Enhancing water security: Statistical measurement and spatiotemporal analysis.

Water security is essential for ensuring energy security, sustainable development, and human survival. However, due to a series of challenges, including rising water demand, environmental pollution, and water resource shortages, the global water security situation remains concerning and poses a threat to global sustainable development. To assess water security in China, this study uses data from 30 provinces in China from 2012 to 2021. A comprehensive evaluation method was applied to determine the level of water resource security in China. The Dagum Gini coefficient, Moran index, and spatial model were used to clarify regional differentiation characteristics and the driving factors. The results indicate that while China's water resource security is relatively low, it has shown steady improvement in recent years. Significant regional disparities exist in water resource security across China, with notable spatial characteristics, and socio-economic factors are the primary causes of these differences. Based on the above research, we put forward policy recommendations from the aspects of water resources management, public participation and inter-regional water resources cooperation, to provide reference for water resources security in developing countries.

Physiological Time-series Fusion with Hybrid Attention for Adaptive Recognition of Pain.

Automatic pain assessment is an application in healthcare serving personalized pain care, and patients cannot self-report pain. Pain at the present is inferred from physiological dynamics at the present and in the near past. However, heterogeneous pain responses cross-subject and cross-type hinder accurate recognition of pain. This work solves the adaptive pain recognition problem across pain types. We concrete the adaptivity problem into recognizing both phasic/short and tonic/long pain from the physiological sequences of the same length. The adaptivity of the proposed solution (TCAtt-PainNet) was ensured by hybrid temporal-channel attention when fusing multivariate time-series of electrocardiogram (ECG) and galvanic skin response (GSR) features. The attention was obtained by learning the dependencies between the point at present and the sequence in the near past, where sequence point temporal attention was constructed via modified self-attention, and the following feature channel attention was constructed by squeeze-and-excitation temporal attention weighted deep feature sequence. The proposed solution successfully enhanced recognition adaptivity by addressing relevant information only from long input sequences when testing with tonic and phasic pain databases, making progress towards automatic pain assessment for real application scenarios with attributes unknown pain.

Feasibility of biomass-based flexible and transparent AuNPs-acetylcellulose membrane for multifarious surface-enhanced Raman spectroscopy detection.

BACKGROUND: Lignocellulosic biomass-based derivatives coupled with surface-enhanced Raman spectroscopy (SERS) technology have emerged as an appealing and indispensable tool in food safety and environmental monitoring for rapidly detecting trace contaminants like pesticide residues. The membrane material, serving as a substrate, ensures both sampling flexibility and test accuracy by directing the diffusion-adsorption process of the molecules. However, the existing membrane substrates, critical for the practical application of SERS, suffer from issues such as costly, intricate fabrication procedures, or restricted detection capabilities. RESULTS: Herein, we present a flexible, transparent, and biodegradable cellulose acetate membrane with gold nanoparticles (AuNPs) uniformly embedded, fabricated using a simple scraping method. This membrane achieved a limit of detection (LOD) of thiram pesticide in water at 10-8 g mL-1. The unique optical transparency of the substrates allowed for in-situ detection on surfaces, with an LOD of thiram reaching 30 ng cm-2. SIGNIFICANCE: Furthermore, SERS substrates made from corn stover-derived cellulose acetate enable the detection of various contaminants, highlighting their cost-effectiveness and eco-friendliness because of the abundance and low environmental impact of the raw materials.

Cerebrospinal fluid-induced stable and reproducible SERS sensing for various meningitis discrimination assisted with machine learning.

Cerebrospinal fluid (CSF)-based pathogen or biochemical testing is the standard approach for clinical diagnosis of various meningitis. However, misdiagnosis and missed diagnosis always occur due to the shortages of unusual clinical manifestations and time-consuming shortcomings, low sensitivity, and poor specificity. Here, for the first time, we propose a simple and reliable CSF-induced SERS platform assisted with machine learning (ML) for the diagnosis and identification of various meningitis. Stable and reproducible SERS spectra are obtained within 30 s by simply mixing the colloidal silver nanoparticles (Ag NPs) with CSF sample, and the relative standard deviation of signal intensity is achieved as low as 2.1%. In contrast to conventional salt agglomeration agent-induced irreversible aggregation for achieving Raman enhancement, a homogeneous and dispersed colloidal solution is observed within 1 h for the mixture of Ag NPs/CSF (containing 110-140 mM chloride), contributing to excellent SERS stability and reproducibility. In addition, the interaction processes and potential enhancement mechanisms of different Ag colloids-based SERS detection induced by CSF sample or conventional NaCl agglomeration agents are studied in detail through in-situ UV-vis absorption spectra, SERS analysis, SEM and optical imaging. Finally, an ML-assisted meningitis classification model is established based on the spectral feature fusion of characteristic peaks and baseline. By using an optimized KNN algorithm, the classification accuracy of autoimmune encephalitis, novel cryptococcal meningitis, viral meningitis, or tuberculous meningitis could be reached 99%, while an accuracy value of 68.74% is achieved for baseline-corrected spectral data. The CSF-induced SERS detection has the potential to provide a new type of liquid biopsy approach in the fields of diagnosis and early detection of various cerebral ailments.

Didymin ameliorates ulcerative colitis-associated secondary liver damage by facilitating Notch1 degradation.

BACKGROUND: Didymin is a dietary flavonoid originally discovered by our group as a potent anti-ulcerative colitis (UC) agent. However, whether didymin plays a protective role in UC-associated inflammatory liver injury is still unclear. PURPOSE: This study aimed to evaluate the therapeutic potential of didymin on UC-associated inflammatory liver injury and explore the underlying mechanism. STUDY DESIGN AND METHODS: Colitis model was established in C57BL/6 mice by exposure to DSS, and didymin was administrated intragastrically for consecutive 10 days. The inflammatory liver injury was assessed by levels of alanine aminotransferase (ALT) and aspartate transaminase (AST) in serum and histopathological damage in the liver. In vitro Kupffer cells and RAW264.7 cells challenged with lipopolysaccharides (LPS) were used to explore the modulatory activity of didymin on pro-inflammatory cytokines secretion and Notch1 signaling pathway activation. RESULTS: Didymin significantly mitigated liver coefficiency, ALT and AST levels in serum, and the hepatic histopathological damage caused by DSS-induced acute and chronic colitis. The mRNA expressions of pro-inflammatory factors including Tnf, Il1, and Il6 in liver tissues, Kupffer cells, and RAW264.7 cells stimulated by the influx of LPS was significantly deprived after didymin treatment. Mechanistically, didymin obstructed the protein expression, nuclear translocation of notch intracellular domain 1 (Notch1-ICD) and mRNA expression of hairy and enhancer of split 1 (Hes1). Further, the inhibitory mechanism of the Notch1-Hes1 pathway was dependent on c-Cbl-mediated Notch1-ICD lysosomal degradation. CONCLUSION: Our study verified for the first time that didymin could prevent UC-associated diseases, such as inflammatory liver injury, and the mechanism was related to facilitating Notch1 lysosomal degradation rather than proteasome degradation via promoting protein expression of c-Cbl in macrophages. Our findings that the inhibition of Notch1 signaling transduction helps to alleviate UC-associated liver injury provides possible therapeutics for the treatment of colitis and also furnishes a research paradigm for the study of flavonoids with similar structures.

Valorization of lignin from aqueous-based lignocellulosic biorefineries.

An additional 100 million tons/year of lignin coproduct will result when lignocellulosic biomass is processed in biorefineries to fiber, sugars, biofuels, and bioproducts. This will double the amount of lignin already generated from pulping and paper production. Unlike pulping that results in lignosulphonate (88% of total) or Kraft lignin (9%), aqueous-based biorefining leaves behind non-sulfonated lignin and aromatic molecules. This new type of lignin provides opportunities for large volume agricultural uses such as controlled-release carriers and soil amendments as well as feedstocks for new chemistries that lead to molecular building blocks for the chemical industry and to precursors for sustainable aviation biofuels.

Alloy Reconstruction in Pyrolytic Bowknot-like Heteronuclear CoFe Clusters for Electrocatalytic Application.

The construction of doped molecular clusters is an intriguing way to perform bimetallic doping for electrocatalysts. However, efficiently harnessing the benefits of a doping strategy and alloy engineering to create a nanostructure for electrocatalytic application at the molecular level has consistently posed a challenge. Here we propose an in situ reconstruction strategy aimed at producing an alloy nanostructure through a pyrolysis process, originating from bowknot-like heterometallic clusters. The Schiff base, denoted as ligand L1 (o-vanillin ethylenediamine), was introduced as a precursor to coordinate Fe and Co metals, thereby yielding a heteronuclear metal cluster [(FeCo)(L1)2O]CH3CN. Subsequently, a comprehensive investigation of the in situ reconstruction process [(FeCo)(L1)2O](CH3CN) → [(FeCo)(L1)2O] → [M-O-M/M-O] [CH3+/CH3O+/H2C═N/C2H5+/C4H4+] → [FeCo/Fe3O4/Fe2O3/Co3O4][carbon layer] led to the formation of MOx/CoFe@NC-700 during the pyrolysis. This process reveals that the metals Fe and Co in the clusters undergo partly in situ evolution into FeCo alloys, resulting in the successful preparation of MOx/CoFe@NC (M = Fe, Co) nanomaterials that leverage the advantages of both doping strategies and alloy engineering. The synergistic interaction between alloy particles and metal oxides establishes active sites that contribute to the excellent oxygen evolution (OER) and hydrogen evolution (HER) catalytic behaviors. Notably, these materials exhibit outstanding OER and HER properties under alkaline conditions, with overpotentials of 191 and 88 mV for OER and HER, respectively, at 10 mA cm-2. Investigation of the in situ conversion of Schiff base bimetal clusters into alloy materials through pyrolysis offers a novel strategy for advancing electrocatalytic applications.

Variability in soybean yield responses to elevated atmospheric CO2: Insights from non-structural carbohydrate remobilisation during seed filling.

The increasing atmospheric CO2 concentration (e[CO2]) has mixed effects on soybean most varieties' yield. This study elucidated the effect of e[CO2] on soybean yield and the underlying mechanisms related to photosynthetic capacity, non-structural carbohydrate (NSC) accumulation, and remobilisation. Four soybean cultivars were cultivated in open-top chambers at two CO2 levels. Photosynthesis rates were determined from R2 to R6. Plants were sampled at R5 and R8 to determine carbohydrate concentrations. There were significant variations in yield responses among the soybean cultivars under e[CO2], from no change in DS1 to a 22% increase in SN14. DS1 and SN14 had the smallest and largest increase, respectively, in daily carbon assimilation capacity. Under e[CO2], DS1, MF5, and XHJ had an increase in Ci, at which point the transition from Rubisco-limited to ribulose-1,5-bisphosphate regeneration-limited photosynthesis occurred, in contrast with SN14. Thus, the cultivars might have distinct mechanisms that enhance photosynthesis under e[CO2] conditions. A positive correlation was between daily carbon assimilation response to e[CO2] and soybean yield, emphasising the importance of enhanced photosynthate accumulation before the R5 stage in determining yield response to e[CO2]. E[CO2] significantly influenced NSC accumulation in vegetative organs at R5, with variation among cultivars. There was enhanced NSC remobilisation during seed filling, indicating cultivar-specific responses to the remobilisation of sucrose and soluble sugars, excluding sucrose and starch. A positive correlation was between leaf and stem NSC remobilisation and yield response to e[CO2], emphasising the role of genetic differences in carbohydrate remobilisation mechanisms in determining soybean yield variation under elevated CO2 levels.

Regional policy options for carbon peaking in the Yangtze River Delta under uncertainty.

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity.

Predicting Soil Organic Matter, Available Nitrogen, Available Phosphorus and Available Potassium in a Black Soil Using a Nearby Hyperspectral Sensor System.

Black soils, which play an important role in agricultural production and food security, are well known for their relatively high content of soil organic matter (SOM). SOM has a significant impact on the sustainability of farmland and provides nutrients for plants. Hyperspectral imaging (HSI) in the visible and near-infrared region has shown the potential to detect soil nutrient levels in the laboratory. However, using portable spectrometers directly in the field remains challenging due to variations in soil moisture (SM). The current study used spectral data captured by a handheld spectrometer outdoors to predict SOM, available nitrogen (AN), available phosphorus (AP) and available potassium (AK) with different SM levels. Partial least squares regression (PLSR) models were established to compare the predictive performance of air-dried soil samples with SMs around 20%, 30% and 40%. The results showed that the model established using dry sample data had the best performance (RMSE = 4.47 g/kg) for the prediction of SOM, followed by AN (RMSE = 20.92 mg/kg) and AK (RMSE = 22.67 mg/kg). The AP was better predicted by the model based on 30% SM (RMSE = 8.04 mg/kg). In general, model performance deteriorated with an increase in SM, except for the case of AP. Feature wavelengths for predicting four kinds of soil properties were recommended based on variable importance in the projection (VIP), which offered useful guidance for the development of portable hyperspectral sensors based on discrete wavebands to reduce cost and save time for on-site data collection.

A Comprehensive Pyrolysis Mechanism of Binuclear Chromium-Based Complexes for Superior OER Activity.

Transition metal oxides are widely pursued as potent electrocatalysts for the oxygen evolution reaction (OER). However, single-metal chromium catalysts remain underexplored due to their intrinsic activity limitations. Herein, we successfully synthesize mixed-valence, nitrogen-doped Cr2O3/CrO3/CrN@NC nanoelectrocatalysts via one-step targeted pyrolysis techniques from a binuclear Cr-based complex (Cr2(Salophen)2(CH3OH)2), which is strategically designed as a precursor. Comprehensive pyrolysis mechanisms were thoroughly delineated by using coupled thermogravimetric analysis and mass spectrometry (TG-MS) alongside X-ray diffraction. Below 800 °C, the generation of a reducing atmosphere was noted, while continuous pyrolysis at temperatures exceeding 800 °C promoted highly oxidized CrO3 species with an elevated +6 oxidation state. The optimized catalyst pyrolyzed at 1000 °C (Cr2O3/CrO3/CrN@NCs-1000) demonstrated remarkable OER activity with a low overpotential of 290 mV in 1 M KOH and excellent stability. Further density functional theory (DFT) calculations revealed a much smaller reaction energy barrier of CrO3 than the low oxidation state species for OER reactivity. This work reveals fresh strategies for rationally engineering chromium-based electrocatalysts and overcoming intrinsic roadblocks to enable efficient OER catalysis through a deliberate oxidation state and compositional tuning.

Impact of carbon emission trading system on green technology innovation of energy enterprises in China.

China proposed establishing a carbon emission trading market in its 12th Five-Year Plan to reduce carbon dioxide emissions through market mechanisms, promote the development of science and technology and help China become an environment-friendly country. To examine the impact of carbon emission trading on green technology innovation in Chinese energy enterprises, data from 1993 to 2020 were collected from 494 A-share-listed energy enterprises. Enterprises located in the pilot area of carbon emissions trading were assigned to the treatment group, while those in the non-pilot area were assigned to the control group. The propensity-score-matching method was utilized to match the treatment group with the control group, and the resulting samples were used as the actual sample data. The difference-in-differences method was then employed to assess the net impact of carbon emission trading and investigate its effect on green technology innovation in energy enterprises. This empirical study suggested that carbon emission trading has a positive impact on green technology innovation in energy enterprises, particularly state-owned ones. Larger enterprises are more willing to engage in green technological innovation than small enterprises. Furthermore, when faced with a carbon emission trading system, 'mature' companies tend to pay more attention to green technology innovation than younger enterprises do. This study puts forward policy measures for establishing a national-level carbon emission market in China in the future.

Evaluation of the learning state of online video courses based on functional near infrared spectroscopy.

Studying brain activity during online learning will help to improve research on brain function based on real online learning situations, and will also promote the scientific evaluation of online education. Existing research focuses on enhancing learning effects and evaluating the learning process associated with online learning from an attentional perspective. We aimed to comparatively analyze the differences in prefrontal cortex (PFC) activity during resting, studying, and question-answering states in online learning and to establish a classification model of the learning state that would be useful for the evaluation of online learning. Nineteen university students performed experiments using functional near-infrared spectroscopy (fNIRS) to monitor the prefrontal lobes. The resting time at the start of the experiment was the resting state, watching 13 videos was the learning state, and answering questions after the video was the answering state. Differences in student activity between these three states were analyzed using a general linear model, 1s fNIRS data clips, and features, including averages from the three states, were classified using machine learning classification models such as support vector machines and k-nearest neighbor. The results show that the resting state is more active than learning in the dorsolateral prefrontal cortex, while answering questions is the most active of the three states in the entire PFC, and k-nearest neighbor achieves 98.5% classification accuracy for 1s fNIRS data. The results clarify the differences in PFC activity between resting, learning, and question-answering states in online learning scenarios and support the feasibility of developing an online learning assessment system using fNIRS and machine learning techniques.

Biochemical component analysis of human myopic corneal stroma using the Raman spectrum.

PURPOSE: This study aimed to measure the Raman spectrum of the human corneal stroma lens obtained from small incision lenticule extraction surgery (SMILE) in Asian myopic eyes using a confocal Raman micro-spectrometer built in the laboratory. METHODS: Forty-three myopic patients who underwent SMILE with equivalent diopters between - 4.00 and - 6.00 D were selected, and the right eye data were collected. Corneal stroma lenses were obtained during surgery, and the Raman spectra were measured after air drying. The complete Raman spectrum of human myopic corneal stroma lens tissue was obtained within the range of 700-4000 cm-1. RESULTS: Thirteen characteristic peaks were found, with the stronger peaks appearing at 937 cm-1, corresponding to proline, valine, and the protein skeleton of the human myopic corneal stroma lens; 1243 cm-1, corresponding to collagen protein; 1448 cm-1, corresponding to the collagen protein and phospholipids; and 2940 cm-1, corresponding to the amino acid and lipids, which was the strongest Raman peak. CONCLUSION: These results demonstrated that Raman spectroscopy has much potential as a fast, cost-effective, and reliable diagnostic tool in the diagnosis and treatment of eye diseases, including myopia, keratoconus, and corneal infection.

Label-free Raman imaging for screening of anti-inflammatory function food.

Natural bioactive compounds and plant constituents are considered to have a positive anti-inflammatory effect. This study aimed to establish a screening technique for anti-inflammatory function in foods based on label-free Raman imaging. A visible anti-inflammatory analysis method based on coherent anti-Stokes Raman scattering (CARS) was established with an LPS-induced RAW264.7 cell model. Dynamic changes in proteins and lipids were determined at laser pump light wavelengths of 2956 cm-1 and 2856 cm-1, respectively. The method was applied to a plant-based formula (JC) with anti-inflammatory activity. Q-TOF-MS and HPLC analyses revealed the main active constituents of JC as quercetin, kaempferol, l-glutamine, and sodium copper chlorophyllin. In in vitro and in vivo verification experiments, JC showed significant anti-inflammatory activity by regulating the TLR4/NF-κB pathway. In conclusion, this study successfully established a label-free and visible method for screening anti-inflammatory constituents in plant-based food products, which will facilitate the evaluation of functional foods.

Facile Bond Exchanging Strategy for Engineering Wet Adhesion and Antioxidant/Antibacterial Thin Layer over a Dynamic Hydrogel via the Carbon Dots Derived from Tannic Acid/ε-Polylysine.

Adhesive hydrogels, playing an essential role in stretchable electronics, soft robotics, tissue engineering, and so forth, upon functioning often need to adhere to various substrates in wet conditions and simultaneously exhibit antibacterial/antioxidant properties while possessing the intrinsic stretchability and elasticity of the hydrogel network intact. Therefore, simple approaches to conveniently access adhesive hydrogels with multifunctional surfaces are being pursued. Herein, a facile strategy has been proposed to construct multifunctional adhesive hydrogels via surface engineering of a multifunctional carbon dot (CD)-decorated polymeric thin layer by dynamic bond exchange. By this strategy, a double cross-linked network hydrogel of polyacrylamide (PAM) and oxidized dextran (ODA) was engineered with a unique dense layer over the Schiff base hydrogel matrix by aqueous solution immersion of PA-120, versatile CDs derived from tannic acid (TA) and ε-polylysine (PL). Without any additional agents, the PA-120 CDs with residual polyphenolic/catechol and amine moieties were incorporated into the surface structure of the hydrogel network by the combined action of the Schiff base and hydrogen bonds to form a dense surface layer that can exhibit high wet adhesive performance via the amine-polyphenol/catechol pair. The armor-like dense architecture also endowed hydrogels with considerably enhanced tensile/compression properties and excellent antioxidant/antibacterial abilities. Besides, the single-sided modified Janus hydrogel and completely surface-modified hydrogel can be flexibly developed through this approach. This strategy will provide new insights into the preparation and application of surface-modified hydrogels featuring multiple functions and tunable interfacial properties.

Revealing the characteristic aroma and boundary compositions of five pig breeds based on HS-SPME/GC-O-MS, aroma recombination and omission experiments.

To clarify the characteristic aroma compounds and flavor discrepancies of five Chinese typical pig species, headspace-solid phase microextraction gas chromatography-olfactometry-mass spectrometry (HS-SPME/GC-O-MS), electronic nose (E-nose), aroma recombination and omission experiments were used to analyze the characteristic aroma and boundary of five boiled pork. A total of 38 volatile compounds were identified, of which 14 were identified as important odorants with odor-activity values (OAVs) greater than 1. Aroma recombination and omission experiments revealed 8 key characteristic aroma compounds, which significantly contributed to the overall aroma. Sensory evaluation of the recombination model with the 8 aroma compounds scored 3.0 to 4.0 out of 5 points. 12 potential markers were identified to distinguish by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), including (E)-2-octenal, 3-ethyl-2-methyl-1,3-hexadiene, (E)-2-heptenal, 2-pentylfuran, cyclooctanol, 1-heptanol, sec-butylamine, D-limonene, N-vinylformamide, 2,3-octanedione, 2-ethylfuran and 3-pentanamine. Alongside benzaldehyde and pentanal, the combinations and fluctuations of these 14 aroma markers were proposed to constitute the aroma boundaries of different pork breeds. The aroma-active substances were able to effectively differentiate different breeds.

Construction of MnO2-Mn3O4 heterostructures to facilitate high-performance aqueous magnesium ion energy storage.

MnO2-Mn3O4 heterostructure materials are applied in aqueous magnesium ion energy storage for the first time. The heterostructure yields an exceptionally high pseudocapacitance contribution, resulting in a specific capacitance of 313.5 F g-1 at 1 A g-1, which contrasts with that of MnO2 (108.8 F g-1) and Mn3O4 (123.5 F g-1). Additionally, it shows potential for practical applications as a cathode for magnesium ion hybrid supercapacitors (MHS).

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks.

Objective. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent ona priorinformation, resulting in a less stable and adaptable method. Data-driven deep learning-based reconstruction avoids the errors of light propagation models and the reliance on experience and a prior by learning the mapping relationship between the surface light distribution and the target directly from the dataset. However, the acquisition of the training dataset and the training of the network itself are time consuming, and the high dependence of the network performance on the training dataset results in a low generalization ability. The objective of this work is to develop a highly robust reconstruction framework to solve the existing problems.Approach. This paper proposes a physical model constrained neural networks-based reconstruction framework. In the framework, the neural networks are to generate a target distribution from surface measurements, while the physical model is used to calculate the surface light distribution based on this target distribution. The mean square error between the calculated surface light distribution and the surface measurements is then used as a loss function to optimize the neural network. To further reduce the dependence ona prioriinformation, a movable region is randomly selected and then traverses the entire solution interval. We reconstruct the target distribution in this movable region and the results are used as the basis for its next movement.Main Results. The performance of the proposed framework is evaluated with a series of simulations andin vivoexperiment, including accuracy robustness of different target distributions, noise immunity, depth robustness, and spatial resolution. The results collectively demonstrate that the framework can reconstruct targets with a high accuracy, stability and versatility.Significance. The proposed framework has high accuracy and robustness, as well as good generalizability. Compared with traditional regularization-based reconstruction methods, it eliminates the need to manually delineate feasible regions and adjust regularization parameters. Compared with emerging deep learning assisted methods, it does not require any training dataset, thus saving a lot of time and resources and solving the problem of poor generalization and robustness of deep learning methods. Thus, the framework opens up a new perspective for the reconstruction of three-dimension optical imaging.

Quantitative analysis of the quality constituents of Lonicera japonica Thunberg based on Raman spectroscopy.

Quantitative analysis of the quality constituents of Lonicera japonica (Jinyinhua [JYH]) using a feasible method provides important information on its evaluation and applications. Limitations of sample pretreatment, experimental site, and analysis time should be considered when identifying new methods. In response to these considerations, Raman spectroscopy combined with deep learning was used to establish a quantitative analysis model to determine the quality of JYH. Chlorogenic acid and total flavonoids were identified as analysis targets via network pharmacology. High performance liquid chromatograph and ultraviolet spectroscopy were used to construct standard curves for quantitative analysis. Raman spectra of JYH extracts (1200) were collected. Subsequently, models were built using partial least squares regression, Support Vector Machine, Back Propagation Neural Network, and One-dimensional Convolutional Neural Network (1D-CNN). Among these, the 1D-CNN model showed superior prediction capability and had higher accuracy (R2 = 0.971), and lower root mean square error, indicating its suitability for rapid quantitative analysis.