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The objective of this study was to explore the clinical characteristics and prognosis of diabetic foot in hospitalized patients with diabetes in Tibet. To achieve that, patients hospitalized in People’s Hospital of Tibet Autonomous Region and diagnosed with diabetic foot ulcer (DFU) from January 1, 2016 to December 31, 2020 were enrolled in the study, and DFU cases of Peking University First Hospital were collected as control group. Analysis and comparison of clinical characteristics of DFU in plateau and plain areas were conducted. Normal distribution data or non-normal distribution data between groups were analyzed by t-test analysis or the nonparametric Mann–Whitney U test, and categorical variants were compared by Chi-square of Pearson. A total of 54 DFU cases were enrolled in the study in the People’s Hospital of Tibet Autonomous Region (Tibet group for short). Males accounted for 83.3% (45 cases) in Tibet group, which was higher than that of Peking University First Hospital (Beijing group for short), which accounted for 67.0%. Compared with the DFU patients in the Beijing group, the Tibet group was younger (58.11 ± 12.25 years vs 64.18 ± 11.37 years, P < 0.05), with a shorter disease duration (7.00 years vs 12.00 years, P < 0.05). In contrast, alcohol consumption was higher in the Tibet group (44.4 vs 27.4%, P < 0.05), and the number of patients with smoking habit was higher in the Beijing group (29.6 vs 43.7%, P < 0.05). The Tibet group had higher HbA1c (10.2 vs 8.7%, P < 0.05) and lower DFU proportion (22.2 vs 44.2%, P < 0.05). There was no statistically significant difference in the proportion of moderate to severe infections between the two groups (58.5 vs 59.6%, P = 0.887). Leukocytes (6.75 × 109/L vs 8.72 × 109/L, P < 0.05) and neutrophils (4.07 × 109/L vs 6.26 × 109/L, P < 0.05) in Tibet group were lower. Although the DFU amputation rate in the Tibet group was lower than that in the Beijing group (9.3 vs 29.8%, P < 0.05), there was no statistically significant difference between the two groups in terms of treatment cost, hospital stay, and mortality. In conclusion, patients with DFU in Tibet had a smaller age, shorter duration of diabetes, and more male predominance. The proportions of gangrene and amputation were lower in Tibet, with gangrene accounting for 80% of all amputees.

Radio frequency fingerprint identification (RFFI) shows great potential as a means for authenticating wireless devices. As RFFI can be addressed as a classification problem, deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance. RFFI is suitable for securing the legacy existing Internet of Things (IoT) networks since it does not require any modifications to the existing end-node hardware and communication protocols. However, most deep learning-based RFFI systems require the collection of a great number of labelled signals for training, which is time-consuming and not ideal, especially for the IoT end nodes that are already deployed and configured with long transmission intervals. Moreover, the long time required to train a neural network from scratch also limits rapid deployment on legacy IoT networks. To address the above issues, two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning. More specifically, they rely on fine-tuning and distance metric learning, respectively, and only require only a small amount of signals from the legacy IoT network. As the dataset used for transfer is small, we propose to apply augmentation in the transfer process to generate more training signals to improve performance. A LoRa-RFFI testbed consisting of 40 commercial-off-the-shelf (COTS) LoRa IoT devices and a software-defined radio (SDR) receiver is built to experimentally evaluate the proposed approaches. The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another IoT network with less than ten signals from each LoRa device. The classification accuracy is over 90%, and the augmentation technique can improve the accuracy by up to 20%.

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.

Background: The impact of gestational diabetes mellitus (GDM) on incident dementia is unknown. Our aim was to evaluate the relationship between GDM and all‐cause dementia and the mediating effects of chronic diseases on this relationship. Methods: This prospective cohort study included women from the UK Biobank who were grouped based on GDM history. Multivariate Cox proportional hazard models were used to explore the associations between GDM and dementia. We further analysed the mediating effects of chronic diseases on this relationship and the interactions of covariates. Results: A total of 1292 women with and 204,171 women without a history of GDM were included. During a median follow‐up period of 45 years after first birth, 2921 women were diagnosed with dementia. Women with a GDM history had a 67% increased risk of incident dementia (hazard ratio 1.67, 95% confidence interval: 1.03–2.69) compared with those without a GDM history. According to mediation analyses, type 2 diabetes, coronary heart disease, chronic kidney disease and comorbidities (diagnosed with any two of the three diseases) explained 34.5%, 8.4%, 5.2% and 18.8% of the mediating effect on the relationship. Subgroup analyses revealed that physical activity modified the association between GDM history and dementia (p for interaction = 0.030). Among physically inactive women, GDM was significantly associated with incident dementia; however, this association was not observed among physically active women. Conclusions: A history of GDM was associated with a greater risk of incident dementia. Type 2 diabetes partially mediated this relationship. Strategies for dementia prevention might be considered for women with a history of GDM. [ABSTRACT FROM AUTHOR]

AHP-3a, a triple-helix acidic polysaccharide isolated from Alpinia officinarum Hance, was evaluated for its anticancer and antioxidant activities. The physicochemical properties and structure of AHP-3a were investigated through gel permeation chromatography, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy, and nuclear magnetic resonance (NMR) spectroscopy. The weight-average molecular weight of AHP-3a was 484 kDa, with the molar percentages of GalA, Gal, Ara, Xyl, Rha, Glc, GlcA, and Fuc being 35.4%, 21.4%, 16.9%, 11.8%, 8.9%, 3.1%, 2.0%, and 0.5%, respectively. Based on the results of the monosaccharide composition analysis, methylation analysis, and NMR spectroscopy, the main chain of AHP-3a was presumed to consist of (1→4)-α-D-GalpA and (1→2)-α-L-Rhap residues, which is a pectic polysaccharide with homogalacturonan (HG) and rhamnogalacturonan-I (RG-I) structural domains containing side chains. In addition, the results of the antioxidant activity assay revealed that the ability of AHP-3a to scavenge DPPH, ABTS, and OH free radicals increased with an increase in its concentration. Moreover, according to the results from the EdU, wound healing, and Transwell assays, AHP-3a can control the proliferation, migration, and invasion of HepG2 and Huh7 hepatocellular carcinoma cells without causing any damage to healthy cells. Thus, AHP-3a may be a natural antioxidant and anticancer component. [ABSTRACT FROM AUTHOR]

Water and fertilizer integration technology can effectively improve nutrient utilization efficiency. However, the existing water and fertilizer machines have some shortcomings, such as huge cost, single fertilizer injection, need for cleaning water and so on, which hinder the development of water and fertilizer integration technology. Aiming at the problems of precise and low-cost compounding of compound fertilizer at the local farm, the water and fertilizer integrated intelligent irrigation and fertilization system were taken as the research object. In this research, new concept of an intelligent sensing system was proposed， and accurate proportioning system of water and fertilizer concentration was constructed and implemented. Firstly, a fast on-line method of intelligent sensing model of water and fertilizer was established based on a series of concentration gradient compound fertilizer solutions. The conductivity values of these formulated solutions were tested by contactless conductivity detection electrodes. Subsequently, the data analysis algorithms were discussed and compared to fit regression model. Based on the intelligent sensing model of water and fertilizer , the framework structure of in-situ intelligent sensing and accurate proportioning system of water and fertilizer concentration was designed, and the working principle of the system was also explained. The system proposed includs a first-level water and fertilizer concentration intelligent perception model building subsystem and a second-level water and fertilizer accurate proportioning subsystem. The first-level subsystem was designed as a portable device, which mainly included a precise pump for quantitative dosing, a large-range online conductivity sensor, a plastic bucket and supporting control and model building software. The second-level subsystem was designed as a dynamic and precise fertilizer distribution device. The effectiveness of the system was verified by three types of water intelligent fertilizer application so as to guide the in-situ water and fertilizer concentration ratio. The testing results showed that the second-order polynomial fitting curve under regularization conditions was the best model to express the relationship between the conductivity and the concentration of water and fertilizer, and the correlation coefficients R2 was higher than 0.999. Combined with the proportion of each index of compound fertilizer, the concentration of each index of compound fertilizer that the user cares about can be obtained according to this model. The results of three types of water intelligent fertilizer application showed that the conductivity of natural water had an effect on the water and fertilizer system, and the relative deviation was more than 0.1. The online water and fertilizer perception and ratio system proposed in this research realized the elimination of the interference of the local water conductivity on the accuracy of the ratio of water and fertilizer, and the accurate calculation of compound fertilizer was achieved through model calculation. This system has a simple structure and accurate ratio, low cost， and can be easily combined with the existing water and fertilizer integrated machine or artificial fertilizer system. The system could be widely used in facility agriculture, orchard cultivation and field cash crop cultivation, et al.