Exploring The Removal Potential of Multi-pollutants from Water Matrices with Innovative Speciality Adsorbents in A Field-scale Filtration System

Driven by excess nutrients in water bodies, eutrophication has long been an issue in water resources management. Harmful algal blooms (HABs) in a highly eutrophic water body lead to hypoxia, creating a “dead zone,” which renders the oxygen levels inadequate for the survival of marine life. This study examined the field-scale filtration performance of two specialty absorbents to improve watershed remediation within a Total Maximum Daily Load program. The goal was to simultaneously remove nutrients and biological pollutants along Canal 23 (C-23) in the St. Lucie River Basin, Florida. The filtration system installed in the C-23 river corridor was equipped with either clay– perlite with sand sorption media (CPS) or zero-valent iron and perlite green environmental media (ZIPGEM). Both media were formulated with varying combinations of sand, clay, perlite, and/or recycled iron based on distinct recipes. Seasonality effects were also evident in nutrient removal efficiencies while the decomposition of dissolved organic nitrogen played a pivotal role in nutrient removal, Overall, ZIPGEM demonstrated a more stable nutrient removal efficiency than CPS in the phase of seasonal changes while biological pollutants can be fully removed over seasons

DENOISER: Rethinking the Robustness for Open-Vocabulary Action Recognition

As one of the fundamental video tasks in computer vision, Open-Vocabulary Action Recognition (OVAR) recently gains increasing attention, with the development of vision-language pre-trainings. To enable generalization of arbitrary classes, existing methods treat class labels as text descriptions, then formulate OVAR as evaluating embedding similarity between visual samples and textual classes. However, one crucial issue is completely ignored: the class descriptions given by users may be noisy, e.g., misspellings and typos, limiting the real-world practicality of vanilla OVAR. To fill the research gap, this paper pioneers to evaluate existing methods by simulating multi-level noises of various types, and reveals their poor robustness. To tackle the noisy OVAR task, we further propose one novel DENOISER framework, covering two parts: generation and discrimination. Concretely, the generative part denoises noisy class-text names via one decoding process, i.e., propose text candidates, then utilize inter-modal and intra-modal information to vote for the best. At the discriminative part, we use vanilla OVAR models to assign visual samples to class-text names, thus obtaining more semantics. For optimization, we alternately iterate between generative and discriminative parts for progressive refinements. The denoised text classes help OVAR models classify visual samples more accurately; in return, classified visual samples help better denoising. On three datasets, we carry out extensive experiments to show our superior robustness, and thorough ablations to dissect the effectiveness of each component

Spatiotemporal Variations of Ecosystem Service Indicators and the Driving Factors Under Climate Change in the Qinghai–Tibet Highway Corridor

In recent decades, the influence of climate change and human activities on the ecosystem services (ES) in the Qinghai–Tibet Plateau (QTP) has been extensively investigated. However, few studies focus on linear traffic corridor area, which is heavily affected by human activities. Taking the Golmud–Lhasa national highway corridor as a case, this study investigated the land-use and land-cover change (LUCC) and spatiotemporal variations of ES indicators using ecosystem indices of fractional vegetation cover (FVC), leaf area index (LAI), evapotranspiration (ET), and net primary productivity (NPP) from 2000 to 2020. The results indicated that LUCC was faster in the last decade, mostly characterized by the conversion from grassland to unused land. In buffer within 3000 m, the proportions of productive areas represented the increased trends with distance. In terms of ES variations, the improved areas outweighed the degraded areas in terms of FVC, LAI, and NPP from 2000 to 2020, mostly positioned in the Qinghai Province. In addition, FVC, LAI, and NPP peaked at approximately 6000 m over time. With regard to influencing factors, precipitation (20.54%) and temperature (14.19%) both positively influenced the spatiotemporal variation of FVC. Nearly 60% of the area exhibited an increased NPP over time, especially in the Qinghai Province, which could be attributed to the temperature increase over the last two decades. In addition, the distance effects of climatic factors on ES indicators exhibited that the coincident effects almost showed an opposite trend, while the reverse effects showed a similar trend. The findings of this study could provide a reference for the ecological recovery of traffic corridors in alpine fragile areas

SigDLA: A Deep Learning Accelerator Extension for Signal Processing

Deep learning and signal processing are closely correlated in many IoT scenarios such as anomaly detection to empower intelligence of things. Many IoT processors utilize digital signal processors (DSPs) for signal processing and build deep learning frameworks on this basis. While deep learning is usually much more computing-intensive than signal processing, the computing efficiency of deep learning on DSPs is limited due to the lack of native hardware support. In this case, we present a contrary strategy and propose to enable signal processing on top of a classical deep learning accelerator (DLA). With the observation that irregular data patterns such as butterfly operations in FFT are the major barrier that hinders the deployment of signal processing on DLAs, we propose a programmable data shuffling fabric and have it inserted between the input buffer and computing array of DLAs such that the irregular data is reorganized and the processing is converted to be regular. With the online data shuffling, the proposed architecture, SigDLA, can adapt to various signal processing tasks without affecting the deep learning processing. Moreover, we build a reconfigurable computing array to suit the various data width requirements of both signal processing and deep learning. According to our experiments, SigDLA achieves an average performance speedup of 4.4$\times$, 1.4$\times$, and 1.52$\times$, and average energy reduction of 4.82$\times$, 3.27$\times$, and 2.15$\times$ compared to an embedded ARM processor with customized DSP instructions, a DSP processor, and an independent DSP-DLA architecture respectively with 17% more chip area over the original DLAs

Environmental Impact Assessment of Watershed Plan Under the “Three Lines and One List” Environmental Governance

AbstractRapid and large-scale watershed development activities have imposed tremendous challenges to the sustainable development while driving economic prosperity in the areas along the watershed in China. Improving effectiveness of environmental impact assessment (EIA) of watershed planning has become a top priority for river ecological civilization. In this regard, the “three lines and one list” (TLOL) environmental governance, was proposed in the latest Chinese environmental management policy, consisting of an ecological conservation red line, an environmental quality bottom line and a resource utilization upper limit line and an environmental permit list are to be taken into account when assessing the potential effects of a watershed development plan. In this paper, an indicator system was established based on the TLOL requirements, and the rapid impact assessment matrix (RIAM) was adopt to asses watershed development alternatives. In an application of this methodology, the Jinjiang watershed development planning in Fujian province was taken as a case study to recommend an optimal alternative. Six alternatives were assessed by conducting a comprehensive comparison. The results showed that, the Alternative 2 is preferred because it has relative advantages in terms of allocating water resource in a reasonable way, safeguarding the ecological water use at downstream, controlling the small scaled hydropower generations. This research shows that the EIA of watershed planning on the basis of the TLOL governance policy is an effective way of integrating environmental management and river ecological civilization requirements into watershed development planning. It proposes not only a universal process for assessing watershed development alternatives, but also a feasible method of maximize the trade-off between water conservancy and hydropower, and other watershed development activities and river ecological protection.</jats:p

A Network Pharmacology Approach for Uncovering the Osteogenic Mechanisms of <i>Psoralea corylifolia</i> Linn

Background and Aim. Psoralea corylifolia Linn (PCL) is an herb that is commonly used for alleviating osteoporosis and vitiligo. Although accumulating evidence has demonstrated the antiosteoporotic effect of PCL, the identities of the osteogenic compounds in PCL and their functional targets remain elusive. To investigate the osteogenic ingredients in PCL and their functional mechanisms, network pharmacology analysis was performed on the targets of PCL and osteogenesis. Methods. The active components of PCL were screened by literature review. The potential protein targets of the active PCL components were predicted with the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Search Tool for Interactions of Chemicals (STITCH), SwissTargetPrediction, and PubChem. The target networks related to PCL and osteogenic differentiation were constructed by using Cytoscape. MC3T3-E1 cells were used to verify the targets. Results. Twenty-three active components of PCL and 162 potential target proteins were identified. Further analysis reduced the number of potential target proteins to 71. Of the 23 components, bavachalcone, psoralen, bavachinin, neobavaisoflavone, methoxsalen, psoradin, bakuchiol, and angelicin may be the main active components of PCL that promote bone formation. PPARγ and aryl hydrocarbon receptor (AhR) were verified as targets of PCL in MC3T3-E1 cells, and the western blot and immunofluorescence staining results showed that compared to the control, PCL reduced the expression of these targets. Conclusions. The active components of PCL and the mechanisms by which they promoted osteogenic differentiation were successfully identified using network pharmacology.</jats:p

Update on the Withdrawal from Long-term Use of Benzodiazepines in Patients with Chronic Insomnia Disorder

Benzodiazepines (BZDs) are indicated for the short-term treatment of chronic insomnia disorder, since real-world use of them in large doses for a long time could cause serious side effects. Patients with long-term use of BZDs are prone to BZDs tolerance and dependence, but sudden withdrawal from BZDs can lead to rebound and withdrawal symptoms, resulting in difficulty in withdrawal. We reviewed recent studies about the withdrawal from long-term use of BZDs in chronic insomnia disorder patients, focusing on the overview of BZDs, timing and strategy of withdrawal, and treatment of withdrawal symptoms. Our review will contribute to enriching the ideas of appropriately discontinuing BZDs in chronic insomnia disorder patients with long-term use of BZDs

Isopsoralen Enhanced Osteogenesis by Targeting AhR/ERα

Isopsoralen (IPRN), one of the main effective ingredients in Psoralea corylifolia Linn, has a variety of biological effects, including antiosteoporotic effects. In vivo studies show that IPRN can increase bone strength and trabecular bone microstructure in a sex hormone deficiency-induced osteoporosis model. However, the mechanism underlying this osteogenic potential has not been investigated in detail. In the present study, we investigated the molecular mechanism of IPRN-induced osteogenesis in MC3T3-E1 cells. Isopsoralen promoted osteoblast differentiation and mineralization, increased calcium nodule levels and alkaline phosphatase (ALP) activity and upregulated osteoblast markers, including ALP, runt-related transcription factor 2 (RUNX2), and collagen type I alpha 1 chain (COL1A1). Furthermore, IPRN limited the nucleocytoplasmic shuttling of aryl hydrocarbon receptor (AhR) by directly binding to AhR. The AhR target gene cytochrome P450 family 1 subfamily A member 1 (CYP1A1) was also inhibited in vitro and in vivo. This effect was inhibited by the AhR agonists indole-3-carbinol (I3C) and 3-methylcholanthrene (3MC). Moreover, IPRN also increased estrogen receptor alpha (ER&alpha;) expression in an AhR-dependent manner. Taken together, these results suggest that IPRN acts as an AhR antagonist and promotes osteoblast differentiation via the AhR/ER&alpha; axis