A Phosphorylated Dendrimer-Supported Biomass-Derived Magnetic Nanoparticle Adsorbent for Efficient Uranium Removal.

A novel biomass-based magnetic nanoparticle (Fe3O4-P-CMC/PAMAM) was synthesized by crosslinking carboxymethyl chitosan (CMC) and poly(amidoamine) (PAMAM), followed by phosphorylation with the incorporation of magnetic ferric oxide nanoparticles. The characterization results verified the successful functionalization and structural integrity of the adsorbents with a surface area of ca. 43 m2/g. Batch adsorption experiments revealed that the adsorbent exhibited a maximum adsorption capacity of 1513.47 mg·g-1 for U(VI) at pH 5.5 and 298.15 K, with Fe3O4-P-CMC/G1.5-2 showing the highest affinity among the series. The adsorption kinetics adhered to a pseudo-second-order model (R2 = 0.99, qe,exp = 463.81 mg·g-1, k2 = 2.15×10-2 g·mg-1·min-1), indicating a chemically driven process. Thermodynamic analysis suggested that the adsorption was endothermic and spontaneous (ΔH° = 14.71 kJ·mol-1, ΔG° = -50.63 kJ·mol-1, 298. 15 K), with increasing adsorption capacity at higher temperatures. The adsorbent demonstrated significant selectivity for U(VI) in the presence of competing cations, with Fe3O4-P-CMC/G1.5-2 showing a high selectivity coefficient. The performed desorption and reusability tests indicated that the adsorbent could be effectively regenerated using 1M HCl, maintaining its adsorption capacity after five cycles. XPS analysis highlighted the role of phosphonate and amino groups in the complexation with uranyl ions, and validated the existence of bimodal U4f peaks at 380.1 eV and 390.1 eV belonging to U 4f7/2 and U 4f5/2. The results of this study underscore the promise of the developed adsorbent as an effective and selective material for the treatment of uranium-contaminated wastewater.

PAFormer: Anomaly Detection of Time Series With Parallel-Attention Transformer.

Time-series anomaly detection is a critical task with significant impact as it serves a pivotal role in the field of data mining and quality management. Current anomaly detection methods are typically based on reconstruction or forecasting algorithms, as these methods have the capability to learn compressed data representations and model time dependencies. However, most methods rely on learning normal distribution patterns, which can be difficult to achieve in real-world engineering applications. Furthermore, real-world time-series data is highly imbalanced, with a severe lack of representative samples for anomalous data, which can lead to model learning failure. In this article, we propose a novel end-to-end unsupervised framework called the parallel-attention transformer (PAFormer), which discriminates anomalies by modeling both the global characteristics and local patterns of time series. Specifically, we construct parallel-attention (PA), which includes two core modules: the global enhanced representation module (GERM) and the local perception module (LPM). GERM consists of two pattern units and a normalization module, with attention weights that indicate the relationship of each data point to the whole series (global). Due to the rarity of anomalous points, they have strong associations with adjacent data points. LPM is composed of a learnable Laplace kernel function that learns the neighborhood relevancies through the distributional properties of the kernel function (local). We employ the PA to learn the global-local distributional differences for each data point, which enables us to discriminate anomalies. Finally, we propose a two-stage adversarial loss to optimize the model. We conduct experiments on five public benchmark datasets (real-world datasets) and one synthetic dataset. The results show that PAFormer outperforms state-of-the-art baselines.

Long noncoding RNA LINC00629 restrains the progression of gastric cancer by upregulating AQP4 through competitively binding to miR-196b-5p.

Gastric cancer continues to be a common cancer in the world with high incidence and mortality. Accumulating evidence has implicated long noncoding RNAs (lncRNAs) in gastric cancer progression. Here, this study identified the potential role of a novel lncRNA, LINC00629 in gastric cancer and to elucidate the underlying mechanism. Initially, microarray-based gene expression profiling of gastric cancer was employed to identify differentially expressed genes. Next, the expression of LINC00629, microRNA-196b-5p (miR-196b-5p) and aquaporin 4 (AQP4) in clinical gastric cancer tissues was determined and the cell line presenting with the lowest LINC00629 expression was selected. The interaction among LINC00629, miR-196b-5p, and AQP4 was identified. Expression of LINC00629, miR-196b-5p, and AQP4 in gastric cancer cells were altered and then biological behaviors of gastric cancer cells were assessed by 5-ethynyl-2'-deoxyuridine and Transwell assays. Tumor formation in vivo was evaluated in nude mice. In gastric cancer, expression of LINC00629 and AQP4 was downregulated, and expression of miR-196b-5p was upregulated. Proliferation, invasion, and migration of gastric cancer cells were reduced after overexpression of LINC00629. LINC00629 competitively bound to miR-196b-5p, while AQP4 was a target of miR-196b-5p. Either downregulating miR-196b-5p or upregulating AQP4 could restrain the development of gastric cancer in vitro. LINC00629 overexpression repressed the growth of transplanted tumors in vivo. Taken together, LINC00629 competitively bound to miR-196b-5p to upregulate AQP4 expression, thereby inhibiting gastric cancer progression. Therefore, understanding of this mechanism may help to improve gastric cancer treatment.

[Effects of environmental temperature on Dendrolimus punctatus development and survival].

In a simulation test, three treatments of low, normal and high temperature were designed to examine the effects of environmental fluctuant temperature on the development and survival of second generation Dendrolimus punctatus larvae. The results showed that under different temperature but same light and dark cycle, the mortality of lst to approximately 3rd instar was apparently different. Under long photophase, the larval mortality of 1st to approximately 3rd instar was 25.55%, 55.13% and 58.23% at low, normal and high temperature, while under short photophase, the mortality was 32.67%, 64.67% and 94.32%, respectively. High temperature resulted in the diapause of larvae. Under the same short photophase, the larvae exposed to high temperature could develop to 5th instar by 40%, while those exposed to normal and low temperature kept diapause, and could not develop to 5th instar after 20 days. After 48 days, the larvae exposed to high temperature became cocoons, while those exposed to normal and low temperature still diapaused. It was suggested that if the second generation larvae of D. punctatus encountered consecutive high temperature, most of them could develop to third generation.