Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism.

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6-7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

Functionalized Single Crystal Perovskite Materials for SERS and Their Potential Detection Applications.

Recent advances in detection and diagnostic tools have improved understanding and identification of plant physiological and biochemical processes. Effective and safe Surface Enhanced Raman Spectroscopy (SERS) can find objects quickly and accurately. Raman enhancement amplifies the signal by 1014-1015 to accurately quantify plant metabolites at the molecular level. This paper shows how to use functionalized perovskite substrates for SERS. These perovskite substrates have lots of surface area, intense Raman scattering, and high sensitivity and specificity. These properties eliminate sample matrix component interference. This study identified research gaps on perovskite substrates' effectiveness, precision, and efficiency in biological metabolite detection compared to conventional substrates. This article details the synthesis and use of functionalized perovskites for plant metabolites measurement. It analyzes their pros and cons in this context. The manuscript analyzes perovskite-based SERS substrates, including single-crystalline perovskites with enhanced optoelectronic properties. This manuscript aims to identify this study gap by comprehensively reviewing the literature and using it to investigate plant metabolite detection in future studies.

Clinical characteristics and prognosis of patients with hypertrophic cardiomyopathy and heart failure with preserved ejection fraction.

BACKGROUND: Whether heart failure with preserved ejection fraction (HFpEF) is associated with an increased risk of developing systolic dysfunction and a poor prognosis in hypertrophic cardiomyopathy (HCM) patients is unknown. OBJECTIVE: We aimed to assess risk factors for the development of end-stage (ES) heart failure (HF) (ejection fraction < 50%) and compare the prognosis of different HF phenotypes. METHODS: This retrospective study was conducted on patients with HCM in China between January 2009 and February 2023. Patients were stratified into three different groups: HCM-non-HF, HCM-HFpEF and HCM-heart failure with reduced ejection fraction (HCM-HFrEF). The primary outcome was a composite of major adverse cardiac events (MACEs), including all-cause deaths, HF hospitalization, sudden cardiac death and ventricular tachycardia. RESULTS: Of 3,620 HCM patients enrolled, 1,553 (42.9%) had non-HF, 1,666 (46.0%) had HFpEF, and 579 patients (11.1%) had HFrEF at baseline. During the median follow-up period of 4.0 years (IQR 1.4-9.4 years), patients with HCM-HFpEF exhibited a higher incidence of ES-HF than those with HCM-non-HF (12.4% vs. 2.7%, P < 0.001). HFpEF was an independent risk factor for ES-HF development (HR 3.84, 2.54-5.80, P < 0.001). MACEs occurred in 26.9% with a higher incidence in HCM-HFpEF than HCM-non-HF (36.6% vs 12.2%, P < 0.001). HFpEF was an independent predictor of MACEs (HR 2.13, 1.75-2.59, P < 0.001). CONCLUSIONS: HFpEF is common in HCM. Compared to non-HF, it increases the risk of LVEF decline and poor prognosis. It may aid in risk stratification and need close echocardiography follow-up.

Naked-eye detection of plant viral disease using polymerase chain reaction amplification and DNAzyme.

Plant viral diseases can seriously affect the yield and quality of crops. In this work, a convenient and highly sensitive biosensor for the visual detection of plant viral disease is proposed by the PCR-induced generation of DNAzyme. In the absence of nucleic acid for a target plant virus, the primers prohibited the production of G-quadruplex by forming a hairpin structure. However, PCR amplification occurred and generated a number of specific PCR products with free G-quadruplex sequences at both ends in the presence of the target cDNA. A catalytically active G-quadruplex DNAzyme was formed with the help of K+ and hemin, resulting in the formation of colored products visible to the naked eye and a strong absorbance by the addition of ABTS2- and H2O2. The absorbance and the logarithm of target cDNA concentrations showed a good linear relationship in the range of 10 fM-1.0 nM, with a linear regression equation of A = 0.1402 lg c + 0.3761 (c: fM) and a detection limit of 0.19 fM. This method was successfully applied to the analysis of emerging tobacco mosaic virus (TMV) infections in tobacco leaf samples collected in the field due to its flexibility and convenience, indicating a potential application for the early detection of plant viral disease.

Mechanisms of Plant Epigenetic Regulation in Response to Plant Stress: Recent Discoveries and Implications.

Plant stress is a significant challenge that affects the development, growth, and productivity of plants and causes an adverse environmental condition that disrupts normal physiological processes and hampers plant survival. Epigenetic regulation is a crucial mechanism for plants to respond and adapt to stress. Several studies have investigated the role of DNA methylation (DM), non-coding RNAs, and histone modifications in plant stress responses. However, there are various limitations or challenges in translating the research findings into practical applications. Hence, this review delves into the recent recovery, implications, and applications of epigenetic regulation in response to plant stress. To better understand plant epigenetic regulation under stress, we reviewed recent studies published in the last 5-10 years that made significant contributions, and we analyzed the novel techniques and technologies that have advanced the field, such as next-generation sequencing and genome-wide profiling of epigenetic modifications. We emphasized the breakthrough findings that have uncovered specific genes or pathways and the potential implications of understanding plant epigenetic regulation in response to stress for agriculture, crop improvement, and environmental sustainability. Finally, we concluded that plant epigenetic regulation in response to stress holds immense significance in agriculture, and understanding its mechanisms in stress tolerance can revolutionize crop breeding and genetic engineering strategies, leading to the evolution of stress-tolerant crops and ensuring sustainable food production in the face of climate change and other environmental challenges. Future research in this field will continue to unveil the intricacies of epigenetic regulation and its potential applications in crop improvement.

Analysis and experimental assessment of an optimized SERS substrate used to detect thiabendazole in apples with high sensitivity.

Pesticides that linger in the environment and ecosystems for an extended period can cause severe and dangerous health problems in humans. To detect pesticides in foods, the development of high-sensitivity and quick screening technologies was required. This research investigated the performance of Au@Ag NPs with varying thicknesses of the silver shell for detecting trace quantities of thiabendazole (TBZ) in apples using surface-enhanced Raman spectroscopy (SERS). The Au@Ag NPs were synthesized by coating 32 nm gold seeds with different thicknesses of silver shell ranging from 2.4 to 8.7 nm, achieved by adjusting the incorporation of AgNO3 and ascorbic acid. The optimized Au@Ag NPs with a 7.3 nm silver shell demonstrated outstanding SERS activity, high sensitivity, and a detection limit of 0.05 µg/mL for TBZ. The R2 values, representing the goodness of fit, were found to be 0.990 and 0.986 for standard and real TBZ samples, respectively, indicating a strong correlation between the measured signal and the TBZ concentration. The recovery analysis showed a reliable and accurate detection capability (96 to 105%), suggesting good reliability and accuracy of the SERS-based detection using the optimal Au@Ag NPs. Overall, this research highlights the potential of SERS with optimal Au@Ag NPs for rapid and effective monitoring of pesticides in the food industry.

High-Level Secretion of Pregnancy Zone Protein Is a Novel Biomarker of DNA Damage-Induced Senescence and Promotes Spontaneous Senescence.

Identification of unique and specific biomarkers to better detect and quantify senescent cells remains challenging. By a global proteomic profiling of senescent human skin BJ fibroblasts induced by ionizing radiation (IR), the cellular level of pregnancy zone protein (PZP), a presumable pan-protease inhibitor never been linked to cellular senescence before, was found to be decreased by more than 10-fold, while the level of PZP in the conditioned medium was increased concomitantly. This observation was confirmed in a variety of senescent cells induced by IR or DNA-damaging drugs, indicating that high-level secretion of PZP is a novel senescence-associated secretory phenotype. RT-PCR examination verified that the transcription of the PZP gene is enhanced in various cells at senescence or upregulated following DNA damage treatment in a p53-independent manner. Moreover, pretreatment with late pregnancy serum containing a high level of PZP led to inhibition of doxorubicin-induced senescence in A549 cells, and depletion of PZP in the pregnancy serum could enhance such inhibition. Finally, the addition of immuno-precipitated PZP complexes into tissue culture attenuated the growth of A549 cells and promoted the spontaneous senescence. Therefore, we revealed that high-level secretion of PZP is a novel and unique feature associated with DNA damage-induced senescence, and secreted PZP is a positive regulator of cellular senescence, particularly during the late stage of gestation.

A deep learning-enabled smartphone platform for rapid and sensitive colorimetric detection of dimethoate pesticide.

A novel deep learning-enabled smartphone platform is developed to assist a colorimetric aptamer biosensor for fast and highly sensitive detection of dimethoate. The colorimetric determination of dimethoate is based on the specific binding of dimethoate and aptamer, which leads to the aggregation of AuNPs in high-concentration NaCl solution, resulting in an obvious color change from red to blue. This color change provides sufficient data for self-learning enabled by a convolutional neural network (CNN) model, which is established to predict dimethoate concentration based on images acquired from a smartphone. To enhance user-friendliness for non-experts, the CNN model is then embedded into a smartphone app, enabling offline detection of dimethoate pesticide in real environments within just 15 min using a pre-configured colorimetric probe. The developed platform exhibits superior performance, achieving a regression coefficient of 0.9992 in the concentration range of 0-10 µM. Moreover, the app's performance is found to be consistent with the ELISA kit. These remarkable findings demonstrate the potential of combining colorimetric biosensors with smartphone-based deep learning methods for the development of portable and affordable tools for pesticide detection.

Unveiling light effect on formation of trisulfur radicals in lithium-sulfur batteries.

is the important electrochemical intermediate in Li-S batteries of highly solvating solvents. Herein, the dissociation of into is deeply studied. light is proven to promote the formation of from the dissociation of . Accordingly, a strategy of pre-introducing highly active into DMSO-based electrolyte is proposed to activate sulfur cathodes of Li-S batteries.

A catalytic hairpin assembly-based Förster resonance energy transfer sensor for ratiometric detection of ochratoxin A in food samples.

Ochratoxin A (OTA) poses severe risks to the environment and human health, making the development of an accurate and sensitive analytical method for OTA detection essential. In this study, a catalytic hairpin assembly (CHA)-based Förster resonance energy transfer (FRET) aptasensor was developed to detect OTA using carbon quantum dots (CDs) and 6-carboxy-fluorescein (FAM) as dual signal readout. In the presence of OTA, the aptamer specifically interacted with OTA to release the helper DNA (HP), which could open the hairpin structure of FAM-labeled hairpin DNA 1 (H1-FAM) modified on the surface of gold nanoparticles (AuNPs). CHA between H1-FAM and hairpin H2 labeled with CDs (H2-CDs) can release HP for the next cycle, resulting in the occurrence of FRET with CDs as the energy donor and FAM as the energy acceptor. According to the ratio of FCDs/FFAM, the proposed aptasensor showed a wide linear range from 5.0 pg/mL to 3.0 ng/mL and a low detection limit of 1.5 pg/mL for OTA detection. Moreover, satisfactory results were obtained for OTA detection in rice, suggesting the potential application of this sensor in food safety analysis.

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices.

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil-water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher's insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.

Follow-up study on ThinPrep cytology test-positive patients in tropical regions.

BACKGROUND: As shown in the statistics from the World Health Organization, it is estimated that approximately 75000 new cases of cervical cancer occur every year in China. In 2008, 33000 people died of cervical cancer in China. It is proven that most women are at risk of cervical cancer. The progression from human papillomavirus (HPV) infection to cervical cancer can be several years or decades, which offers a unique opportunity to prevent cancer. AIM: To observe the changes in ThinPrep cytology tests (TCT) and HPV infection in patients who were detected to be positive via TCT screening of cervical cancer and further explore the biopsy results. METHODS: This paper performed a follow-up study on 206 cervical cancer screening-positive patients of 12231 total cases from our previous research. We conducted an observational study on the TCT results based on the interpretation of The Bethesda System. RESULTS: Over a 5-year period, 10 cases received consistent follow-up. The proportions of cases in which glandular epithelial lesions were detected increased over the follow-up period. The differences between the years were statistically significant (P < 0.01). Over the 5 years, the proportion of patients whose squamous epithelial lesions transformed into glandular epithelial lesions increased yearly. Annual positive rates of HPV infection were: year 1, 73% (24/33); year 2, 43% (6/14); year 3, 36% (9/25); year 4, 50% (9/18); and year 5, 25% (6/24). The positive detection rate after biopsy over a 9-year period was 29%. CONCLUSION: The follow-up study for 5 years to 9 years revealed a tendency to change from squamous epithelial lesions to glandular epithelial lesions and an improvement of the disease (which had not been reported previously). The HPV test indicated a high negative conversion ratio of the viral infection. However, the follow-up cases were not found to have persistent infection of high-risk HPV. Therefore, early intervention of cervical cancer screening is necessary. Low re-examination compliance, patient education, and preventive measures should be enhanced.

Dual-functional SERRS and fluorescent aptamer sensor for abscisic acid detection via charged gold nanorods.

Abscisic acid (ABA) is a plant hormone, which plays an important role in plant growth, crop cultivation and modern agricultural engineering management. Accordingly, the detection of ABA content combined with new techniques and methods has become a more and more popular problem in the field of agricultural engineering. In this work, a SERRS and fluorescence dual-function sensor based on the fluorescence quenching and Raman enhancement properties of gold nanorods (AuNRs) was developed, and applied to the detection of plant hormone ABA. The dual-function reporter molecule Rhodamine isothiocyanate (RBITC) and complementary DNA (cDNA) were modified on AuNRs (AuNRs@RBITC@cDNA) as signal probes and aptamer modified magnetic nanoparticles (Fe3O4MNPs@Apt) as capture probes. Through the specific recognition of ABA aptamer and its complementary chains, an dual-function aptamer sensor based on SERRS and fluorescence was constructed. When ABA molecules were present in the detection system, the signal probes were detached from the capture probes due to the preferential binding between aptamer and ABA molecules. SERS signal of the reporter molecules appeared in the supernatant after magnetic separation, and it increased with the increase of ABA concentration. If the etching agent that can etch AuNRs was added to the supernatant, the AuNRs was etching disappeared, then the signal molecules fall off from the AuNRs, and the fluorescence signal intensity would recovered. The intensity of fluorescence signal also increased with the increase of ABA concentration. Thus, the quantitative relationship between ABA concentration and SERRS intensity and fluorescence intensity of signal molecules was established. The linear range of SERRS detection was 100 fM-0.1 nM, the detection limit was 38 fM; The linear range of fluorescence detection was 1 pM-100 nM, the detection limit is 0.33 p.m. The constructed dual-effect sensor was used in the recovery laboratory of real ABA samples, the recovery rate was up to 85-108%.

Molecular communication network and its applications in crop sciences.

MAIN CONCLUSION: Plant molecular biology and bacterial behaviour research in the future could focus on using genetically engineered bacteria as a sensor, hormonal/disease detector, and target gene expression, as well as establishing a bioluminescence feedback communication system. Over the last two decades, understanding plant signal transduction pathways of plant hormones has become an active research field to understand plant behavior better. To accomplish signal transduction, plants use a variety of hormones for inter- and intra-communication, and biotic or abiotic stressors activate those. Signal transduction pathways refer to the use of various communication methods by effectors to elicit a response at the molecular level. Research methodologies such as inter-kingdom signaling have been introduced to study signal transduction and communication pathways, or what we can term plant molecular communication. However, stochastic qualities are inherent in most technologies used to monitor these biological processes. Molecular communication (MC) is a new research topic that uses the natural features of biological organisms to communicate and aims to manipulate their stochastic nature to achieve the desired results. MC is a multidisciplinary research field inspired by the use of molecules to store, spread, and receive information between biological organisms known as "Biological Nanomachines." It has been used to demonstrate how biological entities may be characterised, modelled, and engineered as communication devices in the same manner as traditional communication technologies are. We attempted to link MC and PLANT'S MC in this study and we believe that reasonable combined efforts may be made to use the functional applications of MC for detecting and understanding molecular-level activities such as signaling transduction pathways in crops.

Ultrasensitive detection of plant hormone abscisic acid-based surface-enhanced Raman spectroscopy aptamer sensor.

Abscisic acid (ABA), as the most common plant hormone in the growth of wheat, can greatly affect the yield when its levels deviate from normal. Therefore, highly sensitive and selective detection of this hormone is greatly needed. In this work, we developed an aptamer sensor based on surface-enhanced Raman spectroscopy (SERS) and applied it for the high sensitivity detection of ABA. Biotin-modified ABA aptamer complement chains were modified on ferrosoferric oxide magnetic nanoparticles (Fe3O4MNPs) and acted as capture probes, and sulfhydryl aptamer (SH-Apt)-modified silver-coated gold nanospheres (Au@Ag NPs) were used as signal probes. Through the recognition of the ABA aptamer and its complementary chains, an aptamer sensor based on SERS was constructed. As SERS internal standard molecules of 4-mercaptobenzoic acid (4-MBA) were encapsulated between the gold core and silver shell of the signal probes; the constructed aptamer sensor generated a strong SERS signal of 4-MBA after magnetic separation. When there were ABA molecules in the detection system, with the preferential binding of ABA aptamer and ABA molecule, the signal probes were released from the capture probes, after magnetic separation, leading to a linear decrease in SERS intensity of 4-MBA. Thus, the detection response was linear over a logarithmic concentration range, with an ultra-low detection limit of 0.67 fM. In addition, the practical use of this assay method was demonstrated in ABA detection from fresh wheat leaves, with a relative error (RE) of 5.43-8.94% when compared with results from enzyme-linked immunosorbent assay (ELISA). The low RE value proves that the aptamer sensor will be a promising method for ABA detection.

Applied surface enhanced Raman Spectroscopy in plant hormones detection, annexation of advanced technologies: A review.

Plant hormones are the molecules that control the vigorous development of plants and help to cope with the stress conditions efficiently due to vital and mechanized physiochemical regulations. Biologists and analytical chemists, both endorsed the extreme problems to quantify plant hormones due to their low level existence in plants and the technological support is devastatingly required to established reliable and efficient detection methods of plant hormones. Surface Enhanced Raman Spectroscopy (SERS) technology is becoming vigorously favored and can be used to accurately and specifically identify biological and chemical molecules. Subsistence molecular properties with varying excitation wavelength require the pertinent substrate to detect SERS signals from plant hormones. Three typical mechanisms of Raman signal enhancement have been discovered, electromagnetic, chemical and Tip-enhanced Raman spectroscopy (TERS). Though, complex detection samples hinder in consistent and reproducible results of SERS-based technology. However, different algorithmic models applied on preprocessed data enhanced the prediction performances of Raman spectra by many folds and decreased the fluorescence value. By incorporating SERS measurements into the microfluidic platform, further highly repeatable SERS results can be obtained. This review paper tends to study the fundamental working principles, methods, applications of SERS systems and their execution in experiments of rapid determination of plant hormones as well as several ways of integrated SERS substrates. The challenges to develop an SERS-microfluidic framework with reproducible and accurate results for plant hormone detection are discussed comprehensively and highlighted the key areas for future investigation briefly.

HOXB13 suppresses proliferation, migration and invasion, and promotes apoptosis of gastric cancer cells through transcriptional activation of VGLL4 to inhibit the involvement of TEAD4 in the Hippo signaling pathway.

Gastric cancer (GC) is one of the most common types of malignancy worldwide and is accompanied by both high mortality and morbidity rates. Homeobox B13 (HOXB13) has been reported to act as a tumor suppressor gene in multiple types of human cancer. The present study aimed to investigate the effects and potential underlying molecular mechanisms of HOXB13 in the progression of GC. The expression of HOXB13 in GC cells was first examined using the Cancer Cell Line Encyclopedia database and subsequently validated in a number of GC cell lines. Following HOXB13 overexpression (Ov­HOXB13), HGC­27 cell proliferation was evaluated by colony formation and Cell Counting Kit­8 assays. Wound healing and Matrigel assays were used to determine the migratory and invasive abilities, respectively. Additionally, cell apoptosis was assessed using TUNEL staining, and the expression of apoptosis­related proteins was detected by western blot analysis. Subsequently, TEA domain transcription factor 4 (TEAD4) was overexpressed to evaluate the effects on HGC­27 cell proliferation, migration, invasion and apoptosis following co­transfection with Ov­HOXB13. The potential binding sites of HOXB13 on the vestigial­like family member 4 (VGLL4) promoter were verified using chromatin immunoprecipitation and dual luciferase reporter assays. Moreover, the expression levels of proteins involved in the Hippo signaling pathway were analyzed using western blotting. The results revealed that the expression of HOXB13 was notably lower in GC cells compared with normal gastric cells. The overexpression of HOXB13 significantly inhibited the proliferation, migration and invasion, but promoted the apoptosis of HGC­27 cells. Moreover, Ov­HOXB13 downregulated TEAD4 expression. Notably, Ov­TEAD4 transfection partially reversed the effects of Ov­HOXB13 on the cellular behaviors of HGC­27 cells. HOXB13 was also confirmed to bind with the VGLL4 promoter. The knockdown of VGLL4 restored the inhibitory effects of Ov­HOXB13 on the expression levels of VGLL4 and Hippo pathway signaling proteins. In conclusion, the findings of the present study suggested that Ov­HOXB13 may suppress the proliferation, migration and invasion, and promote the apoptosis of GC cells through the transcriptional activation of VGLL4 to inhibit the involvement of TEAD4 in the Hippo signaling pathway. These results may provide novel and potent targets for the treatment of GC.

Protein Phosphatase 2A Mediates YAP Activation in Endothelial Cells Upon VEGF Stimulation and Matrix Stiffness.

Angiogenesis is an essential process during development. Abnormal angiogenesis also contributes to many disease conditions such as tumor and retinal diseases. Previous studies have established the Hippo signaling pathway effector Yes-associated protein (YAP) as a crucial regulator of angiogenesis. In ECs, activated YAP promotes endothelial cell proliferation, migration and sprouting. YAP activity is regulated by vascular endothelial growth factor (VEGF) and mechanical cues such as extracellular matrix (ECM) stiffness. However, it is unclear how VEGF or ECM stiffness signal to YAP, especially how dephosphorylation of YAP occurs in response to VEGF stimulus or ECM stiffening. Here, we show that protein phosphatase 2A (PP2A) is required for this process. Blocking PP2A activity abolishes VEGF or ECM stiffening mediated YAP activation. Systemic administration of a PP2A inhibitor suppresses YAP activity in blood vessels in developmental and pathological angiogenesis mouse models. Consistently, PP2A inhibitor also inhibits sprouting angiogenesis. Mechanistically, PP2A directly interacts with YAP, and this interaction requires proper cytoskeleton dynamics. These findings identify PP2A as a crucial mediator of YAP activation in ECs and hence as an important regulator of angiogenesis.

Minimalizing Non-point Source Pollution Using a Cooperative Ion-Selective Electrode System for Estimating Nitrate Nitrogen in Soil.

Nitrate nitrogen ( NO 3 - -N) in the soil is one of the important nutrients for growing crops. During the period of precipitation or irrigation, an excessive NO 3 - -N readily causes its leaching or runoff from the soil surface to rivers due to inaccurate fertilization and water management, leading to non-point source pollution. In general, the measurement of the NO 3 - -N relies upon the laboratory-based absorbance, which is often time-consuming, therefore not suitable for the rapid measurements in the field directly. Ion-selective electrodes (ISEs) support the possibility of NO 3 - -N measurement by measuring the nitrate ( NO 3 - ) ions in soil quickly and accurately due to the high water solubility and mobility of NO 3 - ions. However, such a method suffers from a complicated calibration to remove the influences caused by both temperature and other ions in the measured solution, thus limiting field use. In this study, a kind of all-solid ISE system combined with a temperature sensor and a pH electrode is proposed to automatically measure the concentrations of the NO 3 - -N. In this study, a soil water content calibration function was established, which significantly reduces a relative error (RE) by 13.09%. The experimental results showed that the stabilization time of this electrode system was less than 15 s with a slope of -51.63 mV/decade in the linear range of 10-5-10-2.2 mol/L. Both the limit of detection of 0.5 ppm of the NO 3 - -N and a relative SD of less than 3% were obtained together with the recovery rate of 90-110%. Compared with the UV-Vis spectroscopy method, a correlation coefficient (R 2) of 0.9952 was obtained. The performances of this all-solid ISE system are satisfied for measuring the NO 3 - -N in the field.

Identification of Molecular Subtypes and a Prognostic Signature Based on Inflammation-Related Genes in Colon Adenocarcinoma.

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package "ConsensusClusterPlus" was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT-PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.