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The increasing popularity of the metaverse has led to a growing interest and market size in spatial computing from both academia and industry. Developing portable and accurate imaging and depth sensing systems is crucial for advancing next-generation virtual reality devices. This work demonstrates an intelligent, lightweight, and compact edge-enhanced depth perception system that utilizes a binocular meta-lens for spatial computing. The miniaturized system comprises a binocular meta-lens, a 532 nm filter, and a CMOS sensor. For disparity computation, we propose a stereo-matching neural network with a novel H-Module. The H-Module incorporates an attention mechanism into the Siamese network. The symmetric architecture, with cross-pixel interaction and cross-view interaction, enables a more comprehensive analysis of contextual information in stereo images. Based on spatial intensity discontinuity, the edge enhancement eliminates ill-posed regions in the image where ambiguous depth predictions may occur due to a lack of texture. With the assistance of deep learning, our edge-enhanced system provides prompt responses in less than 0.15 seconds. This edge-enhanced depth perception meta-lens imaging system will significantly contribute to accurate 3D scene modeling, machine vision, autonomous driving, and robotics development.

Abstract Bladder cancer (BCa) stands out as a highly prevalent malignant tumor affecting the urinary system. The Sex determining region Y-box protein family is recognized for its crucial role in BCa progression. However, the effect of Sex determining region Y-box 7 (SOX7) on BCa progression has not been fully elucidated. Herein, RNA-sequencing, western blot (WB), immunohistochemistry (IHC), immunofluorescence (IF) and tissue microarray were utilized to assess SOX7 expression in vitro and in vivo. Additionally, SOX7 expression, prognosis, and SOX7 + cytoglobin (CYGB) score were analyzed using R software. In vitro and vivo experiments were performed with BCa cell lines to validate the effect of SOX7 knockdown and overexpression on the malignant progression of BCa. The results showed that SOX7 exhibits low expression in BCa. It functions in diverse capacities, inhibiting the proliferative, migratory, and invasive capabilities of BCa. In addition, the experimental database demonstrated that SOX7 binds to the promoter of DNA methyltransferase 3 beta (DNMT3B), leading to the transcriptional inhibition of DNMT3B. This subsequently results in a reduced methylation of CYGB promoter, ultimately inhibiting the tumor progression of BCa. SOX7 + CYGB scores were significantly linked to patient prognosis. In conclusion, SOX7 inhibits the malignant progression of BCa via the DNMT3B/CYGB axis. Additionally, the SOX7 + CYGB score is capable of predicting the prognostic outcomes of BCa patients. Therefore, SOX7 and CYGB may play an important role in the progression of bladder cancer, and they can be used as prognostic markers of bladder cancer patients.

Abstract Background m6A modification is currently recognized as a major driver of RNA function that maintains cancer cell homeostasis. Long non-coding (Lnc) RNAs control cell proliferation and play an important role in the occurrence and progression of colorectal cancer (CRC). ZCCHC4 is a newly discovered m6A methyltransferase whose role and mechanism in tumors have not yet been elucidated. Methods The EpiQuik m6A RNA methylation kit was used to detect the level of total RNA m6A in six types of digestive tract tumors. The Kaplan-Meier method and receiver operating characteristic curve were used to evaluate the prognostic and diagnostic value of the newly discovered m6A methyltransferase, ZCCHC4, in CRC. The effects on CRC growth in vitro and in vivo were studied using gain- and loss-of-function experiments. The epigenetic mechanisms underlying ZCCHC4 upregulation in CRC were studied using RIP, MeRIP-seq, RNA pull-down, and animal experiments. Results We reported that the ZCCHC4-LncRNAGHRLOS-KDM5D axis regulates the growth of CRC in vitro and in vivo. We found that ZCCHC4 was upregulated in primary CRC samples and could predict adverse clinical outcomes in patients with CRC. Mechanistically, ZCCHC4 downregulated LncRNAGHRLOS to promote CRC tumorigenesis. As a downstream molecule of LncRNAGHRLOS, KDM5D directly controls CRC cell proliferation, migration, and invasion. Conclusion This study suggests that the ZCCHC4 axis contributes to the tumorigenesis and progression of CRC and that ZCCHC4 may be a potential biomarker for this malignancy.

Abstract. As pancreatic cancer (PC) is highly malignant, its patients tend to develop metastasis at an early stage and show a poor response to conventional chemotherapies. First-line chemotherapies for PC, according to current guidelines, include fluoropyrimidine- and gemcitabine-based regimens. Accumulating research on drug resistance has shown that biochemical metabolic aberrations in PC, especially those involving glycolysis and glutamine metabolism, are highly associated with chemoresistance. Additionally, lipid metabolism is a major factor in chemoresistance. However, emerging compounds that target these key metabolic pathways have the potential to overcome chemoresistance. This review summarizes how PC develops chemoresistance through aberrations in biochemical metabolism and discusses novel critical targets and pathways within cancer metabolism for new drug research.

Objectives This study aims to identify risk factors for acute kidney injury (AKI) in patients with ureterolithiasis and to develop a predictive model for early AKI detection in this population.Methods A retrospective analysis was conducted on data from 1,016 patients with ureterolithiasis who presented to our outpatient emergency department between January 2021 and December 2022. Using multifactorial logistic regression, we identified independent risk factors for AKI and constructed a nomogram to predict AKI risk. The predictive model’s efficacy was assessed through the area under the ROC curve, calibration curves, Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA).Results AKI was diagnosed in 18.7% of the patients. Independent risk factors identified included age, fever, diabetes, hyperuricemia, bilateral calculi, functional solitary kidney, self-medication, and prehospital delay. The nomogram demonstrated excellent discriminatory capabilities, with AUCs of 0.818 (95% CI, 0.775–0.861) for the modeling set and 0.782 (95% CI, 0.708–0.856) for the validation set. Both calibration curve and HL test results confirmed strong concordance between the model’s predictions and actual observations. DCA highlighted the model’s significant clinical utility.Conclusions The predictive model developed in this study provides clinicians with a valuable tool for early identification and management of patients at high risk for AKI, thereby potentially enhancing patient outcomes.

ABSTRACTObjective To establish an efficient nomogram model to predict short-term survival in ICU patients with aplastic anemia (AA).Methods The data of AA patients in the MIMIC-IV database were obtained and randomly assigned to the training set and testing set in a ratio of 7:3. Independent prognosis factors were identified through univariate and multivariate Cox regression analyses. The variance inflation factor was calculated to detect the correlation between variables. A nomogram model was built based on independent prognostic factors and risk scores for factors were generated. Model performance was tested using C-index, receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and Kaplan-Meier curve.Results A total of 1,963 AA patients were included. A nomogram model with 7 variables was built, including SAPS II, chronic pulmonary obstructive disease, body temperature, red cell distribution width, saturation of peripheral oxygen, age and mechanical ventilation. The C-indexes in the training set and testing set were 0.642 and 0.643 respectively, indicating certain accuracy of the model. ROC curve showed favorable classification performance of nomogram. The calibration curve reflected that its probabilistic prediction was reliable. DCA revealed good clinical practicability of the model. Moreover, the Kaplan-Meier curve showed that receiving mechanical ventilation could improve the survival status of AA patients in the short term but did not in the later period.Conclusion The nomogram model of the short-term survival rate of AA patients was built based on clinical characteristics, and early mechanical ventilation could help improve the short-term survival rate of patients.

Summary: Emerging evidence highlights the regulatory role of paired-like (PRD-like) homeobox transcription factors (TFs) in embryonic genome activation (EGA). However, the majority of PRD-like genes are lost in rodents, thus prompting an investigation into PRD-like TFs in other mammals. Here, we showed that PRD-like TFs were transiently expressed during EGA in human, monkey, and porcine fertilized embryos, yet they exhibited inadequate expression in their cloned embryos. This study, using pig as the research model, identified LEUTX as a key PRD-like activator of porcine EGA through genomic profiling and found that LEUTX overexpression restored EGA failure and improved preimplantation development and cloning efficiency in porcine cloned embryos. Mechanistically, LEUTX opened EGA-related genomic regions and established histone acetylation via recruiting acetyltransferases p300 and KAT2A. These findings reveal the regulatory mechanism of LEUTX to govern EGA in pigs, which may provide valuable insights into the study of early embryo development for other non-rodent mammals.

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology.

Sheath blight (ShB) is one of the three major diseases in rice and is prevalent worldwide. Lesions spread vertically from leaf sheaths near the water surface towards the upper parts. This increases the need to develop an approach for the early detection of infection. Hyperspectral remote sensing has been proven to be a potential technology for the early detection of diseases but remains challenging due to redundant information and weak spectral signals. This study proposed a stepwise screening method of spectral features for the early detection of ShB using rice canopy hyperspectral data over two years of successive experiments. The procedure consists of the selection of key wavebands using three algorithms and a further filtration of key wavelengths and vegetation indices considering feature importance, separability, and high correlation. Sheath-blight infection can disrupt the canopy architecture and influence the biochemical parameters in rice plants. The study reported that obvious variations in the chlorophyll content and LAI of rice plants occurred under early stress of ShB, and the sensitive features selected had strong correlations with these two growth factors. By fusing support vector machine with the optimal features, the detection model for early ShB exhibited an overall accuracy of 87%, showing higher accuracy at the current level of early-stage detection of rice ShB at the field scale. The proposed method not only provides methodological support for early detecting rice ShB but also serves as a reference for diagnosing other stalk diseases in crops.

BackgroundRadical cystectomy and urinary diversion (UD) are gold standards for non-metastatic muscle-invasive bladder cancer. Orthotopic neobladder (or Studer), ileal conduit (or Bricker) and cutaneous ureterostomy (CU) are mainstream UD types. Little is known about urinary microbiological changes after UD. MethodsIn this study, urine samples were collected from healthy volunteers and patients with bladder cancer who had received aforementioned UD procedures. Microbiomes of samples were analyzed using 16S ribosomal RNA gene sequencing, and microbial diversities, distributions and functions were investigated and compared across groups. ResultsHighest urine microbial richness and diversity were observed in healthy controls, followed by Studer patients, especially those without hydronephrosis or residual urine, α-diversity indices of whom were remarkably higher than those of Bricker and CU groups. Studer UD type was the only independent factor favoring urine microbial diversity. The urine microflora structure of the Studer group was most similar to that of the healthy individuals while that of the CU group was least similar. Studer patients and healthy volunteers shared many similar urine microbial functions, while Bricker and CU groups exhibited opposite characteristics. ConclusionOur study first presented urinary microbial landscapes of UD patients and demonstrated the microbiological advantage of orthotopic neobladder. Microbiota might be a potential tool for optimization of UD management.

Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology’s precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies.

In this paper, a prediction model based on Empirical Wavelet Transform (EWT) for FGSM-Bi-LSTM network is proposed to address the short-term power load forecasting problem. The model performs noise reduction on the data by combining time windows with EWT. Additionally, the model stability is enhanced by introducing the Fast Gradient Sign Method (FGSM) to generate adversarial samples. Finally, case experiments based on real-world power station load data are conducted. The results demonstrate that compared with prediction models such as LSTM, ARIMA, XGBoost, QR-GRU, and Transformer, the proposed FGSM-Bi-LSTM model reduces RMSE by 85.22%, improves MAE by 62.60%, and increases r by 9.83%. This demonstrates the strong generalization ability of the model to be extended to other time series prediction tasks.

Multi-rotor UAVs (Unmanned Aerial Vehicles) have been increasingly used for hazardous inspection tasks in complex open-air warehouse storage environments due to their high maneuverability and aerial perspective. To facilitate rapid response to patrol missions and improve the efficiency of UAV trajectory planning. This paper established a rotary-wing UAV trajectory plan model considering UAV patrol efficiency, trajectory cost, and power consumption cost. Secondly, an improved SSA (salp swarm algorithm) is incorporated for the shortcomings of low algorithmic search efficiency and unsmooth paths when planning paths in the traditional RRT (Rapidly-exploring Random Trees). The predation mechanism of the salps group is incorporated into the random sampling of the RRT algorithm, which reduces the invalid sampling of random points and introduces the adaptive leader structure, and reverses the search strategy to improve the algorithm’s global search for superiority at the later stage of the search. Finally, the designed LASSA-RRT algorithm is subjected to simulation experiments and compared with RRT, RRT*, IRRT, and PF-RRT* in a cross-sectional manner. The results show that the LASSA-RRT algorithm has an average reduction of 55.83% in sampling times, 51.91% in run time, 13.17% in track length, and 0.1491% in flight cost. In summary, this paper’s UAV trajectory planning method can be effectively applied to complex open storage environments. It can provide a helpful reference direction for UAV trajectory planning.

Spectroscopy can be used for detecting crop characteristics. A goal of crop spectrum analysis is to extract effective features from spectral data for establishing a detection model. An ideal spectral feature set should have high sensitivity to target parameters but low information redundancy among features. However, feature-selection methods that satisfy both requirements are lacking. To address this issue, in this study, a novel method, the continuous wavelet projections algorithm (CWPA), was developed, which has advantages of both continuous wavelet analysis (CWA) and the successive projections algorithm (SPA) for generating optimal spectral feature set for crop detection. Three datasets collected for crop stress detection and retrieval of biochemical properties were used to validate the CWPA under both classification and regression scenarios. The CWPA generated a feature set with fewer features yet achieving accuracy comparable to or even higher than those of CWA and SPA. With only two to three features identified by CWPA, an overall accuracy of 98% in classifying tea plant stresses was achieved, and high coefficients of determination were obtained in retrieving corn leaf chlorophyll content (R2 = 0.8521) and equivalent water thickness (R2 = 0.9508). The mechanism of the CWPA ensures that the novel algorithm discovers the most sensitive features while retaining complementarity among features. Its ability to reduce the data dimension suggests its potential for crop monitoring and phenotyping with hyperspectral data.

Abstract Background It is recorded in the Chinese Pharmacopoeia that deer antlers can be used to tonify the kidney and strengthen bone. Although numerous studies have demonstrated that deer antler has protective effects on the kidney and bone, its molecular mechanisms remain to be elucidated. The aim of this study was to explore the molecular mechanism underlying its effects on the bone and kidney. Methods Water extract of pilose antler was prepared and then filtered through a 0.45 μm Hollow Fiber Cartridge (GE Healthcare, USA). The filtrate was freeze-dried by a Heto PowerDry LL3000 Freeze Dryer (Thermo, USA) and stored at − 80 °C. Rats were treated with deer antler extract (DAE) prepared in advance, and gene regulatory network in the kidney and bone was detected by RNA-Seq technique. Micro-CT was used to detect bone trabecular formation, bone mineral density (BMD) and bone volume fraction (BV/TV). Results The results demonstrate that DAE could jointly heighten renal function by maintaining renal homeostasis, combating renal fibrosis, and reducing renal inflammation by regulating ion transport. Furthermore, DAE can strengthen the bone system by stimulating osteoblast differentiation and regulating bone regeneration and the bone marrow microenvironment. Micro-CT results confirmed that DAE can promote bone trabecular formation and increase BMD and BV/TV. We also identified many genes that can regulate both the kidney and bone simultaneously, which explained the theory of “kidney governing bone” at the molecular level and provided possible strategies for further application of this theory to treat diseases. Conclusions DAE enhances renal function, maintains renal homeostasis, positively regulates skeletal system development, and increases bone mineral density. The underlying mechanism involves improving the expression levels of functional genes involved in renal function and regulation and repair, as well as genes that positively regulate skeletal system development.

BackgroundInsulinoma is a rare type of pancreatic neuroendocrine tumor with low incidence and low-malignant features. While very few insulinomas present with malignant behaviours, such as lymph node and liver metastasis, only a few studies have focused on this field owing to the limitation of samples. Existing evidence suggests that metastatic insulinoma largely derive from non-functional pancreatic neuroendocrine tumor. However, we found a portion of metastatic insulinomas may derive from non-metastatic insulinomas and explored their clinicopathological signatures and genetic characteristics.MethodsFour metastatic insulinoma patients with synchronous liver metastasis or lymph node metastasis at the Peking Union Medical College Hospital between October 2016 and December 2018 were enrolled, and whole exon and genome sequencing were performed on fresh frozen tissues and peripheral blood samples. Clinicopathological information and genomic sequencing results were collected and matched to explore the characteristics of the metastatic insulinomas.ResultsThese four metastatic insulinoma patients underwent surgery or interventional therapy, and their blood glucose levels immediately increased and maintained within standard range after treatment. For these four patients, the proinsulin/insulin molar ratio

Extensive occurrence of rice sheath blight has been observed in China in recent years due to agricultural practices and climatic conditions, posing a serious threat to rice production. Assessing habitat suitability for rice sheath blight at a regional scale can provide important information for disease forecasting. In this context, the present study aims to propose a regional-scale habitat suitability evaluation method for rice sheath blight in Yangzhou city using multisource data, including remote sensing data, meteorological data, and disease survey data. By combining the epidemiological characteristics of the crop disease and the Relief-F algorithm, some habitat variables from key stages were selected. The maximum entropy (Maxent) and logistic regression models were adopted and compared in constructing the disease habitat suitability assessment model. The results from the Relief-F algorithm showed that some remote sensing variables in specific temporal phases are particularly crucial for evaluating disease habitat suitability, including the MODIS products of LAI (4–20 August), FPAR (9–25 June), NDVI (12–20 August), and LST (11–27 July). Based on these remote sensing variables and meteorological features, the Maxent model yielded better accuracy than the logistic regression model, with an area under the curve (AUC) value of 0.90, overall accuracy (OA) of 0.75, and a true skill statistics (TSS) value of 0.76. Indeed, the results of the habitat suitability assessment models were consistent with the actual distribution of the disease in the study area, suggesting promising predictive capability. Therefore, it is feasible to utilize remotely sensed and meteorological variables for assessing disease habitat suitability at a regional scale. The proposed method is expected to facilitate prevention and control practices for rice sheath blight disease.

Introduction: Mushroom-forming fungi comprise diverse species that develop complex multicellular structures. In cultivated species, both ecological adaptation and artificial selection have driven genome evolution. However, little is known about the connections among genotype, phenotype and adaptation in mushroom-forming fungi. Objectives: This study aimed to (1) uncover the population structure and demographic history of Lentinula edodes, (2) dissect the genetic basis of adaptive evolution in L. edodes, and (3) determine if genes related to fruiting body development are involved in adaptive evolution. Methods: We analyzed genomes and fruiting body-related traits (FBRTs) in 133 L. edodes strains and conducted RNA-seq analysis of fruiting body development in the YS69 strain. Combined methods of genomic scan for divergence, genome-wide association studies (GWAS), and RNA-seq were used to dissect the genetic basis of adaptive evolution. Results: We detected three distinct subgroups of L. edodes via single nucleotide polymorphisms, which showed robust phenotypic and temperature response differentiation and correlation with geographical distribution. Demographic history inference suggests that the subgroups diverged 36,871 generations ago. Moreover, L. edodes cultivars in China may have originated from the vicinity of Northeast China. A total of 942 genes were found to be related to genetic divergence by genomic scan, and 719 genes were identified to be candidates underlying FBRTs by GWAS. Integrating results of genomic scan and GWAS, 80 genes were detected to be related to phenotypic differentiation. A total of 364 genes related to fruiting body development were involved in genetic divergence and phenotypic differentiation. Conclusion: Adaptation to the local environment, especially temperature, triggered genetic divergence and phenotypic differentiation of L. edodes. A general model for genetic divergence and phenotypic differentiation during adaptive evolution in L. edodes, which involves in signal perception and transduction, transcriptional regulation, and fruiting body morphogenesis, was also integrated here.

The lyophilized powder of velvet antler blood (VAB) is a by-product of pilose antler, a supplement rich in amino acids, fatty acids, trace elements and vitamins, and it is expected to have anti-osteoporosis effects. The aim of our study was to provide a comprehensive assessment of the anti-osteoporosis effects of VAB in vivo and in vitro. In vivo results indicated that VAB had the protective effects on bone tissue microarchitecture degeneration and bone mass loss in ovariectomized mice. Through an in vitro study, the ability of VAB to promote proliferation, migration and differentiation of primary osteoblasts was confirmed. Further studies have shown that VAB influences cell cycle by regulating Wnt/β-catenin signaling pathway, thus promoting proliferation and differentiation of primary osteoblasts and preventing osteoporosis. According to our research results, VAB is considered to be a potential anti-osteoporosis nutrient, which may help to improve the quality of life of patients with osteoporosis.

Meta-lenses are advanced optical devices composed of artificial nano-antenna arrays. Its flat, light-weight, ultra-thin, compact, customizable, and easy-to-integrate advantages enable widely potential usages in new demands. We demonstrate a GaN-based polarization-independent meta-lens-based camera on a drone. The diameter of the meta-lens is 2.6 mm, and the measured focal length is 5.03 mm under the 532 nm light incident. An array of the 750 nm height cylindrical nano-antennas with various sizes of the meta-lens provides the $2\pi $ phase modulation of the focusing phase distribution. The meta-lens is integrated with an image sensor and mounted on the drone to realize the aerial photography and landing assistance. By taking images of the specific pattern on the ground at different heights through the meta-lens, the flying height of the drone can be detected for landing and flying. We trust meta-lens-camera can reduce the weight burden for prolonging flight time. We believe the meta-lens-based optical devices for imaging and sensing is an important key for micro/nano-robots, micro air vehicles, and intelligent sensing devices in the future.

The piRNA (PIWI-interacting RNA) is P-Element induced wimpy testis (PIWI)-interacting RNA which is a small molecule, non-coding RNA with a length of 24-32nt. It was originally found in germ cells and is considered a regulator of germ cell function. It can interact with PIWI protein, a member of the Argonaute family, and play a role in the regulation of gene transcription and epigenetic silencing of transposable factors in the nucleus. More and more studies have shown that piRNAs are abnormally expressed in a variety of cancer tissues and patient fluids, and may become diagnostic tools, therapeutic targets, staging markers, and prognostic evaluation tools for cancer. This article reviews the recent research on piRNA and summarizes the structural characteristics, production mechanism, applications, and its role in urological tumors, to provide a reference value for piRNA to regulate urological tumors.

Introduction: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive pulmonary fibrotic disease with unknown etiology and poor outcomes. It severely affects the quality of life. In this study, we comprehensively analyzed the expression of N6-methyladenosine (m6A) RNA methylation regulators using gene expression data from various tissue sources in IPF patients and healthy volunteers.Methods: The gene expression matrix and clinical characteristics of IPF patients were retrieved from the Gene Expression Omnibus database. A random forest model was used to construct diagnosis signature m6A regulators. Regression analysis and correlation analysis were used to identify prognosis m6A regulators. Consensus cluster analysis was used to construct different m6A prognosis risk groups, then functional enrichment, immune infiltration and drug sensitivity analysis were performed.Result: Five candidate m6A genes from lung tissue were used to predict the incidence, and the incidence was validated using datasets from bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells. Subsequently, the BALF dataset containing outcomes data was used for the prognosis analysis of m6A regulators. METTL14, G3BP2, and ZC3H13 were independent protective factors. Using correlation analysis with lung function in the lung tissue-derived dataset, METTL14 was a protective factor in IPF. Based on METTL14 and G3BP2, a consensus cluster analysis was applied to distinguish the prognostic m6A regulation patterns. The low-risk group’s prognosis was significantly better than the high-risk group. Biological processes regulated by various risk groups included fibrogenesis and cell adhesion. Analysis of immune cell infiltration showed upregulation of neutrophils in the m6A high-risk group. Subsequently, five m6A high-risk group sensitive drugs and one m6A low-risk group sensitive drug were identified.Discussion: These findings suggest that m6A regulators are involved in the diagnosis and prognosis of IPF, and m6A patterns are a method to identify IPF outcomes.

Abstract. Objective:. Pancreatic cancer is one of the most aggressive digestive system malignant tumors, and its clinical diagnosis and treatment are still challenging. To further understand the current status and improve the multidisciplinary collaboration for diagnosis and treatment of pancreatic cancer in China, we conducted an online questionnaire survey on the diagnosis and treatment status of pancreatic cancer in public tertiary hospitals of China in 2021. Methods:. In this cross-sectional questionnaire-based, observational study, online questionnaires with real-name authentication were used to gather data from 500 clinicians, 50 pharmacists, and 1000 pancreatic cancer patients in tertiary general hospitals or cancer hospitals nationwide. Results:. A total of 485 valid questionnaires were obtained from the clinicians, majority of whom were from economically better developed regions or cities of China. There were multi-disciplinary team treatment (MDT) clinics for pancreatic cancer patients in 60% of the hospitals. Minimally invasive surgeries could be performed in all the surveyed hospitals. However, open surgery was still the mainstream choice in most cases. Gemcitabine-based chemotherapy was the most popular first-line adjuvant regimen for pancreatic cancer. A total of 50 valid questionnaires were collected from pharmacists, 48% of them are not satisfactory with the efficacy of the chemotherapeutic drugs, and myelosuppression, liver, and renal damage were the most concerning side effects. In total, 1011 valid questionnaires were collected from the patients. Approximately, 48.4% of the patients did not know about pancreatic cancer before becoming ill. Over 80% of pancreatic cancer patients reported poor to very poor health-related quality of life, and the estimated overall medical expenses were within ¥400,000 ($58823.53) in 80% of the patients. Clinicians, pharmacists, and patients believe that popularizing scientific knowledge of pancreatic cancer, constructing MDT clinics and fast-lane system, and conducting clinical research will help further improve the diagnosis and treatment of pancreatic cancer. Conclusions:. The MDT clinics for pancreatic cancer have been well developed in most of the public tertiary hospitals. Minimally invasive pancreatic surgery has developed rapidly in China; however, open surgery is still the mainstream choice for pancreatic cancer. The proportion of adjuvant treatment has been significantly improved, and the gemcitabine-based regimen is the most commonly used first-line regimen. Most of the public still lacks the general knowledge of pancreatic cancer, needing further popularization. The construction of a fast-lane treatment system and conducting of high-level clinical studies are the warm expectations of the clinicians and patients. The real-world situation of the diagnosis and treatment of pancreatic cancer in the other types of hospitals of China needs further exploration.

Tea is one of the most common beverages in the world. In order to reduce the cost of artificial tea picking and improve the competitiveness of tea production, this paper proposes a new model, termed the Mask R-CNN Positioning of Picking Point for Tea Shoots (MR3P-TS) model, for the identification of the contour of each tea shoot and the location of picking points. In this study, a dataset of tender tea shoot images taken in a real, complex scene was constructed. Subsequently, an improved Mask R-CNN model (the MR3P-TS model) was built that extended the mask branch in the network design. By calculating the area of multiple connected domains of the mask, the main part of the shoot was identified. Then, the minimum circumscribed rectangle of the main part is calculated to determine the tea shoot axis, and to finally obtain the position coordinates of the picking point. The MR3P-TS model proposed in this paper achieved an mAP of 0.449 and an F2 value of 0.313 in shoot identification, and achieved a precision of 0.949 and a recall of 0.910 in the localization of the picking points. Compared with the mainstream object detection algorithms YOLOv3 and Faster R-CNN, the MR3P-TS algorithm had a good recognition effect on the overlapping shoots in an unstructured environment, which was stronger in both versatility and robustness. The proposed method can accurately detect and segment tea bud regions in real complex scenes at the pixel level, and provide precise location coordinates of suggested picking points, which should support the further development of automated tea picking machines.

Monitoring of rice planting areas plays an important role in maintaining food security. With powerful automatic feature extraction capability, crop mapping based on deep learning methods has become one of the most important research directions of crop remote sensing recognition. However, the training of deep learning models often requires a large number of samples, which restricts the application of these models in areas with a lack of samples. To address this problem, based on time-series Sentinel-1 SAR data, this study pre-trained the temporal feature-based segmentation (TFBS) model with an attention mechanism (attTFBS) using abundant samples from the United States and then performed an inter-continental transfer of the pre-trained model based on a very small number of samples to obtain rice maps in areas with a lack of samples. The results showed that an inter-continental transferred rice mapping model was feasible to achieve accurate rice maps in Northeast China (F-score, kappa coefficient, recall, and precision were 0.8502, 0.8439, 0.8345, and 0.8669, respectively). The study found that the transferred model exhibited a strong spatiotemporal generalization capability, achieving high accuracy in rice mapping in the three main rice-producing regions of Northeast China. The phenological differences of rice significantly affected the generalization capability of the transferred model, particularly the significant differences in transplanting periods, which could have resulted in a decrease in the generalization capability of the model. Furthermore, the study found that the model transferred based on an extremely limited number of samples could attain a rice recognition accuracy equivalent to that of the model trained from scratch with a substantial number of samples, indicating that the proposed method possessed strong practicality, which could dramatically reduce the sample requirements for crop mapping based on deep learning models, thereby decreasing costs, increasing efficiency, and facilitating large-scale crop mapping in areas with limited samples.

As compared with the traditional visual discrimination methods, deep learning and image processing methods have the ability to detect plants efficiently and non-invasively. This is of great significance in the diagnosis and breeding of plant disease resistance phenotypes. Currently, the studies on plant diseases and pest stresses mainly focus on a leaf scale. There are only a few works regarding the stress detection at a complex canopy scale. In this work, three tea plant stresses with similar symptoms that cause a severe threat to the yield and quality of tea gardens, including the tea green leafhopper [Empoasca (Matsumurasca) onukii Matsuda], anthracnose (Gloeosporium theae-sinensis Miyake), and sunburn (disease-like stress), are evaluated. In this work, a stress detection and segmentation method by fusing deep learning and image processing techniques at a canopy scale is proposed. First, a specified Faster RCNN algorithm is proposed for stress detection of tea plants at a canopy scale. After obtaining the stress detection boxes, a new feature, i.e., RGReLU, is proposed for the segmentation of tea plant stress scabs. Finally, the detection model at the canopy scale is transferred to a field scale by using unmanned aerial vehicle (UAV) images. The results show that the proposed method effectively achieves canopy-scale stress adaptive segmentation and outputs the scab type and corresponding damage ratio. The mean average precision (mAP) of the object detection reaches 76.07%, and the overall accuracy of the scab segmentation reaches 88.85%. In addition, the results also show that the proposed method has a strong generalization ability, and the model can be migrated and deployed to UAV scenarios. By fusing deep learning and image processing technology, the fine and quantitative results of canopy-scale stress monitoring can provide support for a wide range of scouting of tea garden.

Combining optical and synthetic aperture radar (SAR) data for crop mapping has become a crucial way to improve classification accuracy, especially in cloudy and rainy areas. However, the acquisition of optical images is significantly unstable due to the influence of cloudy and rainy weather, which seriously restricts the application of this method in practice. To solve this problem, this study proposed an optical-SAR imagery-based rice mapping method which has the advantages of less dependence on optical images, easy operation and high classification accuracy. To account for the trait of sparse availability of optical images, this method only needs one clear sky optical image in the rice growth period and combined it with multi-temporal SAR images to achieve a high accuracy rice mapping result. Meanwhile, this paper also proposed a comprehensively multi-scale segmentation parameter optimization algorithm, which considers the area consistency, shape error and location difference between the segmented object and reference object, and adopts an orthogonal experiment approach. Based on the optical image, the boundaries of the parcel objects can be segmented, which were subsequently used to perform the object-oriented classification. The results show that the overall accuracy of the proposed method in Yangzhou City is 94.64%. Moreover, according to a random pick test, it is encouraging that the proposed method has strong robustness in response to the instability of the acquisition time of SAR images. A relatively high overall accuracy of 90.09% suggested that the proposed method can provide a reliable rice mapping result in cloudy and rainy areas.

ObjectiveTo evaluate the efficacy and safety of standard or low-dose chemotherapy followed by HLA-mismatched allogeneic T-cell infusion (allo-TLI) for the treatment of elderly patients with acute myeloid leukemia (AML) and patients with intermediate-2 to high-risk myelodysplastic syndrome (MDS).MethodsWe carried out a prospective, multicenter, single-arm clinical trial. Totally of 25 patients were enrolled, including 17 AML patients and 8 MDS patients. Each patient received four courses of non-ablative chemotherapy, with HLA-mismatched donor CD3+ allo-TLI 24 h after each course. AML patients received chemotherapy with decitabine, idarubicin, and cytarabine, and MDS patients received decitabine, cytarabine, aclarubicin, and granulocyte colony-stimulating factor.ResultsA total of 79 procedures were performed. The overall response rates of the AML and MDS patients were 94% and 75% and the 1-year overall survival rates were 88% (61–97%) and 60% (13–88%), respectively. The overall 60-day treatment-related mortality was 8%. Compared with a historical control cohort that received idarubicin plus cytarabine (3 + 7), the study group showed significantly better overall response (94% vs. 50%, P=0.002) and overall survival rates (the 1-year OS rate was 88% vs. 27%, P=0.014). Post-TLI cytokine-release syndrome (CRS) occurred after 79% of allo-TLI operations, and 96% of CRS reactions were grade 1.ConclusionElderly AML patients and intermediate-2 to high-risk MDS patients are usually insensitive to or cannot tolerate regular chemotherapies, and may not have the opportunity to undergo allogeneic stem cell transplantation. Our study showed that non-ablative chemotherapy followed by HLA-mismatched allo-TLI is safe and effective, and may thus be used as a first-line treatment for these patients.Clinical Trial Registrationhttps://www.chictr.org.cn/showproj.aspx?proj=20112.

Glycoprotein (GP) Ib is a platelet membrane receptor complex exposed to vascular injury, proposed as an effective target for stroke therapy. Previously, we have observed that the GPIb antagonist anfibatide (ANF) could mitigate blood-brain barrier (BBB) disruption following cerebral ischemia/reperfusion (CI/R) injury. The current study was designed to investigate whether the amelioration of the BBB by ANF is mediated via the Epac signaling pathway. A murine model of CI/R injury was induced following 90 min of transient middle cerebral artery occlusion (MCAO). ANF (4 μg/kg) was intravenously injected 1 h after reperfusion. Herein, ANF ameliorated BBB disruption, increased the expression of tight junction proteins, suppressed F-actin cytoskeleton rearrangement, decreased the permeability of the ischemic brain tissue, and relieved brain edema. ANF-treated mice had smaller infarct volumes and less severe neurological deficits than the MCAO mice. Moreover, the effects of ANF and Epac1 agonists were very similar in the MCAO mice. Epac activation with a cAMP analog, 8-CPT-2′-O-Me-cAMP, mitigated the breakdown of BBB function and CI/R injury. The Epac specific antagonist, ESI-09, worsened barrier damage and cerebral impairment, antagonizing the protective effects afforded by ANF. In addition, ANF upregulated the expression of Epac1 protein in the ischemic cerebral cortex. Collectively, our results indicate that the protective effect of ANF on the BBB after CI/R could be attributed to the activation of the Epac pathway.

A new hyperspectral images (HSIs) denoising method via Interpolated Block-Matching and 3D filtering and Guided Filtering (IBM3DGF) denoising method is proposed. First, inter-spectral correlation analysis is used to obtain inter-spectral correlation coefficients and divide the HSIs into several adjacent groups. Second, high-resolution HSIs are produced by using adjacent three images to interpolate. Third, Block-Matching and 3D filtering (BM3D) is conducted to reduce the noise level of each group; Fourth, the guided image filtering is utilized to denoise HSI of each group. Finally, the inverse interpolation is applied to retrieve HSI. Experimental results of synthetic and real HSIs showed that, comparing with other state-of-the-art denoising methods, the proposed IBM3DGF method shows superior performance according to spatial and spectral domain noise assessment. Therefore, the proposed method has a potential to effectively remove the spatial/spectral noise for HSIs.

Background Immunotherapy has considerably changed the treatment of lung cancer. As immunotherapy has a special mechanism of action, the disease remission that it can induce is unique. Recently, Immune Response Evaluation Criteria in Solid Tumors (iRECIST), which focus on assessing the apparent curative effect of immunotherapy, have become widely accepted. Based on iRECIST criteria, if the response to immunotherapy is determined to be immunity‐confirmed progressive disease or immunity‐unconfirmed progressive disease, and the Eastern Cooperative Oncology Group score is worse than before treatment, immunotherapy should be discontinued. We report two immunity‐confirmed progressive disease cases after pembrolizumab treatment and one immunity‐unconfirmed progressive disease case after nivolumab treatment. All three patients benefited from continued immunotherapy, which indicates that the iRECIST criteria may have limitations in assessing the efficacy of immunotherapy for non‐small cell lung cancer patients.

Traffic accidents have been one of the most important global public problems. It has caused a severe loss of human lives and property every year. Studying the influential factors of accidents can help find the reasons behind. This can facilitate the design of effective measures and policies to reduce the traffic fatality rate and improve road safety. However, most of the existing research either adopted methods based on linear assumption or neglected to further evaluate the spatial relationships. In this paper, we proposed a methodology framework based on XGBoost and grid analysis to spatially analyze the leading factors on traffic fatality in Los Angeles County. Characteristics of the collision, time and location, and environmental factors are considered. Results show that the proposed method has the best modeling performance compared with other commonly seen machine learning algorithms. Eight factors are found to have the leading impact on traffic fatality. Spatial relationships between the eight factors and the fatality rates within the Los Angeles County are further studied using the grid-based analysis in GIS. Specific suggestions on how to reduce the fatality rate and improve road safety are provided accordingly.

Abstract Background Despite a large body of studies have demonstrated the multifaceted behavior of high-density lipoproteins (HDLs) in several physiological and pathological processes, the levels of plasma HDL-cholesterol (HDL-C) that may be associated with endobronchial biopsy (EBB)-related bleeding have never been examined. Methods We conducted a single-center retrospective cohort study of 628 consecutive patients with primary lung cancer who had undergone EBB at a large tertiary hospital between January 2014 and February 2018. Patients were divided into the bleeding group and the non-bleeding group according to the bronchoscopy report. The association between HDL-C levels and EBB-induced bleeding was evaluated using the LASSO regression analysis, multiple regression analysis and smooth curve fitting adjusted for potential confounders. Results There was an inverse association of plasma HDL-C concentration with the incidence of EBB-induced bleeding as assessed by univariate analysis (P

Gemcitabine serves as a first‐line chemotherapy agent for advanced pancreatic cancer (PC). However, the molecular basis by which gemcitabine exerts its effects is not well‐established, and the targeted genetic pathways remain unclear. Pvt1 oncogene (non‐protein coding) (PVT1) has been reported to be an oncogenic long non‐coding RNA in tumorigenesis. In the present study, we show that the expression of PVT1 is correlated with gemcitabine efficacy in PC therapy. Inhibition of PVT1 led to decreased cell growth in PC cells treated with gemcitabine. We also demonstrate that gemcitabine treatment decreases PVT1 levels and increases its encoded miRNAs, such as the miR‐1207 pair (miR‐1207‐5p/3p). Overexpression of the miR‐1207 pair enhanced the chemosensitivity of cells to gemcitabine, whereas silencing of miR‐1207‐5p/3p to prevent its induction by gemcitabine treatment led to increased cell growth. Mechanistic studies revealed that miR‐1207‐5p and miR‐1207‐3p target the SRC proto‐oncogene (non‐receptor tyrosine kinase) and ras homolog family member A in PC cells, respectively. In particular, we observed that gemcitabine induced Drosha ribonuclease III (Drosha) and DGCR8 microprocessor complex subunit (DGCR8) upregulation and then triggered PVT1 processing. Suppression of Drosha and DGCR8 contributed to a dampened efficacy of gemcitabine, indicating that gemcitabine decreased PVT1 expression by promoting its processing into miRNAs, which in turn resulted in blunted oncogenic signaling in PC cells. Moreover, we demonstrate that gemcitabine chemoresistance was a result of decreased expression of Drosha and DGCR8 in AsPC‐1 cells and tumor cell‐engrafted models. Overall, our findings define a novel mechanism for understanding the efficacy of gemcitabine chemotherapy in PC.

Explainable artificial intelligence has been extensively studied recently. However, the research of interpretable methods in the agricultural field has not been systematically studied. We studied the interpretability of deep learning models in different agricultural classification tasks based on the fruit leaves dataset. The purpose is to explore whether the classification model is more inclined to extract the appearance characteristics of leaves or the texture characteristics of leaf lesions during the feature extraction process. The dataset was arranged into three experiments with different categories. In each experiment, the VGG, GoogLeNet, and ResNet models were used and the ResNet-attention model was applied with three interpretable methods. The results show that the ResNet model has the highest accuracy rate in the three experiments, which are 99.11%, 99.4%, and 99.89%, respectively. It is also found that the attention module could improve the feature extraction of the model, and clarify the focus of the model in different experiments when extracting features. These results will help agricultural practitioners better apply deep learning models to solve more practical problems.

Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices.

Rice false smut is known as the cancer of rice. The disease is becoming increasingly prominent and is one of the major diseases in rice. However, prevention and treatment of this disease relies on “Centralized pesticide spraying”. However, indiscriminate spraying leads to more pesticide residue, and impacts ecological and food safety. To obtain more objective results, different experimental planting forms are necessary. This study collected data at a complex planting environment based on “near earth remote sensing” using a frame-based hyperspectral device. We used mixed detection methods to differentiate between healthy rice and U. virens infected rice. There were 49 arrangements and more than 196 differentiation models between healthy and diseased rice, including 7 sowing data plots, 2 farm management types, and 23 pattern recognition methods. Finally, the real accuracy was mostly above 95%. In particular, with the increase of epoch and iteration, feature sequences based on deep learning could achieve better results; most of the accuracies were 100% with 100 epochs. We also found that differentiation accuracy was not necessarily correlated with the sowing dates and farm management. Finally, the detection method was verified according to the actual investigation results in the field. The prescription map of disease incidence was generated, which provided a theoretical basis for the follow-up precision plant protection work.

A blight disease of Flammulina velutipes was identified with symptoms of growth cessation of young fruiting bodies, short stipe, and brown spots on the pileus. The pathogenic bacteria were identified as Arthrobacter arilaitensis and Pseudomonas yamanorum by Koch’s postulate, gram staining, morphological and 16S ribosomal RNA gene sequence analyses. Either of the pathogenic bacteria or both of them can cause the same symptoms. Transcriptome changes in blighted F. velutipes were investigated between diseased and normal samples. Compared to the control group, 1,099 differentially expressed genes (DEGs) were overlapping in the bacteria-infected groups. The DEGs were significantly enriched in pathways such as xenobiotic metabolism by cytochrome P450 and tyrosine metabolism. Based on weighted correlation network analysis (WGCNA), the module most correlated to the pathogen-treated F. velutipes samples and candidate hub genes in the co-regulatory network were identified. Furthermore, a potential diseased mechanism involved in cell wall non-extension, phenolic substrate oxidation, and stress defense response was proposed based on the up-regulation of differentially expressed genes encoding chitin deacetylase, tyrosinase, cytochrome P450, MFS transporter, and clavaminate synthase-like protein. This study provides insights into the underlying reactions of young fruiting body of F. velutipes suffering from blight disease and facilitates the understanding of the pathogenic procedure of bacteriosis in edible mushrooms.

The transverse focal shift (TFS) is a phenomenon that the maximum of a focused field does not occur at the geometrical focus, but is moved a short distance transversely in the focal plane. In this paper, the TFS of vortex beams in a high numerical aperture system is investigated. Four typical types of incident vortex beams are selected and the intensity distributions and the vortices behaviors in the focal region are discussed analytically and numerically. It is found that there are three main parameters, the topological charge, the initial positions of the vortices, and the semiaperture angle influencing the TFS in different ways, and the TFS even can be observed when the vortices annihilate in the focal plane. Our results also show that the intensity maximum can move from the $y$-axis, $x$-axis to the geometrical focus, or move from $+y$-axis to $-y$ -axis in different cases, which may have implications in optical trapping.

Abstract Accumulation of mitochondrial reactive oxygen species (mROS) has been implicated in the pathogenesis of fluorosis. As the main mitochondrial deacetylase, SIRT3 is closely associated with oxidative stress. To investigate the role of SIRT3 in response to sodium fluoride (NaF)-induced nephrotoxicity. Our results showed that NaF treatment impaired mitochondrial ultrastructure, decreased cell viability and increased apoptosis in TCMK-1 cells. Oxidative stress, detected by mROS and 8-Hydroxy-2’-deoxyguanosine (8-OHdG) were higher in NaF-treated cells, accompanied by decreased level of reduced glutathione (GSH). NaF reduces manganese superoxide dismutase (SOD2) expression through SIRT3-mediated DNA-binding activity of FoxO3a and decrease SOD2 activity by inhibiting SIRT3-mediated deacetylation. These effects were ameliorated by overexpression of SIRT3. Peroxisome proliferator-activated receptor-coactivator 1a (PGC-1α) interacted with nuclear factor erythroid 2 (NF-E2)-related factor 2 (NRF2) that bound to SIRT3 promoter to regulate SIRT3 expression. The study provides new insights into a critical NRF2/PGC-1α-SIRT3 pathway in response to NaF-induced nephritic oxidative injury. In vivo treatment of SIRT3-expressing adenovirus protects against NaF-induced nephritic injury in mice. Moreover, mechanistic study revealed that ERK1/2 activation was associated with increased apoptosis induced by NaF. In conclusion, these data shedding light on new approaches for treatment of NaF-induced nephrotoxicity.

MicroRNAs (miRNAs) are a family of highly conserved, non-coding single-stranded RNAs transcribed as ~70 nucleotide precursors to an 18–22 nucleotide product (1). miRNAs can silence their homologous target genes at the post-transcriptional level, and these genes have been revealed to play an important role in tumorigenesis, invasion and metastasis (2). MicroRNA-27a (miR-27a), transcripted by miR-27a gene, has proved to implicate with many kinds of solid tumors, showing potential as a useful biomarker or drug target for clinical application. However, even though miR-27a has been reported in many cancers, the mechanism and signal pathways of miR-27 in oncogenesis, invasion, and metastasis are still obscure. Moreover, recent studies show that miR-27a pays an important role in epithelial-mesenchymal-transition, regulating tumor immune response, and chemoresistance. In this review, we summarize the current literature, demonstrate the established link between miR-27a and tumorigenesis, and focus on recently identified mechanisms. The review also aims to demonstrate the potential of miR-27a as a diagnostic and/or prognostic biomarker in solid tumors and to discuss the possibilities of targeted therapy and drug design.

Glyphosate is the most widely used herbicide in crop production due to the widespread adoption of glyphosate-resistant (GR) crops. However, the spray of glyphosate onto non-target crops from ground or aerial applications can cause severe injury to non-GR corn plants. To evaluate the crop damage of the non-GR corn plants from glyphosate and the recoverability of the damaged plants, we used the hyperspectral imaging (HSI) technique in field experiments with different glyphosate application rates. This study investigated the spectral characteristic of corn plants and assessed the corn plant damage from glyphosate. Based on HSI image analysis, a spectral variation pattern was observed at 1 week after treatment (WAT), 2 WAT, and 3 WAT from the glyphosate-treated non-GR corn plants. It was further found that the corn plants treated with glyphosate rates equal to or higher than 0.5X (X = 0.866 kilograms acid equivalents/hectare (kg ae/ha) represents the recommended spray rate for GR corn) would suffer unrecoverable damage. Using the Jeffries–Matusita distance as the spectral sensitivity criterion, three sensitive bands from the measured spectra were selected to create two spectral indices for crop recoverability differentiation in band ratio and normalization forms, respectively. With the two spectral indices, the corn plants recoverable and unrecoverable from damage were classified with an overall accuracy greater than 95%. Then, three machine learning algorithms (k-nearest neighbors, random forest, and support vector machine) were respectively combined with the successive projections algorithm to create models to relate selected feature spectral bands to glyphosate spray rates. The results indicated that the models achieved reasonable accuracy, especially in the group of recoverable plants. This study illustrated the potential of the hyperspectral imaging technique for evaluating crop damage from herbicides and recoverability of the injured plants using different data analysis and machine learning modeling approaches for practical weed management in crop fields.

The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based inversion, and hybrid regression approaches. The results showed that the modified triangular vegetation index (MTVI2) exhibited the best estimate accuracy for LCC retrieval with a root mean square error (RMSE) of 5.99 μg/cm2 and a relative RMSE (RRMSE) of 10.49%. Several other vegetation indices that were established from red and near-infrared (NIR) bands also exhibited good accuracy. Models established from Gaussian process regression (GPR) achieved the highest accuracy for LCC retrieval (RMSE = 5.50 μg/cm2, RRMSE = 9.62%) compared with other MLRAs. Moreover, red and NIR bands outweighed other bands in terms of GPR modelling. LUT-based inversion methods with the “K(x) = −log (x) + x” cost function that belongs to the “minimum contrast estimates” family showed the best estimation results (RMSE = 8.08 μg/cm2, RRMSE = 14.14%), and the addition of multiple solution regularization strategies effectively improved the inversion accuracy. For hybrid regression methods, the use of active learning (AL) techniques together with GPR for LCC modelling significantly increased the estimation accuracy, and the combination of entropy query by bagging (EQB) AL and GPR had the best accuracy for LCC estimation (RMSE = 12.43 μg/cm2, RRMSE = 21.77%). Overall, our study suggest that Landsat-8 OLI data are suitable for crop LCC retrieval and could provide a basis for LCC estimation with similar multispectral datasets.

Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat.

The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing.

In order to improve the performance of storage and transmission of massive hyperspectral data, a prediction-based spatial-spectral adaptive hyperspectral compressive sensing (PSSAHCS) algorithm is proposed. Firstly, the spatial block size of hyperspectral images is adaptively obtained according to the spatial self-correlation coefficient. Secondly, a k-means clustering algorithm is used to group the hyperspectral images. Thirdly, we use a local means and local standard deviations (LMLSD) algorithm to find the optimal image in the group as the key band, and the non-key bands in the group can be smoothed by linear prediction. Fourthly, the random Gaussian measurement matrix is used as the sampling matrix, and the discrete cosine transform (DCT) matrix serves as the sparse basis. Finally, the stagewise orthogonal matching pursuit (StOMP) is used to reconstruct the hyperspectral images. The experimental results show that the proposed PSSAHCS algorithm can achieve better evaluation results—the subjective evaluation, the peak signal-to-noise ratio, and the spatial autocorrelation coefficient in the spatial domain, and spectral curve comparison and correlation between spectra-reconstructed performance in the spectral domain—than those of single spectral compression sensing (SSCS), block hyperspectral compressive sensing (BHCS), and adaptive grouping distributed compressive sensing (AGDCS). PSSAHCS can not only compress and reconstruct hyperspectral images effectively, but also has strong denoise performance.

Powdery mildew is one of the dominant diseases in winter wheat. The accurate monitoring of powdery mildew is important for crop management and production. Satellite-based remote sensing monitoring has been proven as an efficient tool for regional disease detection and monitoring. However, the information provided by single-date satellite scene is hard to achieve acceptable accuracy for powdery mildew disease, and incorporation of early period contextual information of winter wheat can improve this situation. In this study, a multi-temporal satellite data based powdery mildew detecting approach had been developed for regional disease mapping. Firstly, the Lansat-8 scenes that covered six winter wheat growth periods (expressed in chronological order as periods 1 to 6) were collected to calculate typical vegetation indices (VIs), which include disease water stress index (DSWI), optimized soil adjusted vegetation index (OSAVI), shortwave infrared water stress index (SIWSI), and triangular vegetation index (TVI). A multi-temporal VIs-based k-nearest neighbors (KNN) approach was then developed to produce the regional disease distribution. Meanwhile, a backward stepwise elimination method was used to confirm the optimal multi-temporal combination for KNN monitoring model. A classification and regression tree (CART) and back propagation neural networks (BPNN) approaches were used for comparison and validation of initial results. VIs of all periods except 1 and 3 provided the best multi-temporal data set for winter wheat powdery mildew monitoring. Compared with the traditional single-date (period 6) image, the multi-temporal images based KNN approach provided more disease information during the disease development, and had an accuracy of 84.6%. Meanwhile, the accuracy of the proposed approach had 11.5% and 3.8% higher than the multi-temporal images-based CART and BPNN models’, respectively. These results suggest that the use of satellite images for early critical disease infection periods is essential for improving the accuracy of monitoring models. Additionally, satellite imagery also assists in monitoring powdery mildew in late wheat growth periods.

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