Effects of laser fluence on silicon modification by four-beam laser interference

This paper discusses the effects of laser fluence on silicon modification by four-beam laser interference. In this work, four-beam laser interference was used to pattern single crystal silicon wafers for the fabrication of surface structures, and the number of laser pulses was applied to the process in air. By controlling the parameters of laser irradiation, different shapes of silicon structures were fabricated. The results were obtained with the single laser fluence of 354 mJ/cm, 495 mJ/cm, and 637 mJ/cm, the pulse repetition rate of 10 Hz, the laser exposure pulses of 30, 100, and 300, the laser wavelength of 1064 nm, and the pulse duration of 7-9 ns. The effects of the heat transfer and the radiation of laser interference plasma on silicon wafer surfaces were investigated. The equations of heat flow and radiation effects of laser plasma of interfering patterns in a four-beam laser interference distribution were proposed to describe their impacts on silicon wafer surfaces. The experimental results have shown that the laser fluence has to be properly selected for the fabrication of well-defined surface structures in a four-beam laser interference process. Laser interference patterns can directly fabricate different shape structures for their corresponding applications

Necessary Sequencing Depth and Clustering Method to Obtain Relatively Stable Diversity Patterns in Studying Fish Gut Microbiota

The 16S rRNA gene is one of the most commonly used molecular markers for estimating bacterial diversity during the past decades. However, there is no consistency about the sequencing depth (from thousand to millions of sequences per sample), and the clustering methods used to generate OTUs may also be different among studies. These inconsistent premises make effective comparisons among studies difficult or unreliable. This study aims to examine the necessary sequencing depth and clustering method that would be needed to ensure a stable diversity patterns for studying fish gut microbiota. A total number of 42 samples dataset of Siniperca chuatsi (carnivorous fish) gut microbiota were used to test how the sequencing depth and clustering may affect the alpha and beta diversity patterns of fish intestinal microbiota. Interestingly, we found that the sequencing depth (resampling 1000-11,000 per sample) and the clustering methods (UPARSE and UCLUST) did not bias the estimates of the diversity patterns during the fish development from larva to adult. Although we should acknowledge that a suitable sequencing depth may differ case by case, our finding indicates that a shallow sequencing such as 1000 sequences per sample may be also enough to reflect the general diversity patterns of fish gut microbiota. However, we have shown in the present study that strict pre-processing of the original sequences is required to ensure reliable results. This study provides evidences to help making a strong scientific choice of the sequencing depth and clustering method for future studies on fish gut microbiota patterns, but at the same time reducing as much as possible the costs related to the analysis.</p

Networks are Slacking Off: Understanding Generalization Problem in Image Deraining

Deep deraining networks, while successful in laboratory benchmarks, consistently encounter substantial generalization issues when deployed in real-world applications. A prevailing perspective in deep learning encourages the use of highly complex training data, with the expectation that a richer image content knowledge will facilitate overcoming the generalization problem. However, through comprehensive and systematic experimentation, we discovered that this strategy does not enhance the generalization capability of these networks. On the contrary, it exacerbates the tendency of networks to overfit to specific degradations. Our experiments reveal that better generalization in a deraining network can be achieved by simplifying the complexity of the training data. This is due to the networks are slacking off during training, that is, learning the least complex elements in the image content and degradation to minimize training loss. When the complexity of the background image is less than that of the rain streaks, the network will prioritize the reconstruction of the background, thereby avoiding overfitting to the rain patterns and resulting in improved generalization performance. Our research not only offers a valuable perspective and methodology for better understanding the generalization problem in low-level vision tasks, but also displays promising practical potential

Sensitization of epithelial growth factor receptors by nicotine exposure to promote breast cancer cell growth

Introduction: Tobacco smoke is known to be the main cause of lung, head and neck tumors. Recently, evidence for an increasing breast cancer risk associated with tobacco smoke exposure has been emerging. We and other groups have shown that nicotine, as a non-conventional carcinogen, has the potential to facilitate cancer genesis and progression. However, the underlying mechanisms by which the smoke affects the breast, rather than the lung, remain unclear. Here, we examine possible downstream signaling pathways of the nicotinic acetylcholine receptor (nAChR) and their role in breast cancer promotion. Methods: Using human benign MCF10A and malignant MDA-MB-231 breast cells and specific inhibitors of possible downstream kinases, we identified nAChR effectors that were activated by treatment with nicotine. We further tested the effects of these effector pathways on the regulation of E2F1 activation, cell cycle progression and on Bcl-2 expression and long-term cell survival. Results: In this study, we demonstrated a novel signaling mechanism by which nicotine exposure activated Src to sensitize epidermal growth factor receptor (EGFR)-mediated pathways for breast cancer cell growth promotion. After the ligation of nAChR with nicotine, EGFR was shown to be activated and then internalized in both MCF10A and MDA-MB-231 breast cancer cells. Subsequently, Src, Akt and ERK1/2 were phosphorylated at different time points following nicotine treatment. We further demonstrated that through Src, the ligation of nicotine with nAChR stimulated the EGFR/ERK1/2 pathway for the activation of E2F1 and further cell progression. Our data also showed that Akt functioned directly downstream of Src and was responsible for the increase of Bcl-2 expression and long-term cell survival. Conclusions: Our study reveals the existence of a potential, regulatory network governed by the interaction of nicotine and nAChR that integrates the conventional, mitogenic Src and EGFR signals for breast cancer development

Physics-data-driven intelligent optimization for large-scale meta-devices

Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. Using physics-driven or data-driven optimization algorithms can effectively solve the aforementioned problems. Nevertheless, both of the methods either involve considerable time costs or require a substantial amount of data sets. Here, we propose a physics-data-driven approach based "intelligent optimizer" that enables us to adaptively modify the sizes of the studied meta-atom according to the sizes of its surrounding ones. Such a scheme allows to mitigate the undesired local lattice coupling effect, and the proposed network model works well on thousands of datasets with a validation loss of 3*10-3. Experimental results show that the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a Strehl ratio of 0.94. In contrast to the previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. Our design approach may sets a new paradigm for devising large-scale meta-devices.Comment: manuscripts:19 pages, 4 figures; Supplementary Information: 11 pages, 12 figure

Effects of norfloxacin, copper, and their interactions on microbial communities in estuarine sediment

The discharge of antibiotics and metals in estuaries is of great concern since they threaten microbial communities that are critical for maintaining ecosystem function. To understand single and combined effects of norfloxacin (0–20 μg g−1) and copper (40 μg g−1) on microbial ecology in estuaries, we evaluated changes in bacteria population, inhibition rates, and microbial composition in estuarine sediments over a 28-day period. Bacteria population significantly decreased following single and combined exposure to norfloxacin and copper throughout the incubation period, except on Day 28 in treatments exposed to copper, 20 μg g−1 norfloxacin, or both. These three treatment groups had lower Shannon diversity and Simpson's indices on Day 28 than other treatments and the controls suggesting recovery in bacteria population did not correspond with recovery in richness and evenness. Furthermore, functional predictions revealed that the effect of time and contaminants were significantly different on some microbial community functions on Day 28, especially the combination of Cu and high concentration NFX, including aerobic chemoheterotrophy, methanol oxidation and methylotrophy. Thus, norfloxacin and copper had significant adverse effects on microbial communities in estuarine sediments; however, the combined effects were variable and depended on exposure duration and antibiotic concentration