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.

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.

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.

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.

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.

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.

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.

K-birnessite-MnO2/hollow mulberry-like carbon complexes with stabilized and superior rate performance for aqueous magnesium ion storage.

Manganese oxides are commonly employed as a cathode for magnesium ion storage in aqueous magnesium ion hybrid supercapacitors (MHS). However, sluggish reaction kinetics still hinders their practical application. Herein, we designed K-birnessite-MnO2 and electrostatically spun mulberry-like carbon composites (K-MnO2/HMCs) via an in situ growth technique. Benefiting from the 3D conductive carbon network substrate, the in situ fabricated K-MnO2 exhibits more active sites and provides more interfacial contact area between the electrode material and the electrolyte. This improvement enhances its conductivity, facilitating the rapid transfer of electrons, diffusion of ions, and redox reactions. As a result, K-MnO2/HMC-based MHS achieves a specific capacity of 168 mA h g-1 at 0.5 A g-1, simultaneously exhibiting a superior energy density of 111.1 W h kg-1 at a power density of 505 W kg-1. Furthermore, it demonstrates excellent high rate performance and a long cycling life for aqueous magnesium ion storage, offering new insights for MHS applications.

The correlation of anterior segment structures in primary congenital glaucoma by ultrasound biomicroscopy with disease severity and surgical outcomes.

PURPOSE: To evaluate the anterior segment structures using ultrasound biomicroscopy (UBM) in primary congenital glaucoma (PCG) and explore their correlation with disease severity and surgical outcomes. METHODS: Clinical information of PCG patients who underwent UBM prior to their first glaucoma surgeries from September 2014 to March 2021 were reviewed. The study included 214 UBM images of 154 PCG eyes and 60 fellow unaffected eyes. Anterior segment characteristics were analyzed. UBM parameters, including the iris thickness (IT) at variant distances from the pupil edge and iris root, anterior chamber depth (ACD), and pupil diameter (PD), were compared between two groups and their relationship with clinical factors and surgical outcomes were analyzed in PCG eyes. RESULTS: PCG eyes had unclear scleral spur, thin iris, wide anterior chamber angle, deep anterior chamber, rarefied ciliary body, elongated ciliary processes, and abnormal anterior iris insertion. ITs were thinner, ACD was deeper, and PD was larger in PCG eyes than fellow unaffected eyes (all P < 0.001). In PCG eyes, thinner ITs correlated with bilateral involvement and earlier age at presentation, and larger PD correlated with earlier age at presentation (P = 0.030) and higher intraocular pressure (P < 0.001). Thinner IT2 (P = 0.046) and larger PD (P = 0.049) were identified as risk factors for surgical failure. CONCLUSION: UBM is a powerful technique to exam anterior segment structures in PCG. The anatomical features are associated with disease severity and surgical outcomes, providing essential clinical insights.

Pyrolysis of municipal plastic waste: Chlorine distribution and formation of organic chlorinated compounds.

The release of chlorine during the pyrolysis of actual municipal plastic waste (MPW) was studied. Firstly, thermogravimetry-Fourier transform infrared (TG-FTIR) was analyzed to investigate the chlorine release behavior. Then, the effect of temperature on chlorine migrations was investigated by fast pyrolysis experiments in a fixed bed reactor. Results showed that chlorine released mainly between 241 and 353 °C in the form of HCl or chloroesters during MPW pyrolysis. After pyrolysis, chlorine was mainly distributed in the pyrolytic gas (74.34-82.89 %) and char (10.17-21.29 %). However, the release of chlorine was inhibited due to the melting behavior of MPW at <350 °C. Besides, the relative contents and types of organic chlorinated compounds in liquid products were both decreased with temperature. It was observed that polyethylene terephthalate (PET) was the greatest contributor to the formation of organic chlorinated compounds during MPW pyrolysis. Meanwhile, the pyrolysis of PET was significantly promoted by the HCl released from polyvinyl chloride (PVC). Subsequently, the pathways for the formation of organic chlorinated compounds through the co-pyrolysis of PVC and PET were proposed, including the initial degradation and subsequent chlorination of PET. These findings provided new insights into the release and regulation of chlorine-containing pollutants during actual MPW pyrolysis.

Imaging microglia surveillance during sleep-wake cycles in freely behaving mice.

Microglia surveillance manifests itself as dynamic changes in cell morphology and functional remodeling. Whether and how microglia surveillance is coupled to brain state switches during natural sleep-wake cycles remains unclear. To address this question, we used miniature two-photon microscopy (mTPM) to acquire time-lapse high-resolution microglia images of the somatosensory cortex, along with EEG/EMG recordings and behavioral video, in freely-behaving mice. We uncovered fast and robust brain state-dependent changes in microglia surveillance, occurring in parallel with sleep dynamics and early-onset phagocytic microglial contraction during sleep deprivation stress. We also detected local norepinephrine fluctuation occurring in a sleep state-dependent manner. We showed that the locus coeruleus-norepinephrine system, which is crucial to sleep homeostasis, is required for both sleep state-dependent and stress-induced microglial responses and ß2-adrenergic receptor signaling plays a significant role in this process. These results provide direct evidence that microglial surveillance is exquisitely tuned to signals and stressors that regulate sleep dynamics and homeostasis so as to adjust its varied roles to complement those of neurons in the brain. In vivo imaging with mTPM in freely behaving animals, as demonstrated here, opens a new avenue for future investigation of microglia dynamics and sleep biology in freely behaving animals.

A hematological parameter-based model for distinguishing non-puerperal mastitis from invasive ductal carcinoma.

Purpose: Non-puerperal mastitis (NPM) accounts for approximately 4-5% of all benign breast lesions. Ultrasound is the preferred method for screening breast diseases; however, similarities in imaging results can make it challenging to distinguish NPM from invasive ductal carcinoma (IDC). Our objective was to identify convenient and objective hematological markers to distinguish NPM from IDC. Methods: We recruited 89 patients with NPM, 88 with IDC, and 86 with fibroadenoma (FA), and compared their laboratory data at the time of admission. LASSO regression, univariate logistic regression, and multivariate logistic regression were used to screen the parameters for construction of diagnostic models. Receiver operating characteristic curves, calibration curves, and decision curves were constructed to evaluate the accuracy of this model. Results: We found significant differences in routine laboratory data between patients with NPM and IDC, and these indicators were candidate biomarkers for distinguishing between the two diseases. Additionally, we evaluated the ability of some classic hematological markers reported in previous studies to differentiate between NPM and IDC, and the results showed that these indicators are not ideal biomarkers. Furthermore, through rigorous LASSO and logistic regression, we selected age, white blood cell count, and thrombin time to construct a differential diagnostic model that exhibited a high level of discrimination, with an area under the curve of 0.912 in the training set and with 0.851 in the validation set. Furthermore, using the same selection method, we constructed a differential diagnostic model for NPM and FA, which also demonstrated good performance with an area under the curve of 0.862 in the training set and with 0.854 in the validation set. Both of these two models achieved AUCs higher than the AUCs of models built using machine learning methods such as random forest, decision tree, and SVM in both the training and validation sets. Conclusion: Certain laboratory parameters on admission differed significantly between the NPM and IDC groups, and the constructed model was designated as a differential diagnostic marker. Our analysis showed that it has acceptable efficiency in distinguishing NPM from IDC and may be employed as an auxiliary diagnostic tool.

Optimizing Pt-Based Alloy Electrocatalysts for Improved Hydrogen Evolution Performance in Alkaline Electrolytes: A Comprehensive Review.

The splitting of water through electrocatalysis offers a sustainable method for the production of hydrogen. In alkaline electrolytes, the lack of protons forces water dissociation to occur before the hydrogen evolution reaction (HER). While pure Pt is the gold standard electrocatalyst in acidic electrolytes, since the 5d orbital in Pt is nearly fully occupied, when it overlaps with the molecular orbital of water, it generates a Pauli repulsion. As a result, the formation of a Pt-H* bond in an alkaline environment is difficult, which slows the HER and negates the benefits of using a pure Pt catalyst. To overcome this limitation, Pt can be alloyed with transition metals, such as Fe, Co, and Ni. This approach has the potential not only to enhance the performance but also to increase the Pt dispersion and decrease its usage, thus overall improving the catalyst's cost-effectiveness. The excellent water adsorption and dissociation ability of transition metals contributes to the generation of a proton-rich local environment near the Pt-based alloy that promotes HER. Significant progress has been achieved in comprehending the alkaline HER mechanism through the manipulation of the structure and composition of electrocatalysts based on the Pt alloy. The objective of this review is to analyze and condense the latest developments in the production of Pt-based alloy electrocatalysts for alkaline HER. It focuses on the modified performance of Pt-based alloys and clarifies the design principles and catalytic mechanism of the catalysts from both an experimental and theoretical perspective. This review also highlights some of the difficulties encountered during the HER and the opportunities for increasing the HER performance. Finally, guidance for the development of more efficient Pt-based alloy electrocatalysts is provided.

Migration characteristics of chlorine during pyrolysis of municipal solid waste pellets.

The migration process of chlorine during municipal solid waste (MSW) pellets pyrolysis was studied in a fixed bed reactor. Distribution and speciation changes of chlorine at different pyrolysis temperatures were determined by ion chromatography (IC) and X-ray photoelectron spectroscopy (XPS) analyses. Results showed that chlorine was mainly distributed in pyrolysis char (42.36-65.29 %) and gas (26.66-35.03 %) after MSW pellets pyrolysis. With the temperature increasing, chlorine in char and tar was enriched due to the increase of chlorine release and the decrease of product yields, with chlorine concentration increasing to 3498 ppm and 1415 ppm at 800 °C, respectively. Results of chlorine forms analysis indicated that most of the organic-Cl in MSW was released into the volatiles during pyrolysis due to the dissociation of CCl. Inorganic-Cl became the dominant form of chlorine in char after pyrolysis, with the proportion increasing from 46.69 % (raw) to 61.22 % (500 °C), which also suggested that part of organic-Cl was converted into the inorganic-Cl. Notably, the proportions of inorganic-Cl decreased at >600 °C due to the migration of inorganic. In addition, the pyrolysis release behavior of chlorine was affected by the pore structure of char, which could be inhibited by the unprosperous pores in char, especially at low temperatures (<600 °C). These findings provided a reference for the chlorine regulation of MSW pyrolytic products.

The morphology of angle dysgenesis assessed by ultrasound biomicroscopy and its relationship with glaucoma severity and mutant genes in Axenfeld-Rieger syndrome.

Background: Axenfeld-Rieger syndrome (ARS), a developmental disorder, involves anterior segment abnormalities and can lead to glaucoma. However, limited research has addressed the ultrasound biomicroscopy (UBM) characteristics of ARS. This study aimed to assess the anterior chamber angle features using UBM in ARS and determine their correlation with glaucoma severity and mutant genes. Methods: UBM examination was conducted for 42 patients diagnosed with ARS and glaucoma. The morphology of the anterior chamber angle was classified into 6 types (type A, pure high iris insertion; type B, posterior embryotoxon; type C, iris process; type D, trabecular-iris synechia; type E, peripheral iridocorneal adhesion; type F, goniodysgenesis). Candidate genes were sequenced with next-generation sequencing. Correlations of clinical characteristics with angle dysgenesis types and mutant genes were analyzed. Results: Among the 42 patients recruited, 6 eyes were excluded for poor quality UBM images or lack of glaucoma development. The remaining 78 eyes were categorized into 6 groups according to their dominant type of anterior chamber angle (>2 quadrants). There were statistically significant differences in onset age of glaucoma (P<0.001), untreated intraocular pressure (IOP) (P=0.016), vertical cup to disc ratio (P=0.001), and age at surgery (P<0.001) among the groups. Eyes in the type C and D groups developed glaucoma and underwent surgery at an earlier age, while eyes in the type B, E, and F groups developed glaucoma at a relatively later age. Eyes in type A group developed glaucoma and underwent surgery at the latest age, and had the lowest untreated IOP compared to the other groups. Patients with FOXC1 defects were more likely to have angle type B, type C, and type D (accounting for 93.8% of the total), whereas patients with PITX2 defects were more likely to have angle type A, type E, and type F (accounting for 92.1% of the total). Conclusions: UBM is powerful for evaluating the anterior segment abnormalities in ARS. Combined with genetic testing results, the morphological classification helps to assess the severity of glaucoma.

Harnessing the power of optical microscopy for visualization and analysis of histopathological images.

Histopathology is the foundation and gold standard for identifying diseases, and precise quantification of histopathological images can provide the pathologist with objective clues to make a more convincing diagnosis. Optical microscopy (OM), an important branch of optical imaging technology that provides high-resolution images of tissue cytology and structural morphology, has been used in the diagnosis of histopathology and evolved into a new disciplinary direction of optical microscopic histopathology (OMH). There are a number of ex-vivo studies providing applicability of different OMH approaches, and a transfer of these techniques toward in vivo diagnosis is currently in progress. Furthermore, combined with advanced artificial intelligence algorithms, OMH allows for improved diagnostic reliability and convenience due to the complementarity of retrieval information. In this review, we cover recent advances in OMH, including the exploration of new techniques in OMH as well as their applications, and look ahead to new challenges in OMH. These typical application examples well demonstrate the application potential and clinical value of OMH techniques in histopathological diagnosis.