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Abstract Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4 +-N, NO3 −-N, PO4 3− are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4 +-N, NO3 −-N, and PO4 3−, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.

Abstract 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.

Abstract 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. Graphical Abstract

Plants have evolved intricate signaling pathways, which operate as networks governed by feedback to deal with stressors. Nevertheless, the sophisticated molecular mechanisms underlying these routes still need to be comprehended, and experimental validation poses significant challenges and expenses. Consequently, computational hypothesis evaluation gains prominence in understanding plant signaling dynamics. Biosensors are genetically modified to emit light when exposed to a particular hormone, such as abscisic acid (ABA), enabling quantification. We developed computational models to simulate the relationship between ABA concentrations and bioluminescent sensors utilizing the Hill equation and ordinary differential equations (ODEs), aiding better hypothesis development regarding plant signaling. Based on simulation results, the luminescence intensity was recorded for a concentration of 47.646 RLUs for 1.5 μmol, given the specified parameters and model assumptions. This method enhances our understanding of plant signaling pathways at the cellular level, offering significant benefits to the scientific community in a cost-effective manner. The alignment of these computational predictions with experimental results emphasizes the robustness of our approach, providing a cost-effective means to validate mathematical models empirically. The research intended to correlate the bioluminescence of biosensors with plant signaling and its mathematical models for quantified detection of specific plant hormone ABA.

Background: This study explores the role of leguminous crops like lima bean in enhancing soil quality and ecosystem stability. Despite existing studies on agronomic aspects, there is a significant research gap on its impact on soil organic matter level, microbial activity, soil health, and nutrient availability. Therefore, this study examines the capacity of lima bean to reactivate soil quality, focusing on its impact on soil organic matter level, microbial activity, soil health, and nutrient availability. Methods: The experimental area was set up in 2023 using three replicates and a randomized block design. Two treatments were used: lima bean-planted plots and control plots with various weeds and without lima bean. Post-harvest soil samples were collected from various agroecological zones and sterilely packed, and physical, chemical, and biological indices were examined. Results: lima bean significantly affected nutrients, enzymes, soil microbial respiration, and other markers. Amylase activity (0.41**) was positively correlated with urease activity (0.73**), while dehydrogenase activity positively correlated with both. Dehydrogenase activity was negatively correlated with total nitrogen (0.66**) and sulfur (0.60**). Lima bean significantly affected soil quality, with all locations showing higher ratings (55–77%) than wild land, except for location D (Ilora). A total of 70% of total nitrogen variation may be attributed to soil quality (r2 = 0.696). Lima bean enhanced soil quality, potentially enhancing productivity and reducing dependence on inorganic nitrogen inputs. Conclusions: The symbiotic relationship between lima bean and nitrogen-fixing bacteria improves nutrient cycling, enhancing agricultural productivity and environmental conservation. Future research should explore the economic viability of integrating lima bean into crop rotations or agroforestry systems for sustainable agricultural practices, providing valuable information for farmers.

Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient and accurate soil-specific calibration methods a major challenge. This study aims to propose a plate compression filling technique for soil-specific calibrations and to monitor the extent of soil biomass degradation using dielectric properties. Before and after biodegradation, dielectric measurements of quartz sand and silt loam were made at seven different water contents with three different filling techniques. A third-order polynomial fitting equation explaining the dependence of the dielectric constant on the volumetric water content was obtained using the least-squares method. The suggested plate compression filling method has a maximum mean bias error (MBE) of less than 0.5%, according to experimental results. Depending on the water content, silt loam’s dielectric characteristics change significantly before and after biodegradation. The best water content, measured in gravimetric units, to encourage the decomposition of biomass was discovered to be 24%. It has been demonstrated that the plate compression filling method serves as a simple, convenient, and accurate alternative to the uniform compaction method, while the dielectric method is a reliable indicator for evaluating biomass degradation. This exploration provides valuable insights into the complex relationship between SWC, biomass degradation, and soil dielectric properties.

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements.

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.

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%.

A novel mechanism of sensitivity enhancement of D-shaped fiber-based surface plasmon resonance (SPR) sensors for the optofluidic device has been proposed. The sandwiched structure optofluidic platform is developed with metal-coated D-shaped fiber as the sensing device, while another thin metal layer is situated under the inner wall of the substrate as a second metal layer to construct the sandwiched microchannel. It has been found that the sensitivity of the D-type fiber SPR sensor is enhanced significantly with the sandwiched metal-coated structure of microchannel. In the proposed structure, the measurand analyte is considered as a sandwich channel layer between two thin metal layers. The sensitivity of the proposed structure is dependent on the volume of the measurand and the thickness of the metal layers. The computed sensitivity with a double metal layer and sandwich measurand layer concept is 4085 nm/RIU in the region of 1.33 to 1.36. The sensitivity is enhanced by more than a factor of `2.3' in comparison with the sensitivity of the normal D-shape fiber SPR sensor. It can be enhanced further up to ~12,500 nm/RIU by the deposition of higher RI polymeric overlay just above the second metal layer. The computed resolution of the proposed sensor with standard interrogation technique is ~1 × 10-7 which is quite competitive within the optical fiber sensor domain. A detailed numerical analysis has been accomplished. This structure will be useful in distinct chemical and biological sensing applications where the volume of an analyte is critical. This new concept of enhancement of sensitivity with limited measurand volume will open a new designing methodology for optical fiber biosensors.

Nitrate nitrogen (NO3- -N) in the soil is one of the important nutrients for growing crops. During the period of precipitation or irrigation, an excessive NO3- -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 NO3--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 NO3--N measurement by measuring the nitrate (NO3-) ions in soil quickly and accurately due to the high water solubility and mobility of NO3- 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 NO3--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 NO3--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 (R2) of 0.9952 was obtained. The performances of this all-solid ISE system are satisfied for measuring the NO3--N in the field.

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.

Abstract Background Hereditary cancer syndromes have inherited germline mutations which predispose to benign and malignant tumors. Understanding of the molecular causes in hereditary cancer syndromes has advanced cancer treatment and prevention. However, the causal genes of many hereditary cancer syndromes remain unknown due to their rare frequency of mutation. Methods A large Chinese family with a history of hereditary liver-colon cancer syndrome was studied. The genomic DNA was extracted from the blood samples of involved family members, whole-exome sequencing was performed to identify genetic variants. Functional validation of a candidate variant was carried out using gene expression, gene knockout and immunohistochemistry. Results The whole-exome of the proband diagnosed with colon cancer was sequenced in comparison with his mother. A total of 13 SNVs and 16 InDels were identified. Among these variants, we focused on a mutation of Rab43 gene, a GTPase family member involving in protein trafficking, for further validation. Sanger DNA sequencing confirmed a mutation (c: 128810106C > T, p: A158T) occurred in one allele of Rab43 gene from the proband, that heterozygous mutation also was verified in the genome of the proband’s deceased father with liver cancer, but not in his healthy mother and sister. Ectopic expression of the Rab43 A158T mutant in Huh7 cells led to more enhanced cell growth, proliferation and migration compared to the expression of wild type Rab43. Conversely, knockout of Rab43 in HepG2 cells resulted in slow cell growth and multiple nuclei formation and impaired activation of Akt. Finally, a positive correlation between the expression levels of Rab43 protein and cancer development in that family was confirmed. Conclusions A germline mutation of Rab43 gene is identified to be associated with the onset of a familial liver-colon cancer syndrome. Our finding points to a potential role of protein trafficking in the tumorigenesis of the familial cancer syndrome, and helps the genetic counseling to the affected family members.

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.

In recent years, there has been growing concern among consumers about pesticide contamination in fruits. Therefore, rapid, reliable, and consistent detection methods for OPPs, especially dimethoate, are crucially needed. The existing quantitative methods for detecting dimethoate are not suitable for rapid measuring system such as the dimethoate samples from two channels. Hence this paper examines the utilization of a dual-channel system for utilize the absorption variations of the Localized Surface Plasmon Resonance (LSPR) bands of gold nanoparticles (AuNPs) were investigate for detection of dimethoate. Under optimized conditions, the relationship between concentrations of dimethoate and absorbance ratios (A(520)/A(640)) was linearly found in the concentration range of 10-100 nM. Result from the experiment shows that both channels exhibit a linear correlation coefficient as high as 0.97 and a limit of detection (LOD) as low as 5.5 nM. This LSPR detection system was characterized by testing the dimethoate in apple samples and the recovery rates were found to be in the range of 85.90% to 107.37%. The proposed dual-channel LSPR system for detecting dimethoate creating a new approach for detecting organophosphate insecticide in agricultural fields. It could lay the foundation for designing a high-throughput analysis of the insecticides using a wavelength division multiplexing switch (WDMS).

Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination (R2) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.

The aim of this work is to identify the adulteration of edible gelatin using near-infrared (NIR) spectroscopy combined with supervised pattern recognition methods. The spectral data obtained from a total of 144 samples consisting of six kinds of adulterated gelatin gels with different mixture ratios were processed with multiplicative scatter correction (MSC), Savitzky–Golay (SG) smoothing, and min-max normalization. Principal component analysis (PCA) was first carried out for spectral analysis, while the six gelatin categories could not be clearly distinguished. Further, linear discriminant analysis (LDA), soft independent modelling of class analogy (SIMCA), backpropagation neural network (BPNN), and support vector machine (SVM) were introduced to establish discrimination models for identifying the adulterated gelatin gels, which gave a total correct recognition rate of 97.44%, 100%, 97.44%, and 100%, respectively. For the SIMCA model with significant level α = 0.05, sample overlapping clustering appeared; thus, the SVM model presents the best recognition ability among these four discrimination models for the classification of edible gelatin adulteration. The results demonstrate that NIR spectroscopy combined with unsupervised pattern recognition methods can quickly and accurately identify edible gelatin with different adulteration levels, providing a new possibility for the detection of industrial gelatin illegally added into food products.

Although aptamer-functionalized AuNPs technology exhibits great potential in analytical and biological chemistry, direct analysis of molecules at a low concentration using colorimetric assay remains challenging. The development of intuitive methods has attracted interest for promising detection of melamine in milk samples due to a demand for stable and understandable process. In this study, we propose a rapid and facile colorimetric measurement method of melamine combined aptamer-functionalized AuNPs in contaminated milk samples. To realize the high stability and the lower limit of detection, the aptamer-functionalized surface of AuNPs via a coordinated bond was used in combination with ultra-sonication. The kinetics of this colorimetric assay based on aptamer-functionalized AuNPs was analyzed to illustrate that the higher the concentration of melamine, the faster the aggregation of AuNPs induced. The sensitivity, selectivity, limit of detection and recovery rate were sufficiently validated to understand the measurement principle of melamine using aptamer-functionalized AuNPs. The calibration curve established by the absorption peak ratio (A640 /A520) was linear in the concentration range of 0~1μM of melamine in aqueous solutions with the correlation coefficient (R2) of 0.986 and the limit of detection (LOD) of 22 nM, whereas, the correlation coefficient (R2) of 0.998 and the LOD of 14.9 nM were achieved at the concentration of melamine below 0.5 μM in milk samples. The optimized performance of this colorimetric assay of melamine using aptamer-functionalized AuNPs in milk samples was obtained with 100 μL of 13 nm AuNPs solution, 40 μL of 1 μM (100 dilutions) aptamers and the pre-reaction time of 30 min. This simple colorimetric measurement of melamine using aptamer-functionalized AuNPs provides a promising target for various applications of the sample source with complex sample matrices.

The aim of this study was to develop a circuit for an inexpensive portable biosensing system based on surface plasmon resonance spectroscopy. This portable biosensing system designed for field use is characterized by a special structure which consists of a microfluidic cell incorporating a right angle prism functionalized with a biomolecular identification membrane, a laser line generator and a data acquisition circuit board. The data structure, data memory capacity and a line charge-coupled device (CCD) array with a driving circuit for collecting the photoelectric signals are intensively focused on and the high performance analog-to-digital (A/D) converter is comprehensively evaluated. The interface circuit and the photoelectric signal amplifier circuit are first studied to obtain the weak signals from the line CCD array in this experiment. Quantitative measurements for validating the sensitivity of the biosensing system were implemented using ethanol solutions of various concentrations indicated by volume fractions of 5%, 8%, 15%, 20%, 25%, and 30%, respectively, without a biomembrane immobilized on the surface of the SPR sensor. The experiments demonstrated that it is possible to detect a change in the refractive index of an ethanol solution with a sensitivity of 4.99838 × 105 ΔRU/RI in terms of the changes in delta response unit with refractive index using this SPR biosensing system, whereby the theoretical limit of detection of 3.3537 × 10−5 refractive index unit (RIU) and a high linearity at the correlation coefficient of 0.98065. The results obtained from a series of tests confirmed the practicality of this cost-effective portable SPR biosensing system.

A biosensing system with optical fibers is proposed for the colorimetric detection of melamine in liquid milk samples by using the localized surface plasmon resonance (LSPR) of unmodified gold nanoparticles (AuNPs). The biosensing system consists of a broadband light source that covers the spectral range from 200 nm to 1700 nm, an optical attenuator, three types of 600 μm premium optical fibers with SMA905 connectors and a miniature spectrometer with a linear charge coupled device (CCD) array. The biosensing system with optical fibers is low-cost, simple and is well-proven for the detection of melamine. Its working principle is based on the color changes of AuNPs solution from wine-red to blue due to the inter-particle coupling effect that causes the shifts of wavelength and absorbance in LSPR band after the to-be-measured melamine samples were added. Under the optimized conditions, the detection response of the LSPR biosensing system was found to be linear in melamine detection in the concentration range from 0μM to 0.9 μM with a correlation coefficient (R2) 0.99 and a detection limit 33 nM. The experimental results obtained from the established LSPR biosensing system in the actual detection of melamine concentration in liquid milk samples show that this technique is highly specific and sensitive and would have a huge application prospects.

Abscisic acid (ABA) plays an important role in abiotic stress response and physiological signal transduction resisting to the adverse environment. Therefore, it is very essential for the quantitative detection of abscisic acid (ABA) due to its indispensable role in plant physiological activities. Herein, a new detection method based on localized surface plasmon resonance (LSPR) using aptamer-functionalized gold nanoparticles (AuNPs) is developed without using expensive instrument and antibody. In the presence of ABA, ABA specifically bind with their aptamers to form the ABA-aptamer complexes with G-quadruplex-like structure and lose the ability to stabilize AuNPs against NaCl-induced aggregation. Meanwhile, the changes of the LSPR spectra of AuNP solution occur and therefore the detection of ABA achieved. Under optimized conditions, this method showed a good linear range covering from 5×10-7 M to 5×10-5 M with a detection limit of 0.33 μM. In practice, the usage of this novel method has been demonstrated by its application to detect ABA from fresh leaves of rice with the relative error of 6.59%-7.93% compared with ELISA bioassay. The experimental results confirmed that this LSPR-based biosensor is simple, selective and sensitive for the detection of ABA. The proposed LSPR method could offer a new analytical platform for the detection of other plant hormones by changing the corresponding aptamer.

Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square−support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.

Corn stalk lodging, which involves the breakage of the stalk below the ear following either bad weather, insect infestation or stormy rain, usually leads to harvest loss, increased harvesting time and higher drying costs. The objective of this study was to develop a method that can classify corn stalk lodging resistance. This method, which employed the maximum equivalent force exerted on a corn stalk, corresponding stalk agronomic traits, and the singular value decomposition (SVD) algorithm, showed that the five corn varieties with different stalk lodging resistance from two planting densities of 60,000 plants/ha and 75,000 plants/ha can be effectively classified. A customized device was designed to measure the equivalent forces. Three factors, including the planting density, the stalk diameter, and the maximum equivalent force with comprehensive contributions of −0.4603, 0.4196 and 0.4068, which are related to principal components, play an important role in the classification of corn stalk lodging resistance. The results showed that the corn stalk lodging resistance decreased with increase in planting density; however, with the increase in stalk diameter and maximum equivalent force, the lodging resistance significantly increased. Corn breeders can develop higher lodging resistance-based corn varieties by using this approach.

The physico-chemical parameters including pH and viscosity, and the fluorescence signal induced by fluorescent compounds presenting in yogurts such as riboflavin and porphyrin were measured during one week’s storage at room temperature when five brands of yogurt samples were exposed to ambient air. The fluorescence spectra of yogurt showed four evident emission peaks, 525 nm, 633 nm, 661 nm, and 672 nm. To quantitatively investigate the quality of yogurt during deteriorating, a calculating method of the average rate of change (ARC) was proposed to study the relative change of fluorescence intensity in the spectral range of 600 to 750 nm associated with porphyrin and chlorin compounds. During the storage, the time evolution of two ARC, pH value, and viscosity were regular. Moreover, the ARC showed a good linear relationship with pH value and viscosity of yogurt. Further, multiple linear regression (MLR) models using two ARC as independent variables were developed to verify the dependence of fluorescence signal with pH value and viscosity, which showed a good linear relationship with an R-square of more than 85% for each class of yogurt. The results demonstrate that fluorescence spectra have a great potential to predict the quality of yogurt.

Corn stalk lodging is caused by different factors, including severe wind storms, stalk cannibalization, and stalk rots, and it leads to yield loss. Determining how to rapidly evaluate corn lodging resistance will assist scientists in the field of crop breeding to understand the contributing factors in managing the moisture, chemical fertilizer, and weather conditions for corn growing. This study proposes a non-destructive and direction-insensitive method, using a strain sensor and two single axis angle sensors to measure the corn stalk lodging resistance in the field. An equivalent force whose direction is perpendicular to the stalk is utilized to evaluate the corn lodging properties when a pull force is applied on the corn stalk. A novel measurement device is designed to obtain the equivalent force with the coefficient of variation (CV) of 4.85%. Five corn varieties with two different planting densities are arranged to conduct the experiment using the novel measurement device. The experimental results show that the maximum equivalent force could reach up to 44 N. A strong relationship with the square of the correlation coefficient of 0.88 was obtained between the maximum equivalent forces and the corn field’s stalk lodging rates. Moreover, the stalk lodging angles corresponding to the different pull forces over a measurement time of 20 s shift monotonically along the equivalent forces. Thus, the non-destructive and direction-insensitive method is an excellent tool for rapid analysis of stalk lodging resistance in corn, providing critical information on in-situ lodging dynamics.

Kinetic analysis of biomolecular interactions are powerfully used to quantify the binding kinetic constants for the determination of a complex formed or dissociated within a given time span. Surface plasmon resonance biosensors provide an essential approach in the analysis of the biomolecular interactions including the interaction process of antigen-antibody and receptors-ligand. The binding affinity of the antibody to the antigen (or the receptor to the ligand) reflects the biological activities of the control antibodies (or receptors) and the corresponding immune signal responses in the pathologic process. Moreover, both the association rate and dissociation rate of the receptor to ligand are the substantial parameters for the study of signal transmission between cells. A number of experimental data may lead to complicated real-time curves that do not fit well to the kinetic model. This paper presented an analysis approach of biomolecular interactions established by utilizing the Marquardt algorithm. This algorithm was intensively considered to implement in the homemade bioanalyzer to perform the nonlinear curve-fitting of the association and disassociation process of the receptor to ligand. Compared with the results from the Newton iteration algorithm, it shows that the Marquardt algorithm does not only reduce the dependence of the initial value to avoid the divergence but also can greatly reduce the iterative regression times. The association and dissociation rate constants, ka, kd and the affinity parameters for the biomolecular interaction, KA, KD, were experimentally obtained 6.969×10(5) mL·g(-1)·s(-1), 0.00073 s(-1), 9.5466×10(8) mL·g(-1) and 1.0475×10(-9) g·mL(-1), respectively from the injection of the HBsAg solution with the concentration of 16 ng·mL(-1). The kinetic constants were evaluated distinctly by using the obtained data from the curve-fitting results.

A surface plasmon resonance (SPR) immunoassay with an immobilization of self-assembled molecular identification membrane for the detection of residual Clenbuterol Hydrochloride (CLB) in pork liver was systematically investigated and experimentally validated for its high performance. SPR immunoassay with a regular competitive inhibition assay cannot be directly verified to detect CLB residuals. In this study, the binding of Au film with mercaptopropionic acid was investigated using the known form of the strong S-Au covalent bonds formed by the chemical radical of the mercaptopropionic acid and the Au film. After that, the immunoglobulin IgG of swine (SwIgG-CLB) was bonded with the mercaptopropionic acid by covalent -CO-NH- amide bonding. The modified comprehensive analysis of how the membrane structure works was introduced together with the customized SPR bioanalyzer. In order to evaluate the performance of this biomembrane structure, the concentrations of CLB-contained solutions of 0 ng · mL(-1), 10 ng · mL(-1), 20 ng · mL(-1), 33.3 ng · mL(-1), and 40 ng · mL(-1) were prepared by adding CLB reagents into the solutions of CLB antibody (Clenbuterol Hydrochloride Antibody, CLB-Ab), successively and then the response unit (RU) was measured individually. Using the data collected from the linear CCD array, the fitting curve was established with the R-Square value of 0.9929. Correspondingly, the recovery rate ranged from 88.48% to 103.21% was experimented and the limit of detection of CLB in 1.26 ng · mL(-1) was obtained efficiently. It was concluded that the detection method associated with biomembrane properties is expected to contribute much to the determination of residual CLB in pork liver quantitatively by using the customized SPR bioanalyzer.

A sensitive and label-free analytical approach for the detection of porcine circovirus type 2 (PCV2) instead of PCV2 antibody in serum sample was systematically investigated in this research based on surface plasmon resonance (SPR) with an establishment of special molecular identification membrane. The experimental device for constructing the biosensing analyzer is composed of an integrated biosensor, a home-made microfluidic module, and an electrical control circuit incorporated with a photoelectric converter. In order to detect the PCV2 using the surface plasmon resonance immunoassay, the mercaptopropionic acid has been used to bind the Au film in advance through the known form of the strong S-Au covalent bonds formed by the chemical radical of the mercaptopropionic acid and the Au film. PCV2 antibodies were bonded with the mercaptopropionic acid by covalent -CO-NH- amide bonding. For the purpose of evaluating the performance of this approach, the known concentrations of PCV2 Cap protein of 10 µg/mL, 7.5 µg/mL, 5 µg/mL, 2.5 µg/mL, 1 µg/mL, and 0.5 µg/mL were prepared by diluting with PBS successively and then the delta response units (ΔRUs) were measured individually. Using the data collected from the linear CCD array, the ΔRUs gave a linear response over a wide concentration range of standard known concentrations of PCV2 Cap protein with the R-Squared value of 0.99625. The theoretical limit of detection was calculated to be 0.04 µg/mL for the surface plasmon resonance biosensing approach. Correspondingly, the recovery rate ranged from 81.0% to 89.3% was obtained. In contrast to the PCV2 detection kits, this surface plasmon resonance biosensing system was validated through linearity, precision and recovery, which demonstrated that the surface plasmon resonance immunoassay is reliable and robust. It was concluded that the detection method which is associated with biomembrane properties is expected to contribute much to determine the PCV2 in sample solutions instead of PCV2 antibody in serum samples quantitatively.

The purpose of this study was to develop a portable surface plasmon resonance (SPR) bioanalyzer for the sensitive detection of Escherichia coli O157:H7 in comparison with an enzyme-linked immunosorbent assay (ELISA). The experimental setup mainly consisted of an integrated biosensor and a homemade microfluidic cell with a three-way solenoid valve. In order to detect Escherichia coli O157:H7 using the SPR immunoassay, 3-mercaptopropionic acid (3-MPA) was chemisorbed onto a gold surface via covalent bond for the immobilization of biological species. 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDC) and N-hydroxysuccinimide (NHS) were used as crosslinker reagents to enable the reaction between 3-MPA and Escherichia coli O157:H7 antibodies by covalent –CO–NH– amide bonding. The experimental results were obtained from the Escherichia coli O157:H7 positive samples prepared by 10-, 20-, 40-, 80-, and 160-fold dilution respectively, which show that a good linear relationship with the correlation coefficient R of 0.982 existed between the response units from the portable SPR bioanalyzer and the concentration of Escherichia coli O157:H7 positive samples. Moreover, the theoretical detection limit of 1.87 × 103 cfu/mL was calculated from the positive control samples. Compared with the Escherichia coli O157:H7 ELISA kit, the sensitivity of this portable SPR bioanalyzer is four orders of magnitude higher than the ELISA kit. The results demonstrate that the portable SPR bioanalyzer could provide an alternative method for the quantitative and sensitive determination of Escherichia coli O157:H7 in field.

Ethylene as an indicator for evaluating fruit ripening can be measured by very sensitive electrochemical gas sensors based on a high-resolution current produced by a bias potential applied to the electrodes. For this purpose, a measurement system for monitoring ethylene gas concentrations to evaluate fruit ripening by using the electrochemical ethylene sensor was successfully developed. Before the electrochemical ethylene sensor was used to measure the ethylene gas concentrations released from fruits, a calibration curve was established by the standard ethylene gases at concentrations of 2.99 ppm, 4.99 ppm, 8.01 ppm and 10 ppm, respectively, with a flow rate of 0.4 L·min−1. From the calibration curve, the linear relationship between the responses and concentrations of ethylene gas was obtained in the range of 0–10 ppm with the correlation coefficient R2 of 0.9976. The micropump and a novel signal conditioning circuit were implemented in this measurement, resulting in a rapid response in detecting ethylene concentrations down to 0.1 ppm in air and in under 50 s. In this experiment, three kinds of fruits—apples, pears and kiwifruits—were studied at a low concentration (under 0.8 ppm) of trace ethylene content in the air exhaled by fruits. The experimental results showed that a low cost, compact measurement system constructed by using an electrochemical ethylene sensor has a high sensitivity of 0.3907 V·ppm−1 with a theoretical detection limit of 0.413 ppm, and is non-invasive and highly portable.

Abstract This study aimed to develop and validate prediction models to estimate the risk of death and intensive care unit admission in COVID-19 inpatients. All RT-PCR-confirmed adult COVID-19 inpatients admitted to Fujian Provincial Hospital from October 2022 to April 2023 were considered. Elastic Net Regression was used to derive the risk prediction models. Potential risk factors were considered, which included demographic characteristics, clinical symptoms, comorbidities, laboratory results, treatment process, prognosis. A total of 1906 inpatients were included finally by inclusion/exclusion criteria and were divided into derivation and test cohorts in a ratio of 8:2, where 1526 (80%) samples were used to develop prediction models under a repeated cross-validation framework and the remaining 380 (20%) samples were used for performance evaluation. Overall performance, discrimination and calibration were evaluated in the validation set and test cohort and quantified by accuracy, scaled Brier score (SbrS), the area under the ROC curve (AUROC), and Spiegelhalter-Z statistics. The models performed well, with high levels of discrimination (AUROCICU [95%CI]: 0.858 [0.803,0.899]; AUROCdeath [95%CI]: 0.906 [0.850,0.948]); and good calibrations (Spiegelhalter-Z ICU : − 0.821 (p-value: 0.412); Spiegelhalter-Z death : 0.173) in the test set. We developed and validated prediction models to help clinicians identify high risk patients for death and ICU admission after COVID-19 infection.