A subspace approach to face detection with support vector machines

We present a subspace approach to face detection with Support Vector Machine (SVMs). A linear SVM classifier is trained as a filter to produce a subspace in which a non-linear SVM classifier with Gaussian kernel is trained for face detection. This makes training easier and results in a very efficient face detection algorithm. Experimental results demonstrate their promising performance compared with some well-known existing detectors. 1

Surface wind observations affected by agricultural development over Northwest China

Meteorological stations in Northwest China are surrounded by large proportions of cultivated land. The relations between the change of surface wind speed and the cultivated land fractions (CF) within a 4 km radius at 135 meteorological stations over arid Northwest China are investigated. Stations with larger CF experienced larger declines in surface wind speed from 1960 to 2007. Compared with the wind speed variation in the Tibetan Plateau where agricultural development is negligible, stations with low CF show similar variation, whereas the wind speed at stations with large CF illustrates a sharp decrease in the 1970s–1980s, during which irrigated agriculture developed rapidly. The observed wind speed at the station surrounded by irrigated fields in the Jingtai Irrigation District, shows a rapid wind speed decrease during the same period when the irrigated area expanded. By contrast, rapid wind decrease is not observed at a nearby station with minimal influence of agricultural development

A Fuzzy Transformer Fusion Network (FuzzyTransNet) for Medical Image Segmentation: The Case of Rectal Polyps and Skin Lesions

Skin melanoma, one of the deadliest forms of cancer worldwide, demands precise diagnosis to mitigate cancer-related mortality. While histopathological examination, characterized by its cost-effectiveness and efficiency, remains the primary diagnostic approach, the development of an accurate detection system is pressing due to melanoma’s varying sizes, shapes, and indistinct boundaries shared with normal tissues. To address the efficient segmentation of skin melanoma, we propose an innovative hybrid neural network approach in this study. Initially, a fuzzy neural network is constructed using fuzzy logic to preprocess medical images, supplemented by wavelet transformation for image enhancement. Subsequently, the Swin Transformer V2 and ResNet50 networks are introduced to parallelly extract features and apply them to the task of skin melanoma segmentation. Extensive experimental comparisons are conducted with other classic and advanced medical segmentation algorithms on publicly available skin datasets, namely ISIC 2017 and ISIC 2018. Experimental results reveal that our method outperforms the optimal algorithms by 1.3% in the Dice coefficient and 1.3% in accuracy on the ISIC 2018 dataset. The evaluation metrics indicate the effectiveness of the constructed fuzzy block in identifying uncertain lesion boundaries, while the Transformer–CNN branch adeptly extracts global features while accurately capturing underlying details. Additionally, we successfully apply our method to colon polyp segmentation tasks with similar indistinct boundaries, achieving remarkable segmentation outcomes

Salivary Cortisol Determination on Smartphone-Based Differential Pulse Voltammetry System

Cortisol is commonly used as a significant biomarker of psychological or physical stress. With the accelerated pace of life, non-invasive cortisol detection at the point of care (POC) is in high demand for personal health monitoring. In this paper, an ultrasensitive immunosensor using gold nanoparticles/molybdenum disulfide/gold nanoparticles (AuNPs/MoS2/AuNPs) as transducer was explored for non-invasive salivary cortisol monitoring at POC with the miniaturized differential pulse voltammetry (DPV) system based on a smartphone. Covalent binding of cortisol antibody (CORT-Ab) onto the AuNPs/MoS2/AuNPs transducer was achieved through the self-assembled monolayer of specially designed polyethylene glycol (PEG, SH-PEG-COOH). Non-specific binding was avoided by passivating the surface with ethanolamine. The miniaturized portable DPV system was utilized for human salivary cortisol detection. A series current response of different cortisol concentrations decreased and exhibited a linear range of 0.5–200 nM, the detection limit of 0.11 nM, and high sensitivity of 30 μA M−1 with a regression coefficient of 0.9947. Cortisol was also distinguished successfully from the other substances in saliva. The recovery ratio of spiked human salivary cortisol and the variation of salivary cortisol level during one day indicated the practicability of the immunosensor based on the portable system. The results demonstrated the excellent performance of the smartphone-based immunosensor system and its great potential application for non-invasive human salivary cortisol detection at POC

Corrigendum: A Tree Peony Trihelix Transcription Factor PrASIL1 Represses Seed Oil Accumulation

[This corrects the article DOI: 10.3389/fpls.2021.796181.]

A Tree Peony Trihelix Transcription Factor PrASIL1 Represses Seed Oil Accumulation

In many higher plants, seed oil accumulation is governed by complex multilevel regulatory networks including transcriptional regulation, which primarily affects fatty acid biosynthesis. Tree peony (), a perennial deciduous shrub endemic to China is notable for its seed oil that is abundant in unsaturated fatty acids. We discovered that a tree peony trihelix transcription factor, PrASIL1, localized in the nucleus, is expressed predominantly in developing seeds during maturation. Ectopic overexpression of in leaf tissue and seeds significantly reduced total fatty acids and altered the fatty acid composition. These changes were in turn associated with the decreased expression of multitudinous genes involved in plastidial fatty acid synthesis and oil accumulation. Thus, we inferred that PrASIL1 is a critical transcription factor that represses oil accumulation by down-regulating numerous key genes during seed oil biosynthesis. In contrary, up-regulation of oil biosynthesis genes and a significant increase in total lipids and several major fatty acids were observed in silenced tree peony leaves. Together, these results provide insights into the role of trihelix transcription factor PrASIL1 in controlling seed oil accumulation. can be targeted potentially for oil enhancement in tree peony and other crops through gene manipulation

The Reprocessed Suomi NPP Satellite Observations

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of the Earth and atmosphere for environmental applications with high spatial resolution and sampling rate. The S-NPP and JPSS are equipped with five instruments, each with advanced design in Earth sampling, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Ozone Mapping and Profiler Suite (OMPS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). Among them, the ATMS is the new generation of microwave sounder measuring temperature profiles from the surface to the upper stratosphere and moisture profiles from the surface to the upper troposphere, while CrIS is the first of a series of advanced operational hyperspectral sounders providing more accurate atmospheric and moisture sounding observations with higher vertical resolution for weather and climate applications. The OMPS instrument measures solar backscattered ultraviolet to provide information on the concentrations of ozone in the Earth’s atmosphere, and VIIRS provides global observations of a variety of essential environmental variables over the land, atmosphere, cryosphere, and ocean with visible and infrared imagery. The CERES instrument measures the solar energy reflected by the Earth, the longwave radiative emission from the Earth, and the role of cloud processes in the Earth’s energy balance. Presently, observations from several instruments on S-NPP and JPSS-1 (re-named NOAA-20 after launch) provide near real-time monitoring of the environmental changes and improve weather forecasting by assimilation into numerical weather prediction models. Envisioning the need for consistencies in satellite retrievals, improving climate reanalyses, development of climate data records, and improving numerical weather forecasting, the NOAA/Center for Satellite Applications and Research (STAR) has been reprocessing the S-NPP observations for ATMS, CrIS, OMPS, and VIIRS through their life cycle. This article provides a summary of the instrument observing principles, data characteristics, reprocessing approaches, calibration algorithms, and validation results of the reprocessed sensor data records. The reprocessing generated consistent Level-1 sensor data records using unified and consistent calibration algorithms for each instrument that removed artificial jumps in data owing to operational changes, instrument anomalies, contaminations by anomaly views of the environment or spacecraft, and other causes. The reprocessed sensor data records were compared with and validated against other observations for a consistency check whenever such data were available. The reprocessed data will be archived in the NOAA data center with the same format as the operational data and technical support for data requests. Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations