Sensing prior constraints in deep neural networks for solving exploration geophysical problems.

One of the key objectives in geophysics is to characterize the subsurface through the process of analyzing and interpreting geophysical field data that are typically acquired at the surface. Data-driven deep learning methods have enormous potential for accelerating and simplifying the process but also face many challenges, including poor generalizability, weak interpretability, and physical inconsistency. We present three strategies for imposing domain knowledge constraints on deep neural networks (DNNs) to help address these challenges. The first strategy is to integrate constraints into data by generating synthetic training datasets through geological and geophysical forward modeling and properly encoding prior knowledge as part of the input fed into the DNNs. The second strategy is to design nontrainable custom layers of physical operators and preconditioners in the DNN architecture to modify or shape feature maps calculated within the network to make them consistent with the prior knowledge. The final strategy is to implement prior geological information and geophysical laws as regularization terms in loss functions for training the DNNs. We discuss the implementation of these strategies in detail and demonstrate their effectiveness by applying them to geophysical data processing, imaging, interpretation, and subsurface model building.

Domain Adaptation Based on Semi-Supervised Cross-Domain Mean Discriminative Analysis and Kernel Transfer Extreme Learning Machine.

Good data feature representation and high precision classifiers are the key steps for pattern recognition. However, when the data distributions between testing samples and training samples do not match, the traditional feature extraction methods and classification models usually degrade. In this paper, we propose a domain adaptation approach to handle this problem. In our method, we first introduce cross-domain mean approximation (CDMA) into semi-supervised discriminative analysis (SDA) and design semi-supervised cross-domain mean discriminative analysis (SCDMDA) to extract shared features across domains. Secondly, a kernel extreme learning machine (KELM) is applied as a subsequent classifier for the classification task. Moreover, we design a cross-domain mean constraint term on the source domain into KELM and construct a kernel transfer extreme learning machine (KTELM) to further promote knowledge transfer. Finally, the experimental results from four real-world cross-domain visual datasets prove that the proposed method is more competitive than many other state-of-the-art methods.

Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.

Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features.

Variation, coordination, and trade-offs between needle structures and photosynthetic-related traits across five Picea species: consequences on plant growth.

BACKGROUND: Picea species are distributed and planted world-wide due to their great ecological and economic values. It has been reported that Picea species vary widely in growth traits in a given environment, which reflects genetic and phenotypic differences among species. However, key physiological processes underlying tree growth and the influencing factors on them are still unknown. RESULTS: Here, we examined needle structures, needle chemical components, physiological characteristics and growth traits across five Picea species in a common garden in Tianshui, Gansu province in China: Picea glauca, P. mariana, P. likiangensis, P. koraiensis, and P. crassifolia, among which P. glauca and P. mariana were introduced from North America, P. likiangensis was from Lijiang, Yunan province in China, P. koraiensis was from Yichun, Heilongjiang province in China, and P. crassifolia was native to the experimental site. It was found that nearly all traits varied significantly among species. Tissue-level anatomical characteristics and leaf mass per area (LMA) were affected by needle size, but the variations of them were not associated with the variations in photosynthetic and biochemical capacity among species. Variations in area-based maximum photosynthesis (Pnmax) were affected by stomatal conductance (gs), mesophyll conductance (gm) and biochemical parameters including maximum carboxylation rate (Vcmax), and maximum electron transport rate (Jmax). The fraction of N allocated to different photosynthetic apparatus displayed contrasting values among species, which contributed to the species variations in photosynthetic nitrogen use efficiency (PNUE) and Pnmax. Additionally, all growth traits were positively correlated with Pnmax and PNUE. CONCLUSION: Needle structures are less important than needle biochemical parameters in determining the variations in photosynthetic capacity across the five Picea species. Pnmax and PNUE are closedly associated with the fraction of N allocated to photosynthetic apparatus (Pphoto) compared with leaf N content per area (Narea). The tremendous growth differences among the five Picea species were substantially related to the interspecies variation in Pnmax and PNUE.

Chemoselective Oxyfunctionalization of Functionalized Benzylic Compounds with a Manganese Catalyst.

Whilst allowing for easy access to synthetically versatile motifs and for modification of bioactive molecules, the chemoselective benzylic oxidation reactions of functionalized alkyl arenes remain challenging. Reported in this study is a new non-heme Mn catalyst stabilized by a bipiperidine-based tetradentate ligand, which enables methylene oxidation of benzylic compounds by H2 O2 , showing high activity and excellent chemoselectivity under mild conditions. The protocol tolerates an unprecedentedly wide range of functional groups, including carboxylic acid and derivatives, ketone, cyano, azide, acetate, sulfonate, alkyne, amino acid, and amine units, thus providing a low-cost, more sustainable and robust pathway for the facile synthesis of ketones, increase of complexity of organic molecules, and late-stage modification of drugs.

Deep-learning projector for optical diffraction tomography.

Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been derived by the minimization of an objective function in which the data-fidelity term encourages measurement consistency while the regularization term enforces prior constraints. In this work, we propose to train a convolutional neural network (CNN) as the projector in a projected-gradient-descent method. We iteratively produce high-quality estimates and ensure measurement consistency, thus keeping the best of CNN-based and regularization-based worlds. Our experiments on two-dimensional-simulated and real data show an improvement over other conventional or deep-learning-based methods. Furthermore, our trained CNN projector is general enough to accommodate various forward models for the handling of multiple-scattering events.

Extraction method of a nonuniform auxiliary laser stripe feature for three-dimensional reconstruction of large components.

3D measurement plays an important role in the processing and assembling of large components in the aviation and aerospace industry. However, precision control is a challenging problem due to the complex on-site illumination environment and serious background interference. For the binocular stereovision measurement system based on auxiliary laser scanning, this paper proposes an extraction method of laser stripe for 3D reconstruction. First, an evaluation method for the laser stripe is proposed by analyzing the features of the stripe image. Then, a laser stripe extraction method based on self-adaptive threshold is proposed. To further improve the efficiency of image processing, an improved Kalman filter algorithm is adopted to fast-track and locate the region of interest of laser stripes in the sequence images. Finally, measurement experiments for a large-scale aircraft panel are carried out on-site. The results show that the center extraction error is less than 0.1 pixel and 3D reconstruction error is less than 0.06 mm. The proposed methods improve the efficiency and accuracy of 3D reconstruction of large components, and the feasibility of on-site application is also verified.

Long noncoding RNA UC.98 stabilizes atherosclerotic plaques by promoting the proliferation and adhesive capacity in murine aortic endothelial cells.

Pathological studies have shown that the vulnerability of plaques affects outcomes in patients with atherosclerosis (AS), a chronic inflammatory disease and common cause of morbidity and mortality worldwide. Although emerging technologies have enabled early diagnosis of AS with high-risk vulnerable plaques, more accurate and noninvasive diagnostic methods are urgently required. To this end, molecules involved in genetic or epigenetic regulation of the vulnerability of atherosclerotic plaques have been extensively studied. Here, we evaluated long noncoding RNA (lncRNA) variability by microarray assay in murine aortic endothelial cells (MAECs) bearing vulnerable plaques and identified the novel functional lncRNA UC.98, whose expression pattern was associated with the vulnerability of atherosclerotic plaques. Consistent with this, clinical statistics comparing the peripheral blood specimens from sets of patients with AS with or without vulnerable plaques confirmed the linear relationship between the expression pattern of UC.98 and plaque instability. Moreover, MTT assays and western blot analysis showed that silencing of intrinsic UC.98 in MAECs not only suppressed cell proliferation but also decreased the expressions of vascular cell adhesion molecule-1 and intercellular adhesion molecule-1, thereby inactivating the nuclear factor-κB pathway. In conclusion, our results highlighted the pivotal role of UC.98 in regulating the vulnerability of plaques during AS progression and suggested that UC.98 may be a biomarker of the early diagnosis and prognosis of AS with vulnerable plaques and a potential therapeutic target for slowing AS progression.

Complete plastome sequences of Picea asperata and P. crassifolia and comparative analyses with P. abies and P. morrisonicola.

Picea asperata and P. crassifolia have sympatric ranges and are closely related, but the differences between these species at the plastome level are unknown. To better understand the patterns of variation among Picea plastomes, the complete plastomes of P. asperata and P. crassifolia were sequenced. Then, the plastomes were compared with the complete plastomes of P. abies and P. morrisonicola, which are closely and distantly related to the focal species, respectively. We also used these sequences to construct phylogenetic trees to determine the relationships among and between the four species as well as additional taxa from Pinaceae and other gymnosperms. Analysis of our sequencing data allowed us to identify 438 single nucleotide polymorphism (SNPs) point mutation events, 95 indel events, four inversion events, and seven highly variable regions, including six gene spacer regions (psbJ-petA, trnT-psaM, trnS-trnD, trnL-rps4, psaC-ccsA, and rps7-trnL) and one gene (ycf1). The highly variable regions are appropriate targets for future use in the phylogenetic reconstructions of closely related, sympatric species of Picea as well as Pinaceae in general.

A Knowledge-Driven Approach for 3D High Temporal-Spatial Measurement of an Arbitrary Contouring Error of CNC Machine Tools Using Monocular Vision.

Periodic health checks of contouring errors under unloaded conditions are critical for machine performance evaluation and value-added manufacturing. Aiming at breaking the dimension, range and speed measurement limitations of the existing devices, a cost-effective knowledge-driven approach for detecting error motions of arbitrary paths using a single camera is proposed. In combination with the PNP algorithm, the three-dimensional (3D) evaluation of large-scale contouring error in relatively high feed rate conditions can be deduced from a priori geometrical knowledge. The innovations of this paper focus on improving the accuracy, efficiency and ability of the vision measurement. Firstly, a camera calibration method considering distortion partition of the depth-of-field (DOF) is presented to give an accurate description of the distortion behavior in the entire photography domain. Then, to maximize the utilization of the decimal involved in the feature encoding, new high-efficient encoding markers are designed on a cooperative target to characterize motion information of the machine. Accordingly, in the image processing, markers are automatically identified and located by the proposed decoding method based on finding the optimal start bit. Finally, with the selected imaging parameters and the precalibrated position of each marker, the 3D measurement of large-scale contouring error under relatively high dynamic conditions can be realized by comparing the curve that is measured by PNP algorithm with the nominal one. Both detection and verification experiments are conducted for two types of paths (i.e., planar and spatial trajectory), and experimental results validate the measurement accuracy and advantages of the proposed method.

AMPK/NF-κB signaling pathway regulated by ghrelin participates in the regulation of HUVEC and THP1 Inflammation.

Endothelial inflammation and monocyte plays an essential role in the initiation and progression of atherosclerosis. Ghrelin is beneficial for atherosclerosis progression. However, the detailed and precise molecular mechanisms of how ghrelin regulates endothelial inflammation are not clear. In this study, we investigated the regulation mechanism of ghrelin on TNF-α-activated endothelial inflammation and monocyte adhesion. It was found that TNF-α-induced monocyte adhesion on HUVEC was significantly attenuated by ghrelin. Furthermore, we found that ghrelin effectively suppressed TNF-α-induced inflammatory factors' (including ICAM-1, VCAM-1, MCP-1, and IL-1ß) expression through inhibiting AMPK phosphorylation and p65 expression both in HUVEC and THP-1. This phenomenon was further demonstrated by using AMPK agonist AICAR and inhibitor compound C, respectively. Our findings suggest that ghrelin may mediate TNF-α-induced endothelial inflammation and monocyte adhesion, in part via AMPK/NF-κB signaling pathway. These novel anti-inflammatory and immunoregulatory actions of ghrelin may play a certain role in understanding the formation and development of atherosclerosis.

Anti-proliferation effect of blue light-emitting diodes against antibiotic-resistant Helicobacter pylori.

BACKGROUND AND AIM: Infection by Helicobacter pylori is implicated in a wide range of upper gastrointestinal diseases. Owing to the rapid emergence of antibiotic-resistant strains of H. pylori, the development of novel treatment modalities for antibiotic-resistant H. pylori infection is a key priority. Blue light-emitting diodes (LED) may represent a unique option owing to their antimicrobial effect. In this study, we aimed to evaluate the anti-proliferative effect of blue LED against antibiotic-resistant H. pylori. METHODS: Ten antibiotic-resistant strains and one sensitive H. pylori strain were used in this study. After irradiation by blue LED along time course, the viability of H. pylori was evaluated by enumerating colony forming units. Morphological changes in H. pylori were observed using a scanning electron microscope. Reductase activity was measured as an indicator of bacterial cellular activity. Total reactive oxygen species was monitored using fluorescence intensity and fluorescence microscope imaging. RESULTS: After irradiation by blue LED, the numbers of H. pylori in all the strains were significantly reduced compared with control group. The H. pylori exhibited a short rod-shaped morphology after irradiation; no such change was observed in H. pylori not exposed to blue LED. Re-irradiation of surviving strain after the initial irradiation also exhibited the same anti-proliferation effect. After blue LED irradiation, bacterial cellular activity was lower, and total reactive oxygen species production was significantly higher in blue LED group, compared with that in control. CONCLUSIONS: Blue LED could be a new treatment to eradicate infection with antibiotic-resistant H. pylori.

Chinese Shang Ring Male Circumcision: A Review.

Chinese Shang Ring adult male circumcision (SC) is a safe and effective procedure which is easy to learn and to perform. By a specially designed small device, male circumcision (MC) can be performed in 5 min. Compared with conventional adult MC (CC), SC has shorter operation time, less blood loss, less pain score, higher appearance satisfaction rate, and lower complication rate. SC was first developed in China. As recent studies have demonstrated that MC reduced the risk of acquiring human immunodeficiency virus (HIV) infection via vaginal intercourse in African countries, SC was introduced into Africa to fight HIV. Other sexually transmitted diseases such as human papillomavirus infection may also be prevented by SC. In conclusion, Chinese Shang Ring, a small device, provides an easy, quick, safe, and effective method to perform adult MC.

Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields.

Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient ( σ s o i l ° ). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient ( σ s o i l ° ) and the AIEM-simulated backscattering coefficient ( σ soil-simu ° ). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m³m-3, followed by HH polarization (RMSE = 0.049 m³m-3) and VV polarization (RMSE = 0.053 m³m-3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.

REMEDIAL APPLICATIONS OF EXOSOMES IN CANCER, INFECTIONS AND DIABETES.

Different cell types under normal and diseased states constantly secrete numerous membrane vesicles including exosomes into extracellular space wkich can be isolated from various biological fluids and cell culture supernatants. Exosomal diameter ranges between 40-100 nm. In current research, exosomes are being exploited as biomarkers for pathological diagnosis and potential remedy against various disease conditions such as infections and autoimmune disorders. In cancer immunotherapy, exosomes have promisingly been employed due to the identical immunogenic antigens of exosomes produced by neoplastic cells and the originating tissues. Antigen-specific T-cell activation and immunomodulatory activity is observed to be performed by exosomes isolated from tumor and dendritic cells (DCs). However, more research is still required to uncover the realistic uses of exosomes, particularly as drug delivery tool in autoimmune diseases, cancers and diabetes.

On-line batch production of ferrate with an chemical method and its potential application for greywater recycling with Al(III) salt.

Ferrate(VI) salt is an oxidant and coagulant for water and wastewater treatment. It is considered as a possible alternative method in greywater treatment. However, challenges have existed in putting ferrate(VI) technology into full-scale practice in water and wastewater treatment due to the instability of ferrate solution and high production cost of solid ferrate products. This study demonstrated a new approach of greywater treatment with on-line batch production of Fe(VI) to which Fe(III) salt was oxidized at a weak acidity solution. A series of experiments were conducted to investigate the effect of Fe(VI) on light greywater (total organic carbon (TOC)=19.5mg/L) and dark greywater (TOC=55mg/L) treatment under different conditions with varying pH and Fe(VI) doses. In addition, the combination use of Fe(VI) and Al(III) salts was proved to be more efficient than using the Fe(VI) salts alone at greywater recycling. The optimum dosage of Fe(VI)/Al(III) salts was 25/25mg/L for light greywater, 90/60mg/L for dark greywater, respectively. The TOC values of both light greywater and dark greywater were reduced to less than 3mg/L with the dosages. The cost for treating greywater was 0.06-0.2$/ton at ferrate(VI) dosage of 25-90mg/L and 0.008-0.024$/ton at AlCl3 dosage of 25-60mg/L. The full operating cost needs further assessment before the Fe(VI)/Al(III) technology could be implemented in greywater treatment.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals.

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments.

Asymmetric magma plumbing system beneath Axial Seamount based on full waveform inversion of seismic data.

The architecture of magma plumbing systems plays a fundamental role in volcano eruption and evolution. However, the precise configuration of crustal magma reservoirs and conduits responsible for supplying eruptions are difficult to explore across most active volcanic systems. Consequently, our understanding of their correlation with eruption dynamics is limited. Axial Seamount is an active submarine volcano located along the Juan de Fuca Ridge, with known eruptions in 1998, 2011, and 2015. Here we present high-resolution images of P-wave velocity, attenuation, and estimates of temperature and partial melt beneath the summit of Axial Seamount, derived from multi-parameter full waveform inversion of a 2D multi-channel seismic line. Multiple magma reservoirs, including a newly discovered western magma reservoir, are identified in the upper crust, with the maximum melt fraction of ~15-32% in the upper main magma reservoir (MMR) and lower fractions of 10% to 26% in other satellite reservoirs. In addition, a feeding conduit below the MMR with a melt fraction of ~4-11% and a low-velocity throat beneath the eastern caldera wall connecting the MMR roof with eruptive fissures are imaged. These findings delineate an asymmetric shallow plumbing system beneath Axial Seamount, providing insights into the magma pathways that fed recent eruptions.

[Estimation of vegetation canopy water content using Hyperion hyperspectral data].

Vegetation canopy water content (VCWC) has widespread utility in agriculture, ecology and hydrology. Based on the PROSAIL model, a novel model for quantitative inversion of vegetation canopy water content using Hyperion hyperspectral data was explored. Firstly, characteristics of vegetation canopy reflection were investigated with the PROSAIL radiative transfer model, and it was showed that the first derivative at the right slope (980 - 1 070 nm) of the 970 nm water absorption feature (D98-1 070) was closely related to VCWC, and determination coefficient reached to 0.96. Then, bands 983, 993, 1 003, 1 013, 1 023, 1 033, 1 043, 1 053 and 1 063 nm of Hyperion data were selected to calculate D980-1 070, and VCWC was estimated using the proposed method. Finally, the retrieval result was verified using field measured data in Yingke oasis of the Heihe basin. It indicated that the mean relative error was 12.5%, RMSE was within 0.1 kg x m(-2) and the proposed model was practical and reliable. This study provides a more efficient way for obtaining VCWC of large area.

Designing an Innovative Electrospinning Strategy to Generate PHBV Nanofiber Scaffolds with a Radially Oriented Fibrous Pattern.

Electrospinning has contributed substantially to the construction of nanofibrous scaffolds for potential tissue engineering and regenerative medicine applications. However, conventional electrospinning only has the ability to generate and collect nanofiber scaffolds with a randomly oriented fibrous pattern, which lack the necessary cell alignment guidance function. In this study, a novel electrospinning fiber-collecting device was designed and developed by setting a series of small pin-ring-structured collectors on a large plain plate. Specifically, we demonstrated that the pin-ring-structured collectors, which were constructed by inserting a metal pin into the center of a metal ring, could collect the as-electrospun nanofibers with radially oriented structures in an innovative manner. We first investigated the suitable polymeric concentration for electrospinning poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), and the optimum electrospinning concentration of PHBV was found to be 12% (w/v) PHBV dissolved in hexafluoroisopropyl alcohol (HFIP). Then, 12% (w/v) PHBV solution was electrospun into radially oriented nanofiber scaffolds using our novel electrospinning strategy, and their various performances were further compared with conventionally randomly oriented nanofiber scaffolds that were also produced from 12% (w/v) PHBV solution. The results showed that the radially oriented PHBV nanofiber scaffolds exhibited obviously enhanced mechanical properties and decreased hydrophobicity compared with the randomly oriented PHBV nanofiber scaffold controls. Importantly, the biological properties of radially oriented PHBV nanofiber scaffolds were also demonstrated to be enhanced, compared with randomly oriented PHBV nanofiber scaffolds, by effectively inducing cell alignment and significantly promoting cell proliferation. In sum, the present study indicates that our as-prepared nanofiber scaffolds with a radially oriented pattern are of great interest for advanced applications, such as wound dressings and tissue-engineered scaffolds.