See article vol.29: 1791-1807 In this issue of the Journal of Atherosclerosis and Thrombosis, Umishio et al. report the association between indoor temperature in winter and serum cholesterol based ...upon a cross-sectional analysis of the Smart Wellness Housing Survey in Japan. The indoor temperatures of 2004 participants (1333 households) were measured for 2 weeks in winter, and they were divided into three categories according to average bedroom temperature: 206 warm houses (>-18℃), 940 slightly cold houses (12℃-18℃) and 858 cold houses (<12℃). Compared to the participants in warm houses, the odds ratio of total cholesterol (TC) >220mg/dL was 1.83 (95% confidence interval CI: 1.23-2.71) for slightly cold houses and 1.87 (95% CI: 1.25-2.80) for cold houses. The associations between the bedroom temperature and low-density lipoprotein cholesterol or non-high-density lipoprotein cholesterol were similar to the results for TC.
The effect of sodium chloride on the chemical alteration of calcium silicate hydrate (C–S–H) was measured and discussed. The release of calcium from C–S–H was increased as the concentration of sodium ...chloride in the solution increased. It was observed that sodium sorbed onto the C–S–H phases and some sodium replaced calcium in C–S–H so that the release of calcium was enhanced. An integrated modelling approach employing an ion-exchange model and an incongruent dissolution model of C–S–H is developed. It reasonably and accurately predicted the release of calcium from C–S–H in sodium chloride solution by considering cation exchange and the effect of the ionic strength on the solubility of C–S–H.
Objective
To examine cancer incidence in patients with systemic sclerosis (SSc) through a meta‐analysis of population‐based cohort studies.
Methods
Five different databases (Medline, Scopus, CINAHL ...Cumulative Index to Nursing and Allied Health Literature, Web of Science, and Cochrane Collaboration) were searched for articles published between January 1966 and May 2012; review articles and the reference lists from the articles that resulted from the search were also evaluated. Population‐based cohort studies with data relevant to the determination of cancer risk in patients with SSc were included. All articles that met strict inclusion criteria were analyzed for data on population size, time of followup, and observed‐to‐expected cancer ratios, also known as standardized incidence ratios (SIRs).
Results
Six articles met criteria and were included in the meta‐analysis. The pooled SIR for the incidence of cancer overall was 1.41 (95% confidence interval 95% CI 1.18–1.68), and significant heterogeneity was observed as a consequence of variability in the participants, outcome, study design, and risk of bias among the studies. Men had a significantly higher pooled SIR (1.85 95% CI 1.49–2.31) than women (SIR 1.33 95% CI 1.18–1.49) (P < 0.01), and stratification for sex eliminated heterogeneity, which indicates that variability among the studies greatly contributed to differences between the sexes. There were no differences between limited cutaneous SSc and diffuse cutaneous SSc (P = 0.77). Significant increases were observed in the risk of cancer of the lung, liver, hematologic system, and bladder, as well as of non‐Hodgkin's lymphoma and leukemia.
Conclusion
SSc is associated with an increased risk of cancer, particularly lung, liver, hematologic, and bladder cancers, although absolute risk is relatively low. Men with SSc have a higher risk of developing cancer than women.
Mitigating and preventing beach litter from entering the ocean is urgently required. Monitoring beach litter solely through human effort is cumbersome, with respect to both time and cost. To address ...this problem, an artificial intelligence technique that can automatically identify different-sized beach litter is proposed. The technique was established by training a deep learning model that enables pixel-wise classification (semantic segmentation) using beach images taken by an observer on the beach. Eight segmentation classes that include two beach litter classes were defined, and the results were qualitatively and quantitatively verified. Segmentation performance was adequately high based on three metrics: Intersection over Union (IoU), precision, and recall, although there is room for further improvement. The potency of the method was demonstrated when it was applied to images taken in different places from training data images, and the coverage of artificial litter calculated and discussed using drone images provided ground truth.
•A technique for the pixel-wise classification of beach litter is proposed.•A deep learning model was trained to enable different-sized litter identification.•Semantic segmentation was conducted using images taken by an observer on the beach.•Segmentation results were qualitatively and quantitatively verified.•Application to litter coverage estimation and practical usage were discussed.
Objectives
To clarify the relationship between the baseline value of medial meniscus extrusion (MME) and the radiographic change of knee osteoarthritis (KOA) through a 5-year follow-up.
Methods
...Overall, 472 participants and 944 knees were eligible. MME (mm) was measured at the baseline, and KOA was radiographically evaluated at both baseline and 5-year follow-up by Kellgren-Lawrence grade (KLG). Radiographic KOA (ROA) was defined as the knee showing KLG ≥ 2. Incident ROA (iROA) was defined if the baseline KLG of 0–1 increased to KLG ≥ 2 in 5 years. Progressive ROA (pROA) was defined if the baseline KLG of 2–3 worsened to a higher grade in 5 years. Receiver operating characteristic (ROC) curve and generalized estimating equations were used for analysis.
Results
Of 574 non-ROA knees at the baseline, 43 knees (7.5%) developed iROA; of 370 ROA knees, 47 knees (12.7%) developed pROA. Based on the ROC curves, 4 mm was the optimal cutoff to detect the risk of iROA (area under curve AUC 0.639 right knee; AUC 0.641 left knee) and that of pROA (AUC 0.750 right knee; AUC 0.863 left knee). Multiple regression analysis showed that the 4-mm cutoff of MME was significantly associated with both the prevalence of iROA (regression coefficient
B
1.909;
p
≤ 0.001; adjusted odds ratio aOR 6.746) and that of pROA (
B
1.791;
p
≤ 0.001; aOR 5.993).
Conclusions
On ultrasonography, the participants with more extruded medial meniscus showed a higher prevalence of both iROA and pROA. Ultrasonography could identify patients who had a risk of developing KOA.
Key Points
• Through a 5-year follow-up, the current cohort study was conducted to clarify the relationship between the baseline value of medial meniscus extrusion (MME) and the radiographic change of knee osteoarthritis (KOA).
• More extruded medial meniscus evaluated by ultrasonography was associated with the development of radiographic KOA.
• Ultrasonography could identify the patients who had a risk of developing KOA, and the 4-mm cutoff of MME was optimal to detect this risk.
In the present study, we propose a new approach for determining earthquake hypocentral parameters. This approach integrates computed theoretical seismograms and deep machine learning. The theoretical ...seismograms are generated through a realistic three-dimensional Earth model, and are then used to create spatial images of seismic wave propagation at the Earth’s surface. These snapshots are subsequently utilized as a training data set for a convolutional neural network. Neural networks for determining hypocentral parameters such as the epicenter, depth, occurrence time, and magnitude are established using the temporal evolution of the snapshots. These networks are applied to seismograms from the seismic observation network in the Hakone volcanic region in Japan to demonstrate the suitability of the proposed approach for locating earthquakes. We demonstrate that the determination accuracy of hypocentral parameters can be improved by including theoretical seismograms for different earthquake locations and sizes, in the learning data set for the deep machine learning. Using the proposed method, the hypocentral parameters are automatically determined within seconds after detecting an event. This method can potentially serve in monitoring earthquake activity in active volcanic areas such as the Hakone region.
CD4 ⁺ Treg cells expressing the transcription factor FOXP3 (forkhead box P3) are abundant in tumor tissues and appear to hinder the induction of effective antitumor immunity. A substantial number of ...T cells, including Treg cells, in tumor tissues and peripheral blood express C-C chemokine receptor 4 (CCR4). Here we show that CCR4 was specifically expressed by a subset of terminally differentiated and most suppressive CD45RA ⁻FOXP3 ʰⁱCD4 ⁺ Treg cells designated effector Treg (eTreg) cells, but not by CD45RA ⁺FOXP3 ˡᵒCD4 ⁺ naive Treg cells, in peripheral blood of healthy individuals and cancer patients. In melanoma tissues, CCR4 ⁺ eTreg cells were predominant among tumor-infiltrating FOXP3 ⁺ T cells and much higher in frequency compared with those in peripheral blood. With peripheral blood lymphocytes from healthy individuals and melanoma patients, ex vivo depletion of CCR4 ⁺ T cells and subsequent in vitro stimulation of the depleted cell population with the cancer/testis antigen NY-ESO-1 efficiently induced NY-ESO-1–specific CD4 ⁺ T cells. Nondepletion failed in the induction. The magnitude of the responses was comparable with total removal of FOXP3 ⁺ Treg cells by CD25 ⁺ T-cell depletion. CCR4 ⁺ T-cell depletion also augmented in vitro induction of NY-ESO-1–specific CD8 ⁺ T cells in melanoma patients. Furthermore, in vivo administration of anti-CCR4 mAb markedly reduced the eTreg-cell fraction and augmented NY-ESO-1–specific CD8 ⁺ T-cell responses in an adult T-cell leukemia-lymphoma patient whose leukemic cells expressed NY-ESO-1. Collectively, these findings indicate that anti-CCR4 mAb treatment is instrumental for evoking and augmenting antitumor immunity in cancer patients by selectively depleting eTreg cells.
Detecting seismic events, discriminating between different event types, and picking P- and S-wave arrival times are fundamental but laborious tasks in seismology. In response to the ever-increasing ...volume of seismic observational data, machine learning (ML) methods have been applied to try to resolve these issues. Although it is straightforward to input standard (time-domain) seismic waveforms into ML models, many studies have used time–frequency-domain representations because the frequency components may be effective for discriminating events. However, detailed comparisons of the performances of these two methods are lacking. In this study, we compared the performances of 1D and 2D convolutional neural networks (CNNs) in discriminating events in datasets from two different tectonic settings: tectonic tremor and ordinary earthquakes observed at the Nankai trough, and eruption signals and other volcanic earthquakes at Sakurajima volcano. We found that the 1D and 2D CNNs performed similarly in these applications. Half of the misclassified events were misassigned the same labels in both CNNs, implying that the CNNs learned similar features inherent to the input signals and thus misclassified them similarly. Because the first convolutional layer of a 1D CNN applies a set of finite impulse response (FIR) filters to the input seismograms, these filters are thought to extract signals effective for discriminating events in the first step. Therefore, because our application was the discrimination of signals dominated by low- and high-frequency components, we tested which frequency components were effective for signal discriminations based on the filter responses alone. We found that the FIR filters comprised high-pass and low-pass filters with cut-off frequencies around 7–9 Hz, frequencies at which the magnitude relations of the input signal classes change. This difference in the power of high- and low-frequency components proved essential for correct signal classifications in our dataset.
Graphical Abstract
Purpose
Some but not all randomized controlled trials (RCTs) of soy isoflavones showed their beneficial effect on arterial stiffness, a predictor of cardiovascular events, dementia, and all-cause ...mortality, independent of traditional risk factors. To test the hypothesis that supplementation of soy isoflavones reduces arterial stiffness, we performed a systematic review and meta-analysis of RCTs of soy isoflavones on arterial stiffness.
Methods
The protocol of this systematic review was registered with PROSPERO (CRD42019126128) and written in accordance with PRISMA. The PubMed, Embase, and clinicaltrials.gov databases were searched using the following criteria: human subjects, soy isoflavones as intervention, and arterial stiffness as primary outcome. A random-effects meta-analysis was used to pool estimates across studies. Standardized mean difference (SMD) was used to synthesize quantitative results.
Results
Among 998 articles retrieved, 8 articles met our criteria. Duration of intervention was relatively short (maximum of 12 weeks). Outcome measurements extracted were pulse wave velocity (PWV), systemic arterial compliance (SAC), augmentation index (AI), and cardio-ankle vascular index (CAVI). Soy isoflavones reduced arterial stiffness compared to placebo (standardized mean difference − 0.33, 95% confidence interval − 0.47, − 0.19). Subgroup analyses showed no difference between treatment effects for intervention duration (< 6 weeks vs. ≥ 6 weeks) or gender (women only vs. men only vs. combined). Sensitivity analysis showed no difference in the effect of soy isoflavones between PWV, CAVI, SAC, and AI.
Conclusion
Supplementation of soy isoflavones reduced arterial stiffness. Longer duration trials with larger number of participants are warranted.
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