Enamine Acylation Enabled Desymmetrization of Malonic Esters.

Asymmetric enamine alkylation represents a powerful tool for stereoselective C-C bond formation; in contrast, the development of enantioselective enamine acylation remains elusive. Here, we report that a chiral phosphoric acid can render an in-situ-formed enamine to undergo a stereoselective intramolecular α-carbon acylation, providing an alternative approach for the synthesis of useful pyrrolinones and indolinones bearing tetrasubstituted stereocenters. Utilizing an effective integration of the desymmetrization strategy and bifunctional organocatalysis, the first example of enantioselective enamine acylation is achieved by employing readily available aminomalonic esters and cyclic ketones. Instead of reactive and moisture-sensitive acyl chlorides, common esters with low electrophilicity were successfully used as efficient acylating reagents via hydrogen bonding interactions. The utility is demonstrated in the concise and enantioselective synthesis of (+)-LipidGreen I and II. Experimental studies and DFT calculations establish the reaction pathway and the origin of stereocontrol.

The optimal threshold of PD-L1 combined positive score to predict the benefit of PD-1 antibody plus chemotherapy for patients with HER2-negative gastric adenocarcinoma: a meta-analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) combined with chemotherapy have become the first-line treatment of metastatic gastric and gastroesophageal adenocarcinomas (GEACs). This study aims to figure out the optimal combined positive score (CPS) cutoff value. METHODS: We searched for randomized phase III trials to investigate the efficacy of ICIs plus chemotherapy for metastatic GEACs compared with chemotherapy alone. Pooled analyses of hazard ratios (HRs) based on PD-L1 expression were performed. RESULTS: A total of six trials (KEYNOTE-062, KEYNOTE-590, KEYNOTE-859, ATTRACTION-04, CheckMate 649, and ORIENT-16) were included, comprising 5,242 patients. ICIs plus chemotherapy significantly improved OS (HR: 0.79, 95% CI 0.72-0.86 in global patients; HR: 0.75, 95% CI 0.57-0.98 in Asian patients) and PFS (HR: 0.74, 95% CI 0.68-0.82 in global patients; HR: 0.64, 95% CI 0.56-0.73 in Asian patients) compared with chemotherapy alone. The differences in OS (ratio of HR: 1.05, 95% CI 0.79-1.40; predictive value: - 5.1%) and PFS (ratio of HR: 1.16, 95% CI 0.98-1.36; predictive value: - 13.5%) were not statistically significant between the global and Asian patients. Subgroup analyses indicated that the optimal CPS threshold was at ≥ 5 for OS and ≥ 10 for PFS with the highest predictive values. CONCLUSIONS: The benefit derived from ICIs plus chemotherapy is similar between Asian and global GEAC patients. However, those with a PD-L1 CPS < 5 or CPS < 10 may not have significant benefits from ICIs therapy. Therefore, it is advisable to routinely assess PD-L1 expression in GEAC patients considered for ICIs treatment.

A Machine Learning Method for RNA-Small Molecule Binding Preference Prediction.

The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for the inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features and neglect small molecule characteristics, and resulting in poor performance on unknown small molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on the published RL98 testing set, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown small molecule UNK96 testing set, showing substantial improvement over the existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown small molecule testing. Predicting RNA-small molecule binding preferences can help in the understanding of RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography.

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying RNA small molecule binding nucleotides is essential and requires methods that exhibit a high predictive ability to facilitate drug discovery and inhibitor design. Existing methods can predict the binding nucleotides of simple RNA structures, but it is hard to predict binding nucleotides in complex RNA structures with junctions. To address this limitation, we developed a new deep learning model based on spatial correlation, ZHmolReSTasite, which can accurately predict binding nucleotides of small and large RNA with junctions. We utilize RNA surface topography to consider the spatial correlation, characterizing nucleotides from sequence and tertiary structures to learn a high-level representation. Our method outperforms existing methods for benchmark test sets composed of simple RNA structures, achieving precision values of 72.9% on TE18 and 76.7% on RB9 test sets. For a challenging test set composed of RNA structures with junctions, our method outperforms the second best method by 11.6% in precision. Moreover, ZHmolReSTasite demonstrates robustness regarding the predicted RNA structures. In summary, ZHmolReSTasite successfully incorporates spatial correlation, outperforms previous methods on small and large RNA structures using RNA surface topography, and can provide valuable insights into RNA small molecule prediction and accelerate RNA inhibitor design.

Microfluidic Approaches for Microactuators: From Fabrication, Actuation, to Functionalization.

Microactuators can autonomously convert external energy into specific mechanical motions. With the feature sizes varying from the micrometer to millimeter scale, microactuators offer many operation and control possibilities for miniaturized devices. In recent years, advanced microfluidic techniques have revolutionized the fabrication, actuation, and functionalization of microactuators. Microfluidics can not only facilitate fabrication with continuously changing materials but also deliver various signals to stimulate the microactuators as desired, and consequently improve microfluidic chips with multiple functions. Herein, this cross-field that systematically correlates microactuator properties and microfluidic functions is comprehensively reviewed. The fabrication strategies are classified into two types according to the flow state of the microfluids: stop-flow and continuous-flow prototyping. The working mechanism of microactuators in microfluidic chips is discussed in detail. Finally, the applications of microactuator-enriched functional chips, which include tunable imaging devices, micromanipulation tools, micromotors, and microsensors, are summarized. The existing challenges and future perspectives are also discussed. It is believed that with the rapid progress of this cutting-edge field, intelligent microsystems may realize high-throughput manipulation, characterization, and analysis of tiny objects and find broad applications in various fields, such as tissue engineering, micro/nanorobotics, and analytical devices.

Tumor residue in patients with stage II-IVA nasopharyngeal carcinoma who received intensity-modulated radiation therapy: development and validation of a prediction nomogram integrating postradiotherapy plasma Epstein-Barr virus deoxyribonucleic acid, clinical stage, and radiotherapy dose.

BACKGROUND: To develop and validate a predictive nomogram for tumor residue 3-6 months after treatment based on postradiotherapy plasma Epstein-Barr virus (EBV) deoxyribonucleic acid (DNA), clinical stage, and radiotherapy (RT) dose in patients with stage II-IVA nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT). METHODS: In this retrospective study, 1050 eligible patients with stage II-IVA NPC, who completed curative IMRT and underwent pretreatment and postradiotherapy (-7 to +28 days after IMRT) EBV DNA testing, were enrolled from 2012 to 2017. The prognostic value of the residue was explored using Cox regression analysis in patients (n=1050). A nomogram for predicting tumor residues after 3-6 months was developed using logistic regression analyses in the development cohort (n=736) and validated in an internal cohort (n=314). RESULTS: Tumor residue was an independent inferior prognostic factor for 5-year overall survival, progression-free survival, locoregional recurrence-free survival and distant metastasis-free survival (all P<0.001). A prediction nomogram based on postradiotherapy plasma EBV DNA level (0 vs. 1-499 vs. ≥500 copies/ml), clinical stage (II vs. III vs. IVA), and RT dose (68.00-69.96 vs. 70.00-74.00 Gy) estimated the probability of residue development. The nomogram showed better discrimination (area under the curve (AUC): 0.752) than either the clinical stage (0.659) or postradiotherapy EBV DNA level (0.627) alone in the development and validation cohorts (AUC: 0.728). CONCLUSIONS: We developed and validated a nomogram model integrating clinical characteristics at the end of IMRT for predicting whether tumor will residue or not after 3-6 months. Thus, high-risk NPC patients who might benefit from immediate additional intervention could be identified by the model, and the probability of residue can be reduced in the future.

Evaluation of DNA-protein complex structures using the deep learning method.

Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge. Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

Widely targeted quantitative lipidomics and prognostic model reveal plasma lipid predictors for nasopharyngeal carcinoma.

BACKGROUND: Dysregulation of lipid metabolism is closely associated with cancer progression. The study aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC), based on lipidomics. METHODS: The plasma lipid profiles of 179 patients with locoregionally advanced NPC (LANPC) were measured and quantified using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set (P < 0.05). A deep survival method called DeepSurv was employed to develop a proposed model based on significant lipid species (P < 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. The study also explored the potential role of lipid alterations in the prognosis of NPC. RESULTS: Forty lipids were recognized as distant metastasis-associated (P < 0.05) by univariate Cox regression. The concordance indices of the proposed model were 0.764 (95% confidence interval (CI), 0.682-0.846) and 0.760 (95% CI, 0.649-0.871) in the training and validation sets, respectively. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52-194.80; P < 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways. CONCLUSIONS: Widely targeted quantitative lipidomics reveals plasma lipid predictors for LANPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in LANPC patients.

RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study.

RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA's conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I-III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base-base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.

Catalytic Enantioselective Biltz Synthesis.

The Biltz synthesis establishes straightforward access to 5,5-disubstituted (thio)hydantoins by combining a 1,2-diketone and a (thio)urea. Its appealing features include inherent atom and step economy together with the potential to generate structurally diverse products. However, control of the stereochemistry of this reaction has proven to be a daunting challenge. Herein, we describe the first example of enantioselective catalytic Biltz synthesis which affords more than 40 thiohydantoins with high stereo- and regio-control, irrespective of the symmetry of thiourea structure. A one pot synthesis of corresponding hydantoins is also documented. Remarkably, experimental studies and DFT calculations establish the reaction pathway and origin of stereoselectivity.

A cost-effectiveness analysis of capecitabine maintenance therapy versus routine follow-up for early-stage triple-negative breast cancer patients after standard treatment from a perspective of Chinese society.

BACKGROUND: Capecitabine maintenance therapy is safe and efficacious for early-stage triple-negative breast cancer (TNBC) patients, but the cost-effectiveness of its long-term use has not been investigated. Here, we evaluated the cost-effectiveness of capecitabine maintenance therapy, compared with routine follow-up, in early-stage TNBC patients after standard treatment from a perspective of Chinese society. METHODS: A three-state Markov model based on the data from the SYSUCC-001 trial was constructed to estimate the cost-effectiveness of capecitabine maintenance therapy in a month cycle over a period of 30-year time horizon. A 5% annual discount rate was set for all costs and benefits. One-way and probabilistic sensitivity analyses were performed to explore the model uncertainties. The main outcomes include quality-adjusted life years (QALYs), incremental cost-effectiveness ratio (ICER), and the number needed to treat (NNT) to prevent one additional event. RESULTS: Compared with routine follow-up, 1-year capecitabine maintenance therapy yielded an additional 1.29 quality-adjusted life years (QALYs) at an additional cost of $3391.70, with an ICER of $2630.53 (95% CI: $1159.81-$5090.12) per QALY gained. The ICER was considerably lower than the recommended willingness-to-pay (WTP) threshold (i.e., $28,130.00 per QALY). The results were sensitive to the discount rate, drug cost, and treatment cost after relapse. Further, the NNT to prevent one additional relapse case was 29.2 (95% CI: 13.2-196.6), 16.7 (95% CI: 8.4-111.6), and 12.0 (95% CI: 5.7-82.6) at 1, 2, and 5 years, respectively. CONCLUSIONS: One-year capecitabine maintenance therapy for early-stage TNBC after standard treatment, compared with routine follow-up, was found to be highly cost-effective with promising clinical benefits and acceptable increased costs. Real-world studies are warranted to validate our findings in the future.

The causal relationship between sleep traits and the risk of schizophrenia: a two-sample bidirectional Mendelian randomization study.

BACKGROUND: Observational studies suggest that sleep disturbances are commonly associated with schizophrenia. However, it is uncertain whether this relationship is causal. To investigate the bidirectional causal relation between sleep traits and schizophrenia, we performed a two-sample bidirectional Mendelian randomization (MR) study with the fixed effects inverse-variance weighted (IVW) method. METHODS: As genetic variants for sleep traits, we selected variants from each meta-analysis of genome-wide association studies (GWASs) conducted using data from the UK Biobank (UKB). RESULTS: We found that morning diurnal preference was associated with a lower risk of schizophrenia, while long sleep duration and daytime napping were associated with a higher risk of schizophrenia. Multivariable MR analysis also showed that sleep duration was associated with a higher risk of schizophrenia after adjusting for other sleep traits. Furthermore, genetically predicted schizophrenia was negatively associated with morning diurnal preference and short sleep duration and was positively associated with daytime napping and long sleep duration. CONCLUSIONS: Therefore, sleep traits were identified as a potential treatment target for patients with schizophrenia.

Comparison of Cd subcellular distribution and Cd detoxification between low/high Cd-accumulative rice cultivars and sea rice.

Salt-tolerant rice cultivar (sea rice) is a research hotspot worldwide due to its high yield in high salinity soil. However, knowledge regarding the cadmium (Cd) effects on the growth of sea rice is limited. To determine the short-term and long-term impact of Cd stress, relatively low/high Cd-accumulative rice cultivars and sea rice were grown to compare their growth responses to Cd stress over time. The results showed that sea rice presented the highest Cd concentrations in the root, stem, and leaves under 32-days of Cd stress. Cd stress shortened and thickened the rice root, and decreased the proportion of root diameters in the 0-0.2 mm range. Cd stress remarkably increased the Cd and Fe concentration in dithionite-citrate-bicarbonate (DCB) extracts, and the DCB-Cd and DCB-Fe concentrations were the highest in sea rice. The subcellular distribution of Cd in the rice roots indicated that Cd accumulated the most in the soluble fraction and cell wall. The contents of pectin and hemicellulose 2 in the root cell wall of the low-Cd accumulative rice variety CL755 were higher than those in MXZ and sea rice. Collectively, this work provides a general understanding of the Cd effects on sea rice growth and indicates that sea rice has a relatively high Cd accumulation compared with the other two rice cultivars. However, the specifically-related mechanism remains to be further studied.

Determination of oxytetracycline hydrochloride in milk and egg white samples using Ru(bipy)3 2+ -Ce(SO4 )2 chemiluminescence.

A novel, rapid and sensitive chemiluminescence (CL) method for the determination of oxytetracycline hydrochloride (OTCH) is described in this paper. The presented method was based on the fact that OTCH could immensely enhance the CL of the reaction of cerium sulfate and tris(2,2-bipyridyl) ruthenium (II) in acidic medium. Under optimal experimental conditions, CL intensity was favorably linear for OTCH in the range 5.0 × 10-7 to 5.0 × 10-5  g/ml, with a detection limit of 1.5 × 10-7  g/ml (S/N = 3). The relative standard detection was 4.76% for 5.0 × 10-6  g/ml (n = 11). This method was successfully applied to the analysis of OTCH in milk and egg white samples. According to the results of the kinetic curves for OTCH in the Ru(bipy)3 2+ -Ce(SO4 )2 CL system, together with CL and ultraviolet (UV)-visible spectra, the possible mechanism of the CL reaction is discussed briefly.

Intense Femtosecond Laser-Mediated Electrical Discharge Enables Preparation of Amorphous Nickel Phosphide Nanoparticles.

Reported here is a high-efficiency preparation method of amorphous nickel phosphide (Ni-P) nanoparticles by intense femtosecond laser irradiation of nickel sulfate and sodium hypophosphite aqueous solution. The underlying mechanism of the laser-assisted preparation was discussed in terms of the breaking of chemical bond in reactants via highly intense electric field discharge generated by the intense femtosecond laser. The morphology and size of the nanoparticles can be tuned by varying the reaction parameters such as ion concentration, ion molar ratio, laser power, and irradiation time. X-ray diffraction and transmission electron microscopy results demonstrated that the nanoparticles were amorphous. Finally, the thermogravimetric-differential thermal analysis experiment verified that the as-synthesized noncrystalline Ni-P nanoparticles had an excellent catalytic capability toward thermal decomposition of ammonium perchlorate. This strategy of laser-mediated electrical discharge under such an extremely intense field may create new opportunities for the decomposition of molecules or chemical bonds that could further facilitate the recombination of new atoms or chemical groups, thus bringing about new possibilities for chemical reaction initiation and nanomaterial synthesis that may not be realized under normal conditions.

Association between Cystatin C and SVD in Chinese population.

Leukoaraiosis is an important clinical feature of cerebral small vessel disease. To date, there is no reliable biomarker to reflect the degree of cerebral small vessel disease and white matter damage. This study aimed to explore the relationship between cystatin C levels and the degree of white matter damage in order to assess whether cystatin C could serve as a biomarker for white matter damage. We conducted a retrospective analysis of 408 non-critically ill hospitalized patients. The included patients underwent related biochemical and cerebral magnetic resonance imaging examinations. The magnetic resonance imaging results were assessed using the Fazekas scale in fluid attenuation inversion recovery imaging. We analyzed the association of each risk factor (sex, age, blood glucose, blood lipid, and cystatin C) with the degree of white matter damage using univariate logistic and multivariate cumulative odds logistic regression (stepwise). Serum cystatin C concentration was closely associated with the degree of white matter damage (odds ratio = 2.14), while age, sex, and hypertension were associated with selective damage of brain white matter. Triglycerides and apolipoprotein A may have a protective effect against white matter damage.

Regulation of Vascular Smooth Muscle Cell Dysfunction Under Diabetic Conditions by miR-504.

OBJECTIVE: Diabetes mellitus accelerates proatherogenic and proinflammatory phenotype of vascular smooth muscle cell (VSMC) associated with vascular complications. Evidence shows that microRNAs (miRNAs) play key roles in VSMC functions, but their role under diabetic conditions is unclear. We profiled miRNAs in VSMC from diabetic mice and examined their role in VSMC dysfunction. APPROACH AND RESULTS: High throughput small RNA-sequencing identified 135 differentially expressed miRNAs in VSMC from type 2 diabetic db/db mice (db/dbVSMC) versus nondiabetic db/+ mice. Several of these miRNAs were known to regulate VSMC functions. We further focused on miR-504, because it was highly upregulated in db/dbVSMC, and its function in VSMC is unknown. miR-504 and its host gene Fgf13 were significantly increased in db/dbVSMC and in aortas from db/db mice. Bioinformatics analysis predicted that miR-504 targets including signaling adaptor Grb10 and transcription factor Egr2 could regulate growth factor signaling. We experimentally validated Grb10 and Egr2 as novel targets of miR-504. Overexpression of miR-504 in VSMC inhibited contractile genes and enhanced extracellular signal-regulated kinase 1/2 activation, proliferation, and migration. These effects were blocked by miR-504 inhibitors. Grb10 knockdown mimicked miR-504 functions and increased inflammatory genes. Egr2 knockdown-inhibited anti-inflammatory Socs1 and increased proinflammatory genes. Furthermore, high glucose and palmitic acid upregulated miR-504 and inflammatory genes, but downregulated Grb10. CONCLUSIONS: Diabetes mellitus misregulates several miRNAs including miR-504 that can promote VSMC dysfunction. Because changes in many of these miRNAs are sustained in diabetic VSMC even after in vitro culture, they may be involved in metabolic memory of vascular complications. Targeting such mechanisms could offer novel therapeutic strategies for diabetic complications.

Surface chemistry and stability of metastable corundum-type In2O3.

To account for the explanation of an eventual sensing and catalytic behavior of rhombohedral In2O3 (rh-In2O3) and the dependence of the metastability of the latter on gas atmospheres, in situ electrochemical impedance spectroscopic (EIS), Fourier-transform infrared spectroscopic (FT-IR), in situ X-ray diffraction and in situ thermogravimetric analyses in inert (helium) and reactive gases (hydrogen, carbon monoxide and carbon dioxide) have been conducted to link the gas-dependent electrical conductivity features and the surface chemical properties to its metastability towards cubic In2O3. In particular, for highly reducible oxides such as In2O3, for which not only the formation of oxygen vacancies, but deep reduction to the metallic state (i.e. metallic indium) also has to be taken into account, this approach is imperative. Temperature-dependent impedance features are strongly dependent on the respective gas composition and are assigned to distinct changes in either surface adsorbates or free charge carrier absorbance, allowing for differentiating and distinguishing between bulk reduction-related features from those directly arising from surface chemical alterations. For the measurements in an inert gas atmosphere, this analysis specifically also included monitoring the fate of differently bonded, and hence, differently reactive, hydroxyl groups. Reduction of rh-In2O3 proceeds to a large extent indirectly via rh-In2O3 → c-In2O3 → In metal. As deduced from the CO and CO2 adsorption experiments, rhombohedral In2O3 exhibits predominantly Lewis acidic surface sites. The basic character is less pronounced, directly explaining the previously observed high (inverse) water-gas shift activity and the low CO2 selectivity in methanol steam reforming.

Silicon three-dimensional structures fabricated by femtosecond laser modification with dry etching.

In this paper, a maskless, high efficiency, and flexible technology is developed to fabricate three-dimensional (3D) microstructures on a silicon wafer, which is based on the combination of femtosecond laser modification and subsequent dry etching. The silicon atoms in 2D patterned areas were insufficiently oxidized after femtosecond laser irradiation. Complex 3D structures can be fabricated on the silicon wafer after etching, such as micro gears, comb drive actuators, and micro cantilevers applied in microelectromechanical systems (MEMS) and micro Fresnel zone plates applied in micro optics. What is more, surface roughness of the laser structured wafer can be improved with increased etching time in the dry etching process. This technology shows its unique capacity to fabricate various 3D microstructures for applications in MEMS and micro optics.

Exploring the effect of different typical plant community on human stress reduction: a field experiment.

Research has demonstrated the positive effect of natural environment on human restoration and well-being. Time spent in nature can often alleviate both physiological and psychological stress. However, few studies have discussed the environmental health effects of the nature's components and characteristics. Sixty volunteers were recruited and one manufactured environment and five different natural environments were randomly assigned to them, including coniferous forests (pure coniferous forest-PC and mixed coniferous forest-MC), broad-leaved forests (pure broad-leaved forest-PB and mixed broad-leaved forest-MB), and mixed forest (mixed coniferous and broad-leaved forest-MCB). Each volunteer sat in a built or natural environment and looked around the environment for 15 min. Physiological (HR, HRV, BP, pulse rate and salivary cortisol) and psychological indicators (POMS and STAI) were used to evaluate the changes in their stress level. Results indicated a strong difference in HR, HRV, POMS and STAI between the built and natural environment, which showed that natural environment can lower the stress level. MC had the best effect on relieving physiological stress, whereas MCB is most successful in improving emotional state and reducing anxiety. Broad-leaved forest and mixed forest significantly affected the DBP and vigor level of the subjects, respectively. While coniferous forest did significantly increase the concentration of salivary cortisol in subjects. The study confirmed that compared to the built environment, the natural environment can relieve the human body's physical and psychological stress and negative emotions, while significantly increasing vitality. And different plant communities also have different effects on the physiological and psychological indicators of the subjects. These results will provide scientific basis for the construction and improvement of urban green space environment.