The SMR analysis revealed a potential association between high expression of PCSK9 and rapid kidney function decline (OR = 1.11, 95% CI= [1.001–1.23]; p = 0.044). Similarly, IVW-MR analysis demonstrated a negative association between LDL cholesterol mediated by HMGCR and kidney function decline (OR = 0.74, 95% CI = 0.60–0.90; p = 0.003). Conclusion Genetically predicted inhibition of HMGCR is linked with the progression of kidney function decline, while genetically predicted PCSK9 inhibition is negatively associated with kidney function decline. Future research should incorporate clinical trials to validate the relevance of PCSK9 in preventing kidney function decline.

Abstract Thanks to the success of deep learning over the past few years, the video person re-identification (ReID) algorithms have achieved high accuracy on multiple public benchmark datasets. However, the available video person ReID datasets cover a limited range of real-world scenarios, and they have several obvious limitations: limited camera viewing angles, tiny variations of the shooting scene, and even errors in manual labels. These disadvantages prevent video person ReID from being widely used in real-life scenarios. In this work, a new high-quality multi-situation video person ReID dataset, named MSA-BUPT, is built to promote the video person ReID task in large-scale urban surveillance. Specifically, MSA-BUPT contains 684 identities, 2,665 trajectories, and nearly 250,000 frames from 200-h videos across various complex scenarios. Person attribute annotations and unannotated video data are also provided for other research perspectives, such as cross-modality ReID, cross-domain ReID, and so on. Furthermore, two plug-and-play components are used to improve retrieval capabilities: a new scenario-based data augmentation method is proposed to alleviate the person misalignment problem; a re-ranking strategy based on person attribute is applied to make secondary adjustments to the content to the results of the model. The extensive experimental results show that the above methods improve the performance of some representative state-of-the-art models on the new dataset.

Abstract Background: Optimal adjuvant chemotherapy after laparoscopic surgery in gastric cancer (GC) patients is still undefined. We aimed to evaluate the efficacy of S‐1 plus oxaliplatin (SOX) and capecitabine plus oxaliplatin (CAPOX) in patients with GC after laparoscopic gastrectomy. Methods: A non‐inferiority randomized controlled clinical trial was performed in China. Patients with advanced GC who underwent laparoscopic D2 gastrectomy were randomly assigned to receive SOX and CAPOX regimens. Results: In total, 191 patients were screened between May 2018 and June 2019, and 140 (73.3%) were included in the modified intent‐to‐treat analysis (mITT), of whom 69 and 71 were assigned to the SOX and CAPOX groups, respectively. The SOX group had similar 3‐year overall survival (OS) and disease‐free survival to the CAPOX group. Subgroup analysis revealed significantly better OS in the SOX group for male patients ([HR] = 0.395; 95% [CI], 0.153–1.019; p = 0.045), age >60 (HR = 0.219; 95% [CI], 0.064–0.753; p = 0.016), tumors in the gastric antrum (HR = 0.273; 95% [CI], 0.076–0.981; p = 0.047), and moderately differentiated tumors (HR = 0.338; 95% [CI], 0.110–1.041; p = 0.041). There were no significant differences observed in terms of adverse events and recurrence patterns between the two groups. Conclusion: Adjuvant SOX was non‐inferior to CAPOX treatments for patients with GC who underwent curative laparoscopic D2 gastrectomy. For male patients, aged >60 years, tumors in the gastric antrum, and moderately differentiated tumors, adjuvant SOX may achieve an improvement compared with CAPOX.

The migration of arsenic in groundwater is influenced by the heterogeneity of the medium, and the presence of iron minerals adds complexity and uncertainty to this effect. In this study, a stratified heterogeneous sand column with an embedded hematite lens at the coarse-to-medium sand interface was designed. We introduced an arsenic-laden solution and controlled groundwater flow to investigate the spatiotemporal characteristics of arsenic migration and the impact of hematite dissolution. The results showed that the medium structure significantly influenced the arsenic migration and distribution within the lens-containing sand column. The clay layers directed the lateral migration of arsenic, and the arsenic concentrations in deeper layers were up to seven times greater than those on the surface. The extraction experiments of solid-phase arsenic revealed that the main adsorption modes on quartz sand surfaces were the specific adsorption (F2) and adsorption on weakly crystalline iron–aluminum oxides (F3), correlating to the specific and colloidal adsorption modes, respectively. Monitoring the total iron ions (Fe(aq)) revealed rapid increases within the first 14 days, reaching a maximum on day 15, and then gradually declining; these results indicate that hematite did not continuously dissolve. This study can aid in the prevention and control of arsenic contamination in groundwater.

At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process.

BackgroundContrast-induced nephropathy (CIN) is one of the most common complications after coronary stent implantation due to the extensive development of coronary catheterization technology. Compound Danshen dripping pills (CDDP) are clinically used as cardiovascular drugs, relieving systemic inflammatory response. Previous studies have observed that CDDP can decrease CIN incidence after coronary stent implantation with uncertain effectiveness.MethodsWe conducted a prospective, randomized, single-center, single-blind, controlled trial. We enrolled patients 18 years and older with unstable angina pectoris and NSTEMI who underwent PCI at the Tianjin Chest Hospital between November 1, 2021, and November 31, 2022, and followed for 30 days. Patients were randomized to CDDP and hydration therapy (10 capsules three times/day; N = 411) or hydration only (N = 411). The primary outcome was the contrast nephropathy incidence, defined as an elevation in serum creatinine by more than 25% or 44 μmol/L from baseline within 48–72 h of contrast exposure. Secondary outcomes included major adverse cardiovascular events post-surgery and during follow-up.ResultsAfter 48 h of operation, the two groups had statistical significance in Scr and BUN values (80.0 ± 12.59 vs. 84.43 ± 13.49, P

Iron-containing minerals are key factors controlling arsenic (As) transport in groundwater environments. However, few studies have observed the effect of aqueous Fe [Fe(aq)] on As behavior in a water environment. In this study, river sand in the riparian zone was collected for batch experiments to analyze the effect of Fe(aq) on the adsorption of As on river sand, utilizing characterization analyses to identify the reaction mechanism. The results showed that (1) as the concentration of Fe(aq) in the reaction system increased from 0.1 to 20 mg/L, the equilibrium adsorption capacity (Qe) of river sand for As(III) and As(V) gradually increased. For concentrations of Fe(aq) equal to or greater than 1 mg/L, the Qe for As(V) exceeds that for As(III), whereas at a Fe(aq) concentration of 0.1 mg/L, the Qe for As(III) is higher than that for As(V). (2) Compared to the reaction system without added Fe(aq), the adsorption of As(V) onto river sand was inhibited, while the adsorption of As(III) was enhanced under conditions with low concentrations (0.1, 1 mg/L) of Fe(aq). (3) At higher Fe(aq) concentrations (5, 20 mg/L), the adsorption of both As(V) and As(III) by river sand was more effective than in systems without Fe(aq). Characterization tests confirmed this, while Fe(II) reduced As(V), and Fe(aq) adhered to the surface of river sand to form Fe(OH)3 colloids, thereby facilitating the adsorption of As onto river sand.

In the positioning and anti-swing control of bridge crane, a model-free adaptive control (MFAC) based on data-driven is proposed in order to eliminate the dependence of controller design on the model and the influence of unmodeled dynamics and uncertain disturbances on the controller performance. Only using the input and output data of the bridge crane system, the virtual full format dynamic linearized data model of the bridge crane nonlinear system is obtained through the data-driven modeling method. On the basis of this virtual data model, a model-free adaptive control law and a pseudo-Jacobian matrix estimation algorithm are designed according to the optimization theory under the constraint conditions. The stability of the closed loop system and the convergence of the system error are analyzed and proved by Lipschitz condition and inequality theory. The effectiveness of the control strategy for positioning and anti-swing control of bridge cranes is verified on simulated simulation and experimental platform of bridge crane. The results show that the proposed method is feasible and has good anti-disturbance performance and robustness.

Abstract Gastric cancer is a disease with high heterogeneity, and this heterogeneity may result in an uneven distribution of subclones with varied genetic properties at disease locations (spatial heterogeneity) or temporal changes in subclonal composition (temporal heterogeneity). We present the case of a 69-year-old woman with metastatic gastric cancer who presented for axillary lymph node enlargement and underwent axillary lymphadenectomy. Pathological evidence showed human epidermal growth factor receptor 2 (HER2)(3+). Abdominal computed tomography revealed a mass in the gastric body, gastroscopic biopsy showed HER2(3+). After tumor shrinkage by preoperative translational chemotherapy (oxaliplatin, calcium folate, fluorouracil) and targeted therapy (trastuzumab), she had laparoscopic-assisted total gastrectomy. However, HER2 immunohistochemistry was found to be diffusely negative in the surgically removed tissue, and there was no evidence of HER2 amplification in the whole exon sequencing either. After 10 months of trastuzumab treatment, her disease progressed. Although trastuzumab treatment was initially beneficial, the residual HER2-negative subclones may cause tumor recurrence and metastasis due to temporal heterogeneity, as shown in this case.

Remote sensing scene classification (RSSC) is a very crucial subtask of remote sensing image understanding. With the rapid development of convolutional neural networks (CNNs) in the field of natural images, great progress has been made in RSSC. Compared with natural images, labeled remote sensing images are more difficult to acquire, and typical RSSC datasets are consequently smaller than natural image datasets. Due to the small scale of these labeled datasets, training a network using only remote sensing scene datasets is very difficult. Most current approaches rely on a paradigm consisting of ImageNet pretraining followed by model fine-tuning on RSSC datasets. However, there are considerable dissimilarities between remote sensing images and natural images, and as a result, the current paradigm may present some problems for new studies. In this paper, to break free of this paradigm, we propose a general framework for scene classification (GFSC) that can help to train various network architectures on limited labeled remote sensing scene images. Extensive experiments show that ImageNet pretraining is not only unnecessary but may be one of the causes of the limited performance of RSSC models. Our study provides a solution that not only replaces the ImageNet pretraining paradigm but also further improves the baseline for RSSC. Our proposed framework can help various CNNs achieve state-of-the-art performance using only remote sensing images and endow the trained models with a stronger ability to extract discriminative features from complex remote sensing images.

Objectives Although laparoscopic distal gastrectomy has been widely used for distal gastric cancer, the best functional reconstruction type has not yet been established. Based on previous experience, we propose a modified uncut Roux-en-Y anastomosis. This study aimed to compare the outcomes of different intracorporeal anastomoses after laparoscopic distal gastrectomy. Methods From April 2015 to August 2020, the data of 215 patients who underwent laparoscopic distal gastrectomy was collected. The patients were divided into 4 groups according to the digestive tract reconstruction method, Billroth-I, Billroth-II, Roux-en-Y, and the modified uncut Roux-en-Y. Clinicopathologic characteristics, surgery details, short-term outcomes, and postoperative nutritional status were analyzed. Results The operation time of Billroth-I anastomosis was significantly shorter (216.2 ± 25.8 min, P < .001) than that of other methods. There was no difference in postoperative complications and OS among the 4 reconstruction methods. The incidences of esophagitis, gastritis, and bile reflux were significantly lower in the Roux-en-Y and uncut Roux-en-Y group (P < .001) 1 year after surgery. And the postoperative albumin and PNI levels in uncut Roux-en-Y group were higher than those in other groups(P < .05). On multivariate analysis, age and reconstruction type were independently related to esophagitis, gastritis, and bile reflux. Serum albumin and the prognostic nutritional index were significantly higher in the uncut Roux-en-Y group than other groups (P < .05). Conclusions All 4 reconstruction techniques are feasible and safe. The Roux-en-Y and uncut Roux-en-Y are superior to Billroth-Ⅰ and Billroth-Ⅱ+Braun in terms of reflux esophagitis, gastritis, and bile reflux. Uncut Roux-en-Y may result in better PNI than the others.

Most action recognition tasks now treat the activity as a single event in a video clip. Recently, the benefits of representing activities as a combination of verbs and nouns for action recognition have shown to be effective in improving action understanding, allowing us to capture such representations. However, there is still a lack of research on representational learning using cross-view or cross-modality information. To exploit the complementary information between multiple views, we propose a feature fusion framework, and our framework is divided into two steps: extraction of appearance features and fusion of multi-view features. We validate our approach on two action recognition datasets, IKEA ASM and LEMMA. We demonstrate that multi-view fusion can effectively generalize across appearances and identify previously unseen actions of interacting objects, surpassing current state-of-the-art methods. In particular, on the IKEA ASM dataset, the performance of the multi-view fusion approach improves 18.1% over the performance of the single-view approach on top-1.

The optical system of an imaging spectrometer working on a geostationary earth orbit (GEO) covering a full optical spectrum of 0.3–12.5 μm is analyzed and designed. It enables a ground coverage of 400 × 400 km by internal scanning and achieves a high spatial resolution of 25 m. The full spectrum is divided into five sub-bands, and each band adopts four spectrometers to splice in the field of view to achieve the ultra-long slit required by the wide swath. The total length of the slit is up to 241.3 mm. This paper focuses on compact spectrometers with long slits that can meet the splicing requirements and points out that low spectral distortions, low stray light, high signal-to-noise ratio, and uniform spectral response are necessary for high-fidelity performance. The Offner and Wynne–Offner high-fidelity spectrometers based on convex blazed gratings are designed, and prototypes of each band are developed as well. The properties of long slits and convex blazed gratings are presented. The maximum length of a single slit is 61.44 mm. The groove density of gratings for five bands ranges from 8.8 lp/mm to 312.1 lp/mm, and the peak efficiency is up to 86.4%. The alignment and test of the spectrometers are introduced. Results show that the developed spectrometers have high fidelity and fulfill all requirements.

CeO2 nanoparticle-loaded MnO2 nanoflowers, prepared by a hydrothermal method followed by an adsorption-calcination technique, were utilized for selective catalytic reduction (SCR) of NOx with NH3 at low temperatures. The effects of Ce/Mn ratio and thermal calcination temperature on the NH3–SCR activity of the CeO2-MnO2 nanocomposites were studied comprehensively. The as-prepared CeO2-MnO2 catalysts show high NOx reduction efficiency in the temperature range of 150–300 °C, with a complete NOx conversion at 200 °C for the optimal sample. The excellent NH3–SCR performance could be ascribed to high surface area, intimate contact, and strong synergistic interaction between CeO2 nanoparticles and MnO2 nanoflowers of the well-designed composite catalyst. The in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTs) characterizations evidence that the SCR reaction on the surface of the CeO2-MnO2 nanocomposites mainly follows the Langmuir–Hinshelwood (L-H) mechanism. Our work provides useful guidance for the development of composite oxide-based low temperature NH3–SCR catalysts.

With the rapid development of infrastructure construction, it is an inevitable trend to replace natural sand in short supply with manufactured sand to meet sustainable development. In this paper, the relationship between the particle shape characteristics of manufactured sand and concrete performance is discussed using a morphological analysis and concrete experiments. The particle shape parameters of five types of manufactured sand were obtained by using the aggregate image measurement system (AIMS) and digital image processing (DIP) techniques, and the correlations between different parameters were analyzed. Moreover, the properties of concrete with the five kinds of manufactured sand were tested. The results show that particle size and type have a significant impact on particle shape parameters. Particle shape parameters, especially angularity, correlate well with the workability and compressive strength of concrete while having little effect on the durability of concrete. An accurate understanding of the morphological characteristics of manufactured sand is conducive to the optimization of concrete mix designs. Therefore, it is suggested that a manufactured-sand shape test be included in aggregate specification.

CeO2 nanoparticles prepared by the in-situ synthesis method were used to strengthen the polyamide-imide/polytetrafluoroethylene (PAI/PTFE) lubricating coatings. The crystalline phases and micro-structure of the lubricating coatings were characterized by XRD and SEM. A CSM friction machine was used to test the tribological properties of the coatings. The experimental results showed that CeO2 nanoparticles synthesized in-situ in the PAI/PTFE coatings had good dispersion and a narrow range of particle size (about 80 nm–110nm). The addition of CeO2 nanoparticles could effectively promote the micro-hardness and anti-wear performance of the coatings. Furthermore, the PAI/PTFE lubricating coating filled with 5 wt% CeO2 nanoparticles exhibited superior micro-hardness and wear resistance. In practical applications, The PAI/PTFE lubricating coating filled with 5 wt% CeO2 nanoparticles is more suitable for high max sliding velocity and low applied load.

Working on sludge with electrochemical oxidation is beneficial to promote the subsequent recessive growth of microorganisms in the sludge. To achieve the on-site sludge reduction, this study combined the anoxic/oxic (A/O) process with the electrochemical oxidation process based on the cell lysis-cryptic growth theory by determining the experimental conditions and mechanism of electrochemical cell lysis. The sludge reduction and effluent treatment of the combined process in practical operation were studied. The results showed that the cumulative sludge discharge had been reduced by 37.1% compared with that of the A/O process, and the apparent sludge yield had been reduced by 39.1% during the 30-day operation time, indicating that the electrochemical-A/O combined process could have a considerable sludge reduction effect. After the treatment, chemical oxygen demand (COD), ammonium nitrogen, and total nitrogen in the effluent of the combined process reached 33.02 mg/L, 0.83 mg/L, and 9.95 mg/L, respectively. Due to the limitation of the A/O process, the removal of total phosphorus was poor. As a result, poly aluminum chloride (PAC) was employed to achieve a chemical removal of phosphorus, by which the total phosphorus (TP) of the effluent was controlled to be lower than 0.5 mg/L.

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification.

Otamixaban is a potent (Ki=0.5Â nM) fXa inhibitor currently in late-stage clinical development at Sanofi for the management of acute coronary syndrome. Being unproductive in obtaining a suitable crystal of Otamixaban, the required enantiomeric characterization has been accomplished using vibrational circular dichroism (VCD) spectroscopy. Selected by a spectrum similarity index, the calculated spectra of several higher energy conformers were found to match well with the observed spectra. The characteristic IR bands of these conformers were also identified and attributed to the solvation effect. Combined with both the single crystal x-ray diffraction results for an intermediate and the proton NMR study, the absolute configuration of Otamixaban is unambiguously determined to be (R,R). Keywords: Vibrational circular dichroism, DFT, IR, Absolute configuration, Vicinal protonâproton coupling, scXRD

A facile in situ synthesis approach and a size control strategy were established to obtain Ag2S nanoparticles in polyimide (PI) composite coatings. Such Ag2S nanoparticles in the composite coatings were characterized, and the effects of the as-obtained Ag2S nanoparticles of different sizes on the mechanical and tribological properties of the nanocomposite coatings were investigated. Results indicate that the in situ synthesized Ag2S nanoparticles exhibited good dispersibility and bimodal and multimodal size distribution in the nanocomposite coatings. The size of the Ag2S nanoparticles can be effectively controlled by adjusting the substituent alkyl chain length of single-source precursor, and these Ag2S nanoparticles exhibited superior improvement to mechanical and tribological properties of the nanocomposite coatings. More importantly, the Ag2S nanoparticles with the proper grain size and bimodal size distribution provided the optimal mechanical and tribological properties for the nanocomposite coatings, and the excellent tribological properties were attributed to their outstanding mechanical properties and strong ability to form a homogenous and stable protective tribofilm.

The main purpose of fine-grained classification is to distinguish among many subcategories of a single basic category, such as birds or flowers. We propose a model based on a triple network and bilinear methods for fine-grained bird identification. Our proposed model can be trained in an end-to-end manner, which effectively increases the inter-class distance of the network extraction features and improves the accuracy of bird recognition. When experimentally tested on 1096 birds in a custom-built dataset and on Caltech-UCSD (a public bird dataset), the model achieved an accuracy of 88.91% and 85.58%, respectively. The experimental results confirm the high generalization ability of our model in fine-grained image classification. Moreover, our model requires no additional manual annotation information such as object-labeling frames and part-labeling points, which guarantees good versatility and robustness in fine-grained bird recognition.

With the rapid development of the Internet and speedy increase of the data size, there are more and more data intensive applications which often involve hundreds of megabytes of data. It is important and necessary to obtain the retrieval results from cross-media data quickly and accurately. Large scale cross-media data retrieval based on Hadoop is proposed to speed up the retrieval in this paper. We divide cross-media feature extraction and cross-media retrieval into paralleled pipeline, and implement with the combination of the HDFS, HBase and MapReduce framework. To verify the performance of the proposed method, comparisons with stand-alone mode on different sizes of the image dataset are conducted, and the experimental results demonstrate the good performances of proposed method, which sharply decreases time-consuming, and meanwhile keeps the same query precision.