SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets

Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering. Existing time series clustering methods may fail to capture representative shapelets because they discover shapelets from a large pool of uninformative subsequences, and thus result in low clustering accuracy. This paper proposes a Semi-supervised Clustering of Time Series Using Representative Shapelets (SE-Shapelets) method, which utilizes a small number of labeled and propagated pseudo-labeled time series to help discover representative shapelets, thereby improving the clustering accuracy. In SE-Shapelets, we propose two techniques to discover representative shapelets for the effective clustering of time series. 1) A \textit{salient subsequence chain} ($SSC$) that can extract salient subsequences (as candidate shapelets) of a labeled/pseudo-labeled time series, which helps remove massive uninformative subsequences from the pool. 2) A \textit{linear discriminant selection} ($LDS$) algorithm to identify shapelets that can capture representative local features of time series in different classes, for convenient clustering. Experiments on UCR time series datasets demonstrate that SE-shapelets discovers representative shapelets and achieves higher clustering accuracy than counterpart semi-supervised time series clustering methods

Neural-PBIR Reconstruction of Shape, Material, and Illumination

Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce a robust object reconstruction pipeline combining neural based object reconstruction and physics-based inverse rendering (PBIR). Specifically, our pipeline firstly leverages a neural stage to produce high-quality but potentially imperfect predictions of object shape, reflectance, and illumination. Then, in the later stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction. Experimental results demonstrate our pipeline significantly outperforms existing reconstruction methods quality-wise and performance-wise

Identification and validation of the diagnostic signature associated with immune microenvironment of acute kidney injury based on ferroptosis-related genes through integrated bioinformatics analysis and machine learning

Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning.Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets.Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury.Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature’s versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment

Potential Blood Pressure Goals in IgA Nephropathy: Prevalence, Awareness, and Treatment Rates in Chronic Kidney Disease Among Patients with Hypertension in China (PATRIOTIC) Study

Background/Aims: IgA nephropathy is the most prevalent form of primary glomerulonephritis worldwide. Among patients with kidney disease, hypertension is one of the most important risk factors of disease progression. Considering the limited evidence regarding the appropriate blood pressure (BP) goal for patients with IgA nephropathy, our aim was to critically appraise the potential BP goal in IgA nephropathy. Methods: We performed a retrospective analysis of the BP data from 1055 patients with IgA nephropathy, extracted from the database of a nationwide, multi-center, cross-sectional study, including 61 tertiary hospitals in China. Hypertension was defined by a BP ≥140/90 mmHg. Three BP cutoff levels were evaluated as control values: < 140/90 mmHg, < 130/80 mmHg and < 125/75 mmHg. The primary outcome of our study was the prevalence of BP control among patients with a 24-h proteinuria < 1 g/d or ≥ 1 g/d. Multivariate logistic regression analysis was used to identify demographic and clinical factors associated with a decrease in renal function for the different target levels of BP. Results: The overall prevalence of hypertension was 63.3%. BP was controlled under 140/90 mmHg in 49.1% of patients, with 34.3% of patients with proteinuria < 1 g/d reaching the target BP < 130/80 mmHg and only 12.9% of patients with proteinuria > 1 g/d achieving a BP < 125/75 mmHg. Among patients with proteinuria < 1 g/d, the adjusted odds ratios (OR) and 95% confidence interval (95% CI) of a decrease in renal function, for the 3 target BP levels, were as follows (P > 0.05): < 140/90 mmHg, 0.9 (0.5 - 1.6); < 130/80 mmHg, 1.0 (0.5 - 1.8); and < 125/75 mmHg, 1.0 (0.5 - 2.0). With proteinuria ≥1 g/d, the adjusted ORs (95%CI) of attaining the BP targets of < 140/90 mmHg, < 130/80 mmHg and < 125/75 mmHg were 0.4 (0.2 - 0.6), 0.2 (0.1 - 0.4) and 0.3 (0.1 - 0.5), respectively (P < 0.05). Conclusion: Hypertension was common in IgA nephropathy and hypertensive control was suboptimal. Our result supports a benefit of intensive control of BP < 130/80 mmHg for patients with proteinuria ≥1 g/d. However, in patients with proteinuria < 1 g/d, a renoprotective effect of this BP goal was not identified

Stability of important antibodies for kidney disease: pre-analytic methodological considerations

Background The importance of circulating antibodies as biomarkers of kidney disease has recently been recognized. However, no study has systematically described the methodology of sample preparation and storage regarding antibodies as biomarkers of kidney disease. It remains unknown whether repetitive freeze-thaw cycles, physical disturbances, storage at different temperatures or for different periods of time, or haemolytic or turbid serum samples affect antibody measurements. The aim of this study was to investigate the stabilities of antibodies associated with kidney disease in serum samples under various relevant clinical and research conditions. Methods We stored serum samples in the following different conditions: repetitive freeze-thaw cycles (1, 6 or 12 times), long-term storage (7 or 12 months at −80 °C), physical disturbance (1 or 8 h), and storage at 4 °C (1, 3 or 6 weeks) and room temperature (1 or 7 days). The stabilities of the anti-phospholipase A2 receptor (anti-PLA2R), anti-glomerular basement membrane, anti-myeloperoxidase and anti-proteinase 3 antibodies were evaluated with enzyme-linked immunosorbent assays (ELISA). Results We found that repetitive freeze-thaw cycles did not have a significant effect on the stabilities of the abovementioned antibodies in clear serum samples. The ELISA readings of haemolytic and turbid serum samples tended to increase and decrease, respectively. Neither long-term storage at −80 °C nor physical disturbance had a significant effect on anti-PLA2R antibody stability in sealed serum samples. The concentrations of most of these antibodies increased in unsealed serum samples that were stored at 4 °C for more than 6 weeks or at room temperature for more than 7 days. Discussion Our findings revealed that the abovementioned circulating antibodies that are used as biomarkers for kidney disease had stable physicochemical properties, structures and immunoreactivities such that they were not influenced by repetitive freeze-thaw cycles, physical disturbances or long-term storage at −80 °C. However, the ELISA readings tended to change for haemolytic, turbid and unsealed serum samples

Progress in the study of aging marker criteria in human populations

The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context

Validity and applicability of the global leadership initiative on malnutrition criteria in non-dialysis patients with chronic kidney disease

IntroductionThere are no standardized assessment criteria for selecting nutritional risk screening tools or indicators to assess reduced muscle mass (RMM) in the Global Leadership Initiative on Malnutrition (GLIM) criteria. We aimed to compare the consistency of different GLIM criteria with Subjective Global Assessment (SGA) and protein-energy wasting (PEW).MethodsIn this study, nutritional risk screening 2002 first four questions (NRS-2002-4Q), Nutritional Risk Screening 2002 (NRS-2002), Malnutrition Universal Screening Tool (MUST), and Mini-Nutritional Assessment Short-Form (MNA-SF) tools were used as the first step of nutritional risk screening for the GLIM. The RMM is expressed using different metrics. The SGA and PEW were used to diagnose patients and classify them as malnourished and non-malnourished. Kappa (κ) tests were used to compare the concordance between the SGA, PEW, and GLIM of each combination of screening tools.ResultsA total of 157 patients were included. Patients with Chronic kidney disease (CKD) stage 1–3 accounted for a large proportion (79.0%). The prevalence rates of malnutrition diagnosed using the SGA and PEW were 18.5% and 19.7%, respectively. The prevalence of GLIM-diagnosed malnutrition ranges from 5.1% to 37.6%, depending on the different screening methods for nutritional risk and the different indicators denoting RMM. The SGA was moderately consistent with the PEW (κ = 0.423, p < 0.001). The consistency among the GLIM, SGA, and PEW was generally low. Using the NRS-2002-4Q to screen for nutritional risk, GLIM had the best agreement with SGA and PEW when skeletal muscle index (SMI), fat-free mass index (FFMI), and hand grip strength (HGS) indicated a reduction in muscle mass (SGA: κ = 0.464, 95% CI 0.28–0.65; PEW: κ = 0.306, 95% CI 0.12–0.49).ConclusionThe concordance between the GLIM criteria and the SGA and PEW depended on the screening tool used in the GLIM process. The inclusion of RMM in the GLIM framework is important. The addition of HGS could further improve the performance of the GLIM standard compared to the use of body composition measurements

Arabidopsis CSLD1 and CSLD4 are required for cellulose deposition and normal growth of pollen tubes

The cell wall is important for pollen tube growth, but little is known about the molecular mechanism that controls cell wall deposition in pollen tubes. Here, the functional characterization of the pollen-expressed Arabidopsis cellulose synthase-like D genes CSLD1 and CSLD4 that are required for pollen tube growth is reported. Both CSLD1 and CSLD4 are highly expressed in mature pollen grains and pollen tubes. The CSLD1 and CSLD4 proteins are located in the Golgi apparatus and transported to the plasma membrane of the tip region of growing pollen tubes, where cellulose is actively synthesized. Mutations in CSLD1 and CSLD4 caused a significant reduction in cellulose deposition in the pollen tube wall and a remarkable disorganization of the pollen tube wall layers, which disrupted the genetic transmission of the male gametophyte. In csld1 and csld4 single mutants and in the csld1 csld4 double mutant, all the mutant pollen tubes exhibited similar phenotypes: the pollen tubes grew extremely abnormally both in vitro and in vivo, which indicates that CSLD1 and CSLD4 are not functionally redundant. Taken together, these results suggest that CSLD1 and CSLD4 play important roles in pollen tube growth, probably through participation in cellulose synthesis of the pollen tube wall