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The retention of nanocrystallinity in dense ceramic materials is still a challenge, even with the application of external pressure during sintering. The compaction behavior of high purity and acetate enriched zinc oxide (ZnO) nano-powders was investigated. It was found that acetate in combination with water plays a key role during the compaction into green bodies at moderate temperatures. Application of constant pressure resulted in a homogeneous green body with superior packing density (86% of theoretical value) at moderate temperature (85 °C) in the presence of water. In contrast, no improvement in density could be achieved if pure ZnO powder was used. This compaction behavior offers superior packing of the particles, resulting in a high relative density of the consolidated compact with negligible coarsening. Dissolution accompanying creep diffusion based matter transport is suggested to strongly support reorientation of ZnO particles towards densities beyond the theoretical limit for packing of ideal monosized spheres. Finally, the sintering trajectory reveals that grain growth is retarded compared to conventional processing up to 90% of theoretical density. Moreover, nearly no radial shrinkage was observed after sinter-forging for bodies performed with this advanced processing method.

The quality and safety of tea food production is of paramount importance. In traditional processing techniques, there is a risk of small foreign objects being mixed into Pu-erh sun-dried green tea, which directly affects the quality and safety of the food. To rapidly detect and accurately identify these small foreign objects in Pu-erh sun-dried green tea, this study proposes an improved YOLOv8 network model for foreign object detection. The method employs an MPDIoU optimized loss function to enhance target detection performance, thereby increasing the model’s precision in targeting. It incorporates the EfficientDet high-efficiency target detection network architecture module, which utilizes compound scale-centered anchor boxes and an adaptive feature pyramid to achieve efficient detection of targets of various sizes. The BiFormer bidirectional attention mechanism is introduced, allowing the model to consider both forward and backward dependencies in sequence data, significantly enhancing the model’s understanding of the context of targets in images. The model is further integrated with sliced auxiliary super-inference technology and YOLOv8, which subdivides the image and conducts in-depth analysis of local features, significantly improving the model’s recognition accuracy and robustness for small targets and multi-scale objects. Experimental results demonstrate that, compared to the original YOLOv8 model, the improved model has seen increases of 4.50% in Precision, 5.30% in Recall, 3.63% in mAP, and 4.9% in F1 score. When compared with the YOLOv7, YOLOv5, Faster-RCNN, and SSD network models, its accuracy has improved by 3.92%, 7.26%, 14.03%, and 11.30%, respectively. This research provides new technological means for the intelligent transformation of automated color sorters, foreign object detection equipment, and intelligent sorting systems in the high-quality production of Yunnan Pu-erh sun-dried green tea. It also provides strong technical support for the automation and intelligent development of the tea industry.

Agricultural input and output prices have become extremely volatile in recent years and the global meat industry faces sustainability challenges related to climate change, resource competition, environmental regulations, animal welfare concerns, consumer preferences and industry policies. Additionally, the economic situation of cattle fattening farms has been significantly impacted by two major shocks: the COVID-19 pandemic and the onset of the war in Ukraine. This has led to a growing demand for microsimulation tools that can analyse how these conditions affect the operations of agricultural farms and address various technological challenges at both the farm and sector levels. In this paper, we present a farm model to analyse the cattle farming sector for the pre-Alpine region, using Slovenia, a typical example of this region, as a case study. These farms are particularly important from both social and environmental sustainability perspectives, and it is crucial that economic sustainability follows suit. The results of the SiTFarm model show that, on average, farms in the cattle farming sector achieved modest results between 2018 and 2022, with an average gross margin of 9.57 €/h. However, the variability is significant, with a coefficient of variation 0.74. Only 25% of farms exceeded 17.15 €/h, while 25% did not surpass 4.46 €/h. At the sector level, the gross margin decreased by 12% in 2020 but increased by 99% in 2022 compared to the reference year 2018, highlighting the incredible price volatility over a short period. The model results also indicate greenhouse gas emissions ranging from 5.01 to 7.77 kg CO 2 eq. per kg of daily body weight gain on the analysed farms. Nearly half of the farms have GHG emissions for cattle fattening exceeding 6.1 kg CO 2 eq. per kg daily body weight gain, while about 10% of farms achieve a sustainability target of approximately 5 kg CO 2 eq. per kg of daily body weight gain.

The synthetic promoter E-SARE provides a genetic tool to tag neurons in an activity-dependent manner. The authors show the utility of this tool for labeling populations of neurons that respond to specific stimuli in living mice and for tracking the axonal projection patterns. Identifying the neuronal ensembles that respond to specific stimuli and mapping their projection patterns in living animals are fundamental challenges in neuroscience. To this end, we engineered a synthetic promoter, the enhanced synaptic activity–responsive element (E-SARE), that drives neuronal activity–dependent gene expression more potently than other existing immediate-early gene promoters. Expression of a drug-inducible Cre recombinase downstream of E-SARE enabled imaging of neuronal populations that respond to monocular visual stimulation and tracking of their long-distance thalamocortical projections in living mice. Targeted cell-attached recordings and calcium imaging of neurons in sensory cortices revealed that E-SARE reporter expression correlates with sensory-evoked neuronal activity at the single-cell level and is highly specific to the type of stimuli presented to the animals. This activity-dependent promoter can expand the repertoire of genetic approaches for high-resolution anatomical and functional analysis of neural circuits.

A primary method for studying cellular function is to examine cell morphology after a given manipulation. Fluorescent markers attached to proteins/intracellular structures of interest in conjunction with 3D fluorescent microscopy are frequently exploited for functional analysis. Despite the central role of morphology comparisons in cell biological approaches, few statistical tools are available that allow biological scientists without a high level of statistical training to quantify the similarity or difference of fluorescent images containing multifactorial information. We transform intracellular structures into kernels and develop a multivariate two-sample test that is nonparametric and asymptotically normal to directly and quantitatively compare cellular morphologies. The asymptotic normality bypasses the computationally intensive calculations used by the usual resampling techniques to compute the P-value. Because all parameters required for the statistical test are estimated directly from the data, it does not require any subjective decisions. Thus, we provide a black-box method for unbiased, automated comparison of cell morphology. We validate the performance of our test statistic for finite synthetic samples and experimental data. Employing our test for the comparison of the morphology of intracellular multivesicular bodies, we detect changes in their distribution after disruption of the cellular microtubule cytoskeleton with high statistical significance in fixed samples and live cell analysis. These results demonstrate that density-based comparison of multivariate image information is a powerful tool for automated detection of cell morphology changes. Moreover, the underlying mathematics of our test statistic is a general technique, which can be applied in situations where two data samples are compared.

Many researchers have reported that obesity is a major risk factor for diabetes, cardiovascular diseases, several forms of cancer (such as breast, colon and prostate), pulmonary, osteoarticular and metabolic diseases in the past decades. Recently, the hypolipidemic and anti-obesity effects of green tea in animals and humans have slowly become a hot topic in nutritional and food science research. This review will up-date the information of the anti-obesity effects of green tea in human intervention and animal studies. During recent years, an increasing number of clinical trials have confirmed the beneficial effects of green tea on obesity. However, the optimal dose has not yet been established owing to the very different results from studies with a similar design, which may be caused by differences in the extent of obesity, dietary intake, physical activity intensity, the strength of subjects’ compliance to test instruction, the genetic background of populations, body composition and dietary habits. Therefore, further investigations on a larger scale and with longer periods of observation and tighter controls are needed to define optimal doses in subjects with varying degrees of metabolic risk factors and to determine differences in beneficial effects among diverse populations. Moreover, data from laboratory studies have shown that green tea has important roles in fat metabolism by reducing food intake, interrupting lipid emulsification and absorption, suppressing adipogenesis and lipid synthesis and increasing energy expenditure via thermogenesis, fat oxidation and fecal lipid excretion. However, the exact molecular mechanisms remain elusive.

Obesity is associated with higher risks of developing diabetes and cardiovascular disease. Green tea, rich in polyphenolic compounds such as epigallocatechin gallate (EGCG) and epigallocatechin (EGC), has been shown to display anti-obesity effects. Houttuynia cordata leaves have also been shown to exhibit anti-obesity effects due to their chlorogenic acid content. Lactic acid bacteria are able to increase the production of polyphenolic compounds. This study aims to develop a novel anti-obesity fermentation product by combining H. cordata leaf tea with green tea, using Lactobacillus paracasei subsp. paracasei NTU 101 (NTU 101) for fermentation due to the advantages of bioconverting the polyphenolic compounds. The regulation of adipogenesis factors and the anti-obesity effect of the NTU 101-fermented tea were evaluated in an in vitro 3T3-L1 pre-adipocyte model and an in vivo obese rat model, respectively. The results show that the NTU 101-fermented tea, which contained higher EGCG, EGC, and chlorogenic acid levels than unfermented tea, was able to inhibit the lipogenesis of mature 3T3-L1 adipocytes by the stimulation of lipolysis. Furthermore, the body weight gain, body fat pad, and feeding efficiency of obese rats, induced with a high fat diet, were decreased by the oral administration of NTU 101-fermented tea. The significant anti-obesity effect was probably due to lipolysis. However, NTU 101 bacteria cells and EGCG may also act as functional ingredients to contribute to the anti-obesity effects of NTU 101-fermented products. [Display omitted] •NTU 101-fermented tea produced higher EGCG, EGC, and chlorogenic acid by bioconversion.•NTU 101-fermented tea was able to inhibit lipogenesis of mature 3T3-L1 adipocyte.•The significant in vitro anti-adipogenesis and in vivo anti-obesity effects were contributed from the lipolysis effect.•Both NTU 101 cells and EGCG were the functional ingredients of NTU 101-fermented tea on the anti-obesity effect.

To define a generic diet to protect human health and food system sustainability based on three dimensions: animal:plant ratio, degree of food processing and food diversity. The percentages of maximum animal and ultra-processed energy content were evaluated from scientific papers (Web of Science database) and reports from international scientific institutions. Then, a weekly French standard diet, including these percentages and food diversity (≥42 different foods), was designed to calculate adequacy to nutritional needs. Based on traditional and scientifically based healthy diets, and on foresight scenarios for sustainable diets at horizon 2050, a median daily animal energy content intake of 15 % was found to be protective towards both human health and environment. Based on epidemiological studies associating ultra-processed energy consumption with increased overweight/obesity risk, a precautionary threshold of approximately 15 % ultra-processed energy content was observed. The French diet allows addressing all nutritional needs and other nutritional indicators such as maximum salt and simple sugar consumption, α-linolenic acid:linoleic acid ratio and essential amino acids. This diet was named the '3V rule' for Végétal (plant), Vrai (real) and Varié (varied, if possible organic, local and seasonal). This generic diet can be adapted according to regional traditions and environmental characteristics. Excluding only one dimension of it would threaten both health and food system sustainability. Tending towards a 3V-based diet, while respecting local constraints, should allow preserving human health, environment (greenhouse gas emissions, pollution, deforestation, etc.), small farmers, animal welfare and biodiversity, culinary traditions and socioeconomics (including an alleviation of public health cost).

Wearable devices are widely used in the smart healthcare monitoring system to detect changes in user parameters through applications such as wristwatches, bands, and clothing electronic skin. In addition, multimode devices enable monitoring of vital signs, helping diagnose and prevent diseases. A wearable device detects the user's biological signals such as body temperature, movement, heartbeat, and humidity level, transmits the information to the mobile phone, and sends the information to an emergency center/family/clinician through cloud computing or wireless communication systems. This all‐day monitoring system enables the user's status information to be monitored 24 h a day to ensure appropriate treatment, thereby facilitating highly personalized care due to its human‐centricity. When integrated with higher‐level infrastructure, it is expected to be useful in healthcare scenarios, providing benefits to multiple stakeholders. In addition, it will help protect people exposed to potentially life‐threatening environments such as military personnel, first responders, and deep‐sea and space explorers. In this review, the components for implementing an all‐day monitoring system are described, including the electrode design strategy for realizing a skin attachable e‐skin device. Issues related to flexible storage devices and recent research results are also discussed. The use of an all‐day monitoring system in which a wearable device mounted to or implanted into the human body detects the user's bio‐signals and transmits that information through a communication system will facilitate highly personalized treatment owing to the system's user‐centricity. It is expected to excel in healthcare scenarios integrated with higher‐level infrastructure, benefitting multiple stakeholders.

Objective This study investigates nutritional quality, environmental impact and costs of foods and drinks and their consumption in daily diets according to the degree of processing across the Dutch population. Design The NOVA classification was used to classify the degree of processing (ultra-processed foods (UPF) and ultra-processed drinks (UPD)). Food consumption data were derived from the Dutch National Food Consumption Survey 2012–2016. Indicators assessed were nutritional quality (saturated fatty acids (SFA), sodium, mono and disaccharides (sugar), fibre and protein), environmental impact (greenhouse gas (GHG) emissions and blue water use) and food costs. Setting The Netherlands. Participants Four thousand three hundred thirteen Dutch participants aged 1 to 79 years. Results Per 100 g, UPF were more energy-dense and less healthy than unprocessed or minimally processed foods (MPF); UPF were associated with higher GHG emissions and lower blue water use, and were cheaper. The energy and sugar content of UPD were similar to those of unprocessed or minimally processed drinks (MPD); associated with similar GHG emissions but blue water use was less, and they were also more expensive. In the average Dutch diet, per 2000 kcal, ultra-processed foods and drinks (UPFD) covered 29% (456 g UPF and 437 g UPD) of daily consumption and 61% of energy intake. UPFD consumption was higher among children than adults, especially for UPD. UPFD consumption determined 45% of GHG emissions, 23% of blue water use and 39% of expenses for daily food consumption. UPFD consumption contributed 54% to 72% to daily sodium, sugar and SFA intake. Conclusions Compared with unprocessed or minimally processed foods and drinks, UPF and UPD were found to be less healthy considering their high energy, SFA, sugar and sodium content. However, UPF were associated higher GHG emissions and with less blue water use and food costs. Therefore daily blue water use and food costs might increase if UPF are replaced by those unprocessed or minimally processed. As nutritional quality, environmental impacts and food costs relate differently to the NOVA classification, the classification is not directly applicable to identify win–win-wins of nutritional quality, environmental impact and costs of diets.