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The emergence of dynamic optical switching has opened up new perspectives for lightening the ever growing load on the electrical switches and routers, to meet the increasing demand on high-speed and flexible data processing and management in fiber-optic communications. Despite diversity schemes of optical switching in the single-mode regime, multi-mode switching of the hybrid fiber and chip system enabled by photonic integrated circuits, especially for the fiber-chip-fiber system, is still an outstanding challenge. Here, we propose and demonstrate the mode and polarization transmission and switching fiber-chip-fiber system with few-mode fibers (FMFs), including the FMF links for mode- and polarization-division multiplexing data transmission, the femtosecond (fs)-laser inscribed 3-dimensional (3D) photonic lantern silica chip for (de)multiplexing and coupling between FMFs and chip, and the topology-optimized N × N non-blocking 2-dimensional (2D) silicon switch array chip for switching and routing. Using 30-Gbaud quadrature phase-shift keying signals on wavelength-division multiplexing (WDM) channels, the WDM-compatible hybrid mode/polarization transmission, switching and routing system with FMFs, fs-laser inscribed silica (de)multiplexing chip and silicon switch array chip are demonstrated in the experiment with favorable operation performance. The demonstration may open the door for developing robust multi-dimensional optical data processing in fiber-optic communication systems with versatile fibers and chips.

Background To develop a fully automatic framework for the diagnosis of cause for left ventricular hypertrophy (LVH) via cardiac cine images. Methods A total of 302 LVH patients with cine MRI images were recruited as the primary cohort. Another 53 LVH patients prospectively collected or from multi-centers were used as the external test dataset. Different models based on the cardiac regions (Model 1), segmented ventricle (Model 2) and ventricle mask (Model 3) were constructed. The diagnostic performance was accessed by the confusion matrix with respect to overall accuracy. The capability of the predictive models for binary classification of cardiac amyloidosis (CA), hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD) were also evaluated. Additionally, the diagnostic performance of best Model was compared with that of 7 radiologists/cardiologists. Results Model 3 showed the best performance with an overall classification accuracy up to 77.4% in the external test datasets. On the subtasks for identifying CA, HCM or HHD only, Model 3 also achieved the best performance with AUCs yielding 0.895–0.980, 0.879–0.984 and 0.848–0.983 in the validation, internal test and external test datasets, respectively. The deep learning model showed non-inferior diagnostic capability to the cardiovascular imaging expert and outperformed other radiologists/cardiologists. Conclusion The combined model based on the mask of left ventricular segmented from multi-sequences cine MR images shows favorable and robust performance in diagnosing the cause of left ventricular hypertrophy, which could be served as a noninvasive tool and help clinical decision.

The aim of this study is to evaluate the factors affecting the objective response rate (ORR) after neoadjuvant therapy of taxol plus platinum (TP) regimen combined with programmed cell death protein-1 (PD-1) inhibitors for esophageal cancer, and establish a predictive model for forecasting ORR. According to the inclusion and exclusion criteria, consecutive esophageal cancer patients who were treated in the First Affiliated Hospital of Xi’an Jiaotong University from January 2020 to February 2022 were enrolled in this study as a training cohort, while patients who were treated in the Shaanxi Provincial Cancer Hospital Affiliated to Medical College of Xi’an Jiaotong University from January 2020 to December 2021 were enrolled as a validation cohort. All patients were treated with resectable locally advanced esophageal cancer and received neoadjuvant chemotherapy combined with immunotherapy. The ORR was defined as the sum of complete pathological response, major pathological response and partial pathological response. Logistic regression analysis was performed to determine the factors that might be related to the ORR of the patients after neoadjuvant therapy. The nomogram based on the result of regression analysis was established and verified to predict the ORR. In this study, 42 patients were included as training cohort and 53 patients were included as validation cohort. Chi-square analysis showed that neutrophil, platelet, platelet-to-lymphocytes ratio (PLR), systemic immune-inflammation index (SII), D-dimer and carcinoembryonic antigen (CEA) between ORR group and non-ORR group were significantly different. Logistic regression analysis showed that aspartate aminotransferase (AST), D-dimer and CEA were independent predictors of ORR after neoadjuvant immunotherapy. Finally, a nomogram was established based on AST, D-dimer and CEA. Internal validation and external validation revealed that the nomogram had a good ability to predict ORR after neoadjuvant immunotherapy. In conclusion, AST, D-dimer and CEA were the independent predictors of ORR after neoadjuvant immunotherapy. The nomogram based on these three indicators showed a good predictive ability.

Background Ferroptosis, which is characterized by lipid peroxidation and iron accumulation, is closely associated with the pathogenesis of acute renal injury (AKI). Cyanidin-3-glucoside (C3G), a typical flavonoid that has anti-inflammatory and antioxidant effects on ischemia‒reperfusion (I/R) injury, can induce AMP-activated protein kinase (AMPK) activation. This study aimed to show that C3G exerts nephroprotective effects against I/R-AKI related ferroptosis by regulating the AMPK pathway. Methods Hypoxia/reoxygenation (H/R)-induced HK-2 cells and I/R-AKI mice were treated with C3G with or without inhibiting AMPK. The level of intracellular free iron, the expression of the ferroptosis-related proteins acyl-CoA synthetase long chain family member 4 (ACSL4) and glutathione peroxidase 4 (GPX4), and the levels of the lipid peroxidation markers 4-hydroxynonenal (4-HNE), lipid reactive oxygen species (ROS) and malondialdehyde (MDA) were examined. Results We observed the inhibitory effect of C3G on ferroptosis in vitro and in vivo, which was characterized by the reversion of excessive intracellular free iron accumulation, a decrease in 4-HNE, lipid ROS, MDA levels and ACSL4 expression, and an increase in GPX4 expression and glutathione (GSH) levels. Notably, the inhibition of AMPK by CC significantly abrogated the nephroprotective effect of C3G on I/R-AKI models in vivo and in vitro. Conclusion Our results provide new insight into the nephroprotective effect of C3G on acute I/R-AKI by inhibiting ferroptosis by activating the AMPK pathway.

Infected pancreatic necrosis (IPN) is a highly morbid complication of acute necrotising pancreatitis (ANP). Since there is evidence of early-onset immunosuppression in acute pancreatitis, immune enhancement may be a therapeutic option. This trial aimed to evaluate whether early immune-enhancing Thymosin alpha 1 (Tα1) treatment reduces the incidence of IPN in patients with predicted severe ANP.We conducted a multicentre, double-blind, randomised, placebo-controlled trial involving ANP patients with an Acute Physiology and Chronic Health Evaluation II (APACHE II) score ≥ 8 and a computed tomography (CT) severity score ≥ 5 admitted within 7 days of the advent of symptoms. Enrolled patients were assigned to receive a subcutaneous injection of Tα1 1.6 mg every 12 h for the first 7 days and 1.6 mg once a day for the subsequent 7 days or matching placebos (normal saline). The primary outcome was the development of IPN during the index admission.A total of 508 patients were randomised, of whom 254 were assigned to receive Tα1 and 254 placebo. The vast majority of the participants required admission to the intensive care unit (ICU) (479/508, 94.3%). During the index admission, 40/254(15.7%) patients in the Tα1 group developed IPN compared with 46/254 patients (18.1%) in the placebo group (difference -2.4% [95% CI - 7.4 to 5.1%]; p = 0.48). The results were similar across four predefined subgroups. There was no difference in other major complications, including new-onset organ failure (10.6% vs. 15%), bleeding (6.3% vs. 3.5%), and gastrointestinal fistula (2% vs. 2.4%).The immune-enhancing Tα1 treatment of patients with predicted severe ANP did not reduce the incidence of IPN during the index admission.

Objective To investigate the role of platelet-to-lymphocyte ratio (PLR) in complete pathological response (pCR) of breast cancer (BC) patients after neoadjuvant chemotherapy (NAC), as well as to establish and validate a nomogram for predicting pCR. Methods BC patients diagnosed and treated in the First Affiliated Hospital of Xi’an Jiaotong University from January 2019 to June 2022 were included. The correlation between pCR and clinicopathological characteristics was analyzed by Chi-square test. Logistic regression analysis was performed to evaluate the factors that might affect pCR. Based on the results of regression analysis, a nomogram for predicting pCR was established and validated. Results A total of 112 BC patients were included in this study. 50.89% of the patients acquired pCR after NAC. Chi-square test showed that PLR was significantly correlated with pCR (X2 = 18.878, P Conclusion PLR, PLT, WBC and tumor grade were independent predictors of pCR in BC patients after NAC. The nomogram based on the above positive factors showed a good predictive ability.

Lithium-ion batteries have become one of the most promising technologies for speeding up clean automotive applications, where electrode plays a pivotal role in determining battery performance. Due to the strongly-coupled and highly complex processes to produce battery electrode, it is imperative to develop an effective solution that can predict the properties of battery electrode and perform reliable sensitivity analysis on the key features and parameters during the production process. This paper proposes a novel tree boosting model-based framework to analyze and predict how the battery electrode properties vary with respect to parameters during the early production stage. Three data-based interpretable models including AdaBoost, LPBoost, and TotalBoost are presented and compared. Four key parameters including three slurry feature variables and one coating process parameter are analyzed to quantify their effects on both mass loading and porosity of battery electrode. The results demonstrate that the proposed tree model-based framework is capable of providing efficient quantitative analysis on the importance and correlation of the related parameters and producing satisfying early-stage prediction of battery electrode properties. These can benefit a deep understanding of battery electrodes and facilitate to optimizing battery electrode design for automotive applications.

Catheter ablation of right atrial (RA) tachycardia in patients who have extensive spontaneous RA scarring is challenging due to the complex substrate and the potential for multiple inducible atrial tachycardias (ATs).Eighteen patients with scar-related RA AT and no prior cardiac surgery were enrolled. A total of 52 different ATs (mean 3.2 ± 1.5 ATs per patient) were observed. We endeavored to complete activation maps for 45 ATs.By analyzing activation maps, we classified ATs into six categories. The discrepant location and extension of ESAs were associated with different AT mechanisms.Multiple scar-related RA ATs were observed in patients without previous cardiac surgery. The detailed activation patterns of these ATs could be clearly demonstrated by using an ultra-high-density mapping system.

Efficient and reliable identification and classification of brain tumors from imaging data is essential in the diagnosis and treatment of brain cancer cells. Magnetic resonance imaging (MRI) is the most commonly used imaging modality in the analysis of infected brain tissue. However, manual segmentation requires significant time to process data produced by magnetic resonance imaging. In this study, we present two fast and proficient brain tumor identification techniques based on deep convolutional neural networks (CNNs) using magnetic resonance imaging data for the effective detection and classification of different types of brain tumors. We use two publicly available datasets from Figshare and BraTS 2018, and apply conditional random fields to eliminate forged outputs, considering spatial information on fine segmentation tasks. The first proposed architecture, based on the Figshare dataset, classifies brain tumors as gliomas, meningiomas, or pituitary tumors. The second architecture differentiates between high- and low-grade gliomas (HGG and LGG, respectively). An intensity normalization method is also investigated as a pre-processing step, which proves highly effective at detection and classification of brain tumors in combination with data augmentation techniques. The Figshare and BraTS 2018 datasets included 3062 and 251 images, respectively. The experimental results demonstrate an accuracy of 97.3% and a dice similarity coefficient (DSC) 95.8% on the task of classifying brain tumor as gliomas, meningiomas, or pituitary tumors achieved by the first proposed CNN architecture, while second proposed CNN architecture achieved an accuracy of 96.5% with a DSC of 94.3% on the task of classifying glioma grades as HGG or LGG. Experimental results reveal that our proposed model attained improved performance and increased classification accuracy compared to state-of-the-art methods.

Purpose Lysophosphatidic acid (LPA) is a bioactive molecule which participates in many physical and pathological processes. Although LPA receptor 6 (LPAR6), the last identified LPA receptor, has been reported to have diverse effects in multiple cancers, including breast cancer, its effects and functioning mechanisms are not fully known. Methods Multiple public databases were used to investigate the mRNA expression of LPAR6, its prognostic value, and potential mechanisms in breast cancer. Western blotting was performed to validate the differential expression of LPAR6 in breast cancer tissues and their adjacent tissues. Furthermore, in vitro experiments were used to explore the effects of LPAR6 on breast cancer. Additionally, TargetScan and miRWalk were used to identify potential upstream regulating miRNAs and validated the relationship between miR-27a-3p and LPAR6 via real-time polymerase chain reaction and an in vitro rescue assay. Results LPAR6 was significantly downregulated in breast cancer at transcriptional and translational levels. Decreased LPAR6 expression in breast cancer is significantly correlated with poor overall survival, disease-free survival, and distal metastasis-free survival, particularly for hormone receptor-positive patients, regardless of lymph node metastatic status. In vitro gain and loss-of-function assays indicated that LPAR6 attenuated breast cancer cell proliferation. The analyses of TCGA and METABRIC datasets revealed that LPAR6 may regulate the cell cycle signal pathway. Furthermore, the expression of LPAR6 could be positively regulated by miR-27a-3p. The knockdown of miR-27a-3p increased cell proliferation, and ectopic expression of LPAR6 could partly rescue this phenotype. Conclusion LPAR6 acts as a tumor suppressor in breast cancer and is positively regulated by miR-27a-3p.

In this study, we present a high-k composite of TiO2–ZrO2 (TZO) acted as charge trapping layer for non-volatile memory devices with a structure of p-Si/Al2O3/TZO/Al2O3/metal and investigate the effect of rapid thermal annealing (RTA) on the storage capability. The device after annealing at 800 °C for 90 s in O2 ambient demonstrates a large memory window of ~ 15.5 V under a sweeping voltage of ± 12 V, which is extremely attractive in low-power applications. In addition, a high trapped charge density of ~ 1.55 × 1013 cm−2 was obtained in the fabricated device. The results of XPS analysis show that the peaks of Zr3d3/2 and Zr3d5/2 are shifted to the direction of low binding energy after RTA at high temperature, and it is found that Ti3+ and Ti4+ exist in the TZO films after rapid thermal annealing at 800 °C, which helps to improve the charge trapping ability of the composite dielectric.

Physical phenomena observed before strong earthquakes have been reported for centuries. Precursor signals, which include radon anomalies, electrical signals, water level changes and ground lights near the epicenter, can all be used for earthquake prediction. Anomalous negative signals observed by ground-based atmospheric electric field instruments under fair weather conditions constitute a novel earthquake prediction approach. In theory, the abnormal radiation of heat before an earthquake produces fair weather around the epicenter. To determine the near-epicenter weather conditions prior to an earthquake, 81 global earthquake events with magnitudes of 6 or above from 2008 to 2021 were collected. According to Harrison's fair weather criteria, in 81.48% of all statistical cases, the weather was fair 6 h before the earthquake; in 62.96% of all cases, the weather was fair 24 h before the event. Moreover, most of these cases without fair weather several hours before the earthquake were near the sea. Among the 37 inland earthquakes, 86.49% were preceded by 6 h of fair weather, and 70.27% were preceded by fair weather for 24 h. We conclude that the weather near the epicenter might be fair for several hours before a strong earthquake, especially for inland events.

Developing effective strategies to improve the hydrophilicity or aqueous solubility of hydrophobic molecular scaffolds is meaningful for both academic research and industrial applications. Herein, we demonstrate that stepwise and precise N/O heteroatoms doping on a polycyclic aromatic skeleton can gradually alter these structures from hydrophobic to hydrophilic, even resulting in excellent aqueous solubility. The Hansen solubility parameters (HSP) method shows that the three partial solubility parameters are closely related to N/O doping species, numbers and positions on the molecular panel. The hydrogen bonding solubility parameter indicates that the hydrogen bonding interactions between N/O doped molecules and water play a key role in enhancing hydrophilicity. Moreover, three optimized water-soluble molecules underwent a self-assembly process to form stable nanoparticles in water, thus facilitating better hydrogen bonding interactions disclosed by HSP calculations, NMR and single crystal X-ray analysis. These ensembles even show quasi-solid properties in water from NMR and luminescence perspectives.

A new technical route of organic matter capture and carbon fixation is proposed in response of the increasingly strict emission standards of volatile organic compounds (VOCs) in petrochemical industry and the Chinese national strategic development goals of carbon peak and carbon neutralization. A closed loop from raw materials to adsorbents for gas treatment can be achieved by two key technical characteristics: (1) construct a new mesoporous adsorbent with complete desorption and regeneration function by carbon nanotubes (CNTs); (2) convert gaseous organic matter which cannot be recycled in liquid/gas state to CNTs. It realizes the resource integration of "turning waste into treasure" and maximizes the carbon emission reduction effect of waste gas treatment process without consuming extra precious fossil fuel, compared with the traditional technologies of VOCs treatments, including combustion or catalytic oxidation. What’s more, the increase in supply of various green electricity is expected to change the current situation of large investment and heavy cost burden of environmental protection technology, and make a great contribution to the national carbon peak and carbon neutrality policy.

Deep learning (DL) models are highly research-oriented field in image compressive sensing in the recent studies. In compressive sensing theory, a signal is efficiently reconstructed from very small and limited number of measurements. Block-based compressive sensing is most promising and lenient compressive sensing (CS) approach mostly used to process large-sized videos and images: exploit low computational complexity and requires less memory. In block-based compressive sensing, a number of deep models are needed to train with each corresponding to different sampling rate. Compressive sensing performance is highly degraded through allocating low sampling rates to various blocks within same image or video frames. In this work, we proposed multi-rate method using deep neural networks for block-based compressive sensing of magnetic resonance images with performance that greatly outperforms existing state-of-the-art methods. The proposed approach is capable in smart allocation of exclusive sampling rate for each block within image, based on the image information and removing blocking artifacts in reconstructed MRI images. Each image block is separately sampled and reconstructed with different sampling rate and reassembled into a single image based on inter-correlation between blocks, to remove blocking artifacts. The proposed method surpasses the current state-of-the-arts in terms of reconstruction speed, reconstruction error, low computational complexity, and certain evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and relative l2-norm error (RLNE).

The cell of a rod-shaped bacterium is composed of a cylinder and two hemispherical poles. In recent decades, the molecular mechanism of morphogenesis in rod-shaped bacteria has received extensive research. However, most works have focused on the morphogenesis of cylinders, and the morphogenesis of the hemispherical poles remains unclear. In the past, the pole of bacterial cell wall was considered as a rigid hemispherical structure. However, our work indicated that the pole in the isolated sacculi from Bacillus subtilis was a flat structure instead of a hemisphere form. Further works showed that internal pressure was responsible for shaping the hemispherical poles, indicating an elastic nature of the cell wall in poles. In addition, we found that the internal pressure was able to transform septa into hemispherical shape which is similar to normal poles. Based on our work, we proposed a model for the internal pressure-induced formation of hemispherical poles in B. subtilis, and this work may provide new clues into basic knowledge of the morphogenesis of rod-shaped bacteria.

It is challenging to establish a definitive diagnosis and initiate timely management for skip segment Hirschsprung’s disease (SSHD). Herein, we report three cases of SSHD at our institution between December 2008 and March 2018. Patient #1 was misdiagnosed and underwent three successive operations within 2 years. Patient #2 and #3 were diagnosed timely based on previous experience. A segmental narrowing detected by barium enema may raise the possibility of SSHD. Laparoscopic-guided multipoint biopsy is regarded as a mini-invasive and purposeful way to take representative samples after locating the segmental narrowing intraoperatively and accurate the final diagnosis. The laparoscopic-assisted pull-through procedure with radical resection from the distal rectum to the most proximal margin of the diseased skip segment is safe and effective in treating patients with SSHD. Level of evidence: Level III.