Allethrin Promotes Apoptosis and Autophagy Associated with the Oxidative Stress-Related PI3K/AKT/mTOR Signaling Pathway in Developing Rat Ovaries

The increased concern regarding the reduction in female fertility and the impressive numbers of women undergoing fertility treatment support the existence of environmental factors beyond inappropriate programming of developing ovaries. Among these factors are pyrethroids, which are currently some of the most commonly used pesticides worldwide. The present study was performed to investigate the developmental effects of the pyrethroid-based insecticide allethrin on ovarian function in rat offspring in adulthood. We mainly focused on the roles of oxidative stress, apoptosis, autophagy and the related pathways in ovarian injury. Thirty-day-old Wistar albino female rats were intragastrically administered 0 (control), 34.2 or 68.5 mg/kg body weight allethrin after breeding from Day 6 of pregnancy until delivery. We found that allethrin-induced ovarian histopathological damage was accompanied by elevations in oxidative stress and apoptosis. Interestingly, the number of autophagosomes in allethrin-treated ovaries was higher, and this increase was correlated with the upregulated expression of genes and proteins related to the autophagic marker LC-3. Furthermore, allethrin downregulated the expression of PI3K, AKT and mTOR in allethrin-treated ovaries compared with control ovaries. Taken together, the findings of this study suggest that exposure to the pyrethroid-based insecticide allethrin adversely affects both the follicle structure and function in rat offspring during adulthood. Specifically, allethrin can induce excessive oxidative stress and defective autophagy-related apoptosis, probably through inactivation of the PI3K/AKT/mTOR signaling pathway, and these effects may contribute to ovarian dysfunction and impaired fertility in female offspring.     

Synthesis of Thieno‐Fused Heterocycles through Reiterative Iodocyclization

Iodocyclization of ethynyl methyl sulfides gives 3‐iodo‐2‐thiomethyl heterocycles, setting up the synthesis of thieno‐fused systems through a subsequent iteration of alkyne coupling and iodocylization. This approach can also be exploited in the synthesis of polyfused thiophenes. In developing this protocol it was necessary to address issues associated with unfavourable electronic bias and redox sensitivity in some substrates. The manner in which these have been addressed should prove useful elsewhere in iodocyclization chemisty.

     

Synthesis,Structure Investigation,DFT Analysis And Dielectric Characterization of Substituted Pyridinylidenepropanedinitrile (CMHQCPP) Nanostructure: Novel Approach

Journal of Inorganic and Organometallic Polymers and Materials - For the first time, a novel...     

Removal of leachate pollutants using flocculation method at simultaneous factors of optimum pH and Tacca leontopetaloides biopolymer flocculant (TBPF) dosage via face-centred central composite design

The effect of the interaction factor between pH and dosage is important in leachate wastewater treatment. This study aims to remove leachate pollutants such as turbidity, total suspended solid (TSS), chemical oxygen demand (COD) and colour using simultaneous factors of plant-based Tacca leontopetaloides biopolymer flocculant (TBPF) dosage and leachate pH. The flocculation process was carried out through jar test by applying the perikinetic theory and statical analysis (face-centred central composite design). The results found that the optimum leachate pH and TBPF dosage were pH 3 and 150 mg/L, respectively. The highest removal of leachate pollutants reached up to 69% with a second-order perikinetic model; R² = 0.9545 and k = 9 × 10⁻⁶ L/mg/min were obtained. Simultaneous interaction factors between leachate pH and TBPF dosage on turbidity and TSS removal were found significant and hence can be applied in the actual leachate wastewater treatment industry, particularly at the primary stage using the proposed model.     

A lightweight deep learning with feature weighting for activity recognition

With the development of deep learning, numerous models have been proposed for human activity recognition to achieve state-of-the-art recognition on wearable sensor data. Despite the improved accuracy achieved by previous deep learning models, activity recognition remains a challenge. This challenge is often attributed to the complexity of some specific activity patterns. Existing deep learning models proposed to address this have often recorded high overall recognition accuracy, while low recall and precision are often recorded on some individual activities due to the complexity of their patterns. Some existing models that have focused on tackling these issues are always bulky and complex. Since most embedded systems have resource constraints in terms of their processor, memory and battery capacity, it is paramount to propose efficient lightweight activity recognition models that require limited resources consumption, and still capable of achieving state-of-the-art recognition of activities, with high individual recall and precision. This research proposes a high performance, low footprint deep learning model with a squeeze and excitation block to address this challenge. The squeeze and excitation block consist of a global average-pooling layer and two fully connected layers, which were placed to extract the flattened features in the model, with best-fit reduction ratios in the squeeze and excitation block. The squeeze and excitation block served as channel-wise attention, which adjusted the weight of each channel to build more robust representations, which enabled our network to become more responsive to essential features while suppressing less important ones. By using the best-fit reduction ratio in the squeeze and excitation block, the parameters of the fully connected layer were reduced, which helped the model increase responsiveness to essential features. Experiments on three publicly available datasets (PAMAP2, WISDM, and UCI-HAR) showed that the proposed model outperformed existing state-of-the-art with fewer parameters and increased the recall and precision of some individual activities compared to the baseline, and the existing models.