Evolution mechanism of scratch removal based on the implementation of magnetorheological finishing.

Scratches on optical components induce laser damage and limit the increase in laser power. Magnetorheological finishing (MRF) is a highly deterministic optical manufacturing technology that can improve the surface roughness of optical components. Although MRF has exhibited significant potential for reducing subsurface damage and removing scratches, the principle and mechanism behind the scratch removal are not sufficiently understood. In this study, the theory of fluid mechanics is used to analyze the pressure, velocity, and particle trajectory distribution near a scratch. A physical model was developed for the differential removal of scratches at the bottom and surface of the optical components. The morphological evolution of the scratch was predicted during removal, and detailed experiments were performed to verify the effectiveness of the proposed model. The results indicate that scratches expand laterally rather than being completely removed. Furthermore, scratch removal efficiency is greater when the removal direction is perpendicular to the scratch rather than being parallel. This study offers an intrinsic perspective for a comprehensive understanding of the MRF technique used for scratch removal, which can be beneficial for removing scratches from aspherical optical systems.

Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chronic fibrotic state. It reviewed the cellular and molecular responses of the skin to radiation, highlighting the role of myofibroblasts and the significant impact of Transforming Growth Factor-beta (TGF-ß) in promoting fibroblast-to-myofibroblast transformation. The review delves into the epigenetic regulation of fibrotic gene expression, the contribution of extracellular matrix proteins to the fibrotic microenvironment, and the regulation of the immune system in the context of fibrosis. Additionally, it discusses the potential of biomaterials and artificial intelligence in medical research to advance the understanding and treatment of radiation-induced skin fibrosis, suggesting future directions involving bioinformatics and personalized therapeutic strategies to enhance patient quality of life.

Sustainable building materials-recycled aggregate and concrete: a systematic review of properties, modification techniques, and environmental impacts.

Resource utilization of construction and demolition (C&D) waste has great potential to significantly reduce the consumption of natural resources and improve the environment. Meanwhile, establishing a sound policy system and reducing production are the key ways to solve the problem of C&D waste. Numerous studies on C&D waste, recycled concrete aggregate (RA), and recycled aggregate concrete (RAC) have been reported in the literature, with few systematic summaries. From a global perspective, this paper assessed the current situation of C&D waste and the countermeasure of several major economies. Then, this paper systematically introduces the composition structure and characteristics of RA. Modification techniques from macro and micro perspectives of RA and its effect on RAC were also presented. Paper also reviews the environmental impacts of RA and RAC. The results showed that bonded mortar was the most significant defect of RA than natural aggregate (NA). Thus, RA weakened RAC's microstructure, workability, mechanical properties, and durability. The research on the modification of RA mainly focused on removing bonded mortar and enhancing bonded mortar containing physical or chemical methods. Enhancing bonded mortar was a more effective method than removing bonded mortar. Carbonation and microbially induced calcium carbonate precipitation were highly efficient and environmentally friendly for RA modification. Research progress in quantifying the environmental impacts associated with concrete from waste materials through the LCA methodology is presented. Suggestions and an outlook were given on the critical issues facing RA and RAC. We expect that this work can provide more technical support for C&D waste utilization.

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms.

Objective: The objective of this study is to develop a model to predicts the postoperative Hunt-Hess grade in patients with intracranial aneurysms by integrating radiomics and deep learning technologies, using preoperative CTA imaging data. Thereby assisting clinical decision-making and improving the assessment and prognosis of postoperative neurological function. Methods: This retrospective study encompassed 101 patients who underwent aneurysm embolization surgery. 851 radiomic features were extracted from CTA images. 512 deep learning features are extracted from last layer of ResNet50 deep convolutional neural network model. The feature screening process pipeline encompassed intraclass correlation coefficient analysis, principal component analysis, U test, spearman correlation analysis, minimum redundancy maximum relevance algorithm and Lasso regression, to identify features most correlated with postoperative Hunt-Hess grading. In the model construction phase, three distinct models were constructed: radiomics feature-based model (RSM), deep learning feature-based model (DLM), and deep learning-radiomics feature fusion model (DLRSCM). The study also calculated the radiomics score and combined it with clinical data to construct a Nomogram for predictive modeling. DLM, RSM and DLRSCM model was constructed by 9 base algorithms and 1 ensemble learning algorithm - Stacking ensemble model. Model performance was evaluated based on the area under the Receiver Operating Characteristic (ROC) curve (AUC), Matthews Correlation Coefficient (MCC), calibration curves, and decision curves analysis. Results: 5 significant radiomic feature and 4 significant deep learning features were obtained through the feature selection process. These features were utilized for model construction. Bootstrap resampling method was used for internal validation of the models. In terms of model evaluation, the DLM model, the stacking ensemble algorithm results achieved an AUC of 0.959 and MCC of 0.815. In the RSM model, the stacking ensemble model AUC was 0.935 and MCC was 0.793. The stacking ensemble model in DLRSCM outperformed others, with an AUC of 0.968 and MCC of 0.820. Results indicated that the ANN performed optimally among all base models, while the stacked ensemble learning model exhibited the highest predictive performance. Conclusion: This study demonstrates that the combination of radiomics and deep learning is an effective approach to predict the postoperative Hunt-Hess grade in patients with intracranial aneurysms. This holds significant value in the early identification of postoperative neurological complications and in enhancing clinical decision-making.

Initial Upper Palaeolithic material culture by 45,000 years ago at Shiyu in northern China.

The geographic expansion of Homo sapiens populations into southeastern Europe occurred by ∼47,000 years ago (∼47 ka), marked by Initial Upper Palaeolithic (IUP) technology. H. sapiens was present in western Siberia by ∼45 ka, and IUP industries indicate early entries by ∼50 ka in the Russian Altai and 46-45 ka in northern Mongolia. H. sapiens was in northeastern Asia by ∼40 ka, with a single IUP site in China dating to 43-41 ka. Here we describe an IUP assemblage from Shiyu in northern China, dating to ∼45 ka. Shiyu contains a stone tool assemblage produced by Levallois and Volumetric Blade Reduction methods, the long-distance transfer of obsidian from sources in China and the Russian Far East (800-1,000 km away), increased hunting skills denoted by the selective culling of adult equids and the recovery of tanged and hafted projectile points with evidence of impact fractures, and the presence of a worked bone tool and a shaped graphite disc. Shiyu exhibits a set of advanced cultural behaviours, and together with the recovery of a now-lost human cranial bone, the record supports an expansion of H. sapiens into eastern Asia by about 45 ka.

Ultrafine Iridium Nanoparticles Anchored on Co-Based Metal-Organic Framework Nanosheets for Robust Hydrogen Evolution in Alkaline Media.

A highly promising electrocatalyst has been designed and prepared for the hydrogen evolution reaction (HER). This involves incorporating well-dispersed Ir nanoparticles into a cobalt-based metal-organic framework known as Co-BPDC [Co(bpdc)(H2O)2, BPDC: 4,4'-biphenyldicarboxylic acid]. Ir@Co-BPDC demonstrates exceptional HER activity in alkaline media, surpassing both commercial Pt/C and recent noble-metal catalysts. Theoretical results indicate that electron redistribution, induced by interfacial bonds, optimizes the adsorption energy of water and hydrogen, thereby enhancing our understanding of the superior properties of Ir@Co-BPDC for HER.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma.

BACKGROUND & AIMS: The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model. METHODS: We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison. RESULTS: Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835- 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively). CONCLUSION: CT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

Classification of regulatory T cells and their role in myocardial ischemia-reperfusion injury.

Myocardial ischemia-reperfusion injury (MIRI) is closely related to the final infarct size in acute myocardial infarction (AMI). Therefore, reducing MIRI can effectively improve the prognosis of AMI patients. At the same time, the healing process after AMI is closely related to the local inflammatory microenvironment. Regulatory T cells (Tregs) can regulate various physiological and pathological immune inflammatory responses and play an important role in regulating the immune inflammatory response after AMI. However, different subtypes of Tregs have different effects on MIRI, and the same subtype of Tregs may also have different effects at different stages of MIRI. This article systematically reviews the classification and function of Tregs, as well as the role of various subtypes of Tregs in MIRI. A comprehensive understanding of the role of each subtype of Tregs can help design effective methods to control immune reactions, reduce MIRI, and provide new potential therapeutic options for AMI.

Study on the relation between macro-mechanical properties and micro-structure of geopolymers with different activator modulus.

The fly ash-based geopolymer (FABG) containing slag has distinct advantages in field applications. In this work, given that the activator modulus is a significant parameter affecting the properties of FABG, the influence mechanism of activator modulus (SiO2/Na2O from 1.1 to 1.5) on the macro-mechanical properties and micro-structure composition of FABG containing slag is explored. According to the experimental results, the early product of FABG containing slag is mainly C-A-S-H gel, and N-(C)-A-S-H gel with high cross-linking degree is formed at a later stage. Both C-A-S-H and N-A-S-H gels are distinguished in reaction products by using 29Si NMR. The Si/Al ratio of N-A-S-H gel and C-A-S-H gel decreases with the increase of modulus, resulting in an increase of MCL in C-A-S-H. Appropriate activator modulus can effectively activate slag and fly ash to yield more gels and form a more uniform and dense micro-structure, resulting in a lower threshold pore size and macroporosity, and an associated increase of the material strength. Meanwhile, the gel amount has a positive effect on the strength development in the FABG.

Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram.

BACKGROUND: Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients. METHODS: Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox's proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model's prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics. RESULTS: The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively. CONCLUSION: Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.

Application of locally responsive design of biomaterials based on microenvironmental changes in myocardial infarction.

Morbidity and mortality caused by acute myocardial infarction (AMI) are on the rise, posing a grave threat to the health of the general population. Up to now, interventional, surgical, and pharmaceutical therapies have been the main treatment methods for AMI. Effective and timely reperfusion therapy decreases mortality, but it cannot stimulate myocardial cell regeneration or reverse ventricular remodeling. Cell therapy, gene therapy, immunotherapy, anti-inflammatory therapy, and several other techniques are utilized by researchers to improve patients' prognosis. In recent years, biomaterials for AMI therapy have become a hot spot in medical care. Biomaterials furnish a microenvironment conducive to cell growth and deliver therapeutic factors that stimulate cell regeneration and differentiation. Biomaterials adapt to the complex microenvironment and respond to changes in local physical and biochemical conditions. Therefore, environmental factors and material properties must be taken into account when designing biomaterials for the treatment of AMI. This article will review the factors that need to be fully considered in the design of biological materials.

Allergen-specific IgE in the tear fluid of Chinese patients with common allergic conjunctivitis in autumn and winter.

PURPOSE: In this study, we determined the positive rates of allergen-specific immunoglobulin E (IgE) in the tear fluid of Chinese patients with common allergic conjunctivitis (AC) in autumn and winter, compared systemic and ocular allergen tests, and explored the correlation between the numbers and categories of allergens and clinical AC features. METHODS: This cross-sectional study recruited 44 patients with AC (86 eyes). Specific IgEs for allergens common in China (house dust mite, cat/dog dander, mugwort/ragweed pollen, cottonwood/willow/elm pollen, milk, egg whites, soybeans) were measured in collected tears using kits for allergen-specific IgE antibodies. AC signs and symptoms were graded according to severity. RESULTS: Specific IgE in tears was positive in 87.2% of eyes. House dust mite was the most common allergen (86.0%), followed by cat (24.4%) and dog (7.0%) dander; tree and grass pollen accounted for only 4.7% and 2.3%, respectively. Food allergens were not detected. The positive rates of the systemic allergen tests were lower than in tear fluid tests in both eyes, especially for house dust mites (P = 0.000). In patients with more allergens, itching was more severe (P = 0.035), while conjunctival hyperemia was milder (P = 0.002). CONCLUSION: In autumn and winter, the most common AC allergen in Chinese patients was house dust mites. Compared with systemic allergen tests, measuring specific IgE in tears may be a non-invasive method to diagnose and evaluate AC severity, which may be more suitable to reflect the local conditions of ocular surface inflammation due to its high positive rate and convenience.

Adsorption Kinetics of Poly(benzyl acrylate) Chains onto Alumina Interface during the Flow-Driven Translocation through Cylindrical Nanochannels.

In this work, the adsorption kinetics of the PBAN/AAO system under flushing condition has been investigated, where PBAN and AAO represent poly(benzyl acrylate) and anodic alumina oxide (AAO, average pore radius R0 ≈ 10 nm) nanochannel, respectively. Our specially designed double-pump flushing system is proved to eliminate the overshoot phenomenon and in situ monitor transmembrane pressure (ΔP) as a function of flushing time (t) and flow rate (Q), which gives the effective pore radius (R), cross-sectional coverage factor (χ = [1 - (R/R0)2]), and characteristic ratio (rc) of the increments of χ during each adsorption/desorption cycle at a given bulk solution concentration (Cbulk). Our findings include: (1) by gradient increasing Cbulk from 10 to 200 mg/L at Q = 10 mL/h, the shortest PBA40 displays a saturation adsorption behavior when Cbulk ≥ 80 mg/L and t ≥ 2000 s, which agrees well with the prediction of blob model, whereas for the longer PBAN chains, the chain length (N) and concentration-dependent adsorption tendency get stronger as N increases from 40 to 620 at t ≥ 2000 s, in particular, R/R0 ∼ N-0.20 is observed at Cbulk = 140 mg/L; (2) by focusing on the platform χ in the saturation adsorption regime (χsat), the longer PBAN displays a stronger adsorption trend with partially reversible feature at Q = 5.0 mL/h, namely, as N increases from 40 to 620, χsat increases from 0.15 to 0.83 at Cbulk = 100 mg/L, where rc changes from 0.25 ± 0.10 to 0.80 ± 0.10 as the adsorption/desorption flushing cycle increases from 1 to 8 at Cbulk = 100 mg/L; (3) by further assuming a solvent nonpenetrating and nondraining adsorption layer, χsat determined in the case of curved surface can be comparable to the physical meaning of adsorption thickness (Δad) in the case of flat-surface adsorption, and the fitting result indicates χsat ∼ Δad ∼ N0.58, falling between Δad ∼ N1/2 and Δad ∼ N1.0 predicted by the mean-field and scaling theories for real multichain adsorption, respectively. Overall, the present work not only clarifies some controversies but also provides unambiguous evidence supporting the existence of tightly adsorbed internal and loosely adsorbed external layers.

Enhancing Electrical Generation Efficiency through Parametrical Excitation and Slapping Force in Nonlinear Elastic Beams for Vibration Energy Harvesting.

This study aims to enhance conventional vibration energy harvesting systems (VEHs) by repositioning the piezoelectric patch (PZT) in the middle of a fixed-fixed elastic steel sheet instead of the root, as is commonly the case. The system is subjected to an axial simple harmonic force at one end to induce transversal vibration and deformation. To further improve power conversion, a baffle is strategically installed at the point of maximum deflection, introducing a slapping force to augment electrical energy harvesting. Employing the theory of nonlinear beams, the equation of motion for this nonlinear elastic beam is derived, and the method of multiple scales (MOMS) is used to analyze the phenomenon of parametric excitation. This study demonstrates through experiments and theoretical analysis that the second mode yields better power generation benefits than the first mode. Additionally, the voltage generation benefits of the enhanced system with the added baffle (slapping force) surpass those of traditional VEH systems. Overall, the proposed model proves feasible and holds promising potential for efficient vibration energy harvesting applications in various industrial sectors.

Realization of High Magnetization in Artificially Designed Ni/NiO Layers through Exchange Coupling.

High-magnetization materials play crucial roles in various applications. However, the past few decades have witnessed a stagnation in the discovery of new materials with high magnetization. In this work, Ni/NiO nanocomposites are fabricated by depositing Ni and NiO thin layers alternately, followed by annealing at specific temperatures. Both the as-deposited samples and those annealed at 373 K exhibit low magnetization. However, the samples annealed at 473 K exhibit a significantly enhanced saturation magnetization exceeding 607 emu cm-3 at room temperature, surpassing that of pure Ni (480 emu cm-3 ). Material characterizations indicate that the composite comprises NiO nanoclusters of size 1-2 nm embedded in the Ni matrix. This nanoclustered NiO is primarily responsible for the high magnetization, as confirmed by density functional theory calculations. The calculations also indicate that the NiO clusters are ferromagnetically coupled with Ni, resulting in enhanced magnetization. This work demonstrates a new route toward developing artificial high-magnetization materials using the high magnetic moments of nanoclustered antiferromagnetic materials.

Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods.

Background: Diagnostic models based on gene signatures of nasopharyngeal carcinoma (NPC) were constructed by random forest (RF) and artificial neural network (ANN) algorithms. Least absolute shrinkage and selection operator (Lasso)-Cox regression was used to select and build prognostic models based on gene signatures. This study contributes to the early diagnosis and treatment, prognosis, and molecular mechanisms associated with NPC. Methods: Two gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) associated with NPC were identified by gene expression differential analysis. Subsequently, significant DEGs were identified by a RF algorithm. ANN were used to construct a diagnostic model for NPC. The performance of the diagnostic model was evaluated by area under the curve (AUC) values using a validation set. Lasso-Cox regression examined gene signatures associated with prognosis. Overall survival (OS) and disease-free survival (DFS) prediction models were constructed and validated from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database. Results: A total of 582 DEGs associated with NPC were identified, and 14 significant genes were identified by the RF algorithm. A diagnostic model for NPC was successfully constructed using ANN, and the validity of the model was confirmed on the training set AUC =0.947 [95% confidence interval (CI): 0.911-0.969] and the validation set AUC =0.864 (95% CI: 0.828-0.901). The 24-gene signatures associated with prognosis were identified by Lasso-Cox regression, and prediction models for OS and DFS of NPC were constructed on the training set. Finally, the ability of the model was validated on the validation set. Conclusions: Several potential gene signatures associated with NPC were identified, and a high-performance predictive model for early diagnosis of NPC and a prognostic prediction model with robust performance were successfully developed. The results of this study provide valuable references for early diagnosis, screening, treatment and molecular mechanism research of NPC in the future.

Who's Remembering to Buy the Eggs? The Meaning, Measurement, and Implications of Invisible Family Load.

Although much is known of the observable physical tasks associated with household management and child rearing, there is scant understanding of the less visible tasks that are just as critical. Grounding our research in the extant literature, the broader lay discussion, as well as our own qualitative research, we define, conceptualize, and operationalize this construct, which we label as "invisible family load." Using a mixed method, five-study approach, we offer a comprehensive, multidimensional definition and provide a nine-item, empirically validated scale to measure its component parts-managerial, cognitive, and emotional family load. In addition, we investigate gender differences and find, as expected, that women report higher levels of each dimension. We also examine the implications of invisible family load for employee health, well-being, and job attitudes, as well as family-to-work spillover. Although we substantiated some significant negative consequences, contrary to the popular view that consequences of invisible family load are uniformly negative, our results show some potential benefits. Even after accounting for conscientiousness and neuroticism, managerial family load related to greater family-work enrichment, and cognitive family load related to greater family satisfaction and job performance. Yet, emotional family load had uniformly negative potential consequences including greater family-to-work conflict, sleep problems, family and job exhaustion, and lower life and family satisfaction. Our research sets the stage for scholars to forge a path forward to enhance understanding of this phenomenon and its implications for individuals, their families, and the organizations for which they work. Supplementary Information: The online version contains supplementary material available at 10.1007/s10869-023-09887-7.

Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores.

BACKGROUND: Early identification of severe/critical coronavirus disease 2019 (COVID-19) is crucial for timely treatment and intervention. Chest computed tomography (CT) score has been shown to be a significant factor in the diagnosis and treatment of pneumonia, however, there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution. AIM: To develop a severe/critical COVID-19 prediction model using a combination of imaging scores, clinical features, and biomarker levels. METHODS: This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients. The study also took into consideration the general clinical indicators such as dyspnea, oxygen saturation, alternative lengthening of telomeres (ALT), and androgen suppression treatment (AST), which are commonly associated with severe/critical COVID-19 cases. The study employed lasso regression to evaluate and rank the significance of different disease characteristics. RESULTS: The results showed that blood oxygen saturation, ALT, IL-6/IL-10, combined score, ground glass opacity score, age, crazy paving mode score, qsofa, AST, and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases. The study established a COVID-19 severe/critical early warning system using various machine learning algorithms, including XGBClassifier, Logistic Regression, MLPClassifier, RandomForestClassifier, and AdaBoost Classifier. The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution. CONCLUSION: The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.

Computational study of the role of counterions and solvent dielectric in determining the conductance of B-DNA.

DNA naturally exists in a solvent environment, comprising water and salt molecules such as sodium, potassium, magnesium, etc. Along with the sequence, the solvent conditions become a vital factor determining DNA structure and thus its conductance. Over the last two decades, researchers have measured DNA conductivity both in hydrated and almost dry (dehydrated) conditions. However, due to experimental limitations (the precise control of the environment), it is very difficult to analyze the conductance results in terms of individual contributions of the environment. Therefore, modeling studies can help us to gain a valuable understanding of various factors playing a role in charge transport phenomena. DNA naturally has negative charges located at the phosphate groups in the backbone, which provides both the connections between the base pairs and the structural support for the double helix. Positively charged ions such as the sodium ion (Na^{+}), one of the most commonly used counterions, balance the negative charges at the backbone. This modeling study investigates the role of counterions both with and without the solvent (water) environment in charge transport through double-stranded DNA. Our computational experiments show that in dry DNA, the presence of counterions affects electron transmission at the lowest unoccupied molecular orbital energies. However, in solution, the counterions have a negligible role in transmission. Using the polarizable continuum model calculations, we demonstrate that the transmission is significantly higher at both the highest occupied and lowest unoccupied molecular orbital energies in a water environment as opposed to in a dry one. Moreover, calculations also show that the energy levels of neighboring bases are more closely aligned to ease electron flow in the solution.

Influence of hypobaric hypoxic conditions on ocular structure and biological function at high attitudes: a narrative review.

Background: With the development of science and technology, high-altitude environments, involving aviation, aerospace, and mountainous regions, have become the main areas for human exploration, while such complex environments can lead to rapid decreases in air and oxygen pressure. Although modern aircrafts have pressurized cabins and support equipment that allow passengers and crew to breathe normally, flight crew still face repeated exposure to hypobaric and hypoxic conditions. The eye is a sensory organ of the visual system that responds to light and oxygen plays a key role in the maintenance of normal visual function. Acute hypoxia changes ocular structure and function, such as the blood flow rate, and can cause retinal ischemia. Methods: We reviewed researches, and summarized them briefly in a review. Results: The acute hypobaric hypoxia affects corneal, anterior chamber angle and depth, pupils, crystal lens, vitreous body, and retina in structure; moreover, the acute hypoxia does obvious effect on visual function; for example, vision, intraocular pressure, oculometric features and dynamic visual performance, visual field, contrast sensitivity, and color perception. Conclusion: We summarized the changes in the physiological structure and function of the eye in hypoxic conditions and to provide a biological basis for the response of the human eye at high-altitude.