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Objective: The study was designed to examine the effects of simultaneous combination of aerobic exercise and video game training on executive functions (EFs) and brain functional connectivity in older adults., Design: A four-armed, quasi-experimental study., Setting and Participants: Community-dwelling adults aged 55 years and older., Methods: A total of 97 older adults were divided into one of four groups: aerobic exercise (AE), video game (VG), combined intervention (CI), and passive control (PC). Participants in intervention groups received 32 sessions of training over a 4-month period at a frequency of twice a week. EFs was evaluated using a composite score derived from a battery of neuropsychological tests. The Montreal Cognitive Assessment (MoCA) was employed to evaluate overall cognitive function, while the 6-Minute Walking Test (6MWT) was utilized to gauge physical function. Additionally, the functional connectivity (FC) of the frontal-parietal networks (FPN) was examined as a neural indicator of cognitive processing and connectivity changes., Results: In terms of EFs, both VG and CI groups demonstrated improvement following the intervention. This improvement was particularly pronounced in the CI group, with a large effect size (Hedge's g = 0.83), while the VG group showed a medium effect size (Hedge's g = 0.56). A significant increase in MoCA scores was also observed in both the VG and CI groups, whereas a significant increase in 6MWT scores was observed in the AE and CI groups. Although there were no group-level changes observed in FC of the FPN, we found that changes in FC was behaviorally relevant as increased FC was associated with greater improvement in EFs., Conclusion: The study offers preliminary evidence that both video game training and combined intervention could enhance EFs in older adults. Simultaneous combined intervention may hold greater potential for facilitating EFs gains. The initial evidence for correlated changes in brain connectivity and EFs provides new insights into understanding the neural basis underlying the training gains., (Copyright © 2024. Published by Elsevier Inc.)

Methylmercury (MeHg) production in aquatic ecosystems is a global concern because of its neurotoxic effect. Dissolved organic matter (DOM) plays a crucial role in biogeochemical cycling of Hg. However, owing to its complex composition, the effects of DOM on net MeHg production have not been fully understood. Here, the Hg isotope tracer technique combined with different DOM treatments was employed to explore the influences of DOM with divergent compositions on Hg methylation/demethylation and its microbial mechanisms in eutrophic lake waters. Our results showed that algae-derived DOM treatments enhanced MeHg concentrations by 1.42-1.53 times compared with terrestrial-derived DOM. Algae-derived DOM had largely increased the methylation rate constants by approximately 1-2 orders of magnitude compared to terrestrial-derived DOM, but its effects on demethylation rate constants were less pronounced, resulting in the enhancement of net MeHg formation. The abundance of hgcA and merB genes suggested that Hg-methylating and MeHg-demethylating microbiomes responded differently to DOM treatments. Specific DOM components (e.g., aromatic proteins and soluble microbial byproducts) were positively correlated with both methylation rate constants and the abundance of Hg-methylating microbiomes. Our results highlight that the DOM composition influences the Hg methylation and MeHg demethylation differently and should be incorporated into future Hg risk assessments in aquatic ecosystems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

A lot of plastic floats are presented in the kelp cultivation zone, enabling us to effectively evaluate the differences between surface water (SW) and plastic-attached (PA) microbial communities. In this study, we explored the microbial communities (both bacteria and protists) in SW and PA niches during the kelp cultivation activities. Effects of habitat niches on the diversity and composition of microbial communities were found. Beta partitioning and core taxa analyses showed species turnover and local species pool governed the microbial community assembly, and they contributed more to bacteria and protists, respectively. Based on the results of null model, bacterial communities presented a more deterministic and homogeneous assembly compared to protistan communities. Moreover, microbial communities in PA niche had higher species turnover and homogenizing assembly compared to the SW niche. The results of this study supplemented the theory of microbial community assembly and expanded our understanding of protists in plastisphere., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Hyperuricemia is closely related to a variety of diseases and has been listed as one of the twenty most persistent diseases in the 21st century by the United Nations. Therefore, strengthening the diagnosis of hyperuricemia has become imperative. Here, ordered inverse opal array structures (PAANs) composed of PDMS and gold nanoparticles (AuNPs) have been designed using a bottom-up self-assembly method. The structures exhibit a periodic distribution of hot spots, an enhancement factor (EF) of 4.22 × 10 4 , and a relative standard deviation (RSD) of signal intensity of less than 5%, which can provide high reproducibility of SERS signals. The PAANs substrate is used to detect uric acid in the tears of patients with hyperuricemia, and the limit of detection is 6.03 μM. The significant linear relationship between blood uric acid and tear uric acid indicates that the developed method is a rapid, effective, and non-invasive technique for the determination of uric acid in tears., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Diabetes has become one of the three chronic non-communicable diseases threatening human health in the world, and the detection of glucose concentration is of great importance for the prevention and treatment of diabetes. The noninvasive detection of glucose in tears has attracted interest over the past several decades, however, time-consuming, expensive equipment, and specialist technicians make tear analysis still challenging. Here, flexible surface-enhanced Raman scattering (SERS) substrates composed of gold nanoparticles (AuNPs) and two-dimensional MXene Ti 3 C 2 T X nanosheets have been designed. The GMXeP (gold nanoparticles with MXene nanosheets loaded on paper) SERS substrates show good sensitivity, reproducibility, and stability, yielding an enhancement factor (EF) of 3.7 × 10 5 at the concentration of 10 -9 M. The GMXeP SERS substrates are used to detect glucose of diabetic tears within a linear range of 1-50 μM, the lowest detection concentration is 0.39 μM and the significant correlation between tear glucose and blood glucose indicates that this method is suitable for sensitive and noninvasive detection of blood glucose., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Atmospheric aerosol acidity impacts numerous physicochemical processes, but the determination of particle pH remains a significant challenge due to the nonconservative nature of the H + concentration ([H + ]). Traditional measurements have difficulty in describing the practical state of an aerosol because they comprise chemical components or hypotheses that change the nature of the particles. In this work, we present a direct pH measurement that uses water as a general probe to detect [H + ] in individual particles by micro-Raman spectroscopy. Containing the vibrational bands of ions and water influenced by ions, the spectra of hydrated ion were decomposed from the solution spectra as standard spectra by multivariate curve resolution analysis. Meanwhile, ratios of hydrated ions were calculated between the Raman spectra and standard spectra to evaluate concentration profiles of each ion. It demonstrated that good quantitative models between the ratio and concentration for all ions including H + can be built with correlation coefficients (R 2 ) higher than 0.95 for the solutions. The method was further applied to individual particle pH measurement. The pH value of sulfate aerosol particles was calculated, and the standard error was 0.09 using pH values calculated from the [HSO 4 - ]/[SO 4 2- ] as a reference. Furthermore, the applicability of the method was proven by detecting the pH value of chloride particles. Therefore, utilizing water, the most common substance, as the spectroscopic probe to measure [H + ] without restriction of the ion system, this method has potential to measure the pH value of atmospheric particles with various compounds, although more work needs to be done to improve the sensitivity of the method., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Cryptococcus neoformans (C. neoformans) is a causative agent for acute pulmonary infection, which can further develop to lethal meningoencephalitis if untreated. The meningoencephalitis infection can be prevented, if timely treatment on pulmonary cryptococcal infection can be implemented based on its early diagnosis and accurate assessment. In this study, blood serum surface-enhanced Raman spectroscopy (SERS) method was investigated on identification and assessment of pulmonary C. neoformans infection. The serum SERS measurements were collected from the mice infected with C. neoformans and the healthy mice, in which the infected mice were further divided into four subgroups according to the duration of infection. Based on those SRES measurements, biochemical differences were analyzed among those different groups to investigate the potential biomarkers for identifying and assessing the pulmonary C. neoformans infection. Furthermore, partial least square (PLS) analysis followed by linear discriminant analysis (LDA) model was employed to identify pulmonary cryptococcal infection and to assess the degrees of infection with the accuracies of 96.7% and 85.3%, respectively. Therefore, our study has demonstrated the great clinical potential of using serum SERS technique for an accurate identification and assessment of pulmonary cryptococcal infection., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Monitoring microplastics in the environment based on the Nile red staining protocol has proven to be a newly emerged method in several instances. However, the methodology is still having the limitations of susceptibility, indiscrimination, and complexity, etc. The objectives of this paper are to explore the effects of wavelength, temperature, H 2 O 2 and NaCl addition, plastic property, and fluorescent index on the Nile red staining in microplastics analysis and propose solutions to these inadequacies. Sample co-stained with H 2 O 2 (ω final  = 10%) and NaCl (ω final  = 8.8%) will lower the fluorescence intensity of biogenic materials and reduce their interferences. Based on the fluorescence color and intensity of fused fluorographs, the combined fluorescent index for twelve microplastics was significantly different, thus could be preliminarily distinguished. An elevated staining temperature is propitious to fluorescent tagging with Nile Red. Finally, an improved protocol was proposed, which made the methodology streamlined in microplastics analysis., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging their respective advantages and reducing the dependence of convolutional neural networks on a large amount of data. Using 750 CXR images of lungs of healthy patients as well as those of patients with other pneumonia and novel coronavirus pneumonia, the method can obtain an accuracy of 90.7% and an F1 score of 90.9%, and the sensitivity for detecting COVID-19 can reach 96%. These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia CXR radiography detection., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Aim: Metabolites present in urine reflect the current phenotype of the cancer state. Surface-enhanced Raman spectroscopy (SERS) can be used in urine supernatant or sediment to largely reflect the metabolic status of the body., Materials & Methods: SERS was performed to detect bladder cancer (BCa) and predict tumour grade from urine supernatant, which contains various system metabolites, as well as from urine sediment, which contains exfoliated tumour cells., Results & Discussion: Upon combining the urinary supernatant and sediment results, the total diagnostic sensitivity and specificity of SERS were 100% and 98.85%, respectively, for high-grade tumours and 97.53% and 90.80%, respectively, for low-grade tumours., Conclusion: The present results suggest high potential for SERS to detect BCa from urine, especially when combining both urinary supernatant and sediment results., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The aim of this study is to evaluate the feasibility of using blood serum surface-enhanced Raman spectroscopy (SERS) to identify benign and malignant thyroid nodules. Blood serum samples collected from three different groups including healthy volunteers (n = 22), patients with benign nodules (n = 19) and malignant nodules (n = 22) were measured by SERS. The spectral analysis results demonstrate that biomolecules in serum, such as amino acids, adenine and nucleic acid bases, change differently due to the different progression of nodules. By further combining with partial least square analysis and linear discriminant analysis (PLS-LDA) method, diagnostic accuracies of 93.65% and 82.93%, sensitivities of 92.68% and 81.82% and specificities of 95.45% and 84.21% can be achieved for differentiating healthy versus thyroid nodular groups and benign versus malignant groups, respectively. The above results have suggested that the blood serum SERS technique is helpful for precise diagnosis and timely treatment for patients with thyroid nodules., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500-1800 cm -1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine., Competing Interests: Declaration of competing interest The authors declare that they have no competing financial interests. Financial disclosure This work was supported by the financial support from the National Natural Science Foundation of China (71621061, 61605025, 81372766, 81572532), the 111 Project (B16009), the Fundamental Research Funds for the Central Universities (N182808003, N171904006, N171902001, N172410006-2), Key Research and Development Plan of Liaoning Province (1553837567570), and the Shenyang Young Innovative Talents Support Program (RC180297)., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Quantitative analysis under various perturbations is a difficult problem because the analytical signal changes with different factors. In this work, three-level simultaneous component analysis (3-MSCA) was used for analyzing the near-infrared (NIR) spectra of aqueous solutions under different perturbations. The spectral data of aqueous proline solutions at different pH, concentration and temperature were measured, and a three-level model was built to describe the effects of the three perturbations on the spectra, respectively. The first level model describes the change of the spectra with pH, from which significant aggregation of proline was observed around the isoelectric point. The second and third level model show the spectral change with concentration and temperature, respectively, and the spectral feature has a very good linear relationship with the corresponding influencing factors. Therefore, the pH and concentration scores can be used as the calibration curve for quantitative analysis of the pH and the content of proline, and the temperature scores can be used to predict the temperature of the solutions. In addition, the structural change of water molecules under different conditions is obtained from the loadings. A decline of the bulk water was found with the increase of concentration, implying an ascending trend of the bonded water due to the interaction of proline and water. The dissociation of water clusters with the increase of temperature is also displayed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

For understanding the structures and the hydrogen bonding in alcohol solutions, the changes of the structures and hydrogen bonding with temperature were studied by temperature-dependent near-infrared (NIR) spectroscopy. The spectral features of eight alcohol species including the monomer, dimer and linear or cyclic aggregates (trimer, tetramer and polymer) were found from the resolution-enhanced spectra calculated by continuous wavelet transform. The changes of the eight species with concentration and temperature were analyzed using the intensity variation of the corresponding spectral features and two-dimensional correlation NIR spectroscopy. The aggregates were found to form at a very low concentration and the stability of the seven aggregates with temperature was found in an order of cyclic tetramer > linear polymer > linear tetramer > cyclic trimer > linear trimer > cyclic polymer > dimer. Furthermore, the formation of the aggregates was found to be affected by the chain length. The increase of the chain length is beneficial for the formation of cyclic tetramer and polymer due to the hydrophobic effect, but is an adverse effect for the formation of linear polymer., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Diagnosis based on the compositional variation of biological liquids such as serum has drawn much attention. For exploring the potential diagnostic information from serum samples, temperature-dependent near-infrared (NIR) spectroscopy was developed to obtain the spectral change of water reflecting the interactions in serum solution, and chemometric methods were employed to mine the information from the temperature-dependent NIR spectra. The spectra of 68 healthy controls, 42 patients with the type II diabetes and 16 patients with coronary heart disease were measured and analyzed by chemometric calculations. Continuous wavelet transform (CWT) was used to enhance the resolution of the spectra. From the processed spectra, the information of non-hydrogen-bonded (NHB), weakly hydrogen-bonded (WHB) and strongly hydrogen-bonded (SHB) water species was observed. For explaining the variation of the spectra with temperature, two-dimensional (2D) correlation analysis was adopted. A clear difference in SHB/NHB ratio in the synchronous maps was found between the spectra of the patients and the controls. 86.8% of the controls and 98.3% of the patients can be correctly identified. Furthermore, combining the maps of the synchronous and asynchronous analysis, the correlation between SHB and WHB water species was discovered to have an ability to discriminate the patients of diabetes and heart disease with an accuracy of 83.7% and 75.0%, respectively. Therefore, water may be a probe for providing diagnostic information by temperature-dependent NIR spectroscopy., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Derivative calculation is a powerful method for resolution enhancement in spectral analysis. A high order derivative method based on continuous wavelet transform (CWT) is discussed in the analysis of near infrared (NIR) spectra. The results for a simulated spectrum obtained from conventional numerical differentiation (NM), Fourier transform (FT), Savitzky-Golay (SG) and CWT method were compared. CWT method was found to be as efficient as FT and SG, but easier for high order derivative computation, and the fourth order derivative was proved to be a good choice for resolution enhancement as well as reduction of noise and sidelobe effects. For the NIR spectra of water-ethanol mixtures, the complexity of the spectra can be observed from the fourth derivative, including the spectral features of OH and CH with various intermolecular interactions. Fitting the derivative spectra of the mixtures by those of pure water and ethanol, the obtained coefficients for ethanol show a linear relation with the content but that for water exhibit a non-linear relation, which reveals the influence of ethanol on water structure in the mixture. Furthermore, the information of the water-ethanol clusters was found in the residual spectra after the fitting. Therefore, high order derivative can be an efficient way to improve the resolution of NIR spectra for understanding the interactions in aqueous solutions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Standardization of near infrared (NIR) spectra is indispensable in practical applications because the spectra measured on different instruments are commonly used and the difference between the instruments must be corrected. A two-level standardization method is proposed in this study based on multi-level simultaneous component analysis (MSCA) algorithm for correcting the spectral difference between instruments. A two-level MSCA model is used to model the difference between instruments (the first level) and samples (the second level). With the two models, the spectral difference due to instruments and measurement operation can be corrected, respectively. Three NIR spectral datasets of pharmaceutical tablet, corn and plant leaf are used to evaluate the efficiency of the proposed method. The results show that the score of the first level model describes the overall spectral difference between instruments, and the score of the second level model depictures the spectral difference of the same sample between the measurements. The latter difference may include the spectral variations caused by instrument, operation and the measurement conditions. Therefore, both the spectral difference due to the instrument and measurement can be corrected by adjusting the coefficients in the scores of the two level models, respectively. The proposed method provides a good way for standardizing the spectra measured on different instruments when the measurement is not reproducible., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Temperature dependent near infrared (NIR) spectroscopy has been developed for analyzing multi-component mixtures and understanding the molecular interactions in solutions. In this work, a chemometric method named as mutual factor analysis (MFA) was proposed for the analysis of temperature dependent NIR spectra. The method extracts the common spectral feature contained in the spectra of different temperature or different concentration. The relative quantity of the extracted spectral feature is proportional to the temperature or concentration. From the spectra of water-glucose mixtures, both the spectral variations induced by temperature and concentration are obtained and the variations are correlated with the inducements, respectively, in a very good linearity. Serum samples were used for validation of the method. An acceptable calibration model with a good correlation coefficient (R 2 = 0.8639) was obtained for glucose measurement. The relative deviations of the measured concentrations from the calibration model are in the range of -18.7-8.52%, which are in a reasonable level for clinical uses. More importantly, the calculations are based on the spectral information of water that has interactions with the analyte. This provides a new way for quantitative analyses of bio-systems., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Near infrared (NIR) spectra are sensitive to the variation on water structure caused by perturbations, such as temperature and additives. In this work, water was applied as a probe to detect glucose in aqueous glucose solutions and human serum samples. Spectral changes of water were captured from the temperature dependent NIR spectra using multilevel simultaneous component analysis (MSCA). The first and second level model were established to describe the quantitative spectra-temperature relationship (QSTR) and the quantitative spectra-concentration relationship (QSCR), i.e., the calibration curve, respectively. The score of the first level model shows that the content of free OH in water molecules increases with temperature elevation. The correlation coefficients (R 2 ) of the QSTR model between the score and temperature are higher than 0.99, and that of the calibration model (QSCR) between the spectral features of water clusters and the concentration of glucose are 0.99 and 0.84 for glucose solutions and serum samples, respectively. External validation of the calibration model was further performed with human serum samples. The standard error of the prediction is 0.45. In addition, the linearity of the QSCR models may reveal that glucose interacts with small water clusters and enhances the formation of the hydration shell. Therefore, using water as a probe may provide a new way for quantitative determination of the analytes in aqueous solutions by NIR spectroscopy., (Copyright © 2017 Elsevier B.V. All rights reserved.)