Phosphorus recovery from sewage sludge via Mg-air battery system

A pressing issue in contemporary society is the resource scarcity of phosphorus. Operating on the principle of electrochemical reactions between Mg as the anode and oxygen from air as the cathode, Mg-air batteries (MAB) have been employed to provide new prospects for phosphorus recovery in struvite form. Different phosphorus concentrations and reaction time impact struvite generation in MAB systems; however, the exact mechanism has rarely been investigated. We investigated how varying the initial phosphorus concentration and the reaction time affects phosphorus recovery, electricity generation, and the efficiency of struvite production in MAB. Additionally, we examine the impact of solid carbon sources on phosphorus transformation in sludge. The findings revealed that the incorporation of solid carbon sources facilitated the release of phosphate by changing phosphorus speciation. The electrolyte derived from the conditioned sludge filtrate exhibited a remarkable phosphorus removal efficiency of 91.7 % within 1 h, yielding the highest struvite purity of ∼70 %, whereas that using raw sludge filtrate or extending the reaction time was found to be less effective, even reducing struvite formation. Furthermore, different electrolytes influence the system's ability to passivate anode, and electrolytes with higher phosphorus concentrations have better electricity production performance. The results by Visual MINTEQ model confirmed that longer reaction times and lower initial phosphorus concentrations can negatively affect struvite formation by introducing Mg3(PO4)2 and Mg(OH)2. The integration of agricultural waste as carbon sources with MAB for phosphorus recovery represents a potential methodology for struvite recuperation from sewage sludge, thereby heralding a sustainable strategy for resource recovery.

Computational-aided analysis of the pathway and mechanism of dichloroacetonitrile formation from phenylalanine upon chloramination

Dichloroacetonitrile (DCAN) is an emerging disinfection by-product (DBP) that is widespread in drinking water. However, the pathway for DCAN formation from aromatic amino acids remains unclear, leading to a lack of an understanding of its explicit fate during chloramination. In this study, we investigated the specific formation mechanism of DCAN during the chloramination of phenylalanine based on reaction kinetics and chemical thermodynamics. The reason for differences between aldehyde and decarboxylation pathways was explained, and kinetic parameters of the pathways were obtained through quantum chemistry calculations. The results showed that the reaction rate constant of the rate-limiting step of the aldehyde pathway with 1.9 × 10-11 s-1 was significantly higher than that of decarboxylation (3.6 × 10-16 s-1 M-1), suggesting that the aldehyde pathway is the main reaction pathway for DCAN formation during the chloramination of phenylalanine to produce DCAN. Subsequently, theoretical calculations were performed to elucidate the effect of pH on the formation mechanism, which aligned well with the experimental results. Dehydrohalogenation was found to be the rate-limiting step under acidic conditions with reaction rate constants higher than those of the rate-limiting step (expulsion of amines) under neutral conditions, increasing the rate of DCAN formation. This study highlights the differences in DCAN formation between the decarboxylation and aldehyde pathways during the chloramination of precursors at both molecular and kinetic levels, contributing to a comprehensive understanding of the reaction mechanisms by which aromatic free amino acids generate DCAN.

ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments

Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. It becomes increasingly crucial in the field of embodied AI, with potential applications in autonomous navigation, search and rescue, and human-robot interaction. In this paper, we propose to address a more practical yet challenging counterpart setting - vision-language navigation in continuous environments (VLN-CE). To develop a robust VLN-CE agent, we propose a new navigation framework, ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav performs online topological mapping of environments by self-organizing predicted waypoints along a traversed path, without prior environmental experience. It privileges the agent to break down the navigation procedure into high-level planning and low-level control. Concurrently, ETPNav utilizes a transformer-based cross-modal planner to generate navigation plans based on topological maps and instructions. The plan is then performed through an obstacle-avoiding controller that leverages a trial-and-error heuristic to prevent navigation from getting stuck in obstacles. Experimental results demonstrate the effectiveness of the proposed method. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets, respectively. Our code is available at https://github.com/MarSaKi/ETPNav.

Inhibition of palmitoyltransferase ZDHHC12 sensitizes ovarian cancer cells to cisplatin through ROS-mediated mechanisms

Platinum-based therapies have revolutionized the treatment of high-grade serous ovarian cancer (HGSOC). However, high rates of disease recurrence and progression remain a major clinical concern. Impaired mitochondrial function and dysregulated reactive oxygen species (ROS), hallmarks of cancer, hold potential as therapeutic targets for selectively sensitizing cisplatin treatment. Here, we uncover an oncogenic role of the palmitoyltransferase ZDHHC12 in regulating mitochondrial function and ROS homeostasis in HGSOC cells. Analysis of The Cancer Genome Atlas (TCGA) ovarian cancer data revealed significantly elevated ZDHHC12 expression, demonstrating the strongest positive association with ROS pathways among all ZDHHC enzymes. Transcriptomic analysis of independent ovarian cancer datasets and the SNU119 cell model corroborated this association, highlighting a strong link between ZDHHC12 expression and signature pathways involving mitochondrial oxidative metabolism and ROS regulation. Knockdown of ZDHHC12 disrupted this association, leading to increased cellular complexity, ATP levels, mitochondrial activity, and both mitochondrial and cellular ROS. This dysregulation, achieved by the siRNA knockdown of ZDHHC12 or treatment with the general palmitoylation inhibitor 2BP or the fatty acid synthase inhibitor C75, significantly enhanced cisplatin cytotoxicity in 2D and 3D spheroid models of HGSOC through ROS-mediated mechanisms. Markedly, ZDHHC12 inhibition significantly augmented the anti-tumor activity of cisplatin in an ovarian cancer xenograft tumor model, as well as in an ascites-derived organoid line of platinum-resistant ovarian cancer. Our data suggest the potential of ZDHHC12 as a promising target to improve the outcome of HGSOCs in response to platinum-based chemotherapy.

Dynamic decomposition graph convolutional neural network for SSVEP-based brain-computer interface

The SSVEP-based paradigm serves as a prevalent approach in the realm of brain-computer interface (BCI). However, the processing of multi-channel electroencephalogram (EEG) data introduces challenges due to its non-Euclidean characteristic, necessitating methodologies that account for inter-channel topological relations. In this paper, we introduce the Dynamic Decomposition Graph Convolutional Neural Network (DDGCNN) designed for the classification of SSVEP EEG signals. Our approach incorporates layerwise dynamic graphs to address the oversmoothing issue in Graph Convolutional Networks (GCNs), employing a dense connection mechanism to mitigate the gradient vanishing problem. Furthermore, we enhance the traditional linear transformation inherent in GCNs with graph dynamic fusion, thereby elevating feature extraction and adaptive aggregation capabilities. Our experimental results demonstrate the effectiveness of proposed approach in learning and extracting features from EEG topological structure. The results shown that DDGCNN outperforms other state-of-the-art (SOTA) algorithms reported on two datasets (Dataset 1: 54 subjects, 4 targets, 2 sessions; Dataset 2: 35 subjects, 40 targets). Additionally, we showcase the implementation of DDGCNN in the context of synchronized BCI robotic fish control. This work represents a significant advancement in the field of EEG signal processing for SSVEP-based BCIs. Our proposed method processes SSVEP time domain signals directly as an end-to-end system, making it easy to deploy. The code is available at https://github.com/zshubin/DDGCNN.

Monocytes Release Pro-Cathepsin D to Drive Blood-to-Brain Transcytosis in Diabetes

BACKGROUND

Microvascular complications are the major outcome of type 2 diabetes progression, and the underlying mechanism remains to be determined.

METHODS

High-throughput RNA sequencing was performed using human monocyte samples from controls and diabetes. The transgenic mice expressing human CTSD (cathepsin D) in the monocytes was constructed using CD68 promoter. In vivo 2-photon imaging, behavioral tests, immunofluorescence, transmission electron microscopy, Western blot analysis, vascular leakage assay, and single-cell RNA sequencing were performed to clarify the phenotype and elucidate the molecular mechanism.

RESULTS

Monocytes expressed high-level CTSD in patients with type 2 diabetes. The transgenic mice expressing human CTSD in the monocytes showed increased brain microvascular permeability resembling the diabetic microvascular phenotype, accompanied by cognitive deficit. Mechanistically, the monocytes release nonenzymatic pro-CTSD to upregulate caveolin expression in brain endothelium triggering caveolae-mediated transcytosis, without affecting the paracellular route of brain microvasculature. The circulating pro-CTSD activated the caveolae-mediated transcytosis in brain endothelial cells via its binding with low-density LRP1 (lipoprotein receptor-related protein 1). Importantly, genetic ablation of CTSD in the monocytes exhibited a protective effect against the diabetes-enhanced brain microvascular transcytosis and the diabetes-induced cognitive impairment.

CONCLUSIONS

These findings uncover the novel role of circulatory pro-CTSD from monocytes in the pathogenesis of cerebral microvascular lesions in diabetes. The circulatory pro-CTSD is a potential target for the intervention of microvascular complications in diabetes.

Spatiotemporal link between El Niño Southern Oscillation (ENSO), extreme heat, and thermal stress in the Asia-Pacific region

Climate change is closely monitored and numerous studies reports increasing air temperature and weather extremes across the globe. As a direct consequence of the increase of global temperature, the increased heat stress is becoming a global threat to public health. While most climate change and epidemiological studies focus on air temperature to explain the increasing risks, heat strain can be predicted using comprehensive indices such as Universal Thermal Climate Index (UTCI). The Asia-Pacific region is prone to thermal stress and the high population densities in the region impose high health risk. This study evaluated the air temperature and UTCI trends between 1990 and 2019 and found significant increasing trends for air temperature for the whole region while the increases of UTCI are not as pronounced and mainly found in the northern part of the region. These results indicate that even though air temperature is increasing, the risks of heat stress when assessed using UTCI may be alleviated by other factors. The associations between El Niño Southern Oscillation (ENSO) and heat stress was evaluated on a seasonal level and the strongest regional responses were found during December-January (DJF) and March-May (MAM).

SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

Current understanding of subclinical diabetic retinopathy informed by histology and high-resolution in vivo imaging

The escalating incidence of diabetes mellitus has amplified the global impact of diabetic retinopathy. There are known structural and functional changes in the diabetic retina that precede the fundus photography abnormalities which currently are used to diagnose clinical diabetic retinopathy. Understanding these subclinical alterations is important for effective disease management. Histology and high-resolution clinical imaging reveal that the entire neurovascular unit, comprised of retinal vasculature, neurons and glial cells, is affected in subclinical disease. Early functional manifestations are seen in the form of blood flow and electroretinography disturbances. Structurally, there are alterations in the cellular components of vasculature, glia and the neuronal network. On clinical imaging, changes to vessel density and thickness of neuronal layers are observed. How these subclinical disturbances interact and ultimately manifest as clinical disease remains elusive. However, this knowledge reveals potential early therapeutic targets and the need for imaging modalities that can detect subclinical changes in a clinical setting.

Development of high internal phase Pickering emulsions stabilized by egg yolk and carboxymethylcellulose complexes to improve β-carotene bioaccessibility for the elderly

The work aimed to develop the multi-protein mixture of egg yolk as natural particles to stabilize high internal phase Pickering emulsions (HIPPEs) to improve the bioaccessibility of β-carotene in the elderly. The results showed that the depletion attraction drove the adsorption of egg yolk protein particles at the oil-water interface and the formation of osmotic droplet clusters due to the attachment of particle-coated droplets in the dispersed phase, leading to kinetic blocking and stable gelation of HIPPEs. Rheological measurements showed that HIPPEs had shear thinning, low shear stress, viscoelastic properties, and structural recovery properties, which facilitated easy consumption for the elderly. The stability of HIPPEs was verified by ionic and centrifugal stability tests, demonstrating their potential for application to complex gastric environments. HIPPEs have been applied to the International Dysphagia Dietary Standardization Initiative (IDDSI) test and simulated in vitro digestion in older adults, demonstrating their safe swallowability and high β-carotene bioaccessibility. Our findings suggest solutions for food practitioners facing the aging problem and provide new insights for preparing age-friendly foods.

Unveiling the potential for artificial upwelling in algae derived carbon sink and nutrient mitigation

Mariculture algae may present a crucial part of ocean-based solutions for climate change, with the ability to sequester carbon and remove nutrients. However, the expansion of mariculture algae faces multiple challenges. Here, we measure the changes in algae derived carbon sinks and nitrogen (N) and phosphorus (P) removal between 2010 and 2020 in Shandong Province, China. We further identify the key driving factors, namely area, algal species proportion, and yield, that influence the changes. The results show that algae derived carbon sinks and nutrient removal growth rates in Shandong Province have slowed significantly since 2014, mainly due to area limitations, laver-oriented species change, and unstable yields. Artificial upwelling (AU) has the potential to enhance the yield and subsequently offset the loss of carbon sinks and nutrient removal caused by negative driving factors. Scenario analysis indicates that a complete deployment of AU by 2030 will offset up to a 44.52 % decrease in the mariculture algae area, or a 72.57 % increase in the laver share of the algal species combination compared to 2020. Similar conclusions are reached regarding the role of AU in N and P removal. This study also identifies ancillary challenges such as low energy efficiency and high costs faced by applying AU.

Image-fusion-based object detection using a time-of-flight camera

In this work, we demonstrate an innovative object detection framework based on depth and active infrared intensity images fusion with a time-of-flight (ToF) camera. A slide window weight fusion (SWWF) method provides fuse image with two modalities to localize targets. Then, the depth and intensity information is extracted to construct a joint feature space. Next, we utilize four machine learning methods to achieve object recognition. To verify this method, experiments are performed on an in-house dataset containing 1066 images, which are categorized into six different surface materials. Consequently, the approach performs well on localization with a 0.778 intersection over union (IoU). The best classification results are obtained with K-Nearest Neighbor (KNN) with a 98.01% total accuracy. Furthermore, our demonstrated method is less affected by various illumination conditions.

"Triple-low" radiation dose bronchial artery CT angiography before bronchial artery embolisation: a feasibility study

AIM

To explore the feasibility of a "triple-low" dose (low tube voltage, low tube current, and low contrast agent volume) bronchial artery computed tomography (CT) angiography (CTA) to replace routine dose bronchial artery CTA before bronchial artery embolisation (BAE).

MATERIALS AND METHODS

CTA was obtained from 60 patients with body mass index (BMI) < 30 kg/m2 using a 256 multi-section iCT system, and they were divided into two groups: (1) group A: 100 kVp, 100 mAs, 50 ml contrast medium (CM); (2) group B: 120 kVp, automatic tube current modulation (ACTM), 80 ml CM. CT attenuation of the thoracic aorta, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and subjective image quality scores and traceability scores assessed. The effective radiation dose was calculated.

RESULTS

The radiation dose was reduced by 79.7% in group A compared to group B (p<0.05). The CT attenuation of the thoracic aorta was increased by approximately 13% in group A compared to group B (p<0.05). Higher image noise, lower SNR, and CNR were obtained in group A compared to group B (all p<0.05). Both subjective image quality scores and traceability scores did not differ between groups A and B (both p>0.05).

CONCLUSION

It is feasible to use the "triple-low" dose CTA protocol for patients with a body mass index (BMI) < 30 kg/m2. The radiation dose was reduced by 79.7%, and the dose of contrast medium was reduced by 37.5% to ensure the diagnostic value.

Linear Combination of Hamiltonian Simulation for Nonunitary Dynamics with Optimal State Preparation Cost

We propose a simple method for simulating a general class of nonunitary dynamics as a linear combination of Hamiltonian simulation (LCHS) problems. LCHS does not rely on converting the problem into a dilated linear system problem or on the spectral mapping theorem. The latter is the mathematical foundation of many quantum algorithms for solving a wide variety of tasks involving nonunitary processes, such as the quantum singular value transformation. The LCHS method can achieve optimal cost in terms of state preparation. We also demonstrate an application for open quantum dynamics simulation using the complex absorbing potential method with near-optimal dependence on all parameters.

Extreme precipitation patterns in the Asia-Pacific region and its correlation with El Niño-Southern Oscillation (ENSO)

In the Asia-Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990-2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June-August (JJA), and by precipitation frequency in December-February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March-May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region.

Rechargeable Potassium-Ion Full Cells Operating at -40 °C

Potassium-ion batteries (PIBs) are promising for cryogenic energy storage. However, current researches on low-temperature PIBs are limited to half cells utilizing potassium metal as an anode, and realizing rechargeable full cells is challenged by lacking viable anode materials and compatible electrolytes. Herein, a hard carbon (HC)-based low-temperature potassium-ion full cell is successfully fabricated for the first time. Experimental evidence and theoretical analysis revealed that potassium storage behaviors of HC anodes in the matched low-temperature electrolyte involve defect adsorption, interlayer co-intercalation, and micropore filling. Notably, these unique potassiation processes exhibited low interfacial resistances and small reaction activation energies, enabling an excellent cycling performance of HC with a capacity of 172 mAh g-1 at -40 °C (68% of its room-temperature capacity). Consequently, the HC-based full cells demonstrated impressive rechargeability and high energy density above 100 Wh kg-1cathode at -40 °C, representing a significant advancement in the development of PIBs.

An assessment of microvascular hemodynamics in human macula

An adequate blood supply to meet the energy demands is essential for any tissue, particularly for high energy demand tissues such as the retina. A critical question is: How is the dynamic match between neuronal demands and blood supply achieved? We present a quantitative assessment of temporal and spatial variations in perfusion in the macular capillary network in 10 healthy human subjects using a non-invasive and label-free imaging technique. The assessment is based on the calculation of the coefficient of variation (CoV) of the perfusion signal from arterioles, venules and capillaries from a sequence of optical coherence tomography angiography images centred on the fovea. Significant heterogeneity of the spatial and temporal variation was found within arterioles, venules and capillary networks. The CoV values of the capillaries and smallest vessels were significantly higher than that in the larger vessels. Our results demonstrate the presence of significant heterogeneity of spatial and temporal variation within each element of the macular microvasculature, particularly in the capillaries and finer vessels. Our findings suggest that the dynamic match between neuronal demands and blood supply is achieved by frequent alteration of local blood flow evidenced by capillary perfusion variations both spatially and temporally in the macular region.

Application of fluorescence spectra and molecular weight analysis in the identification of algal organic matter-based disinfection by-product precursors

Algal organic matter (AOM) is considered to be threatening for the consumption of disinfectants and the formation of disinfection by-products (DBPs) during the disinfection process. Incompatible parameters in the conventional pretreatment of algal-laden water will lead to counterproductive results, such as AOM release. Therefore, the generation of AOM and its conversion to DBPs during pretreatment should be observed. The characteristics of DBPs from extracellular organic matter (EOM) and intracellular organic matter (IOM) were epitomized and simulation experiments were conducted in deionized (DI) water and source water under pretreatment conditions. Differences in DBP formation between the different backgrounds during chlorination and powdered activated carbon (PAC) treatment were investigated. Instead of monotonous excitation-emission matrix (EEM) spectra, molecular weight (MW) fractionation was simultaneously applied to elucidate the mechanisms of chlorination and PAC adsorption on AOM-based DBPs. The fluorescence regional integration (FRI) EEM results showed a clear correlation between the fluorescent properties and MW distribution of AOM. A decreasing trend was observed after a rapid increase in fluorescence intensity during the chlorination and PAC treatment of water samples in the simulation experiments in deionized (DI) water and source water. The DBP formation potential (FP) in the source water was consistent with the change in AOM during chlorination and PAC adsorption. In addition, EEM showed decent predictability of AOM-based trihalomethanes (THM) FPs (R² = 0.77-0.99) invoking a combination with MW fractionation. Macromolecular protein compounds were highly correlated with the formation of dichloroacetonitrile (DCAN) (R² = 0.89-0.98). These post-mortems results imply that EEM spectra are a useful tool for identifying AOM-based precursors to reveal the accurate environmental fate and risk assessments of AOM.

Correlation between retinal vein pulse amplitude, estimated intracranial pressure, and postural change

Spaceflight associated neuro-ocular syndrome (SANS) is common amongst astronauts on long duration space missions and is associated with signs consistent with elevated cerebrospinal fluid (CSF) pressure. Additionally, CSF pressure has been found to be elevated in a significant proportion of astronauts in whom lumbar puncture was performed after successful mission completion. We have developed a retinal photoplethysmographic technique to measure retinal vein pulsation amplitudes. This technique has enabled the development of a non-invasive CSF pressure measurement apparatus. We tested the system on healthy volunteers in the sitting and supine posture to mimic the range of tilt table extremes and estimated the induced CSF pressure change using measurements from the CSF hydrostatic indifferent point. We found a significant relationship between pulsation amplitude change and estimated CSF pressure change (p < 0.0001) across a range from 2.7 to 7.1 mmHg. The increase in pulse amplitude was highest in the sitting posture with greater estimated CSF pressure increase (p < 0.0001), in keeping with physiologically predicted CSF pressure response. This technique may be useful for non-invasive measurement of CSF pressure fluctuations during long-term space voyages.

Compensation Method for the Nonlinear Characteristics with Starting Error of a Piezoelectric Actuator in Open-Loop Controls Based on the DSPI Model

Nanopositioning stages with piezoelectric actuators have been widely used in fields such as precision mechanical engineering, but the nonlinear start-up accuracy problem under open-loop control has still not been solved, and more errors will accumulate, especially under open-loop control. This paper first analyzes the causes of the starting errors from both the physical properties of materials and voltages: the starting errors are affected by the material properties of piezoelectric ceramics, and the magnitude of the voltage determines the magnitude of the starting errors. Then, this paper adopts an image-only model of the data separated by a Prandtl-Ishlinskii model (DSPI) based on the classical Prandtl-Ishlinskii model (CPI), which can improve the positioning accuracy of the nanopositioning platform after separating the data based on the start-up error characteristics. This model can improve the positioning accuracy of the nanopositioning platform while solving the problem of nonlinear start-up errors under open-loop control. Finally, the DSPI inverse model is used for the feedforward compensation control of the platform, and the experimental results show that the DSPI model can solve the nonlinear start-up error problem existing under open-loop control. The DSPI model not only has higher modeling accuracy than the CPI model but also has better performance in terms of compensation results. The DSPI model improves the localization accuracy by 99.427% compared to the CPI model. When compared with another improved model, the localization accuracy is improved by 92.763%.

Inactivation mechanism of E. coli in water by enhanced photocatalysis under visible light irradiation

Developing efficient and economical technologies for drinking water disinfection remains a challenge. We synthesized Ag/AgBr/LDH doped with various silver mass concentrations and explored its ability to inactivate E. coli under visible light irradiation (λ ≥ 400 nm). Our results indicated a total inactivation of E. coli (10⁷ CFU·mL^-1) within 80 min using 2 % Ag/AgBr/LDH in a laboratory-scale test. The method was evaluated for disinfecting three effluent samples collected from one drinking water treatment plant, covering representative water treatment processes. After five consecutive runs, the inactivation efficiency decreased slightly to 89 % in CFU·mL^-1, indicating that the photocatalysts had excellent stability and reusability. The mechanisms were analyzed by combining chemical and biological methods. It was verified that singlet oxygen (¹O₂), hydrogen peroxide (H₂O₂), and photo-generated electrons (e^-) were significant contributors to the inactivation process. Scanning electron microscopy images analysis showed the disruption of the membrane integrity of E. coli by photocatalytic oxidation. Internal component leakage and reduced enzyme activity were also observed in terms of K⁺ leakage, β-galactosidase activity, and antioxidant enzyme activity. The results by the transcriptomic analysis implied that Ag/AgBr/LDH regulating the oxidative stress response and cell membrane damage related genes was the main inactivation mechanism.

NbMLP43 Ubiquitination and Proteasomal Degradation via the Light Responsive Factor NbBBX24 to Promote Viral Infection

The ubiquitin-proteasome system (UPS) plays an important role in virus-host interactions. However, the mechanism by which the UPS is involved in innate immunity remains unclear. In this study, we identified a novel major latex protein-like protein 43 (NbMLP43) that conferred resistance to Nicotiana benthamiana against potato virus Y (PVY) infection. PVY infection strongly induced NbMLP43 transcription but decreased NbMLP43 at the protein level. We verified that B-box zinc finger protein 24 (NbBBX24) interacted directly with NbMLP43 and that NbBBX24, a light responsive factor, acted as an essential intermediate component targeting NbMLP43 for its ubiquitination and degradation via the UPS. PVY, tobacco mosaic virus, (TMV) and cucumber mosaic virus (CMV) infections could promote NbMLP43 ubiquitination and proteasomal degradation to enhance viral infection. Ubiquitination occurred at lysine 38 (K38) within NbMLP43, and non-ubiquitinated NbMLP43(K38R) conferred stronger resistance to RNA viruses. Overall, our results indicate that the novel NbMLP43 protein is a target of the UPS in the competition between defense and viral anti-defense and enriches existing theoretical studies on the use of UPS by viruses to promote infection.

Face Mask Identification Using Spatial and Frequency Features in Depth Image from Time-of-Flight Camera

Face masks can effectively prevent the spread of viruses. It is necessary to determine the wearing condition of masks in various locations, such as traffic stations, hospitals, and other places with a risk of infection. Therefore, achieving fast and accurate identification in different application scenarios is an urgent problem to be solved. Contactless mask recognition can avoid the waste of human resources and the risk of exposure. We propose a novel method for face mask recognition, which is demonstrated using the spatial and frequency features from the 3D information. A ToF camera with a simple system and robust data are used to capture the depth images. The facial contour of the depth image is extracted accurately by the designed method, which can reduce the dimension of the depth data to improve the recognition speed. Additionally, the classification process is further divided into two parts. The wearing condition of the mask is first identified by features extracted from the facial contour. The types of masks are then classified by new features extracted from the spatial and frequency curves. With appropriate thresholds and a voting method, the total recall accuracy of the proposed algorithm can achieve 96.21%. Especially, the recall accuracy for images without mask can reach 99.21%.

A hemifused complex is the hub in a network of pathways to membrane fusion

Membrane fusion is a critical step for many essential processes, from neurotransmission to fertilization. For over 40 years, protein-free fusion driven by calcium or other cationic species has provided a simplified model of biological fusion, but the mechanisms remain poorly understood. Cation-mediated membrane fusion and permeation are essential in their own right to drug delivery strategies based on cell-penetrating peptides or cation-bearing lipid nanoparticles. Experimental studies suggest calcium drives anionic membranes to a hemifused intermediate that constitutes a hub in a network of pathways, but the pathway selection mechanism is unknown. Here we develop a mathematical model that identifies the network hub as a highly dynamic hemifusion complex. Multivalent cations drive expansion of this high-tension hemifusion interface between interacting vesicles during a brief transient. The fate of this interface determines the outcome, either fusion, dead-end hemifusion, or vesicle lysis. The model reproduces the unexplained finding that calcium-driven fusion of vesicles with planar membranes typically stalls at hemifusion, and we show the equilibrated hemifused state is a novel lens-shaped complex. Thus, membrane fusion kinetics follow a stochastic trajectory within a network of pathways, with outcome weightings set by a hemifused complex intermediate.

Molecular characterization of disinfection byproduct precursors in filter backwash water from 10 drinking water treatment plants

Organic matter reacts with chlorine forming disinfection byproducts (DBPs) including trihalomethanes (THMs), haloacetamides (HAMs), haloacetic acids (HAAs), and haloacetonitriles (HANs). Filter backwash water (FBW) is either released back to the environment or recycled to the head of the treatment plant after solids settling and the remaining dissolved organic matter is a significant pool of DBP precursors that are not well understood. We characterized dissolved organic matter in FBW from 10 treatment plants and low molecular weight (MW < 1 kDa) organic matter contributed the most to DBP formation. We demonstrated overall similarity of the molecular composition (e.g., elemental ratios, m/z, DBE) of the 10 samples of FBW by Fourier transform ion cyclotron resonance mass spectrometry. Aromatic and more highly oxidized compounds preferentially reacted with chlorine, forming DBPs. Low MW (<450 Da) aliphatic compounds, and highly unsaturated and phenolic compounds were the primary precursors of THMs, HANs, and HAMs, and the formation potentials (FPs) of these groups of DBPs were correlated with multiple individual molecular formulae. HAA FPs were correlated with low MW, highly unsaturated and phenolic compounds. These advances in the understanding of the molecular composition of DBP precursors in FBW may develop the effective strategies to control DBP formation and limit impacts on the quality of finished water, and can be expanded to understanding DBP precursors in drinking water sources.

A global perspective on bacterial diversity in the terrestrial deep subsurface

While recent efforts to catalogue Earth's microbial diversity have focused upon surface and marine habitats, 12-20 % of Earth's biomass is suggested to exist in the terrestrial deep subsurface, compared to ~1.8 % in the deep subseafloor. Metagenomic studies of the terrestrial deep subsurface have yielded a trove of divergent and functionally important microbiomes from a range of localities. However, a wider perspective of microbial diversity and its relationship to environmental conditions within the terrestrial deep subsurface is still required. Our meta-analysis reveals that terrestrial deep subsurface microbiota are dominated by Betaproteobacteria, Gammaproteobacteria and Firmicutes, probably as a function of the diverse metabolic strategies of these taxa. Evidence was also found for a common small consortium of prevalent Betaproteobacteria and Gammaproteobacteria operational taxonomic units across the localities. This implies a core terrestrial deep subsurface community, irrespective of aquifer lithology, depth and other variables, that may play an important role in colonizing and sustaining microbial habitats in the deep terrestrial subsurface. An in silico contamination-aware approach to analysing this dataset underscores the importance of downstream methods for assuring that robust conclusions can be reached from deep subsurface-derived sequencing data. Understanding the global panorama of microbial diversity and ecological dynamics in the deep terrestrial subsurface provides a first step towards understanding the role of microbes in global subsurface element and nutrient cycling.

Topographic distribution and phenotypic heterogeneity of Schlemm's canal endothelium in human donor eyes

Endothelium phenotype is known to be closely associated with flow shear stress. This study is to determine the topographic distribution of endothelial cells and the phenotype of different quadrants and regions of Schlemm's canal using human donor eyes. This study infers differences in flow dynamics based on cell shape and intracellular structure. The Schlemm's canal from 15 human donor eyes were either perfusion labelled using silver stain or dissected for float labeling with Phalloidin to enable visualization of endothelial cell border and intracellular structure. Data were acquired for endothelial cells from the outer and inner wall of Schlemm's canal and grouped according to quadrant of origin. Measurements included endothelial cell length, width, area, and aspect ratio and compared between quadrants. Endothelial cells are mostly spindle-shape and the cell size on the outer wall are larger and longer than those from the inner wall. Significant differences in endothelial cell size and shape were seen in different quadrants. The endothelial cells have varied shapes and orientations close to large ostia in the outer wall and remarkably long endothelial cells were found in the walls of collector channels. F-actin aggregation was found at all endothelial cell borders, and inside some of the endothelial cytoplasm. The presence of various spindle shapes, significant phenotype heterogeneity and F-actin aggregation of endothelial cells indicates aqueous humor flow likely creates variations in shear stress within Schlemm's canal. Further investigation of the relationship between the phenotype heterogeneity and hydrodynamics of aqueous flow may help us understand the mechanisms of outflow resistance changes in glaucoma.

Comprehensive analysis of lysine lactylation in Frankliniella occidentalis

Western flower thrips (Frankliniella occidentalis) are among the most important pests globally that transmit destructive plant viruses and infest multiple commercial crops. Lysine lactylation (Klac) is a recently discovered novel post-translational modification (PTM). We used liquid chromatography-mass spectrometry to identify the global lactylated proteome of F. occidentalis, and further enriched the identified lactylated proteins using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). In the present study, we identified 1,458 Klac sites in 469 proteins from F. occidentalis. Bioinformatics analysis showed that Klac was widely distributed in F. occidentalis proteins, and these Klac modified proteins participated in multiple biological processes. GO and KEGG enrichment analysis revealed that Klac proteins were significantly enriched in multiple cellular compartments and metabolic pathways, such as the ribosome and carbon metabolism pathways. Two Klac proteins were found to be involved in the regulation of the TSWV (Tomato spotted wilt virus) transmission in F. occidentalis. This study provides a systematic report and a rich dataset of lactylation in F. occidentalis proteome for potential studies on the Klac protein of this notorious pest.

Studies of the retinal microcirculation using human donor eyes and high-resolution clinical imaging: Insights gained to guide future research in diabetic retinopathy

The microcirculation plays a key role in delivering oxygen to and removing metabolic wastes from energy-intensive retinal neurons. Microvascular changes are a hallmark feature of diabetic retinopathy (DR), a major cause of irreversible vision loss globally. Early investigators have performed landmark studies characterising the pathologic manifestations of DR. Previous works have collectively informed us of the clinical stages of DR and the retinal manifestations associated with devastating vision loss. Since these reports, major advancements in histologic techniques coupled with three-dimensional image processing has facilitated a deeper understanding of the structural characteristics in the healthy and diseased retinal circulation. Furthermore, breakthroughs in high-resolution retinal imaging have facilitated clinical translation of histologic knowledge to detect and monitor progression of microcirculatory disturbances with greater precision. Isolated perfusion techniques have been applied to human donor eyes to further our understanding of the cytoarchitectural characteristics of the normal human retinal circulation as well as provide novel insights into the pathophysiology of DR. Histology has been used to validate emerging in vivo retinal imaging techniques such as optical coherence tomography angiography. This report provides an overview of our research on the human retinal microcirculation in the context of the current ophthalmic literature. We commence by proposing a standardised histologic lexicon for characterising the human retinal microcirculation and subsequently discuss the pathophysiologic mechanisms underlying key manifestations of DR, with a focus on microaneurysms and retinal ischaemia. The advantages and limitations of current retinal imaging modalities as determined using histologic validation are also presented. We conclude with an overview of the implications of our research and provide a perspective on future directions in DR research.

Multiview behavior and neurotransmitter analysis of zebrafish dyskinesia induced by 6PPD and its metabolites

The typical tire manufacturing additive 6PPD, its metabolites 6PPDQ and 4-Hydroxy should be monitored because of their ubiquitous presence in the environment and the high toxicity of 6PPDQ to coho salmon. The toxic effect of 6PPD and its metabolites have been revealed superficially, especially on behavioral characteristics. However, the behavioral indicators explored so far are relatively simple and the toxic causes are poorly understood. With this in mind, our work investigated the toxic effects of 6PPD, 6PPDQ and 4-Hydroxy on adult zebrafish penetratingly through machine vision, and the meandering, body angle, top time and 3D trajectory are used for the first time to show the abnormal behaviors induced by 6PPD and its metabolites. Moreover, neurotransmitter changes in the zebrafish brain were measured to explore the causes of abnormal behavior. The results showed that high-dose treatment of 6PPD reduced the velocity by 42.4% and decreased the time at the top of the tank by 91.0%, suggesting significant activity inhibition and anxiety. In addition, γ-aminobutyric acid and acetylcholine were significantly impacted by 6PPD, while dopamine exhibited a slight variation, which can explain the bradykinesia, unbalance and anxiety of zebrafish and presented similar symptoms as Huntingdon's disease. Our study explored new abnormal behaviors of zebrafish induced by 6PPD and its metabolites in detail, and the toxic causes were revealed for the first time by studying the changes of neurotransmitters, thus providing an important reference for further studies of the biological toxicity of 6PPD and its metabolites.

Degradation pathways and kinetics of chloroacetonitriles by UV/persulfate in the presence of bromide

Chloroacetonitriles (CANs) are highly toxic nitrogenous disinfection by-products (N-DBPs), which frequently appear in water supply systems and have attracted widespread attention. UV/persulfate (PS) is an effective method to degrade CANs. Bromide (Br^-) is widespread in aquatic environments and reacts with oxidative radicals to produce secondary reactive bromine species (RBS), which affects the degradation of CANs by UV/PS. It was found that the degradation of CANs was highly inhibited by Br^-. The apparent first-order reaction rate constants of monochloroacetonitrile (MCAN), dichloroacetonitrile (DCAN) and trichloroacetonitrile (TCAN) decreased from 2.63 × 10^-3, 2.00 × 10^-3 and 8.66 × 10^-4 s^-1 to 2.58 × 10^-4, 1.61 × 10^-4 and 1.59 × 10^-4 s^-1, respectively after adding 20 μM of Br^-. HO• was the main radicals contributing to the degradation of CANs when the concentration of Br^- was less than 10 μM, compared with SO₄^•- and direct photolysis. When the concentration of Br^- was up to 20 μM, the contributions of RBS accounted for 85.7%, 90.7% and 89.9% of the apparent degradation rate constants of CANs, respectively. During the reaction, about 65% of nitrogen atoms were transformed into NO₃^- by the CC bond cleavage and oxidation. The yields of Cl^- by dechlorination reaction accounted for 83.5%, 71.0% and 41.2% of the chlorine contents in MCAN, DCAN and TCAN, respectively. It was verified that CANs react with free bromine (HOBr) to produce bromochloroacetonitrile (BCAN). DCAN and TCAN are hydrolyzed to produce corresponding haloacetamides (HAMs), which are further reacted with HOBr to produce bromodichloroacetic acid (BDCAA). Furthermore, the generation of bromate was also worth noting via the oxidation of Br^- in the UV/PS system.

Under Psychological Safety Climate: The Beneficial Effects of Teacher-Student Conflict

Previous studies have mainly focused on the negative effects of teacher-student conflict; the positive effects of conflict have rarely been mentioned. This paper suggests that encouraging conflict could act as a teaching method to improve students' innovative competence. This study has two objectives: (1) to examine how various types of teacher-student conflict affects students' innovative competence and (2) to identify the mediating role of a psychological safety climate in the association between conflict and students' innovative competence. To achieve the objectives, we used evidence from 1207 university students. Multivariable logistic regression analysis revealed that conflicts were associated with students' innovative competence, and the mediation role of a psychological safety climate is significant. Specifically, the results revealed that Cognitive Conflict had significant positive effects on students' innovative competence, whereas Affective Conflict had a significant negative effect on students' innovative competence. In addition, we clarified a psychological safety climate as the boundary condition for the relationship between conflict and students' innovative competence.

Structure of Aqueous KNO3 Solutions by Wide-Angle X-ray Scattering and Density Functional Theory

The structure of aqueous KNO₃ solutions was studied by wide-angle X-ray scattering (WAXS) and density functional theory (DFT). The interference functions were subjected to empirical potential structure refinement (EPSR) modeling to extract the detailed hydration structure information. In aqueous KNO₃ solutions with a concentration from 0.50 to 2.72 mol·dm^-3, water molecules coordinate K⁺ with a mean coordination number (CN) from 6.6 ± 0.9 to 5.8 ± 1.2, respectively, and a K-O_W(H₂O) distance of 2.82 Å. To further describe the hydration properties of K⁺, a hydration factor (f_h) was defined based on the orientational angle between the water O-H vector and the ion-oxygen vector. The f_h value obtained for K⁺ is 0.792, 0.787, 0.766, and 0.765, and the corresponding average hydration numbers (HN) are 5.2, 5.1, 4.8, and 4.5. The reduced HN is compensated by the direct binding of oxygen atoms of NO₃^- with the average CN from 0.3 ± 0.7 to 2.6 ± 1.7, respectively, and the K-O_N(NO₃^-) distance of 2.82 Å. The average number of water molecules around NO₃^- slightly reduces from 10.5 ± 1.5 to 9.6 ± 1.7 with r_N-OW = 3.63 Å. K⁺-NO₃^- ion association is characterized by K-O_N and K-N pair correlation functions (PCFs). A K-N peak is observed at 3.81 Å as the main peak with a shoulder at 3.42 Å in g_K-N(r). This finding indicates that two occupancy sites are available for K⁺, i.e., the edge-shared bidentate (BCIP) and the corner-shared monodentate (MCIP) contact ion pairs. The structure and stability of the KNO₃(H₂O)₁₀ cluster were investigated by a DFT method and cross-checked with the results from WAXS.

Improved bat algorithm for roundness error evaluation problem

In the production and processing of precision shaft-hole class parts, the wear of cutting tools, machine chatter, and insufficient lubrication can lead to changes in their roundness, which in turn affects the overall performance of the relevant products. To improve the accuracy of roundness error assessments, Bat algorithm (BA) is applied to roundness error assessments. An improved bat algorithm (IBA) is proposed to counteract the original lack of variational mechanisms, which can easily lead BA to fall into local extremes and induce premature convergence. First, logistic chaos initialisation is applied to the initial solution generation to enhance the variation mechanism of the population and improve the solution quality; second, a sinusoidal control factor is added to BA to control the nonlinear inertia weights during the iterative process, and the balance between the global search and local search of the algorithm is dynamically adjusted to improve the optimization-seeking accuracy and stability of the algorithm. Finally, the sparrow search algorithm (SSA) is integrated into BA, exploiting the ability of explorer bats to perform a large range search, so that the algorithm can jump out of local extremes and the convergence speed of the algorithm can be improved. The performance of IBA was tested against the classical metaheuristic algorithm on eight benchmark functions, and the results showed that IBA significantly outperformed the other algorithms in terms of solution accuracy, convergence speed, and stability. Simulation and example verification show that IBA can quickly find the centre of a minimum inclusion region when there are many or few sampling points, and the obtained roundness error value is more accurate than that of other algorithms, which verifies the feasibility and effectiveness of IBA in evaluating roundness errors.

Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network

An attention (A) based convolutional neural network regression (CNNR) model, namely ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single maize kernel. During the period, a reflectance HSI system was used to collect hyperspectral images of embryo side and non-embryo side of single maize kernel, and the performances of CNNR (without attention mechanism), ACNNR and partial least squares regression (PLSR) were compared. For PLSR, a series of spectral preprocessing and dimensionality reduction methods were used to finally determine the optimal hybrid PLSR model. Whereas for CNNR and ACNNR, only raw spectra were used as their inputs. The results showed that embryo side was more suitable for developing regression models; the attentional mechanism was helpful to reduce the error of prediction, making ACNNR performed best (coefficient of determination of prediction = 0.9198). Overall, the proposed method did not require additional processing on raw spectra, and performed well.

Design and Development of a New Glucagon-Like Peptide-1 Receptor Agonist to Obtain High Oral Bioavailability

PURPOSE

Semaglutide is the only oral GLP-1 RA in the market, but oral bioavailability is generally limited in range of 0.4-1%. In this study, a new GLP-1RA named SHR-2042 was developed to gain higher oral bioavailability than semaglutide.

METHOD

Self-association of SHR-2042, semaglutide and liraglutide were assessed using SEC-MALS. The intestinal perfusion test in SD rats was used to select permeation enhancers (PEs) including SNAC, C10 and LCC. ITC, CD and DLS were used to explore the interaction between SHR-2042 and SNAC. Gastric administrated test in SD rats was used to screen SHR-2042 granules with different SHR-2042/SNAC ratios. The oral bioavailability of SHR-2042 was studied in rats and monkeys.

RESULT

The designed GLP-1RA, SHR-2042, gives a better solubility and lipophilicity than semaglutide. While it forms a similar oligomer with that of semaglutide. During the selection of PEs, SNAC shows better exposure than the other competing PEs including C10 and LCC. SHR-2042 and SNAC bind quickly and exhibit hydrophobic interaction. SNAC could promote monomerization of SHR-2042 and form micelles to trap the monomerized SHR-2042. The oral bioavailability of SHR-2042 paired with SNAC is 0.041% (1:0, w/w), 0.083% (1:10, w/w), 0.32% (1:30, w/w) and 2.83% (1:60, w/w) in rats. And the oral bioavailability of SHR-2042 matched with SNAC is 3.39% (1:30, w/w) in monkeys, which is over 10 times higher than that of semaglutide.

CONCLUSION

We believe that the design and development of oral SHR-2042 will provide a new way to design more and more GLP-1RAs with high oral bioavailability in the future.

Efficacy of Parainfluenza Virus 5 (PIV5)-vectored Intranasal COVID-19 Vaccine as a Single Dose Vaccine and as a Booster against SARS-CoV-2 Variants

Immunization with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines has greatly reduced coronavirus disease 2019 (COVID-19)-related deaths and hospitalizations, but waning immunity and the emergence of variants capable of immune escape indicate the need for novel SARS-CoV-2 vaccines. An intranasal parainfluenza virus 5 (PIV5)-vectored COVID-19 vaccine CVXGA1 has been proven efficacious in animal models and blocks contact transmission of SARS-CoV-2 in ferrets. CVXGA1 vaccine is currently in human clinical trials in the United States. This work investigates the immunogenicity and efficacy of CVXGA1 and other PIV5-vectored vaccines expressing additional antigen SARS-CoV-2 nucleoprotein (N) or SARS-CoV-2 variant spike (S) proteins of beta, delta, gamma, and omicron variants against homologous and heterologous challenges in hamsters. A single intranasal dose of CVXGA1 induces neutralizing antibodies against SARS-CoV-2 WA1 (ancestral), delta variant, and omicron variant and protects against both homologous and heterologous virus challenges. Compared to mRNA COVID-19 vaccine, neutralizing antibody titers induced by CVXGA1 were well-maintained over time. When administered as a boost following two doses of a mRNA COVID-19 vaccine, PIV5-vectored vaccines expressing the S protein from WA1 (CVXGA1), delta, or omicron variants generate higher levels of cross-reactive neutralizing antibodies compared to three doses of a mRNA vaccine. In addition to the S protein, the N protein provides added protection as assessed by the highest body weight gain post-challenge infection. Our data indicates that PIV5-vectored COVID-19 vaccines, such as CVXGA1, can serve as booster vaccines against emerging variants.

LSTM-TCN: dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network

Dissolved oxygen (DO) is an important water quality monitoring parameter of great significance in aquaculture. Accurate prediction of dissolved oxygen can help farmers to take necessary measures in advance to ensure the healthy growth of cultured species. The characteristics of multivariate and long-term correlation of water quality time series in the traditional methods make it difficult to achieve the expected prediction accuracy. To solve this problem, we propose the combined prediction method LSTM-TCN (long short-term memory network and temporal convolutional network). After the preprocessing of time series, the LSTM extracts the features of the series in time dimension, and then combines with TCN to build the fusion prediction model. In this study, we have carried out the DO predictions of LSTM and TCN algorithms separately, followed by the analysis of DO prediction, based on CNN-LSTM and LSTM-TCN combined models. The effects of attention mechanism and window size of historical time on the prediction results were also investigated. The experimental results show that the combined method has high accuracy in dissolved oxygen prediction, and can capture better characteristics of historical data with increasing time window of the historical dissolved oxygen sequence. The LSTM-TCN method achieves better prediction performance, with evaluation index values of MAE = 0.236, MAPE = 3.10%, RMSE = 0.342, and R² = 0.94.

Accumulation of chiral pharmaceuticals (ofloxacin or levofloxacin) onto polyethylene microplastics from aqueous solutions

Drug chirality is attracting increasing attention because the enantiomers of the same chiral pharmaceutical usually exhibit different biological activities, metabolic pathways, and toxicities. The ubiquitous presence of microplastics (MPs) can enrich organic pollutants commonly found in the environment. However, knowledge about the enrichment of pharmaceutical enantiomers to MPs is relatively limited. We investigated the occurrence of enantioselectivity of ofloxacin (OFL) and levofloxacin (LEV) in the adsorption processes on polyethylene (PE) and the interactions influenced by environmental factors. The results showed that the adsorption efficiency of OFL was generally 3-5% (p < 0.05) higher than that of LEV, indicating the different affinities of the enantiomers to PE, but the adsorption process of OFL and LEV on PE was both well described by pseudo-first-order kinetics and liner isotherm models. The chirality of OFL and LEV was not affected by sizes of PE particles and solution salinity due to the identical physicochemical properties. An examination of pH effect indicated that OFL showed better acid-base adaptability than LEV. Moreover, the differences in enantiomeric enrichment between OFL and LEV on PE were promoted with increasing UV light exposure time and natural organic matter (NOM) concentrations. Using Fourier transform infrared spectroscopy (FTIR), we demonstrated that the constituents of the functional groups in chiral NOM were greatly related to the enantiomer stereoselectivity of OFL, subsequently affecting their adsorption in a chiral environment. The excitation-emission matrix (EEM) spectra confirmed the enantioselective behaviors of chiral pharmaceuticals under UV light due to the different optical activity and humic acid-like and fulvic acid-like molecular structure of the enantiomers. These findings imply that the enantioselectivity of drug enantiomers should be considered in presence of microplastics, leading to a more accurate environmental fate and risks assessments of chiral pharmaceuticals.

Macular Vascular Imaging and Connectivity Analysis Using High-Resolution Optical Coherence Tomography

Purpose

To characterize macular blood flow connectivity in vivo using high-resolution optical coherence tomography (HighRes OCT).

Methods

Cross-sectional, observational study. Dense (6-µm interscan distance) perifoveal HighRes OCT raster scans were performed on healthy participants. To mitigate the limitations of projection-resolved OCT-angiography, flow and structural data were used to observe the vascular structures of the superficial vascular complex (SVC) and the deep vascular complex. Vascular segmentation and rendering were performed using Imaris 9.5 software. Inflow and outflow patterns were classified according to vascular diameter and branching order from superficial arteries and veins, respectively.

Results

Eight eyes from eight participants were included in this analysis, from which 422 inflow and 459 outflow connections were characterized. Arteries had direct arteriolar connections to the SVC (78%) and to the intermediate capillary plexus (ICP, 22%). Deep capillary plexus (DCP) inflow derived from small-diameter vessels succeeding ICP arterioles. The most prevalent outflow pathways coursed through superficial draining venules (74%). DCP draining venules ordinarily merged with ICP draining venules and drained independently of superficial venules in 21% of cases. The morphology of DCP draining venules in structural HighRes OCT is distinct from other vessels crossing the inner nuclear layer and can be used to identify superficial veins.

Conclusions

Vascular connectivity analysis supports a hybrid circuitry of blood flow within the human parafoveal macula.

Translational Relevance

Characterization of parafoveal macular blood flow connectivity in vivo using a precise segmentation of HighRes OCT is consistent with ground-truth microscopy studies and shows a hybrid circuitry.

Alpha-Smooth Muscle Actin Expression and Parafoveal Blood Flow Pathways Are Altered in Preclinical Diabetic Retinopathy

Purpose

To investigate differences in alpha smooth muscle actin (αSMA) expression and parafoveal blood flow pathways in diabetic retinopathy (DR).

Methods

Human donor eyes from healthy subjects (n = 8), patients with diabetes but no DR (DR-; n = 7), and patients with clinical DR (DR+; n = 13) were perfusion labeled with antibodies targeting αSMA, lectin, collagen IV, and filamentous actin. High-resolution confocal scanning laser microscopy was used to quantify αSMA staining and capillary density in the parafoveal circulation. Quantitative analyses of connections between retinal arteries and veins within the superficial vascular plexus (SVP), intermediate capillary plexus (ICP) and deep capillary plexus (DCP) were performed.

Results

Mean age between the groups was not different (P = 0.979). αSMA staining was seen in the SVP and ICP of all groups. The DCP was predominantly devoid of αSMA staining in control eyes but increased in a disease stage-specific manner in the DR- and DR+ groups. The increase in αSMA staining was localized to pericytes and endothelia of terminal arterioles and adjacent capillary segments. Capillary density was less in the DCP in the DR+ group (P < 0.001). ICP of the DR- and DR+ groups received more direct arteriole supplies than the control group (P < 0.001). Venous outflow pathways were not altered (all P > 0.284).

Conclusions

Alterations in αSMA and vascular inflow pathways in preclinical DR suggest that perfusion abnormalities precede structural vascular changes such as capillary loss. Preclinical DR may be characterized by a "steal" phenomenon where blood flow is preferentially diverted from the SVP to the ICP and DCP.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

New insights into the cooperative adsorption behavior of Cr(VI) and humic acid in water by powdered activated carbon

Chromium and humic acid often co-exist in wastewater and source waters, and the removal of chromium through sorption by activated carbon may be greatly influenced by humic acid. In this study, we systematically evaluated concurrent adsorption of humic acid (HA) and hexavalent chromium (Cr(VI)) in water by powdered activated carbon (PAC) and further, the effect on conversion to trivalent chromium (Cr(III)). Adsorption of both HA and Cr(VI) was significantly enhanced in the dual adsorbate system as compared to treatments with HA or Cr(VI) alone. The removal of HA increased by 16.0% in the presence of 80 mg/L Cr(VI), while the removal of Cr(VI) similarly increased with increasing levels of HA. However, the promotion effect of HA was found to decrease with increasing pH. With HA at 20 mg/L, removal of Cr(VI) increased from 40.09% to 70.12% at pH 3, which was about twice the increase at pH 10. The cooperative adsorption mechanism was explored using scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS), Raman spectroscopy, Fourier transform infrared spectrometer (FTIR), and X-ray photoelectron spectroscopy (XPS). Comprehensive analysis of spectra suggested that the mutual promotion between HA and Cr(VI) adsorption was attributable to the formation of Cr(VI)-HA and Cr(III)-HA complexes that were readily adsorbed on the PAC surfaces. The higher HA concentrations increased the reduction of Cr(VI) to Cr(III), which was likely due to the electron transfer provided by the functional groups such as -CO, -OH and -COOH in both PAC and HA. At pH 3, 99.1% of Cr adsorbed on the PAC surface was in the form of Cr(III). These findings imply that the interactions between Cr(VI) and HA in the process of water treatment by PAC provides additional and synergistic benefits, leading to a greater removal of chromium.

Differentiating Microaneurysm Pathophysiology in Diabetic Retinopathy Through Objective Analysis of Capillary Nonperfusion, Inflammation, and Pericytes

Microaneurysms are biomarkers of microvascular injury in diabetic retinopathy (DR). Impaired retinal capillary perfusion is a critical pathogenic mechanism in the development of microvascular abnormalities. Targeting fundamental molecular disturbances resulting from capillary nonperfusion, such as increased vascular endothelial growth factor expression, does not always reverse the anatomic complications of DR, suggesting that other pathogenic mechanisms independent of perfusion also play a role. We stratify the effects of capillary nonperfusion, inflammation, and pericyte loss on microaneurysm size and leakage in DR through three-dimensional analysis of 636 microaneurysms using high-resolution confocal scanning laser microscopy. Capillary nonperfusion, pericyte loss, and inflammatory cells were found to be independent predictors of microaneurysm size. Nonperfusion alone without pericyte loss or inflammation was not a significant predictor of microaneurysm leakage. Microaneurysms found in regions without nonperfusion were significantly smaller than those found in regions with nonperfusion, and their size was not associated with pericyte loss or inflammation. In addition, microaneurysm size was a significant predictor of leakage in regions with nonperfusion only. This report refines our understanding of the disparate pathophysiologic mechanisms in DR and provides a histologic rationale for understanding treatment failure for microvascular complications in DR.