F2^2AT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns

Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as self-driving cars, surveillance security, and medical diagnosis. At present, adversarial training is one of the most effective defenses against adversarial examples. However, traditional adversarial training makes it difficult to achieve a good trade-off between clean accuracy and robustness since spurious features are still learned by DNNs. The intrinsic reason is that traditional adversarial training makes it difficult to fully learn core features from adversarial examples when adversarial noise and clean examples cannot be disentangled. In this paper, we disentangle the adversarial examples into natural and perturbed patterns by bit-plane slicing. We assume the higher bit-planes represent natural patterns and the lower bit-planes represent perturbed patterns, respectively. We propose a Feature-Focusing Adversarial Training (F$^2$AT), which differs from previous work in that it enforces the model to focus on the core features from natural patterns and reduce the impact of spurious features from perturbed patterns. The experimental results demonstrated that F$^2$AT outperforms state-of-the-art methods in clean accuracy and adversarial robustness

Quantitative detection and attribution of groundwater level variations in the Amu Darya Delta

In the past few decades, the shrinkage of the Aral Sea is one of the biggest ecological catastrophes caused by human activity. To quantify the joint impact of both human activities and climate change on groundwater, the spatiotemporal groundwater dynamic characteristics in the Amu Darya Delta of the Aral Sea from 1999 to 2017 were analyzed, using the groundwater level, climate conditions, remote sensing data, and irrigation information. Statistics analysis was adopted to analyze the trend of groundwater variation, including intensity, periodicity, spatial structure, while the Pearson correlation analysis and principal component analysis (PCA) were used to quantify the impact of climate change and human activities on the variabilities of the groundwater level. Results reveal that the local groundwater dynamic has varied considerably. From 1999 to 2002, the groundwater level dropped from -189 cm to -350 cm. Until 2017, the groundwater level rose back to -211 cm with fluctuation. Seasonally, the fluctuation period of groundwater level and irrigation water was similar, both were about 18 months. Spatially, the groundwater level kept stable within the irrigation area and bare land but fluctuated drastically around the irrigation area. The Pearson correlation analysis reveals that the dynamic of the groundwater level is closely related to irrigation activity within the irrigation area (Nukus: -0.583), while for the place adjacent to the Aral Sea, the groundwater level is closely related to the Large Aral Sea water level (Muynak: 0.355). The results of PCA showed that the cumulative contribution rate of the first three components exceeds 85%. The study reveals that human activities have a great impact on groundwater, effective management, and the development of water resources in arid areas is an essential prerequisite for ecological protection

Robust memristors based on layered two-dimensional materials

Van der Waals heterostructures are formed by stacking layers of different two-dimensional materials and offer the possibility to design new materials with atomic-level precision. By combining the valuable properties of different 2D systems, such heterostructures could potentially be used to address existing challenges in the development of electronic devices, particularly those that require vertical multi-layered structures. Here we show that robust memristors with good thermal stability, which is lacking in traditional memristors, can be created from a van der Waals heterostructure composed of graphene/MoS2–xO x/graphene. The devices exhibit excellent switching performance with an endurance of up to 107 and a high operating temperature of up to 340 °C. With the help of in situ electron microscopy, we show that the thermal stability is due to the MoS2–xO x switching layer, as well as the graphene electrodes and the atomically sharp interface between the electrodes and the switching layer. We also show that the devices have a well-defined conduction channel and a switching mechanism that is based on the migration of oxygen ions. Finally, we demonstrate that the memristor devices can be fabricated on a polyimide substrate and exhibit good endurance against over 1,000 bending cycles, illustrating their potential for flexible electronic applications

Comparative miRNA expression profile analysis of porcine ovarian follicles: new insights into the initiation mechanism of follicular atresia

Follicular atresia occurs in every stage of ovarian development, which is relevant to female fertility. In the past decade, increasing studies have confirmed that miRNAs, a class of short non-coding RNAs, play an important role in follicular atresia by post-transcription regulation of their target genes. However, the function of miRNAs on follicular atresia initiation is unknown. In the present study, high-throughput small RNA sequencing was performed to analyze differential miRNA expression profiles between healthy (HF) follicles and early atretic (EAF) follicles. A total of 237 conserved miRNA were detected, and the miR-143 is the highest expressed in follicles. Meanwhile, we also found wide sequence variations (isomiRs) in porcine ovarian miRNA, including in 5′un-translation region, core seed sequences and 3′untranslation region. Furthermore, we identified 22 differentially expressed miRNAs in EAF groups compared to HF group, of which 3 miRNAs were upregulated, as well as 19 miRNAs were downregulated, and then the RT-PCR was performed to validate these profiles. The target genes of these differentially expressed miRNAs were predicted by using miRwalk, miRDB, and Targetscan database, respectively. Moreover, the gene ontology and KEGG pathway enrichment established that the regulating functions and signaling pathways of these miRNAs contribute to follicular atresia initiation and cell fate. In conclusion, this study provides new insights into the changes of miRNAs in early atretic follicles to demonstrate their molecular regulation in ovarian follicular atretic initiation

NRF2 Plays a Critical Role in Both Self and EGCG Protection against Diabetic Testicular Damage

Activation of nuclear factor erythroid 2-related factor 2 (NRF2) has been found to ameliorate diabetic testicular damage (DTD) in rodents. However, it was unclear whether NRF2 is required for these approaches in DTD. Epigallocatechin gallate (EGCG) is a potent activator of NRF2 and has shown beneficial effects on multiple diabetic complications. However, the effect of EGCG has not been studied in DTD. The present study aims to explore the role of NRF2 in both self and EGCG protection against DTD. Therefore, streptozotocin-induced diabetic C57BL/6 wild type (WT) and Nrf2 knockout (KO) mice were treated in the presence or absence of EGCG, for 24 weeks. The Nrf2 KO mice exhibited more significant diabetes-induced loss in testicular weight and spermatozoa count, and increase in testicular apoptotic cell death, as compared with the WT mice. EGCG activated NRF2 expression and function, preserved testicular weight and spermatozoa count, and attenuated testicular apoptotic cell death, endoplasmic reticulum stress, inflammation, and oxidative damage in the WT diabetic mice, but not the Nrf2 KO diabetic mice. The present study demonstrated for the first time that NRF2 plays a critical role in both self and EGCG protection against DTD

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Review of Piezoelectric Micromachined Ultrasonic Transducers for Rangefinders

Piezoelectric micromachined ultrasonic transducer (pMUT) rangefinders have been rapidly developed in the last decade. With high output pressure to enable long-range detection and low power consumption (16 μW for over 1 m range detection has been reported), pMUT rangefinders have drawn extensive attention to mobile range-finding. pMUT rangefinders with different strategies to enhance range-finding performance have been developed, including the utilization of pMUT arrays, advanced device structures, and novel piezoelectric materials, and the improvements of range-finding methods. This work briefly introduces the working principle of pMUT rangefinders and then provides an extensive overview of recent advancements that improve the performance of pMUT rangefinders, including advanced pMUT devices and range-finding methods used in pMUT rangefinder systems. Finally, several derivative systems of pMUT rangefinders enabling pMUT rangefinders for broader applications are presented