Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through a contact-sensing method, which is inconvenient and unfriendly for BP monitoring. This paper proposes an efficient end-to-end network for estimating BP values from a facial video to achieve remote BP estimation in daily life. The network first derives a spatiotemporal map of a facial video. Then, it regresses the BP ranges with a designed blood pressure classifier and simultaneously calculates the specific value with a blood pressure calculator in each BP range based on the spatiotemporal map. In addition, an innovative oversampling training strategy was developed to handle the problem of unbalanced data distribution. Finally, we trained the proposed blood pressure estimation network on a private dataset, MPM-BP, and tested it on a popular public dataset, MMSE-HR. As a result, the proposed network achieved a mean absolute error (MAE) and root mean square error (RMSE) of 12.35 mmHg and 16.55 mmHg on systolic BP estimations, and those for diastolic BP were 9.54 mmHg and 12.22 mmHg, which were better than the values obtained in recent works. It can be concluded that the proposed method has excellent potential for camera-based BP monitoring in the indoor scenarios in the real world.

We describe the complete mitochondrial genome of Phiaris dolosana. It is 15,562 bp in length, and contains 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), 2 ribosomal RNA genes (rRNAs), and a 612 bp D-Loop. All PCGs start with ATN codon except for COI gene, which uses CGA as the initiation codon. Nine of 13 PCGs use a typical stop codon of TAA, the rest use incomplete stop codon of T or TA. Phylogenetic analysis of P. dolosana with other 17 leaf rollers is conducted with neighbor-joining method, the result is consistent with the conventional classification.

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Blood pressure indicates cardiac function and peripheral vascular resistance and is critical for disease diagnosis. Traditionally, blood pressure data are mainly acquired through contact sensors, which require high maintenance and may be inconvenient and unfriendly to some people (e.g., burn patients). In this paper, an efficient non-contact blood pressure measurement network based on face videos is proposed for the first time. An innovative oversampling training strategy is proposed to handle the unbalanced data distribution. The input video sequences are first normalized and converted to our proposed YUVT color space. Then, the Spatio-temporal slicer encodes it into a multi-domain Spatiotemporal mapping. Finally, the neural network computation module, used for high-dimensional feature extraction of the multi-domain spatial feature mapping, after which the extracted high-dimensional features are used to enhance the time-domain feature association using LSTM, is computed by the blood pressure classifier to obtain the blood pressure measurement intervals. Combining the output of feature extraction and the result after classification, the blood pressure calculator, calculates the blood pressure measurement values. The solution uses a blood pressure classifier to calculate blood pressure intervals, which can help the neural network distinguish between the high-dimensional features of different blood pressure intervals and alleviate the overfitting phenomenon.

Nine species of Sycacantha Diakonoff, 1959 are recorded from China. Among them, three are described as new: S. typicusivalva, sp. nov., S. camerata, sp. nov., and S. decursiva, sp. nov. Two new combinations are proposed based on DNA barcodes and characters of the male genitalia: S. diserta (Meyrick, 1909), comb. nov., and Phaecasiophora obtundana (Kuznetzov, 1988), comb. nov. Sycacantha complicitana (Walker, 1863) and S. catharia Diakonoff, 1973 are newly recorded from China. Photographs of adults and genitalia of the new species and new combinations are provided, and a key to the species based on genitalia is given.