Seasonal variations and sensory profiles of oolong tea: Insights from metabolic analysis of Tieguanyin cultivar.

The impact of seasonal variations on the quality of oolong tea products remains a subject of ongoing exploration. This study delves into the intricate relationships between seasonality, metabolites, and sensory characteristics in finished oolong tea products. Metabolomic data from 266 Tieguanyin oolong tea products harvested in both spring and autumn, along with corresponding sensory evaluations, were acquired. Using OPLS-DA and PLS-DA models with UPLC-QToF/MS data, our findings showed that seasonal effects were notably more pronounced in light-scented Tieguanyin products (lightly-roasted) compared to strong-scented products (moderately-roasted). Furthermore, over half of the identified key seasonal discriminant metabolites happened to be crucial for determining the sensory grade. The study marks the first-time recognition of triterpene saponins as critical factors in determining both the harvest season and the sensory grade of oolong tea. These insights deepen our understanding of the interplays between seasonal variations, metabolites, and sensory attributes in oolong tea products.

Investigation of feedlot-level use of a direct fed microbial on fecal shedding of E. coli O157:H7.

Our objectives were to determine whether the feedlot-level use of a direct fed microbial (DFM; Lactobacillus animalis LA51 and Propionibacterium freudenreichii PF24; Bovamine Defend®, 2x109 CFU/g) was associated with fecal prevalence and concentration of E. coli O157:H7, and determine pen- and feedlot-level risk factors associated with fecal E. coli O157:H7 prevalence in cattle pens from commercial feedlot operations. Twenty commercial feedlots in Nebraska, ten that included DFM (DFM) and ten that did not (no-DFM), were sampled during the summer of 2017. In each sampling month, 22 pen-floor fecal samples were collected from three pens in each feedlot. Samples were subjected to cultural and molecular procedures for detection of E. coli O157:H7 (immunomagnetic separation, plating on selective media, followed by PCR confirmation) and spiral plating for quantification. A total of 1,320 samples from 180 pens of finishing cattle belonging to 20 feedlots, which were sampled three times throughout a 12-week period, were processed and tested. Across all feedlots and sampling months, mean within-pen prevalence was 13.5% (95% CI = 2.6-47.4%). The association between DFM status and the within-pen prevalence of E. coli O157:H7 depended significantly (p<0.05) on the sampling month. The second sampling month between late July and mid-August, corresponded to the highest within-pen prevalence estimates reported in this study, with no-DFM pens having higher prevalence than DFM pens. After accounting for the DFM status, and based on multivariable analyses, sampling month, average pen body weight and weather conditions were significantly associated with the within-pen fecal prevalence of E. coli O157:H7. Collectively, these findings demonstrate that the use of a DFM containing Lactobacillus animalis LA51 and Propionibacterium freudenreichii PF26 in feedlots showed potential in reducing fecal E. coli O157:H7 prevalence in cattle during times when prevalence peaks.

YOLOv5-POS: research on cabbage pose prediction method based on multi-task perception technology.

Introduction: Accurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance. Methods: This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem. Results and discussion: YOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency.

Investigating the contribution of image time series observations to cauliflower harvest-readiness prediction.

Cauliflower cultivation is subject to high-quality control criteria during sales, which underlines the importance of accurate harvest timing. Using time series data for plant phenotyping can provide insights into the dynamic development of cauliflower and allow more accurate predictions of when the crop is ready for harvest than single-time observations. However, data acquisition on a daily or weekly basis is resource-intensive, making selection of acquisition days highly important. We investigate which data acquisition days and development stages positively affect the model accuracy to get insights into prediction-relevant observation days and aid future data acquisition planning. We analyze harvest-readiness using the cauliflower image time series of the GrowliFlower dataset. We use an adjusted ResNet18 classification model, including positional encoding of the data acquisition dates to add implicit information about development. The explainable machine learning approach GroupSHAP analyzes time points' contributions. Time points with the lowest mean absolute contribution are excluded from the time series to determine their effect on model accuracy. Using image time series rather than single time points, we achieve an increase in accuracy of 4%. GroupSHAP allows the selection of time points that positively affect the model accuracy. By using seven selected time points instead of all 11 ones, the accuracy improves by an additional 4%, resulting in an overall accuracy of 89.3%. The selection of time points may therefore lead to a reduction in data collection in the future.

Innovative approaches to controlling Salmonella in the meat industry.

Salmonella, a Gram-negative, rod-shaped bacterium from the Enterobacteriaceae family, is a significant cause of illnesses in humans and animals. It resides in the digestive tracts of livestock, poultry, and other warm-blooded animals and can contaminate various environments and foods through fecal matter. Salmonella enterica, the main species that affects humans, is widespread in cattle, pigs, and poultry. Despite efforts to control pathogens in meat systems, over 1.4 million human salmonellosis cases occur annually in the U.S., with serotypes S. enteritidis and S. typhimurium being predominant. Advances in meat processing have targeted pathogen reduction at multiple stages, but more innovative approaches are needed for substantial public health impact. This paper discusses current and future strategies to minimize Salmonella in the food supply. It emphasizes pre- and post-harvest Salmonella prevalence by biomapping it through the whole processing chain, focusing on beef and pork interventions such as probiotics. These interventions have shown promise in reducing pathogen loads in cattle manure and lymph nodes. Techniques such as microbiome, whole genome sequencing (WGS), and electron microscopy (EM) provide detailed insights into Salmonella's genetic and bacterial structural-morphological characteristics, aiding in the development of targeted interventions. Integrating rapid detection, biomapping, and enviromapping enhances pathogen tracking in meat production, reducing Salmonella prevalence and improving risk assessment and food safety. The advanced, current, and innovative techniques allow for timely identification, detailed spatial and quantitative data, and more effective interventions. This leads to safer food products and reduces foodborne illnesses.

Modeling long-term, stage-structured dynamics of Tribolium castaneum (Coleoptera: Tenebrionidae) at food facilities with and without two types of long-lasting insecticide-incorporated netting.

The red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), is a cosmopolitan and destructive external-infesting pest at many food facilities. The use of deltamethrin- and α-cypermethrin-incorporated long-lasting insecticide-incorporated netting (LLIN) has shown incredible promise for the management of stored product insects. However, it is unknown how LLIN deployed within food facilities may affect the long-term population dynamics of T. castaneum compared to populations where no LLIN is present. Exposure to LLIN has been shown to affect mortality in the current generation and decrease progeny production in the subsequent generation. Thus, we modeled the long-term population dynamics of T. castaneum at food facilities over 15 generations by incorporating realistic estimates for mortality and progeny reduction after contact with LLIN compared to baseline growth by the species. We parameterized the model with estimates from the literature and used a four-stage structured population (eggs, larvae, pupae, and adults). The model was implemented using the package popbio in R. Our models suggest that deploying LLIN led to significant population reductions based on the estimates of mortality and progeny reduction from prior work, whereas the baseline model exhibited exponential population growth. In addition, there were differences in the frequencies of each life stage under each scenario modeled. As a result, it appears deploying LLIN may contribute to the local extirpation of T. castaneum within as few as 15 generations. Our work contributes to a growing literature about the effectiveness of incorporating LLIN into existing pest management programs for managing stored product insects in food facilities.

Energy Consumption Minimization with SNR Constraint for Wireless Powered Communication Networks.

The article addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a time allocation scheme, named DaTA, which is based on the Different Target Simultaneous Wireless Information and Power Transfer (DT-SWIPT) scheme such that the wireless station can share the remaining energy after transmission to the Hybrid Access Point (HAP) to those who have not transmitted to the HAP to minimize the energy consumption of the WPCN. In addition to proposing a new frame structure, the article also considers the Signal-to-Noise (SNR) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the article, the problem of minimization of energy consumption is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. The simulation results showed that the proposed scheme outperforms the related work in terms of energy consumption and energy efficiency.

Effects of gnotobiotic fermentation on global gene expression of germ-free vegetables.

Existing research has underscored the vital interplay between host organisms and their associated microbiomes, which affects health and function. In both plants and animals, host factors critically shape microbial communities and influence growth, health, and immunity. Post-harvest plants, such as those used in kimchi, a traditional Korean dish, offer a unique avenue for exploring host-microbe dynamics during fermentation. Despite the emphasis on lactic acid bacteria (LAB) in fermentation studies, the roles of host factors remain unclear. This study aimed to investigate the influence of these factors on plant transcriptomes during kimchi fermentation. We individually inoculated nine LAB strains into germ-free kimchi to generate LAB-mono-associated gnotobiotic kimchi and performed RNA-sequencing analysis for the host vegetables during fermentation. The transcriptomes of post-harvest vegetables in kimchi change over time, and microbes affect the transcriptome profiles of vegetables. Differentially expressed gene analyses revealed that microbes affected the temporal expression profiles of several genes in the plant transcriptomes in unique directions depending on the introduced LAB strains. Cluster analysis with other publicly available transcriptomes of post-harvest vegetables and fruits further revealed that the plant transcriptome is more profoundly influenced by the environment harboring the host than by host phylogeny. Our results bridge the gap in understanding the bidirectional relationship between host vegetables and microbes during food fermentation, illuminating the complex interplay between vegetable transcriptomes, fermentative microbes, and the fermentation process in food production. The different transcriptomic responses elicited by specific LAB strains suggest the possibility of microbial manipulation to achieve the desired fermentation outcomes.

The effects of human-caused mortality on mammalian cooperative breeders: a synthesis.

Human-caused mortality can be pervasive and even highly selective for individuals in groups of cooperative breeders. Many studies of cooperative breeders, however, do not address human-caused mortality. Similarly, studies focused on the effects of human-caused mortality on wildlife populations often do not consider the ecology of cooperative breeders. We searched the literature and identified 58 studies where human-caused mortality affected a group characteristic, vital rate, or population state of a cooperative breeder. Of studies reporting population growth or decline, 80% reported a link between human-caused mortality and population declines in cooperative breeders. Such studies often did not identify the mechanism behind population declines, but 28% identified concurrent declines in adult survival and another 21% reported concurrent declines in recruitment or reproduction. There was little overlap between the cooperative breeding and human-caused mortality literatures, limiting our ability to accrue knowledge. Future work would be beneficial if it (i) identified the vital rate(s) causing population declines, (ii) leveraged management actions such as lethal removal to ask questions about the ecology of group-living in cooperative breeders, and (iii) used insights from cooperative breeding theory to inform management actions and conservation of group-living species.

Accurately estimating correlations between demographic parameters: A comment on Deane et al. (2023).

Estimating correlations among demographic parameters is an important method in population ecology. A recent paper by Deane et al. (Ecology and Evolution 13:e9847, 2023) attempted to explore the effects of different priors for covariance matrices on inference when using mark-recovery data. Unfortunately, Deane et al. (2023) made a mistake when parameterizing some of their models. Rather than exploring the effects of different priors, they examined the effects of the use of incorrect equations on inference. In this manuscript, we clearly describe the mistake in Deane et al. (2023). We then demonstrate the use of an alternative and appropriate method and reach different conclusions regarding the effects of priors on inference. Consistent with other recent literature, informative inverse Wishart priors can lead to flawed inference, while vague priors on covariance matrix components have little impact when sample sizes are adequate.

Improving agricultural sustainability - A review of strategies to valorize tomato plant residues (TPR).

Considerations for the modification of agricultural practices and waste management to improve environmental sustainability remain a subject of great importance. Prioritization of intensive mass food production to meet the demand of an increasing human population has introduced a multitude of environmental issues due to, among other factors, the large volumes of waste output. Tomato production in greenhouses, for example, generates tonnes of bio-waste per hectare each harvest including green tomato plant residues (i.e., stems, leaves, branches). Giving value to these green tomato plant residues collected during the growing cycle and after harvest has not proven straightforward despite a massive yearly release of tonnes of carbon dioxide from stems and leaves disposed on landfills. This paper aims to summarize current research in tomato plant residue valorization and to identify considerations for future valorization strategies. Peer reviewed articles, scientific books and governmental, economic and statistical reports on the topic of tomato plant residues were collected and analyzed. Focuses included traditional valorization approaches, bio-refinement strategies and conversion of fiber-rich residues into high value packaging materials. Initiatives for sustainable agriculture, their market relevance, and the strengths and weaknesses of using tomato plant residues in these valorization approaches are discussed. Overall, it was concluded that valorization of tomato plant residues would be a highly integrative endeavor that would require coordination from multiple levels in the agricultural production chain.

Investigation of the aroma profile and blending strategy of Lu'an Guapian teas during grain rain period by sensory evaluation combined with SBSE-GC-MS, GC-O and OAV.

Grain Rain Period (GRP), one of the 24 solar terms in China, signifies a crucial phase for the transformation of tea quality, especially for Lu'an Guapian (LAGP) tea. During GRP, LAGP teas showed 3 distinct aroma profiles, each spanning 3-4 days. Specifically, the sensory evaluation result revealed that LAGP tea exhibited stronger flowery and fresh aromas in the early phase, with the soybean-like aroma significantly intensifying as the harvest period progressed during GRP. Furthermore, the key contributors to the aroma profile and its variation during GRP were identified as indole, δ-decalactone, geraniol, linalool, decanal, jasmone, (E)-ß-ionone, benzeneacetaldehyde, dihydroactinolide, nonanal, octanal, (E)-isoeugenol, (E,E)-2,4-nonadienal, 4-ketoisophorone, (E,Z)-2,6-nonadienal, and 1-octen-3-one. Additionally, we proposed a binary blending strategy using sensory evaluation with the methods of triangle test and normal distribution fitting to predict the blending threshold accurately. This study elucidated the dynamics of LAGP tea aroma during GRP and offered insights for tea blending optimization.

Pre- and post-harvest aflatoxin contamination and management strategies of Aspergillus spoilage in East African Community maize: review of etiology and climatic susceptibility.

Globally, maize (Zea mays L.) is deemed an important cereal that serves as a staple food and feed for humans and animals, respectively. Across the East African Community, maize is the staple food responsible for providing over one-third of calories in diets. Ideally, stored maize functions as man-made grain ecosystems, with nutritive quality changes influenced predominantly by chemical, biological, and physical factors. Food spoilage and fungal contamination are convergent reasons that contribute to the exacerbation of mycotoxins prevalence, particularly when storage conditions have deteriorated. In Kenya, aflatoxins are known to be endemic with the 2004 acute aflatoxicosis outbreak being described as one of the most ravaging epidemics in the history of human mycotoxin poisoning. In Tanzania, the worst aflatoxin outbreak occurred in 2016 with case fatalities reaching 50%. Similar cases of aflatoxicoses have also been reported in Uganda, scenarios that depict the severity of mycotoxin contamination across this region. Rwanda, Burundi, and South Sudan seemingly have minimal occurrences and fatalities of aflatoxicoses and aflatoxin contamination. Low diet diversity tends to aggravate human exposure to aflatoxins since maize, as a dietetic staple, is highly aflatoxin-prone. In light of this, it becomes imperative to formulate and develop workable control frameworks that can be embraced in minimizing aflatoxin contamination throughout the food chain. This review evaluates the scope and magnitude of aflatoxin contamination in post-harvest maize and climate susceptibility within an East African Community context. The paper also treats the potential green control strategies against Aspergillus spoilage including biocontrol-prophylactic handling for better and durable maize production.

Multivariate approach to assess the bioactive compounds of Atractylodes chinensis (DC.) Koidz in different harvest periods.

BACKGROUND: The Atractylodes chinensis (DC.) Koidz (A. chinensis) Chinese herb possesses numerous therapeutic properties and is extensively utilized in the pharmaceutical industry. Its quality is closely associated with the harvest periods. However, the optimal quality and harvest periods of A. chinensis remain elusive. METHODS: The bioactive compounds of perennial A. chinensis were detected by ultra-high-performance liquid chromatography coupled with quadrupole Orbitrap mass spectrometry (UHPLC-Q-Orbitrap/MS) metabolomics, and differentially abundant compounds were selected by multivariate statistical analysis. Then, variations in the content of differential compounds in samples harvested at different periods were analyzed, while correlation analysis was carried out on the differential compounds to determine the suitable harvest period for distinct components. RESULTS: A total of 61 bioactive compounds were detected in all samples, grouped into 9 known classes. The results revealed that the chemical compositions of A. chinensis at different harvest periods were significantly different. The volatile oil content in the four-year-old and five-year-old samples was relatively high, at 31.92 mg/g and 32.42 mg/g, respectively. There were also significant differences in the content of the six active ingredients, for example, the five-year-old sample had the highest content of atractylodin (4.38 mg/g). Indeed, the harvest period was correlated with the abundance of most bioactive compounds. Specifically, quinquennial samples were significantly negatively correlated with the abundance of organic acids and aliphatics while moderately positively correlated with the abundance of other classes of bioactive compounds. CONCLUSIONS: According to the results, the ideal harvest time for atractylenolide Ⅲ was 3 years. Regarding organic acids, the optimal harvest time was around 2-3 years. Taken together, these results offer valuable insights to producers for optimizing the harvest period for A. chinensis.

Integrated nutritional and functional components analyses reveal insights into the peel and pulp quality at different harvest times of 'Dahongpao' tangerine (Citrus reticulata Blanco).

The fruit of Citrus reticulata 'Dahongpao' (DHP) is typically harvested when fully ripe, exhibiting a dark red color, high sweetness, and pleasant taste. However, it remains uncertain whether the optimum harvesting time for its medicinal part (peel) aligns with that of the fruit. The findings of the study indicated that the peel exhibited the highest concentration of total flavonoids (4.018 mg/g) during the middle stage of maturity. Additionally, the total polysaccharide content increased progressively with ripening, reaching its peak (5.36 %) at full maturity. Furthermore, the DHP pulp demonstrated the highest concentration of total polyphenols (11.5 %) and the lowest titrable acid content (0.97 %) during the middle stage of maturity. Furthermore, the peel and pulp of DHP at the middle stage of ripening exhibited the highest antioxidant capacity. Considering the nutritional and functional components at various harvest times of DHP, it is recommended to harvest the peel at the intermediate stage of ripeness. Additionally, during this stage, the pulp also exhibited greater abundance of nutritional components. The findings of this study elucidate the process of accumulation and alteration of nutritional and functional constituents during the ripening of DHP fruit.

Comprehensive physicochemical indicators analysis and quality evaluation model construction for the post-harvest ripening rapeseeds.

Rapeseed (Brassica napus L.) is the second largest globally cultivated oil crop, but the effects of post-harvested ripening on rapeseed quality is unclear and unpredictable. This study reveals the relationship between post-harvest ripening periods (PHR) and physicochemical quality of different rapeseed cultivars using comprehensive physicochemical indicators analysis. The results indicate that PHR led to a gradual decrease in chlorophyll, carotenoid and moisture content but continually increased oil and total phenol content (TPC). Besides, 295 lipid molecules from 13 lipid subclasses were identified, revealing that the relative content of triacylglycerol (TG) was progressively increased while diacylglycerol (DG) demonstrated a consistent decline throughout the PHR. Correlation analysis, hierarchical cluster analysis (HCA) and principal component analysis (PCA) were employed to construct and verify the comprehensive quality evaluation model for rapeseeds in PHR. This paper develops a comprehensive quality evaluation model for post-harvest ripening rapeseeds and advances the development of agricultural products.

Revisiting Fishery Sustainability Targets.

Density-dependent population dynamic models strongly influence many of the world's most important harvest policies. Nearly all classic models (e.g. Beverton-Holt and Ricker) recommend that managers maintain a population size of roughly 40-50 percent of carrying capacity to maximize sustainable harvest, no matter the species' population growth rate. Such insights are the foundational logic behind most sustainability targets and biomass reference points for fisheries. However, a simple, less-commonly used model, called the Hockey-Stick model, yields very different recommendations. We show that the optimal population size to maintain in this model, as a proportion of carrying capacity, is one over the population growth rate. This leads to more conservative optimal harvest policies for slow-growing species, compared to other models, if all models use the same growth rate and carrying capacity values. However, parameters typically are not fixed; they are estimated after model-fitting. If the Hockey-Stick model leads to lower estimates of carrying capacity than other models, then the Hockey-Stick policy could yield lower absolute population size targets in practice. Therefore, to better understand the population size targets that may be recommended across real fisheries, we fit the Hockey-Stick, Ricker and Beverton-Holt models to population time series data across 284 fished species from the RAM Stock Assessment database. We found that the Hockey-Stick model usually recommended fisheries maintain population sizes higher than all other models (in 69-81% of the data sets). Furthermore, in 77% of the datasets, the Hockey-Stick model recommended an optimal population target even higher than 60% of carrying capacity (a widely used target, thought to be conservative). However, there was considerable uncertainty in the model fitting. While Beverton-Holt fit several of the data sets best, Hockey-Stick also frequently fit similarly well. In general, the best-fitting model rarely had overwhelming support (a model probability of greater than 95% was achieved in less than five percent of the datasets). A computational experiment, where time series data were simulated from all three models, revealed that Beverton-Holt often fit best even when it was not the true model, suggesting that fisheries data are likely too small and too noisy to resolve uncertainties in the functional forms of density-dependent growth. Therefore, sustainability targets may warrant revisiting, especially for slow-growing species.

Positive Impact of Late Harvest Date on Polyphenolic Composition of Plavac Mali (Vitis vinifera L.) Wine Depends on Location.

Asynchronous ripening is a significant challenge in winemaking. Green berries reduce alcohol and pH while increasing acidity. Green berries are rich in bitter and astringent compounds, with an unknown impact on wine quality. The aim of this study was to evaluate the impact of harvest date and vineyard location on the polyphenolic composition of Plavac Mali wines in Dalmatia, Croatia. Experiments were conducted in two locations, Split and Zadar, producing fifteen wines per location from four harvest dates (H1-H4), including green berry wines from H1. The first harvest date occurred 27 days after véraison (DAV) and the last at 69 DAV, corresponding to overripeness. Green berry wines of H1 had low alcohol content up to 4.4% (v/v) in Split. Epigallocatechin was the main flavonoid in those wines, followed by dimer B1 in Split and catechin in Zadar. Green wines from Split had a higher concentration of phenolic acids, flavan-3-ol monomers and dimers. Wines of H3 had the highest concentration of malvidin-3-O-glucoside. With a later harvest date, a dramatic decrease in catechin and dimers was observed in wines from Split, and a decrease in epicatechin, epigallocatechin and dimer B1 in those from Zadar. The final expression of the physiochemical and polyphenolic composition of Plavac Mali wine is determined by the dynamics of harvest date, location and their interactions.