Seismic noise in South China Sea: High-quality time-frequency analysis from 0.01 to 125 Hz.

An efficient and precise time-frequency analysis method for real-time ocean bottom seismometer (RTOBS) data in the South China Sea (SCS) is presented. Overcoming the limitations of conventional methods, the method involves temporal segmentation, unique frequency octaves, and Fourier transforms to generate power spectral density (PSD) and probability density function profiles. The method demonstrates superior precision, computational efficiency, and full-bandwidth (0 to Nyquist) capability compared to traditional techniques, as validated through theoretical and empirical evaluations. Applied to SCS RTOBS data, it unveils temporal PSD variations, shedding light on underwater noise sources like earthquakes, offshore blasting, ship-induced disturbances, and tidal effects. Establishing background noise levels in the SCS supports noise source categorization and ocean environment monitoring. Furthermore, comparing onshore and offshore seismic stations advances interdisciplinary research, fostering a comprehensive understanding of acoustics and seismology in the region.

Associations of Dietary Cholesterol Consumption With Incident Diabetes and Cardiovascular Disease: The Role of Genetic Variability in Cholesterol Absorption and Disease Predisposition.

OBJECTIVE: Whether genetic susceptibility to disease and dietary cholesterol (DC) absorption contribute to inconsistent associations of DC consumption with diabetes and cardiovascular disease (CVD) remains unclear. RESEARCH DESIGN AND METHODS: DC consumption was assessed by repeated 24-h dietary recalls in the UK Biobank. A polygenetic risk score (PRS) for DC absorption was constructed using genetic variants in the Niemann-Pick C1-Like 1 and ATP Binding Cassettes G5 and G8 genes. PRSs for diabetes, coronary artery disease, and stroke were also created. The associations of DC consumption with incident diabetes (n = 96,826) and CVD (n = 94,536) in the overall sample and by PRS subgroups were evaluated using adjusted Cox models. RESULTS: Each additional 300 mg/day of DC consumption was associated with incident diabetes (hazard ratio [HR], 1.17 [95% CI, 1.07-1.27]) and CVD (HR, 1.09 [95% CI, 1.03-1.17]), but further adjusting for BMI nullified these associations (HR for diabetes, 0.99 [95% CI, 0.90-1.09]; HR for CVD, 1.04 [95% CI, 0.98-1.12]). Genetic susceptibility to the diseases did not modify these associations (P for interaction ≥0.06). The DC-CVD association appeared to be stronger in people with greater genetic susceptibility to cholesterol absorption assessed by the non-high-density lipoprotein cholesterol-related PRS (P for interaction = 0.04), but the stratum-level association estimates were not statistically significant. CONCLUSIONS: DC consumption was not associated with incident diabetes and CVD, after adjusting for BMI, in the overall sample and in subgroups stratified by genetic predisposition to cholesterol absorption and the diseases. Nevertheless, whether genetic predisposition to cholesterol absorption modifies the DC-CVD association requires further investigation.

Exploration the interaction of cadmium and copper toxic effects in pakchoi (Brassica chinensis L) roots through combinatorial transcriptomic and weighted gene co-expression network analysis.

The interaction between cadmium(Cd) and copper(Cu) during combined pollution can lead to more complex toxic effects on humans and plants.However, there is still a lack of sufficient understanding regarding the types of interactions at the plant molecular level and the response strategies of plants to combined pollution. To assess this, we investigated the phenotypic and transcriptomic patterns of pakchoi (Brassica chinensis L) roots in response to individual and combined pollution of Cd and Cu. The results showed that compared to single addition, the translocation factor of heavy metals in roots significantly decreased (p < 0.05) under the combined addition, resulting in higher accumulation of Cd and Cu in the roots. Transcriptomic analysis of pakchoi roots revealed that compared to single pollution, there were 312 and 1926 differentially expressed genes (DEGs) specifically regulated in the Cd2Cu20 and Cd2Cu100 combined treatments, respectively. By comparing the expression of these DEGs among different treatments, we found that the combined pollution of Cd and Cu mainly affected the transcriptome of the roots in an antagonistic manner. Enrichment analysis indicated that pakchoi roots upregulated the expression of genes involved in glucosetransferase activity, phospholipid homeostasis, proton transport, and the biosynthesis of phenylpropanoids and flavonoids to resist Cd and Cu combined pollution. Using weighted gene co-expression network analysis (WGCNA), we identified hub genes related to the accumulation of Cd and Cu in the roots, which mainly belonged to the LBD, thaumatin-like protein, ERF, MYB, WRKY, and TCP transcription factor families. This may reflect a transcription factor-driven trade-off strategy between heavy metal accumulation and growth in pakchoi roots. Additionally, compared to single metal pollution, the expression of genes related to Nramp, cation/H+ antiporters, and some belonging to the ABC transporter family in the pakchoi roots was significantly upregulated under combined pollution. This could lead to increased accumulation of Cd and Cu in the roots. These findings provide new insights into the interactions and toxic mechanisms of multiple metal combined pollution at the molecular level in plants.

Single and mixture toxicity of cadmium and copper to swim bladder in early life stages of Japanese medaka (Oryzias latipes).

Toxicity observed in aquatic ecosystems often cannot be explained by the action of a single pollutant. Likewise, evaluation standards formulated by a single effect cannot truly reflect the environmental quality requirements. The study of mixtures is needed to provide environmental relevance and knowledge of combined toxicity. In this study, the embryos of Japanese medaka (Oryzias latipes) were treated with individual and binary mixture of copper (Cu) and cadmium (Cd) until 12 days post-fertilization (dpf). Hatching, mortality, development, histology and gene expression were assessed. Our results showed that the highest concentration mixture of Cd (10 mg/L) and Cu (1 mg/L) affected survival, hatching time and hatching success. Occurrence of uninflated swim bladder was the highest (value) with exposure to 10 mg/L Cd. Swim bladder was commonly over-inflated in a mixture (0.1 mg/L Cd + 1.0 mg/L Cu) exposure. Individuals exposed to the mixture (0.1 Cd + 1.0 Cu mg/L) showed up to a 7.69% increase in swim bladder area compared to the control group. The mixtures containing 0.1 or 10 mg/L Cd, each with 1.0 mg/L Cu resulted in significantly increased of Pbx1b expression, higher than any Cd or Cu alone (p < 0.01). In the co-exposure group (0.1/10 Cd + 1.0 Cu mg/L), Pbx1b expression was found at 12 dpf but not 7 dpf in controls. Higher concentrations of Cd may progressively reduce Pbx1b expression, potentially explaining why 75% of individuals in the 10 mg/L Cd group failed to inflate their swim bladders. Additionally, the swim bladder proved to be a valuable bio-indicator for biological evaluation.

Comprehensive assessment of phthalates in indoor dust across China between 2007 and 2019: Benefits from regulatory restrictions.

China is the largest producer and consumer of phthalates in the world. However, it remains unclear whether China's phthalate restrictions have alleviated indoor phthalate pollution. We extracted the concentrations of dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DBP), diisobutyl phthalate (DIBP), benzyl butyl phthalate (BBP), and bis(2-ethylhexyl) phthalate (DEHP) in indoor dust at 2762 sites throughout China between 2007 and 2019 from the published literature. Based on these data, we investigated the effects of phthalate restrictions and environmental factors on the temporal-spatial distribution and sources of phthalates and estimated human exposure and risk of phthalates. The results revealed that the mean concentrations of phthalates in indoor dust throughout China decreased in the following order: DEHP > DBP > DIBP > DMP > DEP > BBP. The concentrations of six phthalates were generally higher in northern and central-western China than in southern regions. BBP and DEHP concentrations decreased by 73.5% and 17.9%, respectively, from 2007 to 2019. Sunshine was a critical environmental factor in reducing phthalate levels in indoor dust. Polyvinyl chloride materials, personal care products, building materials, and furniture were the primary sources of phthalates in indoor dust. The phthalates in indoor dust posed the most significant threat to children and older adults. This study provides a picture of phthalate pollution, thus supporting timely and effective policies and legislation.

Multi-source exposure and health risks of phthalates among university students in Northeastern China.

The endocrine disruptor phthalates (PAEs) are widely used as important chemical additives in a variety of areas around the globe. PAEs are toxic to reproduction and development and may adversely affect the health of adolescents. Risk assessments of exposure to PAEs from different sources are more reflective of actual exposure than single-source assessments. We used personal exposure parameters to estimate the dose of PAEs to 107 university students from six media (including dormitory dust, dormitory air, clothing, food, disposable food containers, and personal care products (PCPs)) and three exposure routes (including ingestion, inhalation, and dermal absorption). Individual factors and lifestyles may affect PAE exposure to varying degrees. Based on a positive matrix factorization (PMF) model, the results indicated that the main sources of PAEs in dust were indoor building materials and plastics, while PCPs and adhesives were the major sources of airborne PAEs. The relative contribution of each source to PAE exposure showed that food and air were the primary sources of dimethyl phthalate (DMP) and dibutyl phthalate (DBP). Air source contributed the most to diethyl phthalate (DEP) exposure, followed by PCPs. Food was the most significant source of diisobutyl phthalate (DiBP), benzyl butyl phthalate (BBP), and bis(2-ethylhexyl) phthalate (DEHP) exposure. Additionally, the exposure of DEHP to dust was not negligible. The ingestion pathway was the most dominant among the three exposure pathways, followed by dermal absorption. The non-carcinogenic risk of PAEs from the six sources was within acceptable limits. DEHP exhibits a low carcinogenic risk. We suggest university students maintain good hygienic and living habits to minimize exposure to PAEs.

A Perifacial EMG Acquisition System for Facial-Muscle-Movement Recognition.

This paper proposes a portable wireless transmission system for the multi-channel acquisition of surface electromyography (EMG) signals. Because EMG signals have great application value in psychotherapy and human-computer interaction, this system is designed to acquire reliable, real-time facial-muscle-movement signals. Electrodes placed on the surface of a facial-muscle source can inhibit facial-muscle movement due to weight, size, etc., and we propose to solve this problem by placing the electrodes at the periphery of the face to acquire the signals. The multi-channel approach allows this system to detect muscle activity in 16 regions simultaneously. Wireless transmission (Wi-Fi) technology is employed to increase the flexibility of portable applications. The sampling rate is 1 KHz and the resolution is 24 bit. To verify the reliability and practicality of this system, we carried out a comparison with a commercial device and achieved a correlation coefficient of more than 70% on the comparison metrics. Next, to test the system's utility, we placed 16 electrodes around the face for the recognition of five facial movements. Three classifiers, random forest, support vector machine (SVM) and backpropagation neural network (BPNN), were used for the recognition of the five facial movements, in which random forest proved to be practical by achieving a classification accuracy of 91.79%. It is also demonstrated that electrodes placed around the face can still achieve good recognition of facial movements, making the landing of wearable EMG signal-acquisition devices more feasible.

Exposure estimates of parabens from personal care products compared with biomonitoring data in human hair from Northeast China.

Parabens (PBs), a class of endocrine-disrupting chemicals (EDCs), are extensively used as additives in personal care products (PCPs); however, distinguishing between endogenous and exogenous contamination from PCPs in hair remains a challenge. We conducted a comprehensive analysis of the levels, distribution patterns, impact factors, and sources of PBs in 119 human hair samples collected from Changchun, northeast China. The detection rates of methylparaben (MeP), propylparaben (PrP), and ethylparaben (EtP) in hair samples were found to be 100%. The concentration of PBs in hair followed the order of MeP (57.48 ng/g) > PrP (46.40 ng/g) > EtP (6.80 ng/g). The concentration of PrP in female hair was significantly higher (65.38 ng/g) than that observed in male hair (7.82 ng/g) (p < 0.05). The levels of excretion rates of MeP (ERMeP) and excretion rates of PrP (ERPrP) in the hair-dying samples (ERMeP: 17.89 ng/day; ERPrP: 14.15 ng/day) were found to be 2.52 and 2.40 times higher, respectively, compared to the non-hair-dying samples (ERMeP: 7.09 ng/day; ERPrP: 6.05 ng/day). However, the system exposure dosage (SED) results revealed that although hair dyes exhibited higher PBs, human exposure was found to be lower than certain PCPs. The results of the correlation analysis revealed that toner, face cream, body lotion, and hair conditioner were identified as the primary sources of PBs in male hair. Furthermore, the human exposure resulting from the utilization of female hair dye and serum exhibited a positive correlation with hair ERMeP and ERPrP levels, indicating in the screening of samples, excluding hair samples using hair dye and haircare essential oil can effectively avoid the interference caused by exogenous contamination from PCPs.

Estimate of the maximum amount of dust adhering to skin and the upper limit of dust-skin adherence factor for young adults: An example from Changchun, China.

Contaminants present in dust adhering to the skin can pose a significant risk to human health through dermal absorption and hand-to-mouth contact. The adhesion capacity of dust differs significantly from that of soil due to its physicochemical properties. Therefore, applying the raw soil exposure parameters to estimate the health risks associated with dermal exposure to dust may lead to erroneous conclusions. In this study, we quantified the maximum amount of dust that adhered to the skin (MADmax) and the upper limit of dust-skin adherence factor (DSAFmax) in 26 adults using element markers as a proxy for dust. The volunteers were exposed to dust and rinse water samples were collected from their hands, forearms, lower legs, and feet. We analyzed both the raw dust samples and the rinse water samples for 11 element markers, including Be, V, Cr, Mn, Co, Ni, Cu, Zn, Se, Ba, and Pb. The results showed that the MADmax of indoor dust and outdoor dust increased by 0.08-0.62 mg and 0.33-0.56 mg following a 1 cm2 increase in skin surface area, respectively. Based on best dust element markers, the body part-weighted dust-skin adherences (WDSAFmaxs) of indoor dust and outdoor dust were 0.35 and 0.64 mg/cm2, respectively. A smaller particle size and higher moisture content resulted in a larger DSAFmax. Only when indoor dust concentrations exceed 24.2 mg/m3 or outdoor dust concentrations exceed 44.3 mg/m3, can the WDSAFmax be applied directly in the health risk assessment of dermal exposure to dust. The method from this study can be re-applied in different regions, and the adherence data can help to improve future studies on the health effects of dermal exposure to dust.

A squarate-pillared titanium oxide quantum sieve towards practical hydrogen isotope separation.

Separating deuterium from hydrogen isotope mixtures is of vital importance to develop nuclear energy industry, as well as other isotope-related advanced technologies. As one of the most promising alternatives to conventional techniques for deuterium purification, kinetic quantum sieving using porous materials has shown a great potential to address this challenging objective. From the knowledge gained in this field; it becomes clear that a quantum sieve encompassing a wide range of practical features in addition to its separation performance is highly demanded to approach the industrial level. Here, the rational design of an ultra-microporous squarate pillared titanium oxide hybrid framework has been achieved, of which we report the comprehensive assessment towards practical deuterium separation. The material not only displays a good performance combining high selectivity and volumetric uptake, reversible adsorption-desorption cycles, and facile regeneration in adsorptive sieving of deuterium, but also features a cost-effective green scalable synthesis using chemical feedstock, and a good stability (thermal, chemical, mechanical and radiolytic) under various working conditions. Our findings provide an overall assessment of the material for hydrogen isotope purification and the results represent a step forward towards next generation practical materials for quantum sieving of important gas isotopes.

Chemoenzymatic synthesis of (+)-isoagatholactone, (+)-spongian-16-one, and 3-deoxychavalone A via biocatalytic polyene cyclization.

The stereoselective cyclization of geranylgeraniol catalysed by squalene-hopene cyclase (SHC) was investigated. By use of this transformation, spongiane diterpenoids (+)-isoagatholactone and (+)-spongian-16-one, and meroterpenoid 3-deoxychavalone A were synthesized in a concise and redox-economic manner. This work showcases the application of SHC-catalysed cyclization as a key step in terpenoid synthesis.

Characteristics and health risks of population exposure to phthalates via the use of face towels.

The production of face towels is growing at an annual rate of about 4% in China, reaching 1.13 million tons by 2021. Phthalates (PAEs) are widely used in textiles, and face towels, as an important household textile, may expose people to PAEs via the skin, further leading to health risks. We collected new face towels and analyzed the distribution characterization of PAEs in them. The changes of PAEs were explored in a face towel use experiment and a simulated laundry experiment. Based on the use of face towels by 24 volunteers, we calculated the estimated daily intake (EDI) and comprehensively assessed the hazard quotient (HQ), hazard index (HI), and dermal cancer risk (DCR) of PAEs exposure in the population. PAEs were present in new face towels at total concentrations of

Dual-ATME: Dual-Branch Attention Network for Micro-Expression Recognition.

Micro-expression recognition (MER) is challenging due to the difficulty of capturing the instantaneous and subtle motion changes of micro-expressions (MEs). Early works based on hand-crafted features extracted from prior knowledge showed some promising results, but have recently been replaced by deep learning methods based on the attention mechanism. However, with limited ME sample sizes, features extracted by these methods lack discriminative ME representations, in yet-to-be improved MER performance. This paper proposes the Dual-branch Attention Network (Dual-ATME) for MER to address the problem of ineffective single-scale features representing MEs. Specifically, Dual-ATME consists of two components: Hand-crafted Attention Region Selection (HARS) and Automated Attention Region Selection (AARS). HARS uses prior knowledge to manually extract features from regions of interest (ROIs). Meanwhile, AARS is based on attention mechanisms and extracts hidden information from data automatically. Finally, through similarity comparison and feature fusion, the dual-scale features could be used to learn ME representations effectively. Experiments on spontaneous ME datasets (including CASME II, SAMM, SMIC) and their composite dataset, MEGC2019-CD, showed that Dual-ATME achieves better, or more competitive, performance than the state-of-the-art MER methods.

CAS(ME)3: A Third Generation Facial Spontaneous Micro-Expression Database With Depth Information and High Ecological Validity.

Micro-expression (ME) is a significant non-verbal communication clue that reveals one person's genuine emotional state. The development of micro-expression analysis (MEA) has just gained attention in the last decade. However, the small sample size problem constrains the use of deep learning on MEA. Besides, ME samples distribute in six different databases, leading to database bias. Moreover, the ME database development is complicated. In this article, we introduce a large-scale spontaneous ME database: CAS(ME) 3. The contribution of this article is summarized as follows: (1) CAS(ME) 3 offers around 80 hours of videos with over 8,000,000 frames, including manually labeled 1,109 MEs and 3,490 macro-expressions. Such a large sample size allows effective MEA method validation while avoiding database bias. (2) Inspired by psychological experiments, CAS(ME) 3 provides the depth information as an additional modality unprecedentedly, contributing to multi-modal MEA. (3) For the first time, CAS(ME) 3 elicits ME with high ecological validity using the mock crime paradigm, along with physiological and voice signals, contributing to practical MEA. (4) Besides, CAS(ME) 3 provides 1,508 unlabeled videos with more than 4,000,000 frames, i.e., a data platform for unsupervised MEA methods. (5) Finally, we demonstrate the effectiveness of depth information by the proposed depth flow algorithm and RGB-D information.

Bioaccumulation of organophosphorus flame retardants in marine organisms in Liaodong Bay and their potential ecological risks based on species sensitivity distribution.

Although organophosphorus flame retardants (OPFRs) in aquatic environments have received increasing concern, little information is available on their bioaccumulation and trophic transfer in marine food webs. Consequently, the risks of OPFRs to marine ecosystems are unknown. In this study, seven OPFR compounds in marine biological samples collected from Liaodong Bay, Bohai Sea, were analyzed to evaluate their level and biological amplification effect in the marine food web. The total OPFRs of marine organisms in Liaodong Bay ranged from 2.60 to 776 ng/g ww, and lipids were critical factors affecting the concentration of OPFRs in marine species. Tris (2-ethylhexyl) phosphate (TEHP) and tris(1-chloro-2-propyl) phosphate (TCIPP) were the OPFRs most frequently detected in marine species. Still, tris(2-chloroethyl) phosphate (TCEP) was dominant in most marine species (16/24), and the content of chlorinated OPFRs was highest. At the same time, alkyl OPFRs and aryl OPFRs accounted for the same proportion. No correlation between OPFR concentration and the trophic level was observed in marine organisms from Liaodong Bay. It was shown in the results of the species sensitivity distribution that TCIPP in Chinese seawater does not pose a potential ecological risk to marine species. However, much work remains to be done on accumulating information and the ecological risks of OPFRs in different marine food webs.

The effect of facial attractiveness on micro-expression recognition.

Micro-expression (ME) is an extremely quick and uncontrollable facial movement that lasts for 40-200 ms and reveals thoughts and feelings that an individual attempts to cover up. Though much more difficult to detect and recognize, ME recognition is similar to macro-expression recognition in that it is influenced by facial features. Previous studies suggested that facial attractiveness could influence facial expression recognition processing. However, it remains unclear whether facial attractiveness could also influence ME recognition. Addressing this issue, this study tested 38 participants with two ME recognition tasks in a static condition or dynamically. Three different MEs (positive, neutral, and negative) at two attractiveness levels (attractive, unattractive). The results showed that participants recognized MEs on attractive faces much quicker than on unattractive ones, and there was a significant interaction between ME and facial attractiveness. Furthermore, attractive happy faces were recognized faster in both the static and the dynamic conditions, highlighting the happiness superiority effect. Therefore, our results provided the first evidence that facial attractiveness could influence ME recognition in a static condition or dynamically.

Inhalation Bioaccessibility and Risk Assessment of Metals in PM2.5 Based on a Multiple-Path Particle Dosimetry Model in the Smelting District of Northeast China.

PM2.5 can deposit and partially dissolve in the pulmonary region. In order to be consistent with the reality of the pulmonary region and avoid overestimating the inhalation human health risk, the bioaccessibility of PM2.5 heavy metals and the deposition fraction (DF) urgently needs to be considered. This paper simulates the bioaccessibility of PM2.5 heavy metals in acidic intracellular and neutral extracellular deposition environments by simulating lung fluid. The multipath particle dosimetry model was used to simulate DF of PM2.5. According to the exposure assessment method of the U.S. Environmental Protection Agency, the inhalation exposure dose threshold was calculated, and the human health risk with different inhalation exposure doses was compared. The bioaccessibility of heavy metals is 12.1−36.2%. The total DF of PM2.5 in adults was higher than that in children, and children were higher than adults in the pulmonary region, and gradually decreased with age. The inhalation exposure dose threshold is 0.04−14.2 mg·kg−1·day−1 for the non-carcinogenic exposure dose and 0.007−0.043 mg·kg−1·day−1 for the carcinogenic exposure dose. Cd and Pb in PM2.5 in the study area have a non-carcinogenic risk to human health (hazard index < 1), and Cd has no or a potential carcinogenic risk to human health. A revised inhalation health risk assessment may avoid overestimation.

Impact of environmental factors and bacterial interactions on dust mite allergens in different indoor dust.

Indoor dust is the main carrier of indoor pollutants, especially dust mite allergens and bacteria, they can trigger asthma, rhinitis, eczema and other allergic diseases. However, the interactions between dust mite allergens and bacterial communities in different types of indoor dust are not clear. The study focused on particulate and flocculent fibrous dust, explored the concentrations of Der p 1 (Dermatophagoides pteronyssinus) and Der f 1 (D. farinae) in 46 households in Changchun and their environmental influences, characterized the bacterial communities by high-throughput sequencing, and the interactions between Der p 1, Der f 1 and bacterial communities were explored. The results showed that Der p 1 and Der f 1 tended to accumulate more in flocculent fibrous dust, and Der p 1 predominated in the indoor dust samples. The floor height, years of housing occupancy and the living areas all affected the concentrations of dust mite allergens. In bacterial community, Proteobacteria, Firmicutes and Actinobacteria were leading phyla in the two types of dust. Kocuria, Blastococcus and Massilia were dominating genera in particulate dust and Acinetobacter, Lactobacillus, Corynebacterium_1 were dominating genera in flocculent fibrous dust. The overall diversity and species richness of bacteria in particulate dust were significantly higher than those in flocculent dust (p < 0.001). The living area was an important environmental factor affecting the bacterial community in flocculent fibrous dust (p < 0.01). The interaction between the relative abundance of Proteobacteria, Firmicutes and Actinobacteria and dust mite allergen concentrations significantly differed between the two dust types, indicating that bacteria could be used both as food and to establish symbiotic relationships with household dust mites (HDMs) hosts and provide nutrition.

The effect of carbonization temperature on the capacity and mechanisms of Cd(II)-Pb(II) mix-ions adsorption by wood ear mushroom sticks derived biochar.

Heavy metals often coexist in contaminated environmental media, and competition between heavy metals for adsorption sites influences the absorption capacity of biochar. In this study, the adsorption mechanism of pyrolytically modified wood ear mushroom sticks (250, 450, and 650 °C) as a new bio-adsorbent for single-ion and mixed-ion solutions Cd2+ and Pb2+ Biochar adsorption experiments showed that the adsorption abilities of Cd2+ and Pb2+ increased with increasing WMBC (wood ear mushroom sticks biochar) pyrolysis temperature. According to the Langmuir model, the maximum adsorption capacity of Cd2+ and Pb2+increased with higher pyrolysis temperature, being 29.84, 39.08, 46.16 mg·g-1and 124.3, 186.8, 234.2 mg·g-1, respectively for three different pyrolysis temperatures 250, 450, and 650 °C. WMBC exhibited a stronger adsorption ability for Pb2+ than for Cd2+. Competition between the two heavy metals severely inhibited the adsorption of Cd2+. Based on X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and Fourier transform infrared spectroscopy (FTIR) analyses, the dominant interaction mechanisms were determined to be complexation, ion exchange, precipitation, and C-π interaction. The results suggest WMBC shows promise as a novel, cheap, and effective adsorbent that can be used to remove both Cd2+ and Pb2+ pollutants from environmental media.

Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture.

COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-020-0679-8.