s-LWSR: Super Lightweight Super-Resolution Network

Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance. However, with the widespread use of mobile phones for taking and retouching photos, this character greatly hampers the deployment of DL-SR models on the mobile devices. To address this problem, in this paper, we propose a super lightweight SR network: s-LWSR. There are mainly three contributions in our work. Firstly, in order to efficiently abstract features from the low resolution image, we build an information pool to mix multi-level information from the first half part of the pipeline. Accordingly, the information pool feeds the second half part with the combination of hierarchical features from the previous layers. Secondly, we employ a compression module to further decrease the size of parameters. Intensive analysis confirms its capacity of trade-off between model complexity and accuracy. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information for performance improvement. Extensive experiments show that our s-LWSR, with limited parameters and operations, can achieve similar performance to other cumbersome DL-SR methods

Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition

Automated medical image classification is the key component in intelligent diagnosis systems. However, most medical image datasets contain plenty of samples of common diseases and just a handful of rare ones, leading to major class imbalances. Currently, it is an open problem in intelligent diagnosis to effectively learn from imbalanced training data. In this paper, we propose a simple yet effective framework, named \textbf{C}lass \textbf{A}ttention to \textbf{RE}gions of the lesion (CARE), to handle data imbalance issues by embedding attention into the training process of \textbf{C}onvolutional \textbf{N}eural \textbf{N}etworks (CNNs). The proposed attention module helps CNNs attend to lesion regions of rare diseases, therefore helping CNNs to learn their characteristics more effectively. In addition, this attention module works only during the training phase and does not change the architecture of the original network, so it can be directly combined with any existing CNN architecture. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. To alleviate the need for manual annotation, we further developed variants of CARE by leveraging the traditional saliency methods or a pretrained segmentation model for bounding box generation. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework with \textit{manual} bounding box annotations. A series of experiments on an imbalanced skin image dataset and a pneumonia dataset indicates that our method can effectively help the network focus on the lesion regions of rare diseases and remarkably improves the classification performance of rare diseases.Comment: Accepted by Neurocomputing on July 2023. 37 page

Multi-influence factor prediction for water bloom based on multi-sensor system

This paper proposes a new multi-influence factors prediction method for water bloom prediction based on a remote monitor system and multi-sensor data taking into account the integrated effect of multiple influential factors along with the periodicity and random effect of environmental variables. Valid and accurate water-bloom prediction can be obtained by combining various multidimensional time series methods. Comparing the proposed model based on multi-sensors data to a traditional one-dimensional time series model based on one-sensor data, it has been found that a multidimensional model is a useful and accurate model for establishing multiple influential factors time series of water bloom. The optimum model can be used not only to predict water bloom but also to determine the period and random change rule of multiple influential factors

A multi-objective control strategy for three phase grid-connected inverter during unbalanced voltage sag

This paper presents a new multi-objective control strategy for inverter-interfaced distributed generation (IIDG) to ensure its safe and continuous operation under unbalanced voltage sags. The proposed control strategy can effectively improve the low voltage ride through (LVRT) capability, reduce active power oscillations, and limit overcurrent simultaneously, which are marked as the most important control objectives of IIDG during unbalanced voltage sags. The advanced voltage support scheme, which utilizes positive sequence component, is firstly proposed to maximize the LVRT capability of IIDG during unbalanced voltage sags. Then, to ensure the safety of IIDG, the active power oscillation suppression and current limitation algorithm are designed individually. Based on the control algorithms of such objectives, the multi-objective control method, including scenario classification and reference current determination, is then presented to achieve such three objectives under various system conditions simultaneously. Finally, case studies and evaluations based on MATLAB/Simulink are carried out to illustrate the effectiveness of the proposed method

Evaluation of the neonatal sequential organ failure assessment and mortality risk in neonates with respiratory distress syndrome: A retrospective cohort study

BackgroundRespiratory distress syndrome (RDS) is one of the leading causes of neonatal death in the neonatal intensive care unit (NICU). Previous studies have suggested that the development of neonatal RDS may be associated with inflammation and lead to organ dysfunction. The neonatal sequential organ failure assessment (nSOFA) scoring system is an operational definition of organ dysfunction, but whether it can be used to predict mortality in neonates RDS is unknown. The aim of this study was to clarify the performance of the nSOFA score in predicting mortality in patients with neonatal RDS, with the aim of broadening the clinical application of the nSOFA score.MethodsNeonates with RDS were identified from the Medical Information Mart for Intensive Care (MIMIC)-III database. Cox proportional hazards model were used to assess the association between nSOFA score and mortality. Propensity score matched analysis were used to assess the robustness of the analytical results.ResultsIn this study of 1,281 patients with RDS of which 57.2% were male, death occurred in 40 cases (3.1%). Patients with high nSOFA scores had a higher mortality rate of 10.7% compared with low nSOFA scores at 0.3%. After adjusting for confounding, multivariate Cox proportional risk analysis showed that an increase in nSOFA score was significantly associated with increased mortality in patients with RDS [adjusted Hazards Ratio (aHR): 1.48, 95% Confidence Interval (CI): 1.32–1.67; p < 0.001]. Similarly, the High nSOFA group was significantly associated with higher mortality in RDS patients (aHR: 19.35, 95% CI: 4.41–84.95; p < 0.001) compared with the low nSOFA group.ConclusionThe nSOFA score was positively associated with the risk of mortality in cases of neonatal RDS in the NICU, where its use may help clinicians to quickly and accurately identify high risk neonates and implement more aggressive intervention

N‑Linked Glycosylation Prevents Deamidation of Glycopeptide and Glycoprotein

Deamidation has been recognized as a common spontaneous pathway of protein degradation and a prevalent concern in the pharmaceutical industry; deamidation caused the reduction of protein/peptide drug efficacy and shelf life in several cases. More importantly, deamidation of physiological proteins is related to several human diseases and considered a timer for the diseases. N-linked glycosylation has a variety of significant biological functions, and it interestingly occurs right on the deamidation site-asparagine. It has been perceived that N-glycosylation could prevent deamidation, but experimental support is still lacking for clearly understanding the role of N-glycosylation on deamidation. Our results presented that deamidation is prevented by naturally occurring N-linked glycosylation. Glycopeptides and corresponding nonglycosylated peptides were used to compare their deamidation rates. All the nonglycosylated peptides have different half-lives ranging from one to 20 days, for the corresponding glycosylated peptides; all the results showed that the deamidation reaction was significantly reduced by the introduction of N-linked glycosylation. A glycoprotein, RNase B, also showed a significantly elongated deamidation half-life compared to nonglycosylated protein RNase A. At last, N-linked glycosylation on INGAP-P, a therapeutic peptide, increased the deamidation half-life of INGAP-P as well as its therapeutic potency

A new control method for three phase inverters under unsymmetrical voltage sag conditions

Under grid fault conditions, especially the unbalanced grid faults, the PCC voltage of DG will suffer notably unbalanced voltage droops, which may cause the unnecessary disconnection of DGs according to the grid codes. Moreover, the overcurrent risk during voltage sag will also result in the disconnection of DGs, and even damage the inverter. In this paper, a new fault control strategy including three control objectives, was proposed to enhance the low-voltage ride-through (LVRT) capability for three-phase inverters. Firstly, the positive sequence (PS) voltage method is proposed to maximize the voltage support capability in any types of unbalanced voltage sags. As to ensure the safe operation of the inverter, a current limitation algorithm is designed based on different operation scenarios. Also, the active power delivery is considered as an ancillary service to fully use the capacity of the inverter. Then, a new control method towards the scenario classification and reference current selection is proposed to simultaneously achieve these control objectives. Finally, the simulation results based on MATLAB/Simulink are presented to verify the effectiveness of the proposed fault control strategy

Secondary infection with Streptococcus suis serotype 7 increases the virulence of highly pathogenic porcine reproductive and respiratory syndrome virus in pigs

<p>Abstract</p> <p>Background</p> <p>Porcine reproductive and respiratory syndrome virus (PRRSV) and <it>Streptococcus suis </it>are common pathogens in pigs. In samples collected during the porcine high fever syndrome (PHFS) outbreak in many parts of China, PRRSV and <it>S. suis </it>serotype 7 (SS7) have always been isolated together. To determine whether PRRSV-SS7 coinfection was the cause of the PHFS outbreak, we evaluated the pathogenicity of PRRSV and/or SS7 in a pig model of single and mixed infection.</p> <p>Results</p> <p>Respiratory disease, diarrhea, and anorexia were observed in all infected pigs. Signs of central nervous system (CNS) disease were observed in the highly pathogenic PRRSV (HP-PRRSV)-infected pigs (4/12) and the coinfected pigs (8/10); however, the symptoms of the coinfected pigs were clearly more severe than those of the HP-PRRSV-infected pigs. The mortality rate was significantly higher in the coinfected pigs (8/10) than in the HP-PRRSV- (2/12) and SS7-infected pigs (0/10). The deceased pigs of the coinfected group had symptoms typical of PHFS, such as high fever, anorexia, and red coloration of the ears and the body. The isolation rates of HP-PRRSV and SS7 were higher and the lesion severity was greater in the coinfected pigs than in monoinfected pigs.</p> <p>Conclusion</p> <p>HP-PRRSV infection increased susceptibility to SS7 infection, and coinfection of HP-PRRSV with SS7 significantly increased the pathogenicity of SS7 to pigs.</p

The association between Toll-like receptor 2 single-nucleotide polymorphisms and hepatocellular carcinoma susceptibility

<p>Abstract</p> <p>Background</p> <p>Toll-like receptors (TLR) are key innate immunity receptors participating in an immune response. Growing evidence suggests that mutations of TLR2/TLR9 gene are associated with the progress of cancers. The present study aimed to investigate the temporal relationship of single nucleotide polymorphisms (SNP) of TLR2/TLR9 and the risk of hepatocellular carcinoma (HCC).</p> <p>Methods</p> <p>In this single center-based case-control study, SNaPshot method was used to genotype sequence variants of TLR2 and TLR9 in 211 patients with HCC and 232 subjects as controls.</p> <p>Results</p> <p>Two synonymous SNPs in the exon of TLR2 were closely associated with risk of HCC. Compared with those carrying wild-type homozygous genotypes (T/T), risk of HCC decreased significantly in individuals carrying the heterozygous genotypes (C/T) of the rs3804099 (adjusted odds ratio (OR), 0.493, 95% CI 0.331 - 0.736, <it>P </it>< 0.01) and rs3804100 (adjusted OR, 0.509, 95% CI 0.342 - 0.759, <it>P </it>< 0.01). There was no significant association found in two TLR9 SNPs concerning the risk of HCC. The haplotype TT for TLR2 was associated significantly with the decreased risk of HCC (OR 0.524, 95% CI 0.394 - 0.697, <it>P </it>= 0.000). Inversely, the risk of HCC increased significantly in patients with the haplotype CC (OR 2.743, 95% CI 1.915 - 3.930, <it>P </it>= 0.000).</p> <p>Conclusions</p> <p>These results suggested that TLR2 rs3804099 C/T and rs3804100 C/T polymorphisms were closely associated with HCC. In addition, the haplotypes composed of these two TLR2 synonymous SNPs have stronger effects on the susceptibility of HCC.</p