Inactivation of Escherichia coli, Salmonella enterica, and Listeria monocytogenes using the Contamination Sanitization Inspection and Disinfection (CSI-D) device.

The Contamination Sanitization Inspection and Disinfection (CSI-D) device is a handheld fluorescence-based imaging system designed to disinfect food contact surfaces using ultraviolet-C (UVC) illumination. This study aimed to determine the optimal CSI-D parameters (i.e., UVC exposure time and intensity) for the inactivation of the following foodborne bacteria plated on non-selective media: generic Escherichia coli (indicator organism) and the pathogens enterohemorrhagic E. coli, enterotoxigenic E. coli, Salmonella enterica, and Listeria monocytogenes. Each bacterial strain was spread-plated on non-selective agar and exposed to high-intensity (10 mW/cm2) or low-intensity (5 mW/cm2) UVC for 1-5 s. Control plates were not exposed to UVC. The plates were incubated overnight at 37 °C and then enumerated. Three trials for each bacterial strain were conducted. Statistical analysis was carried out to determine if there were significant differences in bacterial growth between UVC intensities and exposure times. Overall, exposure to low or high intensity for 3-5 s resulted in consistent inhibition of bacterial growth, with reductions of 99.9-100 % for E. coli, 96.8-100 % for S. enterica, and 99.2-100 % for L. monocytogenes. The 1 s exposure time showed inconsistent results, with a 66.0-100 % reduction in growth depending on the intensity and bacterial strain. When the results for all strains within each species were combined, the 3-5 s exposure times showed significantly greater (p < 0.05) growth inhibition than the 1 s exposure time. However, there were no significant differences (p > 0.05) in growth inhibition between the high and low UVC intensities. The results of this study show that, in pure culture conditions, exposure to UVC with the CSI-D device for ≥3 s is required to achieve consistent reduction of E. coli, S. enterica, and L. monocytogenes.

Hand-sewn gastrojejunal anastomosis reduces delayed gastric emptying after pancreaticoduodenectomy: A single-center retrospective clinical study of 1,077 consecutive patients.

BACKGROUND: Hand-sewn anastomosis and stapled anastomosis are the 2 main types of gastrojejunal anastomotic methods in pancreaticoduodenectomy. There is ongoing debate regarding the most effective anastomotic method for reducing delayed gastric emptying after pancreaticoduodenectomy. This study aims to identify factors that influence delayed gastric emptying after pancreaticoduodenectomy and assess the impact of different anastomotic methods on delayed gastric emptying. METHODS: The study included 1,077 patients who had undergone either hand-sewn anastomosis (n = 734) or stapled anastomosis (n = 343) during pancreaticoduodenectomy between December 2016 and November 2021 at our department. We retrospectively analyzed the clinical data, and a 1:1 propensity score matching was performed to balance confounding variables. RESULTS: After propensity score matching, 320 patients were included in each group. Compared with the stapled anastomosis group, the hand-sewn anastomosis group had a significantly lower incidence of delayed gastric emptying (28 [8.8%] vs 55 [17.2%], P = .001) and upper gastrointestinal tract bleeding (6 [1.9%] vs 17 [5.3%], P = .02). Additionally, the hand-sewn anastomosis group had a significantly reduced postoperative length of stay and lower hospitalization expenses. However, the hand-sewn anastomosis group had a significantly longer operative time, which was consistent with the analysis before propensity score matching. Logistic regression analysis showed that stapled anastomosis, intra-abdominal infection, and clinically relevant postoperative pancreatic fistula were independent prognostic factors for delayed gastric emptying. CONCLUSION: Hand-sewn anastomosis was associated with a lower incidence rate of clinically relevant delayed gastric emptying after pancreaticoduodenectomy. Stapled anastomosis, intra-abdominal infection, and clinically relevant postoperative pancreatic fistula could increase the incidence of postoperative clinically relevant delayed gastric emptying. Hand-sewn anastomosis should be considered by surgeons to reduce the occurrence of postoperative delayed gastric emptying and improve patient outcomes.

Detection of aflatoxins in ground maize using a compact and automated Raman spectroscopy system with machine learning.

Consumption of aflatoxin-contaminated food can cause severe illness when consumed by humans or livestock. Because the mycotoxin frequently occurs in cereal grains and other agricultural crops, it is crucial to develop portable devices that can be used non-destructively and in real-time to identify aflatoxin-contaminated food materials during early stages of harvesting or processing. In this study, an aflatoxin detection method was developed using a compact Raman device that can be used in the field. Data were obtained using maize samples naturally contaminated with aflatoxin, and the data were analyzed using a machine learning method. Of the multiple classification models evaluated, such as linear discriminant analysis (LDA), linear support vector machines (LSVM), quadratic discriminant analysis (QDA), and quadratic support vector machines and spectral preprocessing methods, the best classification accuracy was achieved at 95.7% using LDA in combination with Savitzky-Golay 2nd derivative (SG2) preprocessing. Partial least squares regression (PLSR) models demonstrated a close-range accuracy within the scope of standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing methods, with determination of coefficient values of R2C and R2V of 0.9998 and 0.8322 respectively for SNV, and 0.9916 and 0.8387 respectively for MSC. This study demonstrates the potential use of compact and automated Raman spectroscopy, coupled with chemometrics and machine learning methods, as a tool for rapidly screening food and feed for hazardous substances at on-site field processing locations.

Non-12α-Hydroxylated Bile Acids Improve Piglet Growth Performance by Improving Intestinal Flora, Promoting Intestinal Development and Bile Acid Synthesis.

As an emulsifier and bioactive substance, bile acids (BAs) participate in the absorption of nutrients and in various physiological processes. The objective of this experiment was to investigate the effects of non-12α-hydroxylated BAs (including hyocholic acid, hyodeoxycholic acid and chenodeoxycholic acid, from now on referred to as NBAs) on growth performance, BAs metabolism and the intestinal flora of piglets. The experiment included four groups, with eight piglets per group. The four groups of pigs were fed 0, 60, 120 and 180 mg/kg of NBAs, respectively. The results show that adding NBAs significantly increased the final weight (FW), average daily feed intake (ADFI), average daily gain (ADG), and digestibility of crude fat (EE) and organic matter (OM) in piglets (p < 0.05). Adding NBAs significantly increased the villus height (VH) of the jejunum and ileum (p < 0.05). In addition, NBAs supplementation increased the content of urea nitrogen (BUN) and creatinine (CREA) as well as the ratio of urea nitrogen to creatinine (BUN/CREA) in serum (p < 0.05). Adding NBAs can affect the genes related to BAs enterohepatic circulation. Specifically, adding NBAs significantly decreased the relative mRNA abundance of FXR in the liver (p < 0.05), significantly increased the relative mRNA abundance of CYP27A1 (p < 0.05), and significantly increased the relative mRNA abundance of NTCP (p < 0.05). Adding NBAs also significantly decreased the relative mRNA abundance of FXR in the ileum (p < 0.05). In the full-length 16S rDNA sequencing analysis, ten biomarkers were found from the gate to the species level. NBAs mainly enriched Lactobacillus_Johnsonii and decreased the abundance of Streptococcus_alactolyticus. Short-chain fatty acids (SCFAs) content in the colon was significantly increased (p < 0.05). These results indicate that NBAs supplementation can improve the growth performance of piglets, promote the development of the bile acid replacement pathway and improve intestinal flora.

Advance in Detection Technique of Lean Meat Powder Residues in Meat Using SERS: A Review.

Food that contains lean meat powder (LMP) can cause human health issues, such as nausea, headaches, and even death for consumers. Traditional methods for detecting LMP residues in meat are often time-consuming and complex and lack sensitivity. This article provides a review of the research progress on the use of surface-enhanced Raman spectroscopy (SERS) technology for detecting residues of LMP in meat. The review also discusses several applications of SERS technology for detecting residues of LMP in meat, including the enhanced detection of LMP residues in meat based on single metal nanoparticles, combining metal nanoparticles with adsorbent materials, combining metal nanoparticles with immunizing and other chemicals, and combining the SERS technology with related techniques. As SERS technology continues to develop and improve, it is expected to become an even more widely used and effective tool for detecting residues of LMP in meat.

A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy.

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes.

Protein and lipid content estimation in soybeans using Raman hyperspectral imaging.

Unlike standard chemical analysis methods involving time-consuming, labor-intensive, and invasive pretreatment procedures, Raman hyperspectral imaging (HSI) can rapidly and non-destructively detect components without professional supervision. Generally, the Kjeldahl methods and Soxhlet extraction are used to chemically determine the protein and lipid content of soybeans. This study is aimed at developing a high-performance model for estimating soybean protein and lipid content using a non-destructive Raman HSI. Partial least squares regression (PLSR) techniques were used to develop the model using a calibration model based on 70% spectral data, and the remaining 30% of the data were used for validation. The results indicate that the Raman HSI, combined with PLSR, resulted in a protein and lipid model Rp2 of 0.90 and 0.82 with Root Mean Squared Error Prediction (RMSEP) 1.27 and 0.79, respectively. Additionally, this study successfully used the Raman HSI approach to create a prediction image showing the distribution of the targeted components, and could predict protein and lipid based on a single seeds.

CO Oxidation to CO2 on Uranium Dioxide Molecules through Intermediate O2U(η1-CO).

Investigations on the reactions of uranium oxide molecules with CO offer new inspiration for the design of promising high-efficiency catalysts for CO activation using actinide materials. Herein, we contribute a combined matrix-isolation infrared spectroscopic and theoretical study of CO oxidation to CO2 on uranium dioxide (UO2) molecules in solid argon. The reaction intermediate O2U(η1-CO) is generated spontaneously at the bands of 1893.0, 870.6, and 801.3 cm-1 during codeposition and annealing. Upon the following irradiation, CO2 is substantially produced by the consumption of O2U(η1-CO), indicating the catalytic conversion of CO to CO2 through the intermediate O2U(η1-CO). In C18O isotopic substitution experiments, the yields of 16OC18O convincingly confirm that one of the oxygen atoms in CO2 derives from UO2. The reaction pathways are discussed based on the theoretical and experimental results.

A hyperspectral plant health monitoring system for space crop production.

Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.

A determination method for clenbuterol residue in pork based on optimal particle size gold colloid using SERS.

Clenbuterol is often used as a feed additive to increase the percentage of lean meat in livestock. Meat containing clenbuterol can cause many illnesses and even death for people. In this paper, the particle growth method was used to prepare gold colloids of different sizes, and the enhanced effectiveness of gold colloids of different sizes on clenbuterol in pork was investigated. The results showed that the gold colloid with the best enhanced effectiveness for clenbuterol had a particle size of approximately 90 nm. Second, a sample collection component was designed to detect clenbuterol from bottom to top, solving the problem of poor reproducibility of Surface-enhanced Raman scattering (SERS) detection caused by different droplet sizes and shapes. Then, the influence of different volumes of samples and concentrations of aggregating compounds on the enhanced effectiveness was optimized. The results showed that, based on the sample collection components designed in this article, 5 µL of enhanced substrate, 7.5 µL of clenbuterol and 3 µL of 1 mol/L mixed detection of NaCl solution had the best enhanced performance. Finally, 88 pork samples (0.5, 1, 1.5,…, 10, 12, 14 µg/g) with different concentrations were divided into correction sets and prediction sets in a ratio of 3:1. Unary linear regression models were established between the concentration of clenbuterol residue in the pork and the intensity of the bands at 390, 648, 1259, 1472, and 1601 cm-1. The results showed that the unary linear regression models at 390, 648, and 1259 cm-1 had lower root mean square errors than those at 1472 and 1601 cm-1. The intensity of the three bands and the concentration of clenbuterol residue in the pork were selected to establish a multiple linear regression model, and the concentration of clenbuterol residue in the pork was predicted. The results showed that the determination coefficients (R2) of the correction set and the prediction set were 0.99 and 0.99, respectively. The root mean square errors (RMSE) of the correction set and the prediction set were 0.169 and 0.184, respectively. The detection limit of clenbuterol in pork by this method is 42 ng/g, which can realize the crude screening of pork containing clenbuterol in the market.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Estimation of optical properties of turbid media using spatially resolved diffuse reflectance combined with LSTM-attention network.

The accurate estimation of the optical properties of turbid media by using a spatially resolved (SR) technique remains a challenging task due to measurement errors in the acquired spatially resolved diffuse reflectance (SRDR) and challenges in inversion model implementation. In this study, what we believe to be a novel data-driven model based on a long short-term memory network and attention mechanism (LSTM-attention network) combined with SRDR is proposed for the accurate estimation of the optical properties of turbid media. The proposed LSTM-attention network divides the SRDR profile into multiple consecutive and partially overlaps sub-intervals by using the sliding window technique, and uses the divided sub-intervals as the input of the LSTM modules. It then introduces an attention mechanism to evaluate the output of each module automatically and form a score coefficient, finally obtaining an accurate estimation of the optical properties. The proposed LSTM-attention network is trained with Monte Carlo (MC) simulation data to overcome the difficulty in preparing training (reference) samples with known optical properties. Experimental results of the MC simulation data showed that the mean relative error (MRE) with 5.59% for the absorption coefficient [with the mean absolute error (MAE) of 0.04 cm-1, coefficient of determination (R2) of 0.9982, and root mean square error (RMSE) of 0.058 cm-1] and 1.18% for the reduced scattering coefficient (with an MAE of 0.208 cm-1, R2 of 0.9996, and RMSE of 0.237 cm-1), which were significantly better than those of the three comparative models. The SRDR profiles of 36 liquid phantoms, collected using a hyperspectral imaging system that covered a wavelength range of 530-900 nm, were used to test the performance of the proposed model further. The results showed that the LSTM-attention model achieved the best performance (with the MRE of 14.89%, MAE of 0.022 cm-1, R2 of 0.9603, and RMSE of 0.026 cm-1 for the absorption coefficient; and the MRE of 9.76%, MAE of 0.732 cm-1, R2 of 0.9701, and RMSE of 1.470 cm-1for the reduced scattering coefficient). Therefore, SRDR combined with the LSTM-attention model provides an effective method for improving the estimation accuracy of the optical properties of turbid media.

Resveratrol alleviates oxidative stress induced by oxidized soybean oil and improves gut function via changing gut microbiota in weaned piglets.

BACKGROUND: Oxidized soybean oil (OSO) has been shown to impair growth and exacerbate inflammation, leading to intestinal barrier injury in animals. Recent evidence suggests important roles for resveratrol (RES) in the promoting growth performance, antioxidant capacity, anti-inflammatory, and regulate intestinal barriers in animals. Therefore, The objectives of this study are to investigate the effects of dietary RES (purity 98%) supplementation on the growth performance, antioxidant capacity, inflammatory state, and intestinal function of weaned piglets challenged with OSO. METHODS: A total of 28 castrated weaned male piglets with a similar body weight of 10.19 ± 0.10 kg were randomly assigned to 4 dietary treatments for 28-d feeding trial with 7 replications per treatment and 1 piglet per replicate. Treatments were arranged as a 2 × 2 factorial with oil type [3% fresh soybean oil (FSO) vs. 3% OSO] and dietary RES (0 vs. 300 mg/kg). RESULTS: The results showed that relative to the FSO group, OSO stress tended to decrease the average daily feed intake (ADFI), and decreased the activity levels of lipase, villus/crypt ratio (VCR), the mRNA expression of FABP1, SOD2, IL-10 and ZO-1 in the jejunum, and SOD2, GPX1, occludin and ZO-1 in the colon, the levels of acetic acid in the colonic digesta, whereas up-regulated the mRNA expression of IL-1ß and TNF-α in the jejunum (P < 0.05). Moreover, dietary supplementation with RES increased ether extract (EE), the activity levels of sucrase, lipase, α-amylase, villus height (VH) and VCR, the mRNA expression of FABP1, SOD2, IL-10 and occludin in the jejunum, and FABP1, PPAR-γ, GPX1, occludin and ZO-1 in the colon, and the abundance of Firmicutes, acetic and propionic acid, but decreased the levels of D-lactic acid in the plasma, the abundance of Bacteroidetes in the colonic digesta of weaned piglets compared to the non-RES group (P < 0.05). Meanwhile, in the interaction effect analysis, relative to the OSO group, dietary RES supplementation in the diets supplemented with OSO increased the activity levels of trypsin, VH in the jejunum, the abundance of Actinobacteria, the levels of butyric acid of weaned piglets, but failed to influence the activity levels of trypsin and VH, Actinobacteria abundance, the levels of butyric acid when diets were supplemented with FSO (interaction, P < 0.05). Relative to the OSO group, dietary RES supplementation in the diets supplemented with OSO decreased the activity levels of DAO in the plasma of weaned piglets but failed to influence the activity levels of DAO when diets were supplemented with FSO (interaction, P < 0.05). Relative to the FSO group, dietary RES supplementation in the diets supplemented with FSO decreased the level of propionic acid, whereas RES supplementation failed to influence the level of propionic acid when the diet was supplemented with OSO (interaction, P < 0.01). CONCLUSIONS: Inclusion of OSO intensified inflammatory states and impaired the intestinal health characteristics of weaned piglets. Dietary RES supplementation improved the antioxidant capacity, anti-inflammatory activity, and intestinal morphology. Further studies showed that the protective effects of RES on gut health could be linked to the decreased abundance of Prevotella_1, Clostridium_sensu_stricto_6, and Prevotellaceae_UCG003 and increased levels of acetic and propionic acid.

Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo simulation.

Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food.

C-O Bond Activation in Mononuclear Lanthanide Oxocarbonyl Complexes OLn(η2-CO) (Ln = La, Ce, Pr, and Nd).

Fundamental investigation of metal-CO interactions is of great importance for the development of high-performance catalysts to CO activation. Herein, a series of side-on bonded mononuclear lanthanide (Ln) oxocarbonyl complexes OLn(η2-CO) (Ln = La, Ce, Pr, and Nd) have been prepared and identified in solid argon matrices. The complexes exhibit uncommonly low C-O stretching bands near 1630 cm-1, indicating remarkable C-O bond activation in these Ln analogues. The η2-CO ligand in OLn(η2-CO) can be claimed as an anion on the basis of the experimental observations and quantum chemistry investigations, although the CO anion is commonly considered to be unstable with electron auto-detachment. The CO activation in OLn(η2-CO) is attributed to the photoinduced intramolecular charge transfer from LnO to CO rather than the generally accepted metal → CO π back-donation, which conforms to the traditional Dewar-Chatt-Duncanson motif. Energy decomposition analysis combined with natural orbitals for chemical valence calculations demonstrates that the bonding between LnO and η2-CO arises from the combination of dominant ionic forces (>76%) and normal Lewis "acid-base" interactions. The fundamental findings provide guidelines for the catalyst design of CO activation.

Perinatal exposure to high concentration glyphosate-based herbicides induces intestinal apoptosis by activating endoplasmic reticulum stress in offspring.

Glyphosate-based herbicides (GBHs), the most widely used pesticide worldwide, have been reported to impair organ function in humans and animals. However, research on the effect of maternal GBHs exposure on the intestinal health of offspring has received little attention. Based on the glyphosate limits defined by Codex Alimentarius Commission and European Food Safety Authority, this study established pregnant sow exposure models to investigate the influence of low (L-GBHs, 20 mg/kg) and high concentration GBHs (H-GBHs, 100 mg/kg) on the intestinal health of offspring and proposed the protective mechanism mediated by betaine. The results showed that the intestinal morphology and barrier function of suckling piglets were damaged in the H-GBHs group. H-GBHs increased the activity of glutathione peroxidase (GPX) and levels of methane dicarboxylic aldehyde (MDA), hydrogen peroxide (H2O2) and inflammatory factors (tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), interleukin-6 (IL-6), interleukin-10 (IL-10)) in suckling piglets and activated Nrf2-mediated antioxidant signaling pathway. Subsequently, we found that exposure to H-GBHs triggered endoplasmic reticulum stress (ERS) and further induced apoptosis by upregulating the expression of Bcl-2-associated X protein (Bax), Caspase3, Caspase9 and Caspase12. Moreover, H-GBHs exposure perturbed mitochondrial membrane fusion and electron transport in mitochondrial respiratory chains by increasing the mRNA expression of mitofusin-2 (MFN2) and succinate dehydrogenase subunit A (SDHA), causing mitochondrial dysfunction. Dietary supplementation with betaine provided modest protection against GBHs-induced intestinal damage in suckling piglets. These findings reveal the mechanism of GBHs-induced intestinal damage in offspring, improving our understanding of the risk of GBHs exposure in pregnant women and suggesting the potential protective effects of betaine against GBHs poisoning.

Citrus disease detection using convolution neural network generated features and Softmax classifier on hyperspectral image data.

Identification and segregation of citrus fruit with diseases and peel blemishes are required to preserve market value. Previously developed machine vision approaches could only distinguish cankerous from non-cankerous citrus, while this research focused on detecting eight different peel conditions on citrus fruit using hyperspectral (HSI) imagery and an AI-based classification algorithm. The objectives of this paper were: (i) selecting the five most discriminating bands among 92 using PCA, (ii) training and testing a custom convolution neural network (CNN) model for classification with the selected bands, and (iii) comparing the CNN's performance using 5 PCA bands compared to five randomly selected bands. A hyperspectral imaging system from earlier work was used to acquire reflectance images in the spectral region from 450 to 930 nm (92 spectral bands). Ruby Red grapefruits with normal, cankerous, and 5 other common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were tested. A novel CNN based on the VGG-16 architecture was developed for feature extraction, and SoftMax for classification. The PCA-based bands were found to be 666.15, 697.54, 702.77, 849.24 and 917.25 nm, which resulted in an average accuracy, sensitivity, and specificity of 99.84%, 99.84% and 99.98% respectively. However, 10 trials of five randomly selected bands resulted in only a slightly lower performance, with accuracy, sensitivity, and specificity of 98.87%, 98.43% and 99.88%, respectively. These results demonstrate that an AI-based algorithm can successfully classify eight different peel conditions. The findings reported herein can be used as a precursor to develop a machine vision-based, real-time peel condition classification system for citrus processing.

Perinatal exposure to glyphosate-based herbicides impairs progeny health and placental angiogenesis by disturbing mitochondrial function.

Glyphosate-based herbicides (GBHs) are the most widely used pesticide worldwide and can provoke placental injury. However, whether and how GBHs damage angiogenesis in the placenta is not yet known. This work evaluated the safety of glyphosate on pregnant sows based on the limit level by governments and investigated the effects and mechanism of Low-GBHs (20 mg/kg) and High-GBHs (100 mg/kg) exposure on placental angiogenesis. Results showed that gestational exposure to GBHs decreased placental vessel density and cell multiplication by interfering with the expression of VEGFA, PLGF, VEGFr2 and Hand2 (indicators of angiogenesis), which may be in relation to oxidative stress-induced disorders of mitochondrial fission and fusion as well as the impaired function of the mitochondrial respiratory chain. Additionally, GBHs destroyed barrier function and nutrient transport in the placenta, and was accompanied by jejunum oxidative stress in newborn piglets. However, GBHs exposure had no significant differences on sow reproductive performance. As a natural antioxidant, betaine treatment protected placenta and newborn piglets against GBHs-induced damage. In conclusion, GBHs impaired placental angiogenesis and function and further damaged the health of postnatal progeny, these effects may be linked to mitochondrial dysfunction. Betaine treatment following glyphosate exposure provided modest relief.

Determination of spectral resolutions for multispectral detection of apple bruises using visible/near-infrared hyperspectral reflectance imaging.

This study demonstrates a method to select wavelength-specific spectral resolutions to optimize a line-scan hyperspectral imaging method for its intended use, which in this case was visible/near-infrared imaging-based multiple-waveband detection of apple bruises. Many earlier studies have explored important aspects of developing apple bruise detection systems, such as key wavelengths and image processing algorithms. Despite the endeavors of many, development of a real-time bruise detection system is not yet a simple task. To overcome these problems, this study investigated selection of optimal wavelength-specific spectral resolutions for detecting bruises on apples by using hyperspectral line-scan imaging with the Random Track function for non-contiguous partial readout, with two experimental parts. The first part identified key-wavelengths and the optimal number of key-wavelengths to use for detecting low-, medium-, and high-impact bruises on apples. These parameters were determined by principal component analysis (PCA) and sequential forward selection (SFS) with four classification methods. The second part determined the optimal spectral resolution for each of the key-wavelengths by selecting and evaluating 21 combinations of exposure time and key-wavelength bandwidths, and then selecting the best combination based on the bruise detection accuracies achieved by each classification method. Each of the four classification methods was found to have a different optimized resolution for high accuracy bruise detection, and the optimized resolutions also allowed for use of shorter exposure times. The results of this work can be used to help develop multispectral imaging systems that provide rapid, cost-effective post-harvest processing to identify bruised apples on commercial processing lines.