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INTRODUCTION Early-onset peritonitis (EOP) is a risk factor for mortality in peritoneal dialysis (PD) patients. This study investigates the clinical features and outcomes of EOP in elderly patients. METHODS This multicenter retrospective study evaluated 433 elderly PD patients with end-stage renal disease. The cohort was divided into nonperitonitis group (n = 239), EOP group (≤12 months, n = 109) and late-onset peritonitis (LOP) group (>12 months, n = 85). Clinical data, treatment results, and outcomes were compared between the groups. RESULTS Compared with LOP group, there were no significant intergroup differences in the rate of primary recovery, complete cure, relapse, catheter removal, or death from PDAP (p >0.05) in the most recent PDAP episode. However, Kaplan-Meier analysis showed that patients in the EOP group were likely to have multiple episodes of PD-associated peritonitis (PDAP), technique failure, all-cause death, and composite endpoint in the long-term prognostic outcomes (p

Mutations in CSNK2B lead to Poirier-Bienvenu neurodevelopmental syndrome (POBINDS), a rare neurodevelopmental disorder. Only 14 cases of POBINDS have been reported worldwide. The main manifestations are seizures, often tonic–clonic, with or without intellectual disability, growth retardation, and developmental language retardation. We conducted a comprehensive phenotypic mining and trio-whole exome sequencing on six children with POBINDS for gene diagnosis and analyzed the different variants using bioinformatics analysis software and related experiments. This paper reviews previous literature and discusses two common missense variants that lead to structural changes. Among the six patients, four, one, and one had tonic–clonic, myoclonic, and febrile seizures, respectively. Language development disorder, motor development disorder, and developmental delay/intellectual disability (DD/ID) are the main clinical features. All children had de novo mutations in CSNK2B, including three missense variants (c.410G > T/p.(Cys137Phe), c.494A > G/p.(His165Arg), and c.3G > A/p.(Met1Ile)), two splice variants (c.292-2A > T, c.558-3 T > G), and one frameshift variant (c.499delC/p.(Leu167Serfs*60)). Three missense variants were predicted to be harmful by various software programs, and two splicing variants were found to produce new exonic splicing enhancers by the minigene assay. Western blot analysis showed that the frameshift variant resulted in decreased protein expression. According to a literature review, c.3G > A/p.(Met1Ile), c.292-2A > T, c.558-3 T > G, and c.499delC/p.(Leu167Serfs*60) are novel variants of CSNK2B. The decrease or loss of protein function caused by CSNK2B mutations may be a pathogenic factor in this cohort. The severity of the POBINDS phenotype differs, and refractory epilepsy may be accompanied by a more serious DD/ID, language disorder, and motor retardation. At present, there is no specific treatment, and antiepileptic therapy usually requires the combination of two or more anti-epileptic drugs.

Neurodevelopmental diseases are increasingly recognized to be caused by "de novo" variants with the expanding use of next-generation sequencing. The apparent de novo variants may actually be low-level hereditary parental mosaic variants, which could increase the recurrence risk of disease by >50% and is thought to be an underappreciated cause of neurodevelopmental diseases. Our study aimed to investigate the frequency of parental mosaicism in "de novo" neurodevelopmental diseases. A total of 237 patients (and parents) with neurodevelopmental diseases carrying apparent de novo pathogenic or likely pathogenic variants were recruited consecutively. Deep next-generation sequencing was performed on parental samples to identify parental mosaicism. Fourteen parental disease-causing mosaicism variants (3.0%) in 11 genes were detected with alternate allele frequency (AAF) 0.22%-34%. Three parents showed milder clinical phenotypes than their offspring with relatively high AAF (23.33%, 25%, 34% separately). One recurrent variant was identified prenatally. A review of cohort study on parental mosaicism in neurodevelopmental diseases was performed. Our study highlights that identifying the parental mosaic disease-causing variants especially the low-level mosaicism will contribute to improving the accuracy of genetic counseling and prenatal diagnosis for reproductive risks.

The multi‐terminal hybrid line commutated converter (LCC)/modular multilevel converter (MMC)‐based high voltage dc (HVDC) has been applied to solve the simultaneous commutation failure problem in East China. Here, the propagation of the travelling wave is characterised by transfer functions and the characteristic of the fault current travelling wave in the multi‐terminal hybrid LCC/MMC HVDC system is analysed. Then a directional pilot protection for the multi‐terminal hybrid LCC/MMC HVDC system is proposed. The proposed method is implemented to the designed HCM5000 dc control and protection device and verified in the Kun–Liu–Long three‐terminal hybrid LCC/MMC HVDC real‐time digital simulator (RTDS) test system. The test results show that the proposed method meets the engineering requirement of multi‐terminal hybrid LCC/MMC HVDC system for line protection.

The optical performance of fused silica optics used in high-power lasers is known to depend not only on their surface damage resistance, but also on their surface quality. Previous studies have shown that good fused silica damage performance and surface quality can be achieved by the use of reactive ion etching (RIE), followed by HF-based wet shallow etching (3 μm). In this study, two kinds of HF-based etchants (aqueous HF and HF/NH4F solutions) were employed to investigate the effect of HF-based etching on the optical performance of reactive-ion-etched fused silica surfaces at various HF-based shallow etching depths. The results showed that the addition of NH4F to HF solution makes it possible to produce a high-quality optical surface with a high laser-induced damage threshold, which is strongly associated with the surface roughness and fluorescence defect density. Additionally, changing the HF-based etching depth over the range from 1 μm to 3 μm can affect the surface damage resistance and absorption performance of RIE-treated fused silica. The light-scattering results indicate that the point defect density plays an important role in the determination of the HF-based etching depth. Understanding these trends can enable the advantages of the combined technique of RIE and HF-based etching during the fabrication of high-quality fused silica optics.

BACKGROUND The number of end-stage renal disease patients with diabetes mellitus (DM) who are undergoing peritoneal dialysis is increasing. Peritoneal dialysis-associated peritonitis (PDAP) is a serious complication of peritoneal dialysis leading to technical failure and increased mortality in patients undergoing peritoneal dialysis. The profile of clinical symptoms, distribution of pathogenic organisms, and response of PDAP to medical management in the subset of end-stage renal disease patients with DM have not been reported previously. Discrepant results have been found in long-term prognostic outcomes of PDAP in patients with DM. We inferred that DM is associated with bad outcomes in PDAP patients. AIM To compare the clinical features and outcomes of PDAP between patients with DM and those without. METHODS In this multicenter retrospective cohort study, we enrolled patients who had at least one episode of PDAP during the study period. The patients were followed for a median of 31.1 mo. They were divided into a DM group and a non-DM group. Clinical features, therapeutic outcomes, and long-term prognostic outcomes were compared between the two groups. Risk factors associated with therapeutic outcomes of PDAP were analyzed using multivariable logistic regression. A Cox proportional hazards model was constructed to examine the influence of DM on patient survival and incidence of technical failure. RESULTS Overall, 373 episodes occurred in the DM group (n = 214) and 692 episodes occurred in the non-DM group (n = 395). The rates of abdominal pain and fever were similar in the two groups (P > 0.05). The DM group had more infections with coagulase-negative Staphylococcus and less infections with Escherichia coli (E. coli) as compared to the non-DM group (P < 0.05). Multivariate logistic regression analysis revealed no association between the presence of diabetes and rates of complete cure, catheter removal, PDAP-related death, or relapse of PDAP (P > 0.05). Patients in the DM group were older and had a higher burden of cardiovascular disease, with lower level of serum albumin, but a higher estimated glomerular filtration rate (P < 0.05). Cox proportional hazards model confirmed that the presence of diabetes was a significant predictor of all-cause mortality (hazard ratio = 1.531, 95% confidence interval: 1.091-2.148, P < 0.05), but did not predict the occurrence of technical failure (P > 0.05). CONCLUSION PDAP patients with diabetes have similar symptomology and are predisposed to coagulase-negative Staphylococcus but not E. coli infection compared those without. Diabetes is associated with higher all-cause mortality but not therapeutic outcomes of PDAP.

The fast and selective protection using fast dc circuit breaker to break the fault current and isolate faulty lines satisfies the requirement of fault clearance in HVDC grids. However, traditional derivative-based traveling wave protection suffers from low-sensitivity problem when high-impedance faults occur. Based on the analysis of the fault initial traveling wave, it is concluded that the distortion degree of the fault initial traveling wave is related to the location of the fault, and the amplitude of the fault initial traveling wave is related to the fault impedance. Then a Levenberg–Marquart (LM) optimal approximation method is proposed to extract the fault distance information from zero-sequence fault current initial traveling wave. The optimal approximation parameters respectively reflecting the fault distance and fault impedance can be obtained. A non-unit traveling wave protection scheme is proposed based on the parameters. A ±400 kV modular multilevel converter (MMC) HVDC grid test system is built in PSCAD/EMTDC to verify the proposed method. The simulation results show that the parameters can directly correspond to the fault distance and fault impedance, and the proposed method can fast and reliably identify dc line faults. Finally, the sensitivity of the proposed method is studied by comparing with three different methods.

The insufficiency and contamination of supervision information are the main factors affecting the performance of support vector machines (SVMs) in real-world applications. To address this issue, novel correntropy loss functions and Laplacian SVM (LapSVM) are utilized for robust semi-supervised classification. It is known that correntropy loss functions have been used in robust learning and achieved promising results. However, the potential for more diverse priors has not been extensively explored. In this paper, a correntropy loss function induced from Laplace kernel function, called LK-loss, is applied to LapSVM for the construction of robust semi-supervised classifier. Properties of LK-loss are demonstrated including robustness, symmetry, boundedness, Fisher consistency and asymptotic approximation behaviors. Moreover, the asymmetric version of LK-loss is introduced to further improve the performance. Concave-convex procedure (CCCP) technique is used to handle the non-convexity of Laplace kernel-induced correntropy loss functions iteratively. Experimental results show that in most cases, the proposed methods have better generalization performance than the comparing ones, which demonstrate the feasibility and effectiveness of the proposed semi-supervised classification framework.

The structural information of data contains useful prior knowledge and thus is important for designing classifiers. Extreme learning machine (ELM) has been a potential technique in handling classification problems. However, it only simply considers the prior class-based structural information and ignores the prior knowledge from statistics and geometry of data. In this paper, to capture more structural information of the data, we first propose a Fisher-regularized extreme learning machine (called Fisher-ELM) by applying Fisher regularization into the ELM learning framework, the main goals of which is to build an optimal hyperplane such that the output weight and within-class scatter are minimized simultaneously. The proposed Fisher-ELM reflects both the global characteristics and local properties of samples. Intuitively, the Fisher-ELM can approximatively fulfill the Fisher criterion and can obtain good statistical separability. Then, we exploit graph structural formulation to obtain semi-supervised Fisher-ELM version (called Lap-FisherELM) by introducing manifold regularization that characterizes the geometric information of the marginal distribution embedded in unlabeled samples. An efficient successive overrelaxation algorithm is used to solve the proposed Fisher-ELM and Lap-FisherELM, which converges linearly to a solution, and can process very large datasets that need not reside in memory. The proposed Fisher-ELM and Lap-FisherELM do not need to deal with the extra matrix and burden the computations related to the variable switching, which makes them more suitable for relatively large-scale problems. Experiments on several datasets verify the effectiveness of the proposed methods.

Marchiafava-Bignami disease (MBD) is a rare neurological disease, and is characterized by callosal lesions consisting of necrosis and demyelination. Posterior reversible encephalopathy syndrome (PRES) is a clinical syndrome, and refers to a disorder with reversible subcortical vasogenic brain edema, predominantly involving the bilateral parieto-occipital regions. Never before had articles reported that the two diseases occurred simultaneously. We report a 49-year-old male alcoholic with both MBD and PRES who had a good prognosis after correct diagnosis and timely treatment.

Background PPP3CA gene encodes the catalytic subunit A of a calcium-dependent protein phosphatase called calcineurin. However, two distinct mechanisms in PPP3CA deficiency would cause two clinically different diseases. Gain-of-function mutations in the autoinhibitory domain at the C-terminus would cause ACCIID that stands for arthrogryposis, cleft palate, craniosynostosis and impaired intellectual development. While loss-of-function mutations in PPP3CA would cause infantile or early childhood onset epileptic encephalopathy1, named as IECEE1. IECEE1 is a severe epileptic neurodevelopmental disorder and mainly characterized by psychomotor delay. Here, we report a Chinese patient who was clinically and genetically diagnosed as IECEE1. We also extensively analyzed electroencephalogram (EEG) features of the patient in this study. Case presentation A 2-year-old Chinese patient who had recurrent polymorphic seizures was clinically and genetically diagnosed as IECEE1. A frameshift variant c.1283insC (p.T429NfsX22) was identified in this case. Multiple types of abnormal features were observed in the EEG, comparing with the previous reports. Conclusions These findings could expand the spectrum of PPP3CA mutations and might also support the diagnosis and further study of IECEE1.

Ensemble learning is an effective technique to improve performance and stability compared to single classifiers. This work proposes a selective ensemble classification strategy to handle missing data classification, where an uncertain extreme learning machine with probability constraints is used as individual (or base) classifiers. Then, three selective ensemble frameworks are developed to optimize ensemble margin distributions and aggregate individual classifiers. The first two are robust ensemble frameworks with the proposed loss functions. The third is a sparse ensemble classification framework with the zero-norm regularization, to automatically select the required individual classifiers. Moreover, the majority voting method is applied to produce ensemble classifier for missing data classification. We demonstrate some important properties of the proposed loss functions such as robustness, convexity and Fisher consistency. To verify the validity of the proposed methods for missing data, numerical experiments are implemented on benchmark datasets with missing feature values. In experiments, missing features are first imputed by using expectation maximization algorithm. Numerical experiments are simulated in filled datasets. With different probability lower bounds of classification accuracy, experimental results under different proportion of missing values show that the proposed ensemble methods have better or comparable generalization compared to the traditional methods in handling missing-value data classifications.

ObjectiveTo analyze the clinical manifestations, imaging, electroencephalography, treatment, and prognosis of 35 cases of autoimmune glial fibrillary acidic protein astrocytopathy (GFAP-A) in children.MethodsChildren hospitalized in the Department of Neurology, Hunan Children’s Hospital, China, between January 2015 and June 2021, owing to autoimmune diseases of the central nervous system were subjected to a cell-based assay (CBA). The assay identified 40 children positive for GFAP-immunoglobulin (Ig)G antibodies in the serum and/or the cerebrospinal fluid. Based on clinical manifestations and imaging characteristics, five children who were only positive for GFAP-IgG antibodies in serum were excluded, and the remaining 35 children were diagnosed with autoimmune GFAP-A. The clinical data derived from the 35 children were retrospectively analyzed.ResultsA total of 35 children, including 23 males and 12 females with a mean age of 6.3 ± 0.6 years, manifested clinical symptoms of fever (62.9%), headache (42.9%), convulsions (42.9%), abnormal mental behavior (51.4%), disorders of consciousness (54.3%), visual disturbance (22.9%), ataxia (11.4%), paralysis (40%), and autonomic dysfunction (25.7%). One child exhibited only the clinical symptom of peripheral facial nerve palsy. Eleven out of 35 children were also positive for other antibodies. In addition to the common overlapping autoimmune syndromes, one case of autoimmune GFAP-A also manifested as Bickerstaff’s brainstem encephalitis. Linear periventricular enhancement upon MRI was significantly less frequent in children (8.5%) than in adults. In pediatric patients, MRI contrast enhancement was principally seen in the meninges and brain lobes. Although repeated relapse (17.1%) and sequelae symptoms (20%) occurred in some cases, most children showed a favorable prognosis. Spearman’s rank correlation showed that the antibody titer was not significantly associated with the severity of the initial disease conditions.ConclusionsThe disease diagnosis in children seropositive for GFAP antibodies only should receive a comprehensive diagnosis based on their clinical symptoms, imaging, electroencephalographic characteristics, and treatment responses. Some patients with relapses should receive repeated gamma globulin and corticosteroid therapy or the addition of immunosuppressants to their therapeutic regimen, and slow-dose tapering of corticosteroids and extended treatment are recommended for patients with overlapping autoimmune syndromes.

Ischemic heart disease (IHD) is a considerable health burden worldwide with high mortality and morbidity. Treatments for IHD are mainly focused on decreasing oxygen demand or increasing myocardial oxygen supply, including pharmacological, interventional, and surgical treatment, but there are also some limitations. Therefore, it is important to find a simple, effective, and economical treatment. As non-invasive and safe physiotherapy, electrical stimulation (ES) has a promising application in the treatment of IHD. Current studies suggest that ES can affect the occurrence and development of IHD by promoting angiogenesis, regulating autophagy and apoptosis, inhibiting the inflammatory response and oxidative stress. In this review, we focus predominantly on the mechanism of ES and the current progress of ES therapy in IHD, furthermore, give a brief introduction to the forms of ES in clinical application.

Atherosclerosis is a multifactorial chronic inflammatory arterial disease forming the pathological basis of many cardiovascular diseases such as coronary heart disease, heart failure, and stroke. Numerous studies have implicated inflammation as a key player in the initiation and progression of atherosclerosis. Galectin-3 (Gal-3) is a 30 kDaβ-galactose, highly conserved and widely distributed intracellularly and extracellularly. Gal-3 has been demonstrated in recent years to be a novel inflammatory factor participating in the process of intravascular inflammation, lipid endocytosis, macrophage activation, cellular proliferation, monocyte chemotaxis, and cell adhesion. This review focuses on the role of Gal-3 in atherosclerosis and the mechanism involved and several classical Gal-3 agonists and antagonists in the current studies.

Modelling internal combustion engines (ICEs) with multidimensional computational fluid dynamics (CFD) is challenging due to the fuel spray, combustion chemistry and moving piston involved. To accur...

In this paper, we define a mixture correntropy criterion where two different kernel functions are combined. We induce a more general nonconvex robust loss function by this heterogenous mixture correntropy. The proposed mixture correntropy is also a local similarity measure that not only improves the limitations of correntropy under a single kernel, but also handles heterogeneous data more flexibly and stably. The induced loss amalgamates the superiors of the state-of-the-art robust loss functions and is more effective. What’s more, we verify the Fisher consistency of the induced loss and analyze the robustness from the view point of robust estimation. With this induced loss, we propose a robust support vector machine (SVM) framework and adopt half quadratic optimization algorithm to handle the nonconvexity and further improve convergent rate. Furthermore, we generate heterogenous structured artificial datasets and impose different levels of label noise on benchmark datasets. Implements on these two types of datasets show the superior flexibility and effectiveness of the proposed framework.

Underwater imaging is attractive but challenging. Images could be severely degraded by the particles in turbid water because of backscatter generation and signal light attenuation. In this paper, we focus on the scheme of underwater imaging and study the methods of visibility enhancement of turbid underwater images based on polarimetric imaging utilizing active unpolarized illumination. Compared with traditional polarimetric imaging using linearly polarized illumination, using unpolarized illumination ensures the polarization effect of the signal light could be neglected, no matter the depolarization degree of the object is high or low, which expands the application range of underwater polarimetric imaging and makes the underwater polarimetric imaging scheme more reliable and robust. Experimentally, the visibility and the contrast of underwater images are enhanced effectively. In addition, it is demonstrated that our method is applicable for objects of different materials and different imaging distances in turbid water. The contrast of underwater images could be promoted at least 100%, meaning that this kind of technique can be potentially used in many underwater environments.

In this work we propose a minimax probability extreme learning machine framework (MPME), which combines the benefits of minimax probability machine (MPM) with extreme learning machine (ELM). For binary classification problems, we illustrate that the proposed MPME can be interpreted geometrically by minimizing the maximum of Mahalanobis distances to the two classes. Then two variants of the MPME are presented based on the l 2 -norm loss and l 1 -norm loss functions (called LSEMPME and LADMPME) respectively. Without making specific assumption on the data distribution, the proposed methods can provide explicit upper-bounds for the generalization error, moreover the LSEMPME and LADMPME minimize empirical risk simultaneously. The decision hyperplanes of the proposed methods pass through the origin in ELM feature space with few decision variables. By using the multivariate Chebyshev–Cantelli inequality, all the proposed problems can be reformulated as second-order cone programming (SOCP) with global solutions. Furthermore, numerical experiments have been carried out on two databases that are drawn from UCI benchmark database and a practical application database. First, the proposed methods are evaluated for a practical application consisting on the analysis of licorice seeds using near-infrared spectral (NIR) data. Experiments in six different spectral regions illustrate that the proposed methods can improve generalization in most cases. Then the proposed methods are evaluated on benchmark datasets. In comparison with traditional methods including MPM, ELM and support vector machine (SVM), experiments show that the proposed methods achieve comparable results in generalization. With few decision variables, the proposed methods are easy to implement for nonlinear classification and to estimate a lower-bound on the prediction accuracy.

With the explosive growth of spent lithium-ion batteries (LIBs), the effective recycling of graphite as a key negative electrode material has become economically attractive and environmentally significant. This review reports the recent research progress in recycling strategies for spent graphite from the perspectives of separation and reuse. First, technologies for separating graphite powder after direct crushing and artificially splitting are introduced, and the shortcomings of cost control and separation efficiency are reported. Subsequently, the reuse of recycled spent graphite is systematically summarized in terms of regeneration into battery materials, low-value utilization, and high-value conversion. Special attention has been paid to different aging degrees of retired batteries, as well as the performance and applications of regenerated graphite. Finally, upcoming research efforts on evaluation the standard establishment, development of advanced technology, and potential value enhancement to meet practical the industrial conditions and facilitate industry installation are proposed.

This paper proposes a novel model for an high-efficiency tunable broadband near-infrared absorber. The proposed absorber consists of an Al bottom mirror, SiO2-VO2 hybrid spacing layer, and certain MoS2 top nanostructures. Owing to the thermal tunability of the refractive index of VO2 materials, the near field coupling resonance in the multilayer metamaterials can be tuned by regulating the temperature, and henceforth, the efficiency of the absorber and the absorption band are also tunable. MoS2 has an excellent thermal-stability in the near-infrared range, which can nullify the influence of the temperature regulation of VO2. The results from our study demonstrated that the absorber achieved an average absorbance of 86.5% at 75 °C for a broadband range of 800–2350 nm. At 25 °C, the absorber attained an average absorbance of 96.6% for the wavelength range of 800–1160 nm, and a narrow-band absorption peak around 1489 nm. The absorber we have studied, which was based on tunable metasurfaces, displays tremendous potential for the applications such as camouflage coatings, solar energy, information sensing, and atmospheric environment monitoring.

OBJECTIVE: :To explore the value of quantitative perfusion histogram parameters of dynamic contrast-enhanced magnetic resonance imaging （DCE-MRI） in pathological classification of uterine leiomyoma and its correlation with Ki-67 protein expression. METHODS: Thirty five patients with uterine leiomyoma confirmed by operation and pathology at Shaoxing People’s Hospital from October 2015 to September 2017 were analyzed retrospectively，including 15 cases of ordinary type，8 cases of cellular type and 12 cases of degenerative type. All patients were examined by pelvic DCE-MRI before operation，and the histogram parameters （median，mean，skewness，kurtosis，energy，entropy） of various quantitative perfusion parameters，including volume transport constant （K (trans)），rate constant （K (ep)），extravascular extracellular space distribute volume per unit tissue volume （V (e)），blood plasma volume per unit volume of tissue （V (p)） were calculated，and the efficacy of different parameters in pathological classification of uterine leiomyoma was evaluated by ROC curve. The expression of Ki-67 protein in uterine leiomyoma was detected by immunohistochemical method，and the correlation between histogram parameters and Ki-67 protein expression was analyzed by Pearson and Spearman correlation analysis. RESULTS: The median and mean values of K (trans)，K (ep)，V (e) and V (p) in the cellular group were higher than those in the degenerative group and the ordinary group（ P

Background Developmental and epileptic encephalopathies (DEEs) are a heterogeneous group of chronic encephalopathies characterized by epilepsy with comorbid intellectual disability that are frequently associated with de novo nonsynonymous coding variants in ion channels, cell-surface receptors, and other neuronally expressed genes. Mutations in TRPM3 were identified as the cause of DEE. We report a novel patient with DEE carrying a de novo missense mutation in TRPM3, p.(S1202T); this missense mutation has never been reported. Case presentation A 7-year and 2-month-old Chinese patient who had recurrent polymorphic seizures was clinically diagnosed with DEE. A de novo missense mutation in TRPM3, which has not yet been reported, was identified in this case. The patient had a clinical phenotype consistent with previous reports. Conclusions These findings could expand the spectrum of TRPM3 mutations and might also support that de novo substitutions of TRPM3 are a cause of DEE.

We investigate the role of each step in the combined treatment of reactive ion etching (RIE) and dynamic chemical etching (DCE) for improving the laser-induced damage resistance of fused silica optics. We employ various surface analytical methods to identify the possible damage precursors on fused silica surfaces treated with different processes (RIE, DCE, and their combination). The results show that RIE-induced defects, including F contamination, broken Si-O bonds, luminescence defects (i.e., NBOHCs and ODCs), and material densification, are potential factors that limit the improvement of laser-induced damage resistance of the optics. Although being capable of eliminating the above factors, the DCE treatment can achieve rough optical surface with masses of exposed scratches and pits which might serve as reservoirs of the deposits such as inorganic salts, thus limiting the further improvement in damage resistance of fused silica. The study guides us to a deep understanding of the laser-induced damage process in achieving fused silica optics with enhanced resistance to laser-induced damage by the combined treatment of RIE and DCE.

BackgroundWe conduct a study in developing and validating four MRI-based radiomics models to preoperatively predict the risk classification of gastrointestinal stromal tumors (GISTs).MethodsForty-one patients (low-risk = 17, intermediate-risk = 13, high-risk = 11) underwent MRI before surgery between September 2013 and March 2019 in this retrospective study. The Kruskal–Wallis test with Bonferonni correction and variance threshold was used to select appropriate features, and the Random Forest model (three classification model) was used to select features among the high-risk, intermediate-risk, and low-risk of GISTs. The predictive performance of the models built by the Random Forest was estimated by a 5-fold cross validation (5FCV). Their performance was estimated using the receiver operating characteristic (ROC) curve, summarized as the area under the ROC curve (AUC). Area under the curve (AUC), accuracy, sensitivity, and specificity for risk classification were reported. Linear discriminant analysis (LDA) was used to assess the discriminative ability of these radiomics models.ResultsThe high-risk, intermediate-risk, and low-risk of GISTs were well classified by radiomics models, the micro-average of ROC curves was 0.85, 0.81, 0.87 and 0.94 for T1WI, T2WI, ADC and combined three MR sequences. And ROC curves achieved excellent AUCs for T1WI (0.85, 0.75 and 0.82), T2WI (0.69, 0.78 and 0.78), ADC (0.85, 0.77 and 0.80) and combined three MR sequences (0.96, 0.92, 0.81) for the diagnosis of high-risk, intermediate-risk, and low-risk of GISTs, respectively. In addition, LDA demonstrated the different risk of GISTs were correctly classified by radiomics analysis (61.0% for T1WI, 70.7% for T2WI, 83.3% for ADC, and 78.9% for the combined three MR sequences).ConclusionsRadiomics models based on a single sequence and combined three MR sequences can be a noninvasive method to evaluate the risk classification of GISTs, which may help the treatment of GISTs patients in the future.

Objective: Anti-N-methyl-D-aspartate (anti-NMDA) receptor encephalitis is the most common autoimmune encephalitis in pediatric patients. The study aimed to investigate the clinical characteristics and prognostic factors of anti-NMDA receptor encephalitis in children in South China.Methods: This was a retrospective study of children diagnosed with anti-NMDA receptor encephalitis between 01/2014 and 12/2017 at Hunan Children's Hospital. Laboratory, brain magnetic resonance imaging (MRI), and electroencephalography data were collected. The short-term (6-month) outcomes were assessed using the Liverpool score by the same pediatric neurologist. The children were divided into good (scores 4–5) and poor (score Results: Among the 51 patients, 21 (41.2%) were male. The most common clinical symptoms were dyskinesia (88.2%), personality change (84.3%), seizure (82.4%), and cognitive disorder (31.4%). Two were transferred to another hospital, 45 (91.8%) received intravenous immunoglobulins, 41 (83.7%) received methylprednisolone, and 8 (16.3%) received plasma exchange. Eight (16.3%) received rituximab for second-line treatment, six after intravenous immunoglobulin and methylprednisolone treatment, and two after plasma exchange therapy failed. Seven were lost to follow-up. The short-term outcome was good in 23 patients. Cognitive disorder [odds ratio (OR): 23.97, 95% confidence interval (CI): 1.12–513.30, P = 0.042) and abnormal brain MRI (OR: 14.29, 95% CI: 1.36–150.10, P = 0.027] were independently associated with a poor short-term outcome after adjustment for age, GCS, and rituximab use.Conclusions: MRI abnormalities and cognitive disorders are independently associated with poor short-term outcomes in children with anti-NMDA receptor encephalitis. The use of rituximab is not associated with the 6-month outcomes.

Rationale: Mutations of connector enhancer of kinase suppressor of Ras-2 (CNKSR2) gene were identified as the cause of Houge type of X-linked syndromic mental retardation. The mutations of CNKSR2 gene are rare, we reporta patient carrying a novel nonsense mutation of CNKSR2,c.625C > T(p.Gln209∗) and review the clinical features and mutations of CNKSR2 gene for this rare condition considering previous literature. Patient concerns: We report a case of a 7-year and 5-month-old Chinese patient with clinical symptoms of intellectual disability, language defect, epilepsy and hyperactivity. Genetic study revealed a novel nonsense variant of CNKSR2, which has not been reported yet. Diagnosis: According to clinical manifestations, genetic pattern and ACMG classification of mutation site as Class 1-cause disease, the patient was diagnosed as Houge type of X-linked syndromic mental retardation caused by CNKSR2 gene mutation. Interventions: The patient was administrated with a gradual titration of valproic acid (VPA). Outcomes: On administration of valproic acid, he had no further seizures. Lessons: This is the first time to report a nonsense variant in CNKSR2, c.625C > T(p.Gln209∗), this finding could expand the spectrum of CNKSR2 mutations and might also support the further study of Houge type of X-linked syndromic mental retardation.

AimsAtherosclerosis underlies most cardiovascular diseases, among which acute coronary syndrome (ACS) caused by plaque rupture (PR) often leads to death. Immune-related GTPases (IRGM/Irgm1) have been extensively studied in inflammatory diseases, but their role in atherosclerosis is unclear. Determining how IRGM/Irgm1 promotes atherosclerotic plaque vulnerability will provide information for new biomarkers and/or therapeutic targets.Methods and resultsWe identified ruptured and unruptured plaques by optical coherence tomography, and found that serum IRGM was highly expressed in patients with ST-segment elevation myocardial infarction in PR. We used ApoE-/-Irgm1+/+, ApoE-/-Irgm1+/- mice and chimeric mice to establish a model of advanced atherosclerosis. The results of pathological experiments showed that Irgm1 caused plaque necrosis. The ratio of neutral lipids and cholesterol crystals increases, while the content of collagen fibers decreases, aggravating the destabilization of atherosclerotic plaques. In vitro, we used multiple approaches to confirm that Irgm1 promotes macrophage apoptosis by promoting the production of reactive oxygen species and activating the MAPK signaling pathway.ConclusionsIRGM may be a potential risk factor for PR. Mechanistic studies have shown that IRGM/Irgm1 contributes to the formation and rupture of fragile plaques. This is partly mediated by the induction of macrophage apoptosis via the MAPK signaling pathway. IRGM may offer new strategies for early treatment of ACS.Translation viewOur findings indicate that IRGM/Irgm1 contributes to formation and rupture of vulnerable plaques. It suggests that IRGM may provide a new target for the early treatment of ACS.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curative therapy for most of acute myeloid leukemia (AML) patients. But relapse remains the leading cause of mortality after transplant. Ex vivo expanded allogeneic CD4- and CD8- double negative T cells (allo-DNTs) effectively target AML cells without attacking normal tissues in preclinical models. In this first-in-human phase I/IIa trial, twelve patients with relapsed AML after allo-HSCT were treated with allo-DNTs. Nine patients had hematological relapse, 2 with molecular relapse, and 1 had both molecular and extramedullary relapse. The median times from transplant to relapse and relapse to DNT therapy were 10 months (3-18 months) and 4.7 months (1.75-11.7 months), respectively. At the time of DNT-treatment, 11/12 patients had active or residual disease. After lymphodepleting preconditioning, patients received allo-DNTs in three dose cohorts (5x107-4x108/kg). None of the patients developed graft-versus-host disease or > grade 2 DNT treatment-related toxicity; hence, the maximum tolerated dose was not reached. One patient withdrew from this trial. The initial response was observed in 7 out of 11 patients, where 4 patients achieved complete remission (CR), 2 achieved CR with incomplete hematologic recovery, and 1 achieved partial remission. Five patients remain alive and 4 are in continuing CR 374 to 609 days after DNT-treatment. The predicted 1-year overall survival after DNT treatment is 45.5% (95% CI, 16.7%-70.7%). Collectively, the study supports the feasibility, safety, and potential efficacy of allo-DNTs as a new treatment option for patients with relapsing/relapsed AML after allo-HSCT. Trial Registration: The trial was registered on the Chinese Clinical Trial Registry (ChiCTR-IPR-1900022795). Funding: This work was supported by the National Natural Science Foundation of China (grant # 81670165), International Cooperation Projects in Anhui Province (grant # 1804b06020352), the Fundamental Research Funds for the Central Universities (grants # WK9110000001 and WK9110000060), the Canadian Cancer Society Impact Grant (grant # 704121), Canadian Institutes of Health Research (grant # 419699), the Leukemia & Lymphoma Society (grant #R6509-18) and WYZE Biotech Inc. Declaration of Interest: LZ is a co-inventor on several DNT cell technology related patents and intellectual properties (IPs), received a grant and consulting fees from WYZE Biotech Co. Ltd. WYZE Biotech Co. Ltd licensed the Chinese right for one of the patents; and LZ owns shares in WYZE Biotech Co. Ltd.. LY, JL, and ADS are co-inventor on DNT cell technology related patents/IPs. LY, ZX, MW and PT own shares in WYZE Biotech Co., Ltd and are employees of WYZE Biotech Co., Ltd. MISSING REMAINING AUTHORS Ethical Approval: This study protocol was approved by Chinese Ethics Committee of Registering Clinical Trials. Written informed consent was obtained from all the patients or guardians in accordance with the Declaration of Helsinki.

Additive manufacturing, including 3D printing (3DP), is one of the critical pillars of Industry 4.0 and the next construction revolution. Several countries, including China, have utilized 3DP on larger scales or real projects. However, reviews of the lessons learned from previous large-sized practices of 3DP utilization are scarce. This paper presents a few practical applications of implementing 3DP over the past decade and suggests a direction for future research work. Recent publications on 3DP practices are systematically reviewed using an interpretivist philosophical lens, and more specifically, the nozzle characteristics are focused upon. The Scopus and China National Knowledge Infrastructure (CNKI) journal databases are utilized, resulting in the examination of 54 English and 62 Chinese papers. The selected practices from Mainland China, Hong Kong, Taiwan and Macao are considered for this review. A content critical review approach is adopted, and the identified papers are critically reviewed. These papers reported key challenges and advantages from their reported practices, such as limitations in aggregate sizes, nozzle sizes, standards, post-occupancy satisfaction, final product quality, productivity challenges and other associated risks. The paper reports upon prominent limitations and signposts directions for future investigations.

Background BCL11B encodes B cell lymphoma/leukemia 11B, a transcription factor that participates in the differentiation and migration of neurons and lymphocyte cells. De novo mutations of BCL11B have been associated with neurodevelopmental disorder and immunodeficiency. These mutations were identified in patients with Immunodeficiency 49 (IMD49) or Intellectual developmental disorder with speech delay, dysmorphic facies, and T-cell abnormalities (IDDSFTA). However, the pathogenesis of these two types of neurodevelopmental disorder and T-cell deficiency is still mysterious. The strategy to distinguish these two diseases in detail is also unclear. Methods We identified a patient with unique clinical features and applied multiple examinations for evaluation. Whole-exome sequencing (WES) and Sanger sequencing were also used to determine the disease-causing mutation. Results We report a 17-month-old girl of intellectual disability with motor and speech delay. She presented dysmorphic facial features and week functional movement. MRI displayed the abnormal myelination of the white matter. Immunological analysis indicated normal levels of RTEs and γδT cells but a deficiency of naive T-cells. Sequencing analysis identified that a de novo heterozygous frameshift c.1192_1196delAGCCC presented in the BCL11B gene. Conclusions We reported the first IDDSFTA patient of East Asian origin. The unreported neurological display, immunophenotype, and a novel disease-causing mutation of the patient extended the spectrum of clinical features and genotype of IDDSFTA. Keywords: BCL11B, neurodevelopmental disease, immunodeficiency, developmental disorder, immune system abnormalities

Introduction: The impact of early-onset peritonitis (EOP) on patients with diabetes undergoing peritoneal dialysis (PD) has not been adequately addressed. We therefore sought to investigate the effects of EOP on the therapeutic response to management and long-term prognostic outcomes in patients with diabetes undergoing PD. Methods: For this retrospective cohort study, we analyzed the data for patients with end-stage renal disease, who were also suffering from diabetes mellitus and had undergone PD between January 1, 2013, and December 31, 2018. EOP was defined as the first episode of peritoneal dialysis-related peritonitis (PDAP) occurring within 12 months of PD initiation. All patients were divided into an EOP group and a later-onset peritonitis (LOP) group. Clinical data, treatment results, and outcomes were compared between groups. Results: Ultimately, 202 patients were enrolled for the analysis. Compared to the EOP group, the LOP group had more Streptococcus (p = 0.033) and Pseudomonas (p = 0.048). Patients with diabetes in the EOP group were less likely to have PDAP-related death (OR 0.13, CI: 0.02–0.82, p = 0.030). Patients with diabetes in the EOP group were more likely to have multiple episodes of PDAP and had higher rates of technical failure and poorer patient survival than those in the LOP group, as indicated by Kaplan-Meier analysis (p = 0.019, p = 0.004, and p < 0.001). In the multivariate Cox proportional hazards model, EOP was a significant predictor for multiple PDAP (HR 4.20, CI: 1.48–11.96, p = 0.007), technical failure (HR 6.37, CI: 2.21–18.38, p = 0.001), and poorer patient survival (HR 3.09, CI: 1.45–6.58, p = 0.003). Conclusions: The occurrence of EOP is significantly associated with lower rates of PDAP-related death and poorer clinical outcomes in patients with diabetes undergoing PD.

Emerging evidence has revealed widespread stroke-induced brain dysconnectivity, which leads to abnormal network organization. However, there are apparent discrepancies in dysconnectivity between structural connectivity and functional connectivity studies. In this work, resting-state fMRI and structural diffusion tensor imaging were obtained from 26 patients with subacute (10-14 days) intracerebral hemorrhage (ICH) and 20 matched healthy participants (patients/controls = 21/18 after head motion rejection). Graph theoretical approaches were applied to multimodal brain networks to quantitatively compare topological properties between both groups. Prominent small-world properties were found in the structural and functional brain networks of both groups. However, a significant deficit in global integration was revealed in the structural brain networks of the patient group and was associated with more severe clinical manifestations of ICH. Regarding ICH-related nodal deficits, reduced nodal interconnectivity was mainly detected in motor-related regions. Moreover, in the functional brain network, topological properties were mostly comparable between patients with ICH and healthy participants. Beyond the prominent small-world architecture in multimodal brain networks, there are dissociable alterations between structural and functional brain networks in patients with ICH. These findings highlight the potential for using aberrant network metrics as neural biomarkers for evaluation of the severity of ICH. Graphical abstract Intracerebral hemorrhage (ICH) also known as cerebral bleed, a major type of stroke, would significantly affect brain structure and function. Using multimodal neuroimaging, Zhang et al. investigate the ICH-related dysconnectivity in structural and functional brain networks and show a significantly disintegrated structural brain network with a preserved functional network topology in subacute phase (10-14 days).

Based on minimizing misclassification cost, a new robust loss function is designed in this paper to deal with the imbalanced classification problem under noise environment. It is nonconvex but maintains Fisher consistency. Applying the proposed loss function into support vector machine (SVM), a robust SVM framework is presented which results in a Bayes optimal classifier. However, nonconvexity makes the model difficult to optimize. We develop an alternative iterative algorithm to solve the proposed model. What’s more, we analyze the robustness of the proposed model theoretically from a re-weighted SVM viewpoint and the obtained optimal solution is consistent with Bayesian optimal decision rule. Furthermore, numerical experiments are carried out on databases that are drawn from UCI Machine Learning Repository and a practical application. With two different types of noise environments, one with label noise and one with feature noise, experiment results show that on these two databases the proposed method achieves better generalization results compared to other SVM methods.

Objectives: This study aimed to investigate the influence of neutrophil-to-lymphocyte ratio (NLR) on the severity and short-time curative effect of myasthenia gravis (MG) in children.Methods: Data of 132 MG children were retrospectively analyzed, and data of 140 healthy controls (HC group) in the same period were collected. The data of both groups were compared and analyzed.Results: NLR of MG group was significantly higher than that of HC group (Z = 2.644, p = 0.008). According to NLR level, patients were divided into 3 groups: N1 (NLR 2.17). Significant differences in white blood cell counts, course of disease, uric acid, albumin and the time of hospital stay among the 3 groups were observed (p < 0.05, 0.01). The results of logistic regression revealed that NLR (adjusted OR = 3.874, 95% CI 1.359-11.045, p = 0.011) was the risk factor of MG, and it was risk factor of higher QMG during admission (adjusted OR = 2.989, 95% CI 1.247-7.160, p = 0.014) as well. Using the NLR level for the MG diagnostic test, the area under the receiver operating characteristic (ROC) curve was 0.765 [95%CI (0.710-0.820), p = 0.000], with a cut-off value of 1.39, sensitivity of 0.833, and specificity of 0.479. Cox regression analysis suggested that NLR (N1: Wald = 9.262, p = 0.010, N2: HR = 12.267, 95%CI 2.432-61.863, p = 0.000, and N3: HR = 8.142, 95%CI 1.209-77.754, p = 0.032) was associated with poor efficacy at discharge. Elevated NLR was considered as an independent risk factor of poor outcomes during discharge.Conclusion: NLR could reflect disease severity and short time curative effect in children with MG to some extent. It may also be a potential marker in indicating diagnosis and severity of MG in children.

Correntropy is a local similarity measure between two arbitrary variables, and it has been applied in a variety of learning algorithms to improve noise insensitivity. In this paper, based on the correntropy, a non-convex and bounded loss function is obtained which contains second and higher order moments of the classification margin. And the novel loss function is robust to noises and close to the 0–1 loss function. Then we introduce it into extreme learning machine (ELM), and propose a correntropy-based robust ELM framework for classification, trained by half quadratic optimization to cope with non-convexity of the algorithm. To evaluate robustness, feature noise and label noise are simulated to provide noisy environments. Experimental results on benchmark datasets demonstrate that the proposed algorithm is better than original algorithms and robust algorithms. Moreover, the superiority of proposed algorithm in noisy environment is more evident, which further proves its robustness to noises.

Two extreme learning machine (ELM) frameworks are proposed to handle supervised and semi-supervised classifications. The first is called lagrangian extreme learning machine (LELM), which is based on optimality conditions and dual theory. Then LELM is extended to semi-supervised setting to obtain a semi-supervised extreme learning machine (called Lap-LELM), which incorporates the manifold regularization into LELM to improve performance when insufficient training information is available. In order to avoid the inconvenience caused by matrix inversion, Sherman-Morrison-Woodbury (SMW) identity is used in LELM and Lap-LELM, which leads to two smaller sized unconstrained minimization problems. The proposed models are solvable in a space of dimensionality equal to the number of sample points. The resulting iteration algorithms converge globally and have low computational burden. So as to verify the feasibility and effectiveness of the proposed method, we perform a series of experiments on a synthetic dataset, near-infrared (NIR) spectroscopy datasets and benchmark datasets. Compared with the traditional methods, experimental results demonstrate that the proposed methods achieve better performances than the traditional supervised and semi-supervised methods in most of considered datasets.

Extreme learning machine (ELM) has demonstrated great potential in machine learning owing to its simplicity, rapidity and good generalization performance. However, the traditional ELM is sensitive to noise and outliers due to using traditional least square loss function. In this paper, we present a new mixed loss function from a combination of pinball loss and least square loss. Then three robust ELM frameworks are proposed based on rescaled hinge loss function, pinball loss function and mixed loss function respectively to enhance noise robustness. To train the proposed ELM with rescaled hinge loss, the half-quadratic optimization algorithm is used to handle nonconvexity, and we demonstrate the convergence of the resulting algorithm. Furthermore, the proposed methods are applied to various datasets including classification data and regression data, with different types of noises such as feature noise and target noise. Compared with traditional methods, experiment results on UCI benchmark datasets show that the proposed methods are less sensitive to noises and achieve better performance in classification and regression applications.

Nonalcoholic fatty liver disease (NAFLD) is a worldwide chronic liver disease. Few studies have investigated the association between NAFLD and Lipoprotein-associated phospholipase A2 (Lp-PLA2), a unique enzyme correlated with oxidative stress. The aim of this study was to assess the relationship between Lp-PLA2 and NAFLD in a Chinese community-based cohort. A total of 1587 adults aged ≥40 years were enrolled in the current study. Participants underwent a standardized evaluation. The serum Lp-PLA2 concentration was measured by ELISA and NAFLD was diagnosed by ultrasonography. Multivariable logistic regression was used to assess the association between Lp-PLA2 and NAFLD. Increased Lp-PLA2 levels were significantly associated with decreased NAFLD prevalence after adjusting for other potential confounders. The adjusted ORs of NAFLD in Q2, Q3 and Q4 compared with Q1 were 0.88 (0.64–1.21), 0.71 (0.51–0.98) and 0.67 (0.48–0.95), respectively (P

Support vector machine(SVM) with pinball loss(PINSVM) has been recently proposed and shown its advantages in pattern recognition. In this paper, we present a robust bounded loss function (called L t -loss) that truncates pinball loss function. Then a novel robust SVM formulation with L t -loss(called TPINSVM) is proposed to enhance noise robustness. Moreover, we demonstrate that the proposed TPINSVM satisfies Bayes rule and it has a certain sparseness. However, the non-convexity of the proposed TPINSVM makes it difficult to optimize. We develop a continuous optimization method, DC(difference of convex functions) programming method, to solve the proposed TPINSVM. The resulting DC optimization algorithm converges finitely. Furthermore, the proposed TPINSVM is directly applied to recognize the purity of hybrid maize seeds using near-infrared spectral data. Experiments show that the proposed method achieves better performance than the traditional methods in most spectral regions. Meanwhile we simulate the proposed TPINSVM in benchmark datasets in different situations. In noiseless setting, the proposed TPINSVM either improves or shows no significant difference in generalization compared to the traditional approaches. While in noise situations, TPINSVM improves generalization in most cases.

Uncertain or missing data may occur in many practical applications. A principled strategy for handling this problem would therefore be very useful. We consider two-class and multi-class classification problems where the mean and covariance of each class are assumed to be known. With simple structure, fast speed and good performance, extreme learning machine (ELM) has been an important technology in machine learning. In this work, from the viewpoint of probability, we present a robust ELM framework (RELM) for missing data classification. Applying the Chebyshev–Cantelli inequality, the proposed RELM is reformulated as a second-order cone programming with global optimal solution. The proposed RELM only relates to the second moments of input samples and makes no assumption about the data probability distribution. Expectation maximization algorithm is used to fill in missing values and then obtain complete data. Numerical experiments are simulated in various datasets from UCI database and a practical application database. Experimental results show that the proposed method can achieve better performance than traditional methods. These results illustrate the feasibility and effectiveness of the proposed method for missing data classification.

Insulin-induced hypoglycemia in diabetes is associated with impaired glucagon secretion. In this study, we tested whether stimulation of GPR119, a G-protein–coupled receptor expressed in pancreatic islet as well as enteroendocrine cells and previously shown to stimulate insulin and incretin secretion, might enhance glucagon secretion during hypoglycemia. In the study, GPR119 agonists were applied to isolated islets or perfused pancreata to assess insulin and glucagon secretion during hypoglycemic or hyperglycemic conditions. Insulin infusion hypoglycemic clamps were performed with or without GPR119 agonist pretreatment to assess glucagon counterregulation in healthy and streptozotocin (STZ)-induced diabetic rats, including those exposed to recurrent bouts of insulin-induced hypoglycemia that leads to suppression of hypoglycemia-induced glucagon release. Hypoglycemic clamp studies were also conducted in GPR119 knockout (KO) mice to evaluate whether the pharmacological stimulatory actions of GPR119 agonists on glucagon secretion during hypoglycemia were an on-target effect. The results revealed that GPR119 agonist-treated pancreata or cultured islets had increased glucagon secretion during low glucose perfusion. In vivo, GPR119 agonists also significantly increased glucagon secretion during hypoglycemia in healthy and STZ-diabetic rats, a response that was absent in GPR119 KO mice. In addition, impaired glucagon counterregulatory responses were restored by a GPR119 agonist in STZ-diabetic rats that were exposed to antecedent bouts of hypoglycemia. Thus, GPR119 agonists have the ability to pharmacologically augment glucagon secretion, specifically in response to hypoglycemia in diabetic rodents. Whether this effect might serve to diminish the occurrence and severity of iatrogenic hypoglycemia during intensive insulin therapy in patients with diabetes remains to be established.

SUMMARY Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders., In Brief Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines.

A new method is proposed to calibrate the gain ratio for polarization lidars in this paper, which involves a half-wave plate (HWP) placed immediately upstream of the polarizing beam splitter (PBS) in the receiver of polarization lidars. Gain ratio can be determined by rotating the HWP by 45° without key assumptions. The calibration results of this new method are independent of the monitored atmosphere as well as the system errors related to the components that are upstream of the HWP. Both theoretical calculations in the framework of the Mueller–Stokes formalism and experimental measurements are carried out to validate the calibration method.

Whole slide imaging (WSI) has moved the traditional manual slide inspection process to the era of digital pathology. A typical WSI system translates the sample to different positions and captures images using a high numerical aperture (NA) objective lens. Performing oil-immersion microscopy is a major obstacle for WSI as it requires careful liquid handling during the scanning process. Switching between dry objective and oil-immersion lens is often impossible as it disrupts the acquisition process. For a high-NA objective lens, the sub-micron depth of field also poses a challenge to acquiring in-focus images of samples with uneven topography. Additionally, it implies a small field of view for each tile, thus limiting the system throughput and resulting in a long acquisition time. Here we report a deep learning-enabled WSI platform, termed DeepWSI, to substantially improve the system performance and imaging throughput. With this platform, we show that images captured with a regular dry objective lens can be transformed into images comparable to that of a 1.4-NA oil immersion lens. Blurred images with defocus distance from −5 µm to +5 µm can be virtually refocused to the in-focus plane post measurement. We demonstrate an equivalent data throughput of >2 gigapixels per second, the highest among existing WSI systems. Using the same deep neural network, we also report a high-resolution virtual staining strategy and demonstrate it for Fourier ptychographic WSI. The DeepWSI platform may provide a turnkey solution for developing high-performance diagnostic tools for digital pathology.

The rapid urbanisation and infrastructure development projects have increased infinite property regeneration and construction projects in the developed countries. This study aims to examine the current construction waste management literature, including key authors, university networks, and relevant information systems, considering four selected countries, including Australia, Canada, the United Kingdom, and the United States. This paper uses a set of novel metrics, query-based search, and social network analysis techniques for evaluating a dataset of 2337 papers published in a decade by authors from different countries investigating the topic of recycling and reuse of construction and demolition waste in order to identify prominent researchers, influential universities, collaboration practices, and research trends. The dataset of publications is collected from Scopus and analysed using a set of network analysis techniques and statistical analysis. An additional dataset of keywords was collected from the Twitter pages of engineering firms and other relevant industry organisations to assess any possible connection between industry interests and research trends. In order to present a solid analysis of the current investigations in the field, a scientometric analysis along with a rigor statistical method was adopted to carefully identify trends, detailed sub-topics, and the materials investigated in the literature. A probit regression model for testing differences between countries in recycling practices was utilised in the STATA environment. The findings show a set of network analysis and clusters that can help scholars to set their future studies based on the current advances and limitations identified in the literature. Within the dataset, 42% of publications referred to concrete waste, whilst only 2.2% included glass, 2.5% included brick, 4.3% included steel and none included wood, which shows the most common building materials have not been fully covered in the literature. Only 48 publications, or 7.4% of the dataset, originated from the three selected concrete-focused journals. It was also found that information systems appeared in the database lately; however, there is less discussion in the literature on how the integration of Geographic Information System (GIS), Buidling Information Model (BIM), 3D Printing and geo-spatial data analysis can help waste management in the field of construction. The bibliographic analysis of the selected cases shows that universities in Canada and the US exhibit more evidence of domestic collaboration and industry partnerships than universities in Australia and the UK. Additionally, there is evidence to suggest that publications with at least one industry co-author are more likely to address the issue of regulation. The outcome of this paper enables academics, engineers, and regulators to predict research trends, improve industry collaborations and utilise information systems and assign more research resources to address the identified gaps in the literature.

Background and objective Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 from community-acquired pneumonia (CAP) is urgently needed. However, the spatial uncertainty and morphological diversity of COVID-19 lesions in the lungs, and subtle differences with respect to CAP, make differential diagnosis non-trivial. Methods We propose a deep represented multiple instance learning (DR-MIL) method to fulfill this task. A 3D volumetric CT scan of one patient is treated as one bag and ten CT slices are selected as the initial instances. For each instance, deep features are extracted from the pre-trained ResNet-50 with fine-tuning and represented as one deep represented instance score (DRIS). Each bag with a DRIS for each initial instance is then input into a citation k-nearest neighbor search to generate the final prediction. A total of 141 COVID-19 and 100 CAP CT scans were used. The performance of DR-MIL is compared with other potential strategies and state-of-the-art models. Results DR-MIL displayed an accuracy of 95% and an area under curve of 0.943, which were superior to those observed for comparable methods. COVID-19 and CAP exhibited significant differences in both the DRIS and the spatial pattern of lesions (p

We investigated the clinical value of fractional flow reserve (FFR) for the prognostic evaluation of patients with percutaneous coronary intervention. We enrolled 120 patients who were admitted to the hospital to undergo percutaneous coronary intervention for acute coronary syndromes between May 2014 and June 2015. The 120 patients were divided into two groups, the observation group and the control group, according to the post-surgery level of FFR. Each cohort contained 60 patients. These patients were divided into the occurrence group (n=45) and the non-occurrence group (n=75), classified according to the occurrence of major adverse cardiovascular events (MACE). There were no statistically significant differences in the comparison of the occurrence rate of MACE within 30 days after surgery, such as lethal or non-lethal myocardial infarction or non-lethal cerebrovascular events, between the observational group and the control group (P>0.05). For the observation group, the 1-year survival cases were 56 with a survival rate of 93.3%, and 2-year survival cases were 50 with a survival rate of 83.3%. In the control group, the 1-year and 2-year survival cases were respectively 55 and 49 (survival rate of 91.7 and 81.7%). The occurrence rates of hyperlipidemia and ratio of patients with a history of smoking and drinking in the occurrence group were significantly higher than those in the non-occurrence group (P0.05), but the post-treatment FFR in the occurrence group was significantly lower than that in the non-occurrence group (P