Assessing the impacts of fine sediment removal on endogenous pollution release and microbial community structure in the shallow lakes.

Resuspension is a crucial process for releasing endogenous pollution from shallow lakes into the overlying water. Fine particle sediment, which has a higher contamination risk and longer residence time, is the primary target for controlling endogenous pollution. To this end, a study coupling aqueous biogeochemistry, electrochemistry, and DNA sequencing was conducted to investigate the remediation effect and microbial mechanism of sediment elution in shallow eutrophic water.

Assessing B-Z DNA Transitions in Solutions via Infrared Spectroscopy.

Z-DNA refers to the left-handed double-helix DNA that has attracted much attention because of its association with some specific biological functions. However, because of its low content and unstable conformation, Z-DNA is normally difficult to observe or identify. Up to now, there has been a lack of unified or standard analytical methods among diverse techniques for probing Z-DNA and its transformation conveniently.

CMRD-Net: a deep learning-based damage symptom rotated detection framework for in-field survey.

The damage symptoms of is an important evaluation index for pest prevention and control. However, due to various shapes, arbitrary-oriented directions and heavy overlaps of damage symptoms under complex field conditions, generic object detection methods based on horizontal bounding box cannot achieve satisfactory results. To address this problem, we develop a damage symptom rotated detection framework called CMRD-Net.

Reducing Power Line Interference from sEMG Signals Based on Synchrosqueezed Wavelet Transform.

Power line interference (PLI) is a major source of noise in sEMG signals. As the bandwidth of PLI overlaps with the sEMG signals, it can easily affect the interpretation of the signal. The processing methods used in the literature are mostly notch filtering and spectral interpolation.

Machine learning models for diabetic neuropathy diagnosis using microcirculatory parameters in type 2 diabetes patients.

Background: Diabetic peripheral neuropathy (DPN) is a primary cause of diabetic foot, early detection of DPN is essential. This study aimed to construct a machine learning model for DPN diagnosis based on microcirculatory parameters, and identify the most predictive parameters for DPN.

Methods: Our study involved 261 subjects, including 102 diabetics with neuropathy (DMN), 73 diabetics without neuropathy (DM), and 86 healthy controls (HC).

Structure and Reactivity of a Seven-Coordinate Ruthenium Iodosylbenzene Complex.

Seven-coordinate (CN7) ruthenium-oxo species have attracted much attention as highly reactive intermediates in both organic and water oxidation. Apart from metal-oxo, other metal-oxidant adducts, such as metal-iodosylarenes, have also recently emerged as active oxidants. We reported herein the first example of a CN7 Ru-iodosylbenzene complex, [Ru(bdpm)(pic)(O)I(Cl)Ph] (Hbdpm = [2,2'-bipyridine]-6,6'-diylbis(diphenylmethanol); pic = 4-picoline).

Insights into Regulating Mechanism of Mutagenesis Strains of Elizabethkingia meningoseptica sp. F2 by Omics Analysis.

Vitamin K plays an important role in electron transport, blood coagulation, and calcium homeostasis, and researchers have been trying to use microbes to produce it. Although our previous studies have shown that gradient radiation, breeding, and culture acclimation can improve vitamin K production in Elizabethkingia meningoseptica, the mechanism is still unclear. This study is the first which performs genome sequencing of E.

Simultaneous removal of norfloxacin and chloramphenicol using cold atmospheric plasma jet (CAPJ): Enhanced performance, synergistic effect, plasma-activated water (PAW) contribution, mechanism and toxicity evaluation.

The extensive abuse and inadvertent discharge of various antibiotics into the environment has become a serious problem for posing a big threat to human health. In order to deal with this problem, we utilized cold atmospheric plasma jet (CAPJ) to treat two different antibiotics, namely, norfloxacin and chloramphenicol, and investigated the efficiencies and corresponding mechanisms for removing the mixed antibiotics. In the application of the CAPJ technique, we made use of not only the direct plasma processing, but also the indirect plasma-activated water (PAW) treatment.

Pan-cancer analysis reveals the associations between MMP13 high expression and carcinogenesis and its value as a serum diagnostic marker.

Background: Matrix metalloproteinase-13 (MMP13) is a member of the endopeptidase matrix metalloproteinase family, which is involved in many normal physiological processes and even tumorigenesis. However, its co-carcinogenic signature in different cancers is not fully understood.

Methods: In this study, we first analyzed the expression of MMP13 in pan-cancer and its association with prognosis, immune infiltration, and cancer-related signaling pathways through integrated bioinformatics.

In Situ Synergistic Catalysis Hydrothermal Liquefaction of Spirulina by CuO-CeO and Ni-Co to Improve Bio-oil Production.

Hydrothermal liquefaction (HTL) is one of the most promising technologies for biofuel production. The preparation and application of catalysts for HTL have been the research focus in recent years. In this study, a new synergistic catalytic process strategy is proposed.

Research on the Optimal Regulation of Sudden Water Pollution.

For the needs of the whole region's emergency regulation of the nullah sudden water pollution event, the emergency regulation strategy of the accident section and upstream and downstream of the sudden water pollution event is studied. For the accident section, the duration of the whole emergency event is calculated using the parameter quantification method; for the upstream of the accident section, the NSGA-II is used to adjust the gate opening to ensure the water level stability of the upstream pools; for the downstream section, the optimized partition method is used to identify the unfavorable pools and close the unfavorable pool to extend the water supply time. Based on the example of an emergency event in the section of the Liyanghe gate-Guyunhe gate of the middle line project, the research results are as follows: the accident section is identified as the Xiaohe gate-Hutuohe gate, the upstream of the accident section is the Liyanghe gate-Xiaohe gate, and the downstream of the accident section is the Hutuohe gate-Gangtou Tunnel gate.

Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification.

Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions.

Inhibitors against Two PDZ Domains of MDA-9 Suppressed Migration of Breast Cancer Cells.

Melanoma differentiation-associated gene 9 (MDA-9) is a small adaptor protein with tandem PDZ domains that promotes tumor progression and metastasis in various human cancers. However, it is difficult to develop drug-like small molecules with high affinity due to the narrow groove of the PDZ domains of MDA-9. Herein, we identified four novel hits targeting the PDZ1 and PDZ2 domains of MDA-9, namely PI1A, PI1B, PI2A, and PI2B, using a protein-observed nuclear magnetic resonance (NMR) fragment screening method.

Prior knowledge auxiliary for few-shot pest detection in the wild.

One of the main techniques in smart plant protection is pest detection using deep learning technology, which is convenient, cost-effective, and responsive. However, existing deep-learning-based methods can detect only over a dozen common types of bulk agricultural pests in structured environments. Also, such methods generally require large-scale well-labeled pest data sets for their base-class training and novel-class fine-tuning, and these significantly hinder the further promotion of deep convolutional neural network approaches in pest detection for economic crops, forestry, and emergent invasive pests.

The Tumorigenic Effect of the High Expression of Ladinin-1 in Lung Adenocarcinoma and Its Potential as a Therapeutic Target.

The oncogenic role of Ladinin-1 (LAD1), an anchoring filament protein, is largely unknown. In this study, we conducted a series of studies on the oncogenic role of LAD1 in lung adenocarcinoma (LUAD). Firstly, we analyzed the aberrant expression of LAD1 in LUAD and its correlation with patient survival, tumor immune infiltration, and the activation of cancer signaling pathways.

Extraction and identification of new flavonoid compounds in dandelion Taraxacum mongolicum Hand.-Mazz. with evaluation of antioxidant activities.

Due to the interest in the potential pharmacological application of dandelion, the chemical constituents and activities of Taraxacum mongolicum Hand.-Mazz were studied. Box-Behnken response surface methodology was employed to optimize the protocol for extraction of flavonoid from dandelion.

Graphene/MoSO/graphene photomemristor with tunable non-volatile responsivities for neuromorphic vision processing.

Conventional artificial intelligence (AI) machine vision technology, based on the von Neumann architecture, uses separate sensing, computing, and storage units to process huge amounts of vision data generated in sensory terminals. The frequent movement of redundant data between sensors, processors and memory, however, results in high-power consumption and latency. A more efficient approach is to offload some of the memory and computational tasks to sensor elements that can perceive and process the optical signal simultaneously.

Evaluation of Polysaccharide Content in Shiitake Culinary-Medicinal Mushroom, Lentinula edodes (Agaricomycetes), via Near-Infrared Spectroscopy Integrated with Deep Learning.

Polysaccharide is one of the bioactive ingredients extracted from the fruiting body of Lentinula edodes (=L. edodes), which has many medicinal functions. While the content of polysaccharide can be measured by near-infrared (NIR) spectroscopy, the NIR analytical models established previously only covered L.

Therapeutic potential and nutritional significance of - a comprehensive review from 2010 to 2022.

With a long history in traditional Asian medicine, () is a mushroom species suggested to improve health and extend life. Its medicinal reputation has merited it with numerous attributes and titles, and it is evidenced to be effective in the prevention and treatment of various metabolic disorders owing to its unique source of bioactive metabolites, primarily polysaccharides, triterpenoids, and polyphenols, attributed with antioxidant, anti-inflammatory, anticancer, hepatoprotective, antidiabetic activities, . These unique potential pharmaceutical properties have led to its demand as an important resource of nutrient supplements in the food industry.

Effect of Noninvasive Static Human Data on Maximum Data in Exercise.

Maximum data in exercise (Max-Ex), including maximum heart rate (HRmax), peak oxygen uptake (VO2pk), maximum power (MaxP), etc., are frequently used, whether it is for the determination of exercise intensity, the measurement of an athlete’s performance, assessment of recovery from disease, and so on. However, very often this choice does not take into account the targeted individual.

Detection of amylase activity and moisture content in rice by reflectance spectroscopy combined with spectral data transformation.

In this study, reflectance spectroscopy was used to achieve rapid and non-destructive detection of amylase activity and moisture content in rice. Since rice husk can interfere with spectral measurements, spectral data transformation was used to remove the husk interference. Reflectance spectra of rice were transformed by direct standardization, convolutional autoencoder network, and kernel regression (KR).

AgriPest-YOLO: A rapid light-trap agricultural pest detection method based on deep learning.

Light traps have been widely used for automatic monitoring of pests in the field as an alternative to time-consuming and labor-intensive manual investigations. However, the scale variation, complex background and dense distribution of pests in light-trap images bring challenges to the rapid and accurate detection when utilizing vision technology. To overcome these challenges, in this paper, we put forward a lightweight pest detection model, AgriPest-YOLO, for achieving a well-balanced between efficiency, accuracy and model size for pest detection.

Research on the identification and detection of field pests in the complex background based on the rotation detection algorithm.

As a large agricultural and population country, China's annual demand for food is significant. The crop yield will be affected by various natural disasters every year, and one of the most important factors affecting crops is the impact of insect pests. The key to solving the problem is to detect, identify and provide feedback in time at the initial stage of the pest.