Rolipram plays an anti-fibrotic effect in ligamentum flavum fibroblasts by inhibiting the activation of ERK1/2.

BACKGROUND: Fibrosis is an important factor and process of ligamentum flavum hypertrophy. The expression of phosphodiesterase family (PDE) is related to inflammation and fibrosis. This article studied the expression of PDE in hypertrophic ligamentum flavum fibroblasts and investigated whether inhibition of PDE4 activity can play an anti-fibrotic effect. METHODS: Samples of clinical hypertrophic ligamentum flavum were collected and patients with lumbar disc herniations as a control group. The collagenase digestion method is used to separate fibroblasts. qPCR is used to detect the expression of PDE subtypes, type I collagen (Col I), type III collagen (Col III), fibronectin (FN1) and transforming growth factor ß1 (TGF-ß1). Recombinant TGF-ß1 was used to stimulate fibroblasts to make a fibrotic cell model and treated with Rolipram. The morphology of the cells treated with drugs was observed by Sirius Red staining. Scratch the cells to observe their migration and proliferation. WB detects the expression of the above-mentioned multiple fibrotic proteins after drug treatment. Finally, combined with a variety of signaling pathway drugs, the signaling mechanism was studied. RESULTS: Multiple PDE subtypes were expressed in ligamentum flavum fibroblasts. The expression of PDE4A and 4B was significantly up-regulated in the hypertrophic group. Using Rolipram to inhibit PDE4 activity, the expression of Col I and TGF-ß1 in the hypertrophic group was inhibited. Col I recovered to the level of the control group. TGF-ß1 was significantly inhibited, which was lower than the control group. Recombinant TGF-ß1 stimulated fibroblasts to increase the expression of Col I/III, FN1 and TGF-ß1, which was blocked by Rolipram. Rolipram restored the increased expression of p-ERK1/2 stimulated by TGF-ß1. CONCLUSION: The expressions of PDE4A and 4B in the hypertrophic ligamentum flavum are increased, suggesting that it is related to the hypertrophy of the ligamentum flavum. Rolipram has a good anti-fibrosis effect after inhibiting the activity of PDE4. This is related to blocking the function of TGF-ß1, specifically by restoring normal ERK1/2 signal.

Photon-limited single-pixel imaging.

Photon-limited imaging technique is desired in tasks of capturing and reconstructing images by detecting a small number of photons. However, it is still a challenge to achieve high photon-efficiency. Here, we propose a novel photon-limited imaging technique that explores the consistency of photon detection probability in a single pulse and light intensity distribution in a single-pixel correlated imaging system. We demonstrated theoretically and experimentally that our technique can reconstruct a high-quality 3D image by using only one pulse each frame, thereby achieving a high photon efficiency of 0.01 detected photons per pixel. Long-distance field experiments for 100 km cooperative target and 3 km practical target are conducted to verify its feasibility. Compared with the conventional single-pixel imaging, which requires hundreds or thousands of pulses per frame, our technique saves two orders of magnitude in the consumption of total light power and acquisition time.

Image reconstruction through dynamic scattering media based on deep learning.

Under complex scattering conditions, it is very difficult to capture clear object images hidden behind the media by modelling the inverse problem. With regard to dynamic scattering media, the challenge increases. For solving the inverse problem, we propose a new class-specific image reconstruction algorithm. The method based on deep learning classifies blurred scattering images according to scattering conditions and then recovers to clear images hidden behind the media. The deep learning network is used to learn the mapping relationship between the object and the scattering image rather than characterizing the scattering media explicitly or parametrically. 25000 scattering images are obtained under five sets of dynamic scattering condition to verify the feasibility of the proposed method. In addition, the generalizability of the method has been verified successfully. Compared with common CNN method, it's confirmed that our algorithm has better performance in reconstructing higher-quality images. Furthermore, for a given scattering image with unknown scattering condition, the closest scattering condition information can be given by classification network, and then the corresponding clear image is restored by reconstruction network.

Photon-limited face image super-resolution based on deep learning.

With one single photon camera (SPC), imaging under ultra weak-lighting conditions may have wide-ranging applications ranging from remote sensing to night vision, but it may seriously suffer from the problem of under-sampled inherent in SPC detection. Some approaches have been proposed to solve the under-sampled problem by detecting the objects many times to generate high-resolution images and performing noise reduction to suppress the Poission noise inherent in low-flux operation. To address the under-sampled problem more effectively, a new approach is developed in this paper to reconstruct high-resolution images with lower-noise by seamlessly integrating low-light-level imaging with deep learning. In our new approach, all the objects are detected only once by SPC, where a deep network is learned to reduce noise and reconstruct high-resolution images from the detected noisy under-sampled images. In order to demonstrate our proposal is feasible, we first select a special category to verify by experiment, which are human faces. Such deep network is able to recover high-resolution and lower-noise face images from new noisy under-sampled face images and the resolution can achieve 4× up-scaling factor. Our experimental results have demonstrated that our proposed method can generate high-quality images from only ~0.2 detected signal photon per pixel.

Cross-cultural validation of simplified Chinese version of spine functional index.

BACKGROUND: No effective constructs were available in mainland China to assess the whole spine function. The SFI was developed to evaluate spinal function based on the concept of a single kinetic chain concept for whole spine. The SFI has been translated to Spanish and Turkish with accepted psychometric properties. It is imperative to introduce the SFI in mainland China and further to explore the measurement properties. METHODS: The English versions of the SFI was cross-culturally translated according to international guidelines. Measurement properties (content validity, construct validity and reliability) were tested in accordance with the COSMIN checklists. A total of 271 patients were included in this study, and 61 participants with neck pain and 64 participants with back pain paid a second visit three to seven days later. Confirmatory factor analysis (CFA) and principal factor analysis (PCA) were applied to test the factor structure. The Functional Rating Index (FRI), Neck Disability Index (NDI), Oswestry Disability Index (ODI), SF-12 and a Visual Analogue Scale (VAS) were employed to evaluate the construct validity. Cronbach's alpha and an intra-class correlation coefficient (ICC) were calculated for internal consistency and reproducibility. RESULTS: The means score of SC-SFI was 63.60 in patients with spinal musculoskeletal disorders. A high response rate was acquired (265/271). No item was removed due to abnormal distribution or low item-total correlation. Results of CFA did not support that one-factor structure was in goodness of fit (CMIN/DF = 3.306, NNFI = 0.687, CFI = 0.756, GFI = 0.771 and RMSEA = 0.092). Yet, PCA suggested a one-factor structure was the best, accounting for 32% of the total variance. For structural validity, the SC-SFI correlated highly with the FRI, NDI, ODI, and PF, BP in SF-12 (r = 0.661, 0.610, 0.750, 0.709, 0.605, respectively). All the a priori hypotheses were verified. The Cronbach's alpha for the SC-SFI was 0.91, and ICC was 0.96 (95% CI, 0.94-0.98). Bland-Altman plot also confirmed excellent test-retest reliability. CONCLUSIONS: The SFI has been culturally adapted into SC-SFI with remarkable clinical acceptance, excellent internal consistency, reproducibility, and construct validity when applied to patients with spinal musculoskeletal disorders. The results of current study suggest that SC-SFI can be applied by physicians and researchers to measure whole-spine functional status in mainland China.

The antitumor activity of Meconopsis horridula Hook, a traditional Tibetan medical plant, in murine leukemia L1210 cells.

BACKGROUND: Meconopsis horridula Hook (M. horridula) has been used as a traditional Tibetan medicine to relieve heat and pain as well as mobilize static blood, and it is recognized as a good treatment for bruises. This study is the first trial to evaluate the tumor inhibitory activity of M. horridula extract and its underlying mechanism in the hope of providing evidence to support the anticancer function of M. horridula. METHODS AND RESULTS: M. horridula extract was cytotoxic to L1210 cells in a dose- and time-dependent manner. SEM (scanning electron microscope) observation revealed obvious morphological changes in L1210 cells after M. horridula treatment. Flow cytometry analysis demonstrated that the extract dose-dependently induced early apoptosis. Additional apoptosis parameters, such as alterations in nuclear morphology and DNA damage, were also observed. Furthermore, M. horridula treatment induced G2/M arrest. M. horridula treatment significantly increased reactive oxygen species (ROS) production, suggesting that ROS are a key factor in M. horridula-induced apoptosis. Volatile constituent detection found 15 abundant chemicals in M. horridula, which may contribute to its anticancer effect. CONCLUSION: In conclusion, M. horridula extract induced L1210 cell apoptosis and inhibited proliferation through G2/M phase arrest, and ROS were involved in the process.

Induction of Mitochondrial Dependent Apoptosis in Human Leukemia K562 Cells by Meconopsis integrifolia: A Species from Traditional Tibetan Medicine.

OBJECTIVES: Meconopsis integrifolia (M. integrifolia) is one of the most popular members in Traditional Tibetan Medicine. This study aimed to investigate the anticancer effect of M. integrifolia and to detect the underlying mechanisms of these effects. METHODS: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and trypan blue assay were used to evaluate the cytotoxicity of M. integrifolia. Changes in cell nuclear morphology and reactive oxygen species (ROS) level were observed by fluorescent microscopy. Apoptosis ratio, DNA damage and mitochondrial membrane potential (MMP) loss were analyzed by flow cytometry. Western blotting assay was adopted to detect the proteins related to apoptosis. Immunofluorescence was used to observe the release of cytochrome C. RESULTS: The obtained data revealed that M. integrifolia could significantly inhibit K562 cell viability, mainly by targeting apoptosis induction and cell cycle arrest in G2/M phase. Collapse in cell morphology, chromatin condensation, DNA damage and ROS accumulation were observed. Further mechanism detection revealed that mitochondrion might be a key factor in M. integrifolia-induced apoptosis. CONCLUSIONS: M. integrifolia could induce mitochondria mediated apoptosis and cell cycle arrest in G2/M phase with little damage to normal cells, suggesting that M. integrifolia might be a potential and efficient anticancer agent that deserves further investigation.

Combination of heterogeneous Fenton-like reaction and photocatalysis using Co-TiO2nanocatalyst for activation of KHSO5 with visible light irradiation at ambient conditions.

A novel coupled system using Co-TiO2was successfully designed which combined two different heterogeneous advanced oxidation processes, sulfate radical based Fenton-like reaction (SR-Fenton) and visible light photocatalysis (Vis-Photo), for degradation of organic contaminants. The synergistic effect of SR-Fenton and Vis-Photo was observed through comparative tests of 50mg/L Rhodamine B (RhB) degradation and TOC removal. The Rhodamine B degradation rate and TOC removal were 100% and 68.1% using the SR-Fenton/Vis-Photo combined process under ambient conditions, respectively. Moreover, based on XRD, XPS and UV-DRS characterization, it can be deduced that tricobalt tetroxide located on the surface of the catalyst is the SR-Fenton active site, and cobalt ion implanted in the TiO2lattice is the reason for the visible light photocatalytic activity of Co-TiO2. Finally, the effects of the calcination temperature and cobalt concentration on the synergistic performance were also investigated and a possible mechanism for the synergistic system was proposed. This coupled system exhibited excellent catalytic stability and reusability, and almost no dissolution of Co²âº was found.

Attribute Prototype-guided Iterative Scene Graph for Explainable Radiology Report Generation.

The potential of automated radiology report generation in alleviating the time-consuming tasks of radiologists is increasingly being recognized in medical practice. Existing report generation methods have evolved from using image-level features to the latest approach of utilizing anatomical regions, significantly enhancing interpretability. However, directly and simplistically using region features for report generation compromises the capability of relation reasoning and overlooks the common attributes potentially shared across regions. To address these limitations, we propose a novel region-based Attribute Prototype-guided Iterative Scene Graph generation framework (AP-ISG) for report generation, utilizing scene graph generation as an auxiliary task to further enhance interpretability and relational reasoning capability. The core components of AP-ISG are the Iterative Scene Graph Generation (ISGG) module and the Attribute Prototype-guided Learning (APL) module. Specifically, ISSG employs an autoregressive scheme for structural edge reasoning and a contextualization mechanism for relational reasoning. APL enhances intra-prototype matching and reduces inter-prototype semantic overlap in the visual space to fully model the potential attribute commonalities among regions. Extensive experiments on the MIMIC-CXR with Chest ImaGenome datasets demonstrate the superiority of AP-ISG across multiple metrics.

Picture fuzzy Additive Ratio Assessment Method (ARAS) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for multi-attribute decision problem and their application.

The purpose of this paper is to study the multi-attribute decision-making problem under the fuzzy picture environment. First, a method to compare the pros and cons of picture fuzzy numbers (PFNs) is introduced in this paper. Second, the correlation coefficient and standard deviation (CCSD) method is used to determine the attribute weight information under the picture fuzzy environment regardless of whether the attribute weight information is partially unknown or completely unknown. Third, the ARAS and VIKOR methods are extended to the picture fuzzy environment, and the proposed PFNs comparison rules are also applied in the PFS-ARAS and PFS-VIKOR methods. Fourth, the problem of green supplier selection in a picture-ambiguous environment is solved by the method proposed in this paper. Finally, the method proposed in this paper is compared with some methods and the results are analyzed.

Extraordinarily Time-and Memory-Efficient Large-Scale Canonical Correlation Analysis in Fourier Domain: From Shallow to Deep.

Canonical correlation analysis (CCA) is a correlation analysis technique that is widely used in statistics and the machine-learning community. However, the high complexity involved in the training process lays a heavy burden on the processing units and memory system, making CCA nearly impractical in large-scale data. To overcome this issue, a novel CCA method that tries to carry out analysis on the dataset in the Fourier domain is developed in this article. Appling Fourier transform on the data, we can convert the traditional eigenvector computation of CCA into finding some predefined discriminative Fourier bases that can be learned with only element-wise dot product and sum operations, without complex time-consuming calculations. As the eigenvalues come from the sum of individual sample products, they can be estimated in parallel. Besides, thanks to the data characteristic of pattern repeatability, the eigenvalues can be well estimated with partial samples. Accordingly, a progressive estimate scheme is proposed, in which the eigenvalues are estimated through feeding data batch by batch until the eigenvalues sequence is stable in order. As a result, the proposed method shows its characteristics of extraordinarily fast and memory efficiencies. Furthermore, we extend this idea to the nonlinear kernel and deep models and obtained satisfactory accuracy and extremely fast training time consumption as expected. An extensive discussion on the fast Fourier transform (FFT)-CCA is made in terms of time and memory efficiencies. Experimental results on several large-scale correlation datasets, such as MNIST8M, X-RAY MICROBEAM SPEECH, and Twitter Users Data, demonstrate the superiority of the proposed algorithm over state-of-the-art (SOTA) large-scale CCA methods, as our proposed method achieves almost same accuracy with the training time of our proposed method being 1000 times faster. This makes our proposed models best practice models for dealing with large-scale correlation datasets. The source code is available at https://github.com/Mrxuzhao/FFTCCA.

SWsnn: A Novel Simulator for Spiking Neural Networks.

Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other fields and can also be applied to artificial intelligence, machine learning, and other fields. At present, many simulators using central processing unit (CPU) or graphics processing unit (GPU) have been developed. However, due to the randomness of connections between neurons and spiking events in SNN simulation, this causes a lot of memory access time. To alleviate this problem, we developed an SNN simulator SWsnn based on the new Sunway SW26010pro processor. The SW26010pro processor consists of six core groups, each with 16 MB of local data memory (LDM). LDM has the characteristics of high-speed read and write, which is suitable for performing simulation tasks similar to SNNs. Experimental results show that SWsnn runs faster than other mainstream GPU-based simulators when simulating a certain scale of neural network, showing a strong performance advantage. To conduct larger scale simulations, SWsnn designed a simulation computation based on a large shared model of Sunway processor and developed a multiprocessor version of SWsnn based on this mode, achieving larger scale SNN simulations.

Magnetic Resonance Imaging-CCCFLS Scoring System: Toward Predicting Clinical Symptoms and C5 Paralysis.

STUDY DESIGN: A retrospective study. OBJECTIVE: To develop a new MRI scoring system to assess patients' clinical characteristics, outcomes and complications. METHODS: A retrospective 1-year follow-up study of 366 patients with cervical spondylosis from 2017 to 2021. The CCCFLS scores (cervical curvature and balance (CC), spinal cord curvature (SC), spinal cord compression ratio (CR), cerebrospinal fluid space (CFS). Spinal cord and lesion location (SL). Increased Signal Intensity (ISI) were divided into Mild group (0-6), Moderate group (6-12), and Severe group (12-18) for comparison, and the Japanese Orthopaedic Association (JOA) scores, visual analog scale (VAS), numerical rating scale (NRS), Neck Disability Index (NDI) and Nurick scores were evaluated. Correlation and regression analyses were performed between each variable and the total model in relation to clinical symptoms and C5 palsy. RESULTS: The CCCFLS scoring system was linearly correlated with JOA, NRS, Nurick and NDI scores, with significant differences in JOA scores among patients with different CC, CR, CFS, ISI scores, with a predictive model (R2 = 69.3%), and significant differences in preoperative and final follow-up clinical scores among the 3 groups, with a higher rate of improvement in JOA in the severe group (P < .05), while patients with and without C5 paralysis had significant differences in preoperative SC and SL (P < .05). CONCLUSIONS: CCCFLS scoring system can be divided into mild (0-6). moderate (6-12), severe (12-18) groups. It can effectively reflect the severity of clinical symptoms, and the improvement rate of JOA is better in the severe group, while the preoperative SC and SL scores are closely related to C5 palsy. LEVEL OF EVIDENCE: III.

A Survey of Visual Transformers.

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pioneering works have recently been done on employing Transformer-liked architectures in the computer vision (CV) field, which have demonstrated their effectiveness on three fundamental CV tasks (classification, detection, and segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data). Because of their competitive modeling capabilities, the visual Transformers have achieved impressive performance improvements over multiple benchmarks as compared with modern convolution neural networks (CNNs). In this survey, we have reviewed over 100 of different visual Transformers comprehensively according to three fundamental CV tasks and different data stream types, where taxonomy is proposed to organize the representative methods according to their motivations, structures, and application scenarios. Because of their differences on training settings and dedicated vision tasks, we have also evaluated and compared all these existing visual Transformers under different configurations. Furthermore, we have revealed a series of essential but unexploited aspects that may empower such visual Transformers to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between the visual Transformers and the sequential ones. Finally, two promising research directions are suggested for future investment. We will continue to update the latest articles and their released source codes at.

Detection of Common Anatomical Landmarks and Vertical Trajectories for Freehand Pedicle Screw Placement.

OBJECTIVE: It is clinically important for pedicle screws to be placed quickly and accurately. Misplacement of pedicle screws results in various complications. However, the incidence of complications varies greatly due to the different professional titles of physicians and surgical experience. Therefore, physicians must minimize pedicle screw dislocation. This study aims to compare the three nail placement methods in this study, and explore which method is the best for determining the anatomical landmarks and vertical trajectories. METHODS: This study involved 70 patients with moderate idiopathic scoliosis who had undergone deformity correction surgery between 2018 and 2021. Two spine surgeons used three techniques (preoperative computed tomography scan [CTS], visual inspection-X-freehand [XFH], and intraoperative detection [ID] of anatomical landmarks) to locate pedicle screws. The techniques used include visual inspection for 287 screws in 21 patients, preoperative planning for 346 screws in 26 patients, and intraoperative probing for 309 screws in 23 patients. Observers assessed screw conditions based on intraoperative CT scans (Grade A, B, C, D). RESULTS: There were no significant differences between the three groups in terms of age, sex, and degree of deformity. We found that 68.64% of screws in the XFH group, 67.63% in the CTS group, and 77.99% in the ID group were placed within the pedicle margins (grade A). On the other hand, 6.27% of screws in the XFH group, 4.33% in the CTS group, and 6.15% in the ID group were considered misplaced (grades C and D). The results show that the total amount of upper thoracic pedicle screws was fewer, meanwhile their placement accuracy was lower. The three methods used in this study had similar accuracy in intermediate physicians (P > 0.05). Compared with intermediate physicians, the placement accuracy of three techniques in senior physicians was higher. The intraoperative detection group was better than the other two groups in the good rate and accuracy of nail placement (P < 0.05). CONCLUSION: Intraoperative common anatomical landmarks and vertical trajectories were beneficial to patients with moderate idiopathic scoliosis undergoing surgery. It is an optimal method for clinical application.

Boundary-aware context neural network for medical image segmentation.

Medical image segmentation can provide a reliable basis for further clinical analysis and disease diagnosis. With the development of convolutional neural networks (CNNs), medical image segmentation performance has advanced significantly. However, most existing CNN-based methods often produce unsatisfactory segmentation masks without accurate object boundaries. This problem is caused by the limited context information and inadequate discriminative feature maps after consecutive pooling and convolution operations. Additionally, medical images are characterized by high intra-class variation, inter-class indistinction and noise, extracting powerful context and aggregating discriminative features for fine-grained segmentation remain challenging. In this study, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information, which incorporates encoder-decoder architecture. In each stage of the encoder sub-network, a proposed pyramid edge extraction module first obtains multi-granularity edge information. Then a newly designed mini multi-task learning module for jointly learning segments the object masks and detects lesion boundaries, in which a new interactive attention layer is introduced to bridge the two tasks. In this way, information complementarity between different tasks is achieved, which effectively leverages the boundary information to offer strong cues for better segmentation prediction. Finally, a cross feature fusion module acts to selectively aggregate multi-level features from the entire encoder sub-network. By cascading these three modules, richer context and fine-grain features of each stage are encoded and then delivered to the decoder. The results of extensive experiments on five datasets show that the proposed BA-Net outperforms state-of-the-art techniques.

Adaptive Selection of Reference Frames for Video Object Segmentation.

Video object segmentation is a challenging task in computer vision because the appearances of target objects might change drastically along the time in the video. To solve this problem, space-time memory (STM) networks are exploited to make use of the information from all the intermediate frames between the first frame and the current frame in the video. However, fully using the information from all the memory frames may make STM not practical for long videos. To overcome this issue, a novel method is developed in this paper to select the reference frames adaptively. First, an adaptive selection criterion is introduced to choose the reference frames with similar appearance and precise mask estimation, which can efficiently capture the rich information of the target object and overcome the challenges of appearance changes, occlusion, and model drift. Secondly, bi-matching (bi-scale and bi-direction) is conducted to obtain more robust correlations for objects of various scales and prevents multiple similar objects in the current frame from being mismatched with the same target object in the reference frame. Thirdly, a novel edge refinement technique is designed by using an edge detection network to obtain smooth edges from the outputs of edge confidence maps, where the edge confidence is quantized into ten sub-intervals to generate smooth edges step by step. Experimental results on the challenging benchmark datasets DAVIS-2016, DAVIS-2017, YouTube-VOS, and a Long-Video dataset have demonstrated the effectiveness of our proposed approach to video object segmentation.

Guided Filter Network for Semantic Image Segmentation.

The existing publicly available datasets with pixel-level labels contain limited categories, and it is difficult to generalize to the real world containing thousands of categories. In this paper, we propose an approach to generate object masks with detailed pixel-level structures/boundaries automatically to enable semantic image segmentation of thousands of targets in the real world without manually labelling. A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from an existed dataset, and such GFN then transfers the learned segmentation knowledge to generate initial coarse object masks for the target images. These coarse object masks are treated as pseudo labels to self-optimize the GFN iteratively in the target images. Our experiments on six image sets have demonstrated that our proposed approach can generate object masks with detailed pixel-level structures/boundaries, whose quality is comparable to the manually-labelled ones. Our proposed approach also achieves better performance on semantic image segmentation than most existing weakly-supervised, semi-supervised, and domain adaptation approaches under the same experimental conditions.

Modeling amide-I vibrations of alanine dipeptide in solution by using neural network protocol.

Infrared spectroscopy is a powerful tool for the understanding of molecular structure and function of polypeptides. Theoretical interpretation of IR spectra relies on ab initio calculations may be very costly in computational resources. Herein, we developed a neural network (NN) modeling protocol to evaluate a model dipeptide's backbone amide-I spectra. DFT calculations were performed for the amide-I vibrational motions and structural parameters of alanine dipeptide (ALAD) conformers in different micro-environments ranging from polar to non-polar ones. The obtained backbone dihedrals, C = O bond lengths and amide-I frequencies of ALAD were gather together for NN architecture. The applications of built NN protocols for the prediction of amide-I frequencies of ALAD in other solvation conditions are quite satisfactory with much less computational cost comparing with electronic structure calculations. The results show that this cost-effective way enables us to decipher the polypeptide's dynamic secondary structures and biological functions with their backbone vibrational probes.

Effects of hydrophobic coating on properties of hydrochar produced at different temperatures: Specific surface area and oxygen-containing functional groups.

Hydrochar's specific surface area (SSA) is important in environmental remediation; however, a hydrophobic coating formed on hydrochar creates a physical barrier that reduces that SSA. The formation and composition of the hydrophobic coating and its effects on hydrochar properties are unclear. In this study, hydrochar was produced from Chinese fan palm (Livistona chinensis) leaves at different temperatures. The resulting hydrophobic coatings were investigated by in situ characterization and then extracted with acetone for composition identification. Additionally, hydrochar properties were compared before and after hydrophobic coating removal. The results showed that the hydrophobic coating of the hydrochar produced at 180 °C was the insoluble cuticle layer of raw biomass, while the hydrophobic coatings formed above 180 °C were the depolymerization products of cutin. For the hydrochar above 180 °C, especially at 260 °C, the removal of the hydrophobic coating from hydrochar increased both its SSA and its oxygen-containing functional groups.