Adaptive Weighted Error-Correction Method Based on the Error Distribution Characteristics of Multi-Channel Alignment.

As process nodes of advanced integrated circuits continue to decrease below 10 nm, the requirement for overlay accuracy is becoming stricter. The alignment sensor measures the position of the alignment mark relative to the wafer; thus, sub-nanometer alignment position accuracy is vital. The Phase Grating Alignment (PGA) method is widely used due to its high precision and stability. However, the alignment error caused by the mark asymmetry is the key obstacle preventing PGA technology from achieving sub-nanometer alignment accuracy. This error can be corrected using many methods, such as process verification and multi-channel weighted methods based on multi-diffraction, multi-wavelength and multi-polarization state alignment sensors. However, the mark asymmetry is unpredictable, complex and difficult to obtain in advance. In this case, the fixed-weight method cannot effectively reduce the alignment error. Therefore, an adaptive weighted method based on the error distribution characteristic of a multi-channel is proposed. Firstly, the simulation result proves that the error distribution characteristic of the multi-alignment result has a strong correlation with the mark asymmetry. Secondly, a concrete method of constructing weight values based on error distribution is described. We assume that the relationship between the weight value of each channel and the deviations of all channels' results is second-order linear. Finally, without other prior process correction in the simulation experiment, the residual error's Root Mean Square (RMS) of fixed weighted method is 14.0 nm, while the RMS of the adaptive weighted method is 0.01 nm, when dealing with five typical types of mark asymmetry. The adaptive weighted method exhibits a more stable error correction effect under unpredictable and complicated mark asymmetry.

Study on the Effect of Cations on the Surface Energy of Nano-SiO2 Particles for Oil/Gas Exploration and Development Based on the Density Functional Theory.

Although nano SiO2 exhibits excellent application potential in the field of oil and gas exploration and development, such as drilling fluid, enhanced oil/gas recovery, etc., it is prone to agglomeration and loses its effectiveness due to the action of cations in saline environments of oil and gas reservoirs. Therefore, it is crucial to study the mechanism of the change in energy between nano SiO2 and cations for its industrial application. In this paper, the effect of cations (Na+, K+, Ca2+, and Mg2+) on the surface energy of nano SiO2 particles is investigated from the perspective of molecular motion and electronic change by density functional theory. The results are as follows: Due to the electrostatic interactions, cations can migrate towards the surface of nano SiO2 particles. During the migration process, monovalent cations are almost unaffected by water molecules, and they can be directly adsorbed on the surface by nano SiO2 particles. However, when divalent cations migrate from a distance to the surface of nano SiO2 particles, they can combine with water molecules to create an energy barrier, which can prevent them from moving forward. When divalent cations break through the energy barrier, the electronic kinetic energy between them and nano SiO2 particles changes more strongly, and the electrons carried by them are more likely to break through the edge of the atomic nucleus and undergo charge exchange with nano SiO2 particles. The change in interaction energy is more intense, which can further disrupt the configuration stability of nano SiO2. The interaction energy between cations and nano SiO2 particles mainly comes from electrostatic energy, followed by Van der Waals energy. From the degree of influence of four cations on nano SiO2 particles, the order from small to large is as follows: K+ < Na+ < Mg2+ < Ca2+. The research results can provide a theoretical understanding of the interaction between nano SiO2 particles and cations during the application of nano SiO2 in the field of oil and gas exploration and development.

Deep learning-enhanced fluorescence microscopy via confocal physical imaging model.

Confocal microscopy is one of the most widely used tools for high-resolution cellular, tissue imaging and industrial inspection. Micrograph reconstruction based on deep learning has become an effective tool for modern microscopy imaging techniques. While most deep learning methods neglect the imaging process mechanism, which requires a lot of work to solve the multi-scale image pairs aliasing problem. We show that these limitations can be mitigated via an image degradation model based on Richards-Wolf vectorial diffraction integral and confocal imaging theory. The low-resolution images required for network training are generated by model degradation from their high-resolution counterparts, thereby eliminating the need for accurate image alignment. The image degradation model ensures the generalization and fidelity of the confocal images. By combining the residual neural network with a lightweight feature attention module with degradation model of confocal microscopy ensures high fidelity and generalization. Experiments on different measured data report that compared with the two deconvolution algorithms, non-negative least squares algorithm and Richardson-Lucy algorithm, the structural similarity index between the network output image and the real image reaches a high level above 0.82, and the peak signal-to-noise ratio can be improved by more than 0.6 dB. It also shows good applicability in different deep learning networks.

Depth-of-field expansion method based on multidimensional structure and edge-guided correction.

Multi-focus image fusion is a method to extend the depth of field to generate fully focused images. The effective detection of image focusing pixels and the optimization of image regions are the key to it. A method based on multidimensional structure and edge-guided correction (MSEGC) is proposed. The pixel-level focusing evaluation function is redesigned to preserve image details and non-texture regions. Edge-guided decision correction is used to suppress edge artifacts. With public data and semiconductor detection images for verification, the results show that compared with other methods, the objective evaluation is improved by 22-50%, providing better vision.

The enrichment of more functional microbes induced by the increasing hydraulic retention time accounts for the increment of autotrophic denitrification performance.

With pyrite (FeS2) and polycaprolactone (PCL) as electron donors, three denitrification systems, namely FeS2-based autotrophic denitrification (PAD) system, PCL-supported heterotrophic denitrification (PHD) system and split-mixotrophic denitrification (PPMD) system, were constructed and operated under varying hydraulic retention times (HRT, 1-48 h). Compared with PAD or PHD, the PPMD system could achieve higher removals of NO3--N and PO43--P, and the effluent SO42- concentration was greatly reduced to 7.28 mg/L. Similarly, the abundance of the dominant genera involved in the PAD (Thiobacillus, Sulfurimonas, and Ferritrophicum, etc.) or PHD (Syntrophomonas, Desulfomicrobium, and Desulfovibrio, etc.) process all increased in the PPMD system. Gene prediction completed by PICRUSt2 showed that the abundance of the functional genes involved in denitrification and sulfur oxidation all increased with the increase of HRT. This also accounted for the increased contribution of autotrophic denitrification to total nitrogen removal in the PPMD system. In addition, the analysis of metabolic pathways disclosed the specific conversion mechanisms of nitrogen and sulfur inside the reactor.

Changes in the composition of rhizosphere bacterial communities in response to soil types and acid rain.

It is widely known how acid rain negatively impacts plant physiology. However, the magnitude of these effects may depend on soil types. Although the response of aboveground parts has received much attention, the effects of soil types and acid rain on underground processes are yet to be studied, specifically with respect to the composition and diversity of bacterial communities in the rhizosphere. Based on a high throughput sequencing approach, this study examined how different soil types, acid rain of different pH, and interactions between the two factors influenced the growth and rhizosphere bacterial communities of Jatropha curcas L. The present study pointed out that the soil pH, total nitrogen (TN), total phosphorus (TP), total potassium (TK), and total organic carbon/total nitrogen (C/N) were more related to soil type than to acid rain. The growth of J. curcas aboveground was mainly affected by acid rain, while the underground growth was mainly influenced by soil type. Changes in bacterial abundance indicated that the genera (Burkholderia-Paraburkholde, Bryobacter, Cupriavidus, Mycobacterium, and Leptospirillu) and phyla (Acidobacteria and Actinobacteria) could likely resist acid rain to some extent, with Acidobacteria, Gemmatimonadetes and Proteobacteria being well adapted to the copiotrophic environments. Results of correlational analyses between Firmicutes and soil properties (pH, TN, TK) further indicated that this phylum was also well adapted to a nutrient-deficient habitat of low pH. Finally, while Mycobacterium and Bradyrhizobium could adapt to low pH, high soil TK contents were not conducive to their enrichment. The results also showed that acid rain shifted the bacterial groups from fast-growing copiotrophic populations to slow-growing oligotrophic ones. The RDA analysis, and Pearson's rank correlation coefficients indicated that soil pH and TK were the main factors influencing bacterial richness.

Molecular ecological networks reveal the spatial-temporal variation of microbial communities in drinking water distribution systems.

Microbial activity and regrowth in drinking water distribution systems is a major concern for water service companies. However, previous studies have focused on the microbial composition and diversity of the drinking water distribution systems (DWDSs), with little discussion on microbial molecular ecological networks (MENs) in different water supply networks. MEN analysis explores the potential microbial interaction and the impact of environmental stress, to explain the characteristics of microbial community structures. In this study, the random matrix theory-based network analysis was employed to investigate the impact of seasonal variation including water source switching on the networks of three DWDSs that used different disinfection methods. The results showed that microbial interaction varied slightly with the seasons but was significantly influenced by different DWDSs. Proteobacteria, identified as key species, play an important role in the network. Combined UV-chlorine disinfection can effectively reduce the size and complexity of the network compared to chlorine disinfection alone, ignoring seasonal variations, which may affect microbial activity or control microbial regrowth in DWDSs. This study provides new insights for analyzing the dynamics of microbial interactions in DWDSs.

Simulation-driven learning: a deep learning approach for image scanning microscopy via physical imaging models.

Image reconstruction based on deep learning has become an effective tool in fluorescence microscopy. Most deep learning reconstruction methods ignore the mechanism of the imaging process where a large number of datasets are required. In addition, a lot of time is spent solving the aliasing problem from multi-scaled image pairs for data pre-processing. Here we demonstrate an improved generative adversarial network for image scanning microscopy (ISM) that can be trained by simulation data and has good generalization. Based on physical imaging models, this method can generate matching image pairs from simulation images and uses them as datasets for network training, without capturing a large number of real ISM images and avoiding image alignment preprocessing. Simulation and experimental results show that this simulation data-driven method improves the imaging quality of conventional microscopic images and reduces the cost of experiments. This method provides inspiration for optimizing network generalizability of the deep learning network.

Lensfree on-chip microscopy based on single-plane phase retrieval.

We propose a novel single-plane phase retrieval method to realize high-quality sample reconstruction for lensfree on-chip microscopy. In our method, complex wavefield reconstruction is modeled as a quadratic minimization problem, where total variation and joint denoising regularization are designed to keep a balance of artifact removal and resolution enhancement. In experiment, we built a 3D-printed field-portable platform to validate the imaging performance of our method, where resolution chart, dynamic target, transparent cell, polystyrene beads, and stained tissue sections are employed for the imaging test. Compared to state-of-the-art methods, our method eliminates image degradation and obtains a higher imaging resolution. Different from multi-wavelength or multi-height phase retrieval methods, our method only utilizes a single-frame intensity data record to accomplish high-fidelity reconstruction of different samples, which contributes a simple, robust, and data-efficient solution to design a resource-limited lensfree on-chip microscope. We believe that it will become a useful tool for telemedicine and point-of-care application.

Design, synthesis and fungicidal activity of 3,4-dichloroisothiazolocoumarin-containing strobilurins.

Twenty-one novel 3,4-dichloroisothiazolocoumarin-containing strobilurins were rationally designed and synthesized. Preliminary bioassay showed that compounds 7c, 7h and 7l exhibited over 80% inhibitory rate against Sclerotinia sclerotiorum at 50 µg/mL, 7c exhibited good activity against S. sclerotiorum with median effective concentration (EC50) of 4.08 µg/mL, while the positive control coumoxystrobin showed EC50 of 1.00 µg/mL. In addition, 7d showed better fungicidal activity with a lower EC50 value of 7.65 µg/mL against Botrytis cinerea than that of positive control trifloxystrobin with its EC50 value of 21.96 µg/mL. This study indicated that 3,4-dichloroisothiazolocoumarin-containing strobilurin was a promising fungicide lead deserved for further study.

Efficacy of Pharmaceutical Care in Patients with Type 2 Diabetes Mellitus and Hypertension: A Randomized Controlled Trial.

Background: The rates of treatment adherence and treat-to-target rates of blood pressure and blood glucose-related indexes in patients with hypertension and type 2 diabetes mellitus (T2DM) are associated with prognosis. This study aimed to investigate the efficacy of pharmaceutical care postdischarge for treatment adherence in hypertensive patients with type 2 diabetes mellitus (T2DM). Methods: This was a randomized controlled trial of patients with combined T2DM and hypertension treated between January and May 2018. Pharmaceutical care included free access to a clinical pharmacist, education material, a WeChat account for live discussion, and a telephone follow-up. The primary endpoint was the 3-month medication adherence. The secondary endpoints included the achieving levels of target rates and values of fasting plasma glucose (FPG), 2 h postprandial glucose (2hPG), and hemoglobin A1c (HbA1c) and the rates of reaching the achieving target rate of blood pressure target <130/80. Results: In 80 participants, the 3-month medication adherence was higher in the pharmaceutical care group than the routine group (90.0% vs. 52.5%, P < 0.001). In terms of FPG, 2hPG, and HbA1c, there were also significant differences between the pharmaceutical care and routine groups (FPG, 6.50 (6.00, 7.18) vs. 7.00 (6.83, 7.78) mmol/L, P=0.004; 2hPG, 8.45 (7.45, 9.28) vs. 9.35 (8.23, 10.15), P=0.007; HbA1c, 6.5% (6.3%, 7.0%) vs. 7.0% (6.5%, 7.4%), P=0.007). The achieving target rate of reaching the blood pressure target in the pharmaceutical care group (92.5%) was significantly higher than that in the routine group (62.5%; P < 0.05). Conclusion: The postdischarge pharmaceutical care program in patients with T2DM and hypertension improves medication adherence.

Elevated AST/ALT ratio is associated with all-cause mortality and cancer incident.

BACKGROUND: The aspartate transaminase (AST)-to-alanine aminotransferase (ALT) ratio, which is used to measure liver injury, has been found to be associated with some chronic diseases and mortality. However, its relevance to cancer incidence resulting from population-based prospective studies has rarely been reported. In this study, we investigated the correlation of the AST/ALT ratio as a possible predictor of mortality and cancer incidence. METHODS: A total of 9,946 participants fulfilled the inclusion criteria for a basic public health service project of the Health Checkup Program conducted by the BaiYun Community Health Service Center, Taizhou. Deceased participants and cancer incident cases were from The Taizhou Chronic Disease Information Management System. Odds ratios (ORs) and interval of quartile range (IQR) computed by logistic regression analysis and cumulative incidence rate were calculated by the Kaplan-Meier survival method and compared with log-rank test statistics. RESULTS: Serum ALT and AST levels were both increased in patients with chronic diseases, but the ratio of AST/ALT was generally decreased. The cancer incident cases (488 new cases) had a greater baseline ratio (median =1.23, IQR: 0.96-1.54) than noncancer cases (median =1.15, IQR: 0.91-1.44). Compared to the first quartile of the AST/ALT ratio, the population in the top quartile had a higher cumulative cancer incidence rate (7.54% vs. 4.44%) during follow-up period. Furthermore, an elevated AST/ALT ratio increased the risk of all-cause mortality. CONCLUSIONS: The ratio of AST/ALT is a potential biomarker to assess healthy conditions and long-term mortality. Especially for cancer, the AST/ALT ratio not only increases at baseline but also predicts the future development of cancer. The clinical value and potential mechanism deserve further research.

Monitoring the Dynamic Regulation of the Mitochondrial GTP-to-GDP Ratio with a Genetically Encoded Fluorescent Biosensor.

The interconversion of guanosine triphosphate (GTP) and guanosine diphosphate (GDP) is known to be integral to a wide variety of biological cellular activities, yet to date there are no analytical methods available to directly detect the ratio of intracellular GTP to GDP. Herein, we report GRISerHR, a genetically encoded fluorescent biosensor to monitor the GTP : GDP ratio in multiple cell types and in various organelles under metabolic perturbation. Additionally, we characterized the differential mitochondrial GTP : GDP ratios resulting from genetic modulation of two isoforms of a tricarboxylic acid (TCA) cycle enzyme (succinyl-CoA synthetase; SCS-ATP and SCS-GTP) and of a phosphoenolpyruvate (PEP) cycle enzyme (PEPCK-M). Thus, our GRISerHR sensor achieves spatiotemporally precise detection of dynamic changes in the endogenous GTP : GDP ratio in living cells and can help deepen our understanding about the energy metabolic contributions of guanosine nucleotides in biology.

Deep learning enables confocal laser-scanning microscopy with enhanced resolution.

Theoretical resolution enhancement of confocal laser-scanning microscopy (CLSM) is sacrificed for the best compromise between optical sectioning and the signal-to-noise ratio (SNR). The pixel reassignment reconstruction algorithm can improve the effective spatial resolution of CLSM to its theoretical limit. However, current implementations are not versatile and are time-consuming or technically complex. Here we present a parameter-free post-processing strategy for laser-scanning microscopy based on deep learning, which enables a spatial resolution enhancement by a factor of ∼1.3, compared to conventional CLSM. To speed up the training process for experimental data, transfer learning, combined with a hybrid dataset consisting of simulated synthetic and experimental images, is employed. The overall resolution and SNR improvement, validated by quantitative evaluation metrics, allowed us to correctly infer the fine structures of real experimental images.

Shear deformation response of a holographic sensor based on elastic poly(MMA-co-LMA) photopolymer.

A holographic sensor based on camphorquinon doped poly (methyl methacrylate-co-lauryl methacrylate) (poly (MMA-co-LMA)) elastic photopolymer is developed for characterizing the shear deformation of material. A shear angle and its transverse displacement, which are induced by a couple of shear stresses, are analyzed using a diffraction spectrum of a transmission holographic sensor. The dependence of the peak wavelength shift on the shear deformation presents a good linear relationship which provides a quantitative characterization means. The detectable maximum of the shear angle exceeds 26.1 deg, and the peak wavelength shift closes to 4.0 nm. The available sensitivity is better than 3.33 deg/0.5 nm (shear angle/wavelength shift) using a commercial spectrometer with 0.5 nm of resolution. Finally, the reversibility response of shear deformation further confirmed the practical applicability of the elastic polymer-based shear deformation sensor. The spectrum measurement of shear deformation provides a novel measurement means for the mechanical deformation of materials and expands the application of a holographic sensor.

Characterizing spatial uniformity of tensile deformation with an elastic polymer based holographic sensor.

Tensile deformation uniformity of material has been studied with a stretchable polymer based holographic sensor. The diffraction spectrum distribution of a holographic grating with a large area as a main response parameter is scanned. A linear spatial distribution of peak wavelength provides an important foundation for exploring the tensile uniformity. The same ratio of wavelength to position confirms that the tensile deformation of the material is uniform in a small spot size. Over the entire length of the materials, gradually increasing deformation accumulation is the main uniformity feature of tensile deformation. The uniformity response is expected to apply in sensing the deformation and stress fluctuation distribution in the middle of the thin surface. The non-uniform distribution of stress can be expressed by the nonlinear distribution of the grating diffraction spectrum. The optical measurement of tensile deformation uniformity further validates the applicability of a stretchable polymer based holographic sensor.

SAF-189s, a potent new-generation ROS1 inhibitor, is active against crizotinib-resistant ROS1 mutant-driven tumors.

The ROS1 fusion kinase is an attractive antitumor target. Though with significant clinical efficacy, the well-known first-generation ROS1 inhibitor (ROS1i) crizotinib inevitably developed acquired resistance due to secondary point mutations in the ROS1 kinase. Novel ROS1is effective against mutations conferring secondary crizotinib resistance, especially G2032R, are urgently needed. In the present study, we evaluated the antitumor efficacy of SAF-189s, the new-generation ROS1/ALK inhibitor, against ROS1 fusion wild-type and crizotinib-resistant mutants. We showed that SAF-189s potently inhibited ROS1 kinase and its known acquired clinically resistant mutants, including the highly resistant G2032R mutant. SAF-189s displayed subnanomolar to nanomolar IC50 values against ROS1 wild-type and mutant kinase activity and a selectivity vs. other 288 protein kinases tested. SAF-189s blocked cellular ROS1 signaling, and in turn potently inhibited the cell proliferation in HCC78 cells and BaF3 cells expressing ROS1 fusion wild-type and resistance mutants. In nude mice bearing BaF3/CD74-ROS1 or BaF3/CD74-ROS1G2032R xenografts, oral administration of SAF-189s dose dependently suppressed the growth of both ROS1 wild-type- and G2032R mutant-driven tumors. In a patient-derived xenograft model of SDC4-ROS1 fusion NSCLC, oral administration of SAF-189s (20 mg/kg every day) induced tumor regression and exhibited notable prolonged and durable efficacy. In addition, SAF-189s was more potent than crizotinib and comparable to lorlatinib, the most advanced ROS1i known against the ROS1G2032R. Collectively, these results suggest the promising potential of SAF-189s for the treatment of patients with the ROS1 fusion G2032R mutation who relapse on crizotinib. It is now recruiting both crizotinib-relapsed and naive ROS1-positive NSCLC patients in a multicenter phase II trial (ClinicalTrials.gov Identifier: NCT04237805).

Model-Independent Lens Distortion Correction Based on Sub-Pixel Phase Encoding.

Lens distortion can introduce deviations in visual measurement and positioning. The distortion can be minimized by optimizing the lens and selecting high-quality optical glass, but it cannot be completely eliminated. Most existing correction methods are based on accurate distortion models and stable image characteristics. However, the distortion is usually a mixture of the radial distortion and the tangential distortion of the lens group, which makes it difficult for the mathematical model to accurately fit the non-uniform distortion. This paper proposes a new model-independent lens complex distortion correction method. Taking the horizontal and vertical stripe pattern as the calibration target, the sub-pixel value distribution visualizes the image distortion, and the correction parameters are directly obtained from the pixel distribution. A quantitative evaluation method suitable for model-independent methods is proposed. The method only calculates the error based on the characteristic points of the corrected picture itself. Experiments show that this method can accurately correct distortion with only 8 pictures, with an error of 0.39 pixels, which provides a simple method for complex lens distortion correction.

A Ratiometric Fluorescent Biosensor Reveals Dynamic Regulation of Long-Chain Fatty Acyl-CoA Esters Metabolism.

Despite increasing awareness of the biological impacts of long-chain fatty acyl-CoA esters (LCACoAs), our knowledge about the subcellular distribution and regulatory functions of these acyl-CoA molecules is limited by a lack of methods for detecting LCACoAs in living cells. Here, we report development of a genetically encoded fluorescent sensor that enables ratiometric quantification of LCACoAs in living cells and subcellular compartments. We demonstrate how this FadR-cpYFP fusion "LACSer sensor" undergoes LCACoA-induced conformational changes reflected in easily detectable fluorescence responses, and show proof-of-concept for real-time monitoring of LCACoAs in human cells. Subsequently, we applied LACSer in scientific studies investigating how disruption of ACSL enzymes differentially reduces cytosolic and mitochondrial LCACoA levels, and show how genetic disruption of an acyl-CoA binding protein (ACBP) alters mitochondrial accumulation of LCACoAs.

Multifunctionality of biocrusts is positively predicted by network topologies consistent with interspecies facilitation.

The potential of biodiversity loss to impair the delivery of ecosystem services has motived ecologists to better understand the relationship between biodiversity and ecosystem functioning. Although increasing evidence underlines the collective contribution of different biodiversity components on the simultaneous performance of multiple functions (multifunctionality), we know little about the trade-offs between individual diversity effects and the extent to which they determine multifunctionality differentially. Here, at a subcontinental scale of 62 dryland sites, we show in phototrophic microbiota of biological soil crusts (biocrusts) that, whereas richness alone is unable to guarantee the maxima of multifunctional performance, interspecies facilitation and compositional identity are particularly stronger but often neglected predictors. The inconsistent effects of different biodiversity components imply that soil multifunctionality can be lost despite certain species remaining present. Moreover, we reveal a significant empirical association between species functional importance and its topological feature in co-occurrence networks, indicating a functional signal of species interaction. Nevertheless, abundant species tend to isolate and merely interact within small topological structures, but rare species were tightly connected in complicated network modules. Our findings suggest that abundant and rare species of soil phototrophs exhibit distinct functional relevance. These results give a comprehensive view of how soil constructive species drive multifunctionality in biocrusts and ultimately promote a deeper understanding of the consequences of biodiversity loss in real-world ecosystems.