Global, regional, and national analyses of the burden of colorectal cancer attributable to diet low in milk from 1990 to 2019: longitudinal observational study.

Background: Globally, diet low in milk is the third greatest risk factor for colorectal cancer (CRC). However, there has been a lack of detailed worldwide analysis of the burden and trends of CRC attributable to diet low in milk. Objective: We aim to assess the spatiotemporal trends of CRC-related mortality and disability-adjusted life-years (DALYs) attributable to diet low in milk at the global, regional, and national levels from 1990 to 2019. Methods: Data of mortality, DALYs, age-standardized mortality rate (ASMR), and age-standardized DALY rate (ASDR) of CRC attributable to diet low in milk were extracted from the Global Burden of Disease (GBD) 2019 study. The burden of CRC attributable to diet low in milk was estimated using the ASMR and ASDR, while accounting for sex, age, country, and socio-demographic index (SDI). From 1990 to 2019, the estimated annual percentage change (EAPC) was calculated to clarify the temporal trends in the ASMR and ASDR attributable to diet low in milk. Results: In 2019, there were 166,456 (95% UI = 107,221-226,027) deaths and 3,799,297 (95% UI = 2,457,768-5,124,453) DALYs attributable to diet low in milk, accounting for 15.3 and 15.6% of CRC-related deaths and DALYs in 2019. CRC-related deaths and DALYs attributed to diet low in milk increased by 130.5 and 115.4%, from 1990 to 2019. The burden of CRC attributable to diet low in milk varied notably among regions and nations. High-middle SDI regions had the highest ASDR and ASMR of CRC linked to diet low in milk, while there was a slight downward trend high SDI regions. Among geographical regions, East Asia had the highest number of CRC-related deaths and DALYs attributable to diet low in milk. Notably, the burden of CRC was highest in males and the elderly. With coefficients of -0.36 and -0.36, the EAPC in ASMR and ASDR was significantly inversely correlated with the Human Development Index in 2019. Conclusion: Globally, the number of CRC deaths attributable to diet low in milk has continued to increase over the last 30 years. Therefore, government and authorities should conduct education campaigns to encourage individuals to increase daily milk intake.

Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network.

In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time-frequency images as inputs to convolutional neural networks (CNNs) or recurrent neural networks (RNNs). However, with the advancement of graph neural networks (GNNs), a new approach has been introduced involving transforming time series data into graph structures. In this study, we propose a CNN-transformer graph neural network (CTGNet) for modulation classification, to uncover complex representations in signal data. First, we apply sliding window processing to the original signals, obtaining signal subsequences and reorganizing them into a signal subsequence matrix. Subsequently, we employ CTGNet, which adaptively maps the preprocessed signal matrices into graph structures, and utilize a graph neural network based on GraphSAGE and DMoNPool for classification. Extensive experiments demonstrated that our method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. This underscores CTGNet's significant advantage in capturing key features in signal data and providing an effective solution for modulation classification tasks.

Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation.

Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies and bandwidths. In this work, a deep learning-based framework named SigdetNet is proposed, which takes the power spectrum as the network's input to localize the spectral locations of the signals. In the proposed framework, Welch's periodogram is applied to reduce the variance in the power spectral density (PSD), followed by logarithmic transformation for signal enhancement. In particular, an encoder-decoder network with the embedding pyramid pooling module is constructed, aiming to extract multi-scale features relevant to signal detection. The influence of the frequency resolution, network architecture, and loss function on the detection performance is investigated. Extensive simulations are carried out to demonstrate that the proposed multi-signal detection method can achieve better performance than the other benchmark schemes.

Cerebral Venous Sinus Thrombosis Caused by Neuro-Behçet Disease Accidentally Detected by 68Ga-FAPI PET/CT.

ABSTRACT: A 26-year-old man presented with recurrent oral ulcer, temporal headache, and blurred vision for 4 months. The giant cell arteritis was suspected, and 18F-FDG and 68Ga-FAPI PET/CT were done. There was no hypermetabolic lesion in 18F-FDG PET/CT. However, 68Ga-FAPI PET/CT showed multiple increased FAPI uptake lesions in the cerebral venous sinus. High-resolution MR venography revealed multiple chronic cerebral venous sinus thrombosis. Neuro-Behçet disease was diagnosed finally. This case showed 68Ga-FAPI PET/CT may play some role in the management of neuro-Behçet disease.

Matching daily home health-care demands with supply in service-sharing platforms.

The availability of innovative technologies (e.g., the Internet of Things, big data analytics, blockchain, the cloud, and applications) has led to a shift in the provision of home health-care (HHC) services from traditional institutions to service-sharing platforms. In the HHC context, one main challenge faced by service-sharing platforms is the matching of demand with supply, while considering the heterogeneity of care requests and service providers. From a centralized perspective of service-sharing platforms regarding three stakeholders (i.e., platform, caregiver, and customer), different matching strategies are used, including the "self-interested", "customer-first", "hard-work-happy-life", and "social-welfare" strategies. When addressing the matching problem at an operational level, the platforms must comply with various requirements and rules, including break requirements, temporal dependencies, and flexible service durations. In this study, mixed-integer linear programming models and a branch-and-price approach are designed to match demand with supply using different matching strategies while satisfying all of the requirements and rules. The effects of key factors on performance indicators (e.g., platform revenue, caregiver profit, and customer surplus) are examined, and the matching strategies are compared. The results indicate that the "customer-first" and "self-interested" strategies benefit more from flexible service durations, however they are more and less negatively affected by break requirements and temporal dependencies, respectively, as compared to the "social-welfare" and "hard-work-happy-life" strategies. A comparison between the "social-welfare" strategy and the other three strategies indicates that the former strategy is beneficial for all three stakeholders of the service-sharing platforms as well as the government. Another comparison between the service-sharing platforms and traditional HHC institutions indicates the sharing economy has a positive impact on caregiver profit and customer surplus.

Rapid ILs-polishing Processes Toward Flexible Nanostructured Paper with Dually High Transparency and Haze.

Biodegradable highly nanostructured paper has received great interest in past years due to its excellent optical properties which facilitate its wide applications in green flexible electronics and devices. However, energy and/or time-consuming procedure during the process of fabricating most nanostructured transparent paper are presently the main obstacle to their scalable production. In this work, we demonstrated a novel nanostructured paper with dually high transparency (∼91%) and high haze (∼89%) that was directly fabricated from original paper with rapid ILs-polishing processes. The whole fabricating time only requires 10 min. Compared to the previously reported nanopaper made of the isolated cellulose nanofibers by pure mechanical and/or chemical approaches, this work presented herein is devoted to use green ILs to polish directly the micrometer-sized fibrous paper into the nanostructured paper. This new method brings a rapid fabrication of transparent nanostructured paper while also retaining dual intriguing properties both in optical transmittance and haze. This work is capable of fabricating next-generation flexible and highly transparent and haze paper by a high-speed roll-to-roll manufacturing process with a much lower cost.

Rapid fabrication of transparent film directly from wood fibers with microwave-assisted ionic liquids technology.

Presently flexibly transparent film or nanopaper from all cellulose was mostly fabricated by assembling cellulose nanofibers disintegrated from macroscopic wood fibers which mostly suffers from potential environmental toxicity or high cost. In this work, we firstly reported an all-cellulose transparent film fabricated by a novel microwave-assisted ionic liquids technology (MILT). The use of MILT for treating the original all-cellulose paper brings nearly 2.6 fold-increases in optical transmission, and 2.0 fold-increases in tensile property compared to those without microwave assistance. More importantly, by contrast with the partial dissolution of cellulose in typical DMAC/LiCl, ILs, NaOH/urea, the MILT is extremely time-saved with responding to the highest increase in mechanical property because the high efficient surface dissolution and welding bind individual sheets together under a micro environment.

Investigation on left ventricular multi-directional deformation in patients of hypertension with different LVEF.

BACKGROUND: This study is aimed at investigating myocardial multi-directional systolic deformation in hypertensive with different left ventricular ejection fraction (LVEF), and exploring its contribution to LVEF. METHODS: One hundred and twenty-three patients with primary hypertension (HT) were divided into group A (LVEF ≥ 55%), group B (45% ≤ LVEF < 50%, or 50% ≤ LVEF < 55% + LVEDVI ≥ 97 ml/m2), and group C (LVEF < 45%). Two-dimensional strain echocardiography (2DSE) including LV longitudinal strain (SL), radial strain (SR) and circumferential strain (SC) were measured. RESULTS: SL decreased gradually from group A, B to C (all p < 0.05) while SR and SC were reduced only in group B and C (all p < 0.05). All strain measurements correlated to LVEF, with the strongest correlation in SC (r = -0.82, p < 0.01) and the second in SL (r = -0.76). The diastolic E/e increased from group A, B to C. CONCLUSIONS: Left ventricular multi-directional deformation correlated well to LVEF in hypertension and particularly SC, indicating that it was SC, not SL or SR, that makes the prominent contribution to left ventricular pump function.

Value of segmental myocardial strain by 2-dimensional strain echocardiography for assessment of scar area induced in a rat model of myocardial infarction.

OBJECTIVES: Two-dimensional strain echocardiography (2DSE) technique has enabled accurate quantification of regional myocardial function. This experimental study was aimed to investigate the value of 2DSE in detection of segmental regional myocardial dysfunction induced by fibrosis following myocardial infarction in a small animal (rat) model. METHODS: A rat model of myocardial infarction was established by ligation of the proximal left anterior descending coronary artery in 17 SD rats. Regional myocardial function was detected by 2DSE at baseline and 4-weeks post-infarction, including end-systolic radial strain and strain rate (SR and SrR) and end-systolic circumferential strain and strain rate (SC and SrC) of each of six segments at papillary level. According to the size of scar found by histologic Masson staining, the optimal cutoff points of parameters for detecting scar area were analyzed and the sensitivity and specificity of every parameter to detect myocardial scar were obtained using ROC. RESULTS: (1) Comparing with parameters measured at baseline, there were significant decreases in SR, SrR, SC and SrC of each segment at 4 weeks post-infarction, with the worst in the infarct area (32.90 ± 8.79 vs 11.18 ± 3.89, 6.28 ± 1.35 vs 3.18 ± 0.47, -14.46 ± 2.21 vs -6.30 ± 2.17 and 4.93 ± 0.95 vs 2.59 ± 1.16, respectively) (all P < 0.05). (2)By 4 weeks, the myocardium of infarct area (anteroseptum, anterior and anterolateral) had fibrosis (31.33 ± 9.89, 73.42 ± 13.21 and 13.99 ± 3.24%, respectively) with minimal fibrosis in inferoseptal segment (0.32 ± 0.19%), no fibrosis was found in the inferior and inferolateral segments. (3)Significant negative correlations were found between the size of segmental scar and 2DSE parameters (r-value -0.61 ~ -0.80, all P < 0.01) with the strongest correlation in SR. SR less than 10% has 84% sensitivity and 98% specificity for detecting segments of scar area greater than 30% with AUC = 0.97. CONCLUSIONS: 2DSE is able to assess regional myocardial dysfunction in a rat model of myocardial infarction and has high accuracy in detecting infarct segments with scar area greater than 30%.