[2]Pseudorotaxane-Based Supramolecular Optical Indicator for the Visual Detection of Cellular Cyanide Excretion

Cyanide is extremely hazardous to living organisms and the environment. Owing to its wide range of applications and high toxicity, the development of functional materials for cyanide detection and sensing is highly desirable. Host–guest complexation between bis(p-phenylene)-34-crown-10 H and N-methylacridinium salt G remarkably decreases the detection limit for cyanide anions compared with that of the guest itself. The [2]pseudorotaxane selectively recognizes the cyanide anion with high optical sensitivity as a result of the nucleophilic addition of the cyanide anion at the 9-position of G . The host–guest complexation is further incorporated into supramolecular materials for the visual detection of cyanide anions, especially the detection of cellular cyanide excretion with a detection limit of 0.6 μm . This supramolecular method provides an extremely distinct strategy for the visual detection of cyanide anions.     

基于级联卷积神经网络的前列腺磁共振图像分类

针对深度学习训练成本高，以及基于磁共振图像的前列腺癌临床诊断需要大量医学常识且极为耗时的问题，本文提出了一种基于级联卷积神经网络（Convolutional Neural Network，CNN）和磁共振图像的前列腺癌（Prostate Cancer，PCa）自动分类诊断方法，该网络以Faster-RCNN作为前网络，对前列腺区域进行提取分割，用于排除前列腺附近组织器官的干扰；以基于ResNet改进的网络结构CNN40_bottleneck作为后网络，用于对前列腺区域病变进行分类.后网络由瓶颈结构串联组成，其中使用批量标准化（Batch Normalization，BN）、全局平均池化（Global Average Pooling，GAP）进行优化.实验结果证明，本文方法对前列腺癌诊断结果较好，而且缩减了训练时间和参数量，有效降低了训练成本.     

Vertical copper oxide nanowire arrays attached three-dimensional macroporous framework as a self-supported sensor for sensitive hydrogen peroxide detection

A hierarchical nanostructure consisting of uniform copper oxide nanowires vertically grown on three-dimensional copper framework (CuO NWs/3D-Cu foam) was prepared by a two-step synthetic process. The uniform CuO NWs anchored onto the 3D foam exhibited outstanding electrocatalytic activity towards hydrogen peroxide reduction due to the unique one‐dimensional direction with its excellent catalytic activity and large surface area of 3D substrate, which enhanced electroactive sites and charge conductivity. As a result, a wide linear detection range of 1 µM–1 mM, good sensitivity of 8.87 µA/(mM ⋅ cm²), low detection limit of 0.98 µM, and rapid response time of 5 s to hydrogen peroxide were achieved under a working potential of −0.4 V in phosphate buffer solution (pH of 7.4). In addition, the CuO NWs/3D-Cu foam material showed excellent selectivity to hydrogen peroxide and good resistance against poisonous interferents, including ascorbic acid, dopamine, urea, uric acid, and potassium chloride. Furthermore, the CuO NWs/3D-Cu foam presented good reproducibility, stability, and accurate detection for hydrogen peroxide in real sample; therefore, it may be considered to be a potential free-standing hydrogen peroxide sensor in practical analysis applications.     

A Highly Stable (4, 8)-Connected Tb-MOF Exhibiting Efficiently Luminescent Sensing towards Nitroimidazole Antibiotics

A robust 4,8-connected Tb-based metal-organic framework (Tb-MOF) with paddle wheel-shaped {Tb₂(COO)₄} subunits extended by C₃-symmetric 3,3',3''-[1,3,5-benzenetriyltris(carbonylimino)]tris-benzoate connector was hydrothermally synthesized, showing highly environmental stability, good dispersion and intense green emission in water system. Resulting critically from the well suppressed absorption towards the excitation energy, the Tb-MOF exhibits rapid and efficient fluorescent response towards nitroimidazole antibiotics with strong quenching constants and low detection limits of 1.59 × 10⁴ m ^–1 and 2.4 μM for metronidazole as well as 1.62 × 10⁴ m ^–1 and 2.9 μM for dimetridazole. Moreover, the sensitive and selective identification of the Tb-MOF has strong anti-interference and excellent regeneration ability, which endows the promising applications of the Tb-MOF as fluorescent sensing materials.     

基于多尺度学习与深度卷积神经网络的遥感图像土地利用分类

土地利用信息是国土资源管理的基础和重要依据，随着高分辨率遥感图像数据的日益增多，迫切需要快速准确的土地利用分类方法。目前应用较广的面向对象的分类方法对空间特征的利用尚不够充分，在特征选择上存在一定的局限性。为此，提出一种基于多尺度学习与深度卷积神经网络（deep convolutional neural network，DCNN）的多尺度神经网络（multi-scale neural network，MSNet）模型，基于残差网络构建了100层编码网络，通过并行输入实现输入图像的多尺度学习，利用膨胀卷积实现特征图像的多尺度学习，设计了一种端到端的分类网络。以浙江省0.5 m分辨率的光学航空遥感图像为数据源进行了实验，总体分类精度达91.97%，并将其与传统全卷积网络（fully convolutional networks，FCN）方法和基于支持向量机（support vector machine，SVM）的面向对象方法进行了对比，结果表明，本文所提方法分类精度更高，分类结果整体性更强。     

Trichothecenes in Food and Feed,Relevance to Human and Animal Health and Methods of Detection: A Systematic Review

Trichothecene mycotoxins are sesquiterpenoid compounds primarily produced by fungi in taxonomical genera such as Fusarium, Myrothecium, Stachybotrys, Trichothecium, and others, under specific climatic conditions on a worldwide basis. Fusarium mold is a major plant pathogen and produces a number of trichothecene mycotoxins including deoxynivalenol (or vomitoxin), nivalenol, diacetoxyscirpenol, and T-2 toxin, HT-2 toxin. Monogastrics are sensitive to vomitoxin, while poultry and ruminants appear to be less sensitive to some trichothecenes through microbial metabolism of trichothecenes in the gastrointestinal tract. Trichothecene mycotoxins occur worldwide however both total concentrations and the particular mix of toxins present vary with environmental conditions. Proper agricultural practices such as avoiding late harvests, removing overwintered stubble from fields, and avoiding a corn/wheat rotation that favors Fusarium growth in residue can reduce trichothecene contamination of grains. Due to the vague nature of toxic effects attributed to low concentrations of trichothecenes, a solid link between low level exposure and a specific trichothecene is difficult to establish. Multiple factors, such as nutrition, management, and environmental conditions impact animal health and need to be evaluated with the knowledge of the mycotoxin and concentrations known to cause adverse health effects. Future research evaluating the impact of low-level exposure on livestock may clarify the potential impact on immunity. Trichothecenes are rapidly excreted from animals, and residues in edible tissues, milk, or eggs are likely negligible. In chronic exposures to trichothecenes, once the contaminated feed is removed and exposure stopped, animals generally have an excellent prognosis for recovery. This review shows the occurrence of trichothecenes in food and feed in 2011–2020 and their toxic effects and provides a summary of the discussions on the potential public health concerns specifically related to trichothecenes residues in foods associated with the exposure of farm animals to mycotoxin-contaminated feeds and impact to human health. Moreover, the article discusses the methods of their detection.     

Peroxidase-Like Platinum Clusters Synthesized by Ganoderma lucidum Polysaccharide for Sensitively Colorimetric Detection of Dopamine

The sensitive and selective detection of dopamine (DA) is very important for the early diagnosis of DA-related diseases. In this study, we reported the colorimetric detection of DA using Ganoderma lucidum polysaccharide (GLP) stabilized platinum nanoclusters (Pt_n-GLP NCs). When Pt₆₀₀-GLP NCs was added, 3,3’,5,5’-tetramethylbenzidine (TMB) was rapidly catalyzed and oxidized to blue oxTMB, indicating the peroxidase-like activity of Pt₆₀₀-GLP NCs. The catalytic reaction on the substrate TMB followed the Michaelis-Menton kinetics with the ping-pong mechanism. The mechanism of the colorimetric reaction was mainly due to the formation of hydroxyl radical (•OH). Furthermore, the catalytic reaction of Pt₆₀₀-GLP NCs was used in the colorimetric detection of DA. The linear range for DA was 1–100 μM and the detection limit was 0.66 μM. The sensitive detection of DA using Pt-GLP NCs with peroxidase-like activity offers a simple and practical method that may have great potential applications in the biotechnology field.     

ABLE: Attention based learning for enzyme classification

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper proposes an attention-based bidirectional-LSTM model (ABLE) trained on over sampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the PDB. We baseline our model against eighteen other machine learning and deep learning networks, including CNN, LSTM, Bi-LSTM, GRU, and the state-of-the-art DeepEC model. We conduct experiments with two different oversampling techniques, SMOTE and ADASYN. To corroborate the obtained results, we perform extensive experimentation and statistical testing.     

Detection challenges in quantitative polymer analysis by liquid chromatography

Accurate quantification of polymer distributions is one of the main challenges in polymer analysis by liquid chromatography. The response of contemporary detectors is typically influenced by compositional features such as molecular weight, chain composition, end groups, and branching. This renders the accurate quantification of complex polymers of which there are no standards available, extremely challenging. Moreover, any (programmed) change in mobile‐phase composition may further limit the applicability of detection techniques. Current methods often rely on refractive index detection, which is not accurate when dealing with complex samples as the refractive‐index increment is often unknown. We review current and emerging detection methods in liquid chromatography with the aim of identifying detectors, which can be applied to the quantitative analysis of complex polymers.     

Graphene and Carbon Nanotube-based Electrochemical Sensing Platforms for Dopamine

Dopamine (DA) is an important neurotransmitter, which is created and released from the central nervous system. It plays a crucial role in human activities, like cognition, emotions, and response to anything. Maladjustment of DA in human blood serum results in different neural diseases, like Parkinson's and Schizophrenia. Consequently, researchers have started working on DA detection in blood serum, which is undoubtedly a hot research area. Electrochemical sensing techniques are more promising to detect DA in real samples. However, utilizing conventional electrodes for selective determination of DA encounters numerous problems due to the coexistence of other materials, such as uric acid and ascorbic acid, which have an oxidation potential close to DA. To overcome such problems, researchers have put their focus on the modification of bare electrodes. The aim of this review is to present recent advances in modifications of most used bare electrodes with carbonaceous materials, especially graphene, its derivatives, and carbon nanotubes, for electrochemical detection of DA. A brief discussion about the mechanistic phenomena at the electrode interface has also been included in this review.