Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care. Full article

Membrane proteins constitute about 20% of the human proteome and play crucial roles in cellular functions. However, a complete understanding of their structure and function is limited by their hydrophobic nature, which poses significant challenges in purification and stabilization. Detergents, essential in the isolation process, risk destabilizing or altering the proteins’ native conformations, thus affecting stability and functionality. This study leverages single-particle cryo-electron microscopy to elucidate the structural nuances of membrane proteins, focusing on the SLAC1 bacterial homolog from Haemophilus influenzae (HiTehA) purified with diverse detergents, including n-dodecyl β-D-maltopyranoside (DDM), glycodiosgenin (GDN), β-D-octyl-glucoside (OG), and lauryl maltose neopentyl glycol (LMNG). This research not only contributes to the understanding of membrane protein structures but also addresses detergent effects on protein purification. By showcasing that the overall structural integrity of the channel is preserved, our study underscores the intricate interplay between proteins and detergents, offering insightful implications for drug design and membrane biology. Full article

Movement and muscle control are crucial for the survival of all free-living organisms. This study aimed to explore differential patterns of cortical and subcortical activation across different stages of muscle control using functional magnetic resonance imaging (fMRI). An event-related design was employed. In each trial, participants (n = 10) were instructed to gently press a button with their right index finger, hold it naturally for several seconds, and then relax the finger. Neural activation in these temporally separated stages was analyzed using a General Linear Model. Our findings revealed that a widely distributed cortical network, including the supplementary motor area and insula, was implicated not only in the pressing stage, but also in the relaxation stage, while only parts of the network were involved in the steady holding stage. Moreover, supporting the direct/indirect pathway model of the subcortical basal ganglia, their substructures played distinct roles in different stages of muscle control. The caudate nucleus exhibited greater involvement in muscle contraction, whereas the putamen demonstrated a stronger association with muscle relaxation; both structures were implicated in the pressing stage. Furthermore, the subthalamic nucleus was exclusively engaged during the muscle relaxation stage. We conclude that even the control of simple muscle movements involves intricate automatic higher sensory–motor integration at a neural level, particularly when coordinating relative muscle movements, including both muscle contraction and muscle relaxation; the cortical and subcortical regions assume distinct yet coordinated roles across different stages of muscle control. Full article

Coatomer Protein Complex-II (COPII) mediates anterograde vesicle transport from the endoplasmic reticulum (ER) to the Golgi apparatus. Here, we report that the COPII coatomer complex is constructed dependent on a small GTPase, Sar1, in spermatocytes before and during Drosophila male meiosis. COPII-containing foci co-localized with transitional endoplasmic reticulum (tER)-Golgi units. They showed dynamic distribution along astral microtubules and accumulated around the spindle pole, but they were not localized on the cleavage furrow (CF) sites. The depletion of the four COPII coatomer subunits, Sec16, or Sar1 that regulate COPII assembly resulted in multinucleated cell production after meiosis, suggesting that cytokinesis failed in both or either of the meiotic divisions. Although contractile actomyosin and anilloseptin rings were formed once plasma membrane ingression was initiated, they were frequently removed from the plasma membrane during furrowing. We explored the factors conveyed toward the CF sites in the membrane via COPII-mediated vesicles. DE-cadherin-containing vesicles were formed depending on Sar1 and were accumulated in the cleavage sites. Furthermore, COPII depletion inhibited de novo plasma membrane insertion. These findings suggest that COPII vesicles supply the factors essential for the anchoring and/or constriction of the contractile rings at cleavage sites during male meiosis in Drosophila. Full article

Deep learning has profoundly influenced various domains, particularly medical image analysis. Traditional transfer learning approaches in this field rely on models pretrained on domain-specific medical datasets, which limits their generalizability and accessibility. In this study, we propose a novel framework called real-world feature transfer learning, which utilizes backbone models initially trained on large-scale general-purpose datasets such as ImageNet. We evaluate the effectiveness and robustness of this approach compared to models trained from scratch, focusing on the task of classifying pneumonia in X-ray images. Our experiments, which included converting grayscale images to RGB format, demonstrate that real-world-feature transfer learning consistently outperforms conventional training approaches across various performance metrics. This advancement has the potential to accelerate deep learning applications in medical imaging by leveraging the rich feature representations learned from general-purpose pretrained models. The proposed methodology overcomes the limitations of domain-specific pretrained models, thereby enabling accelerated innovation in medical diagnostics and healthcare. From a mathematical perspective, we formalize the concept of real-world feature transfer learning and provide a rigorous mathematical formulation of the problem. Our experimental results provide empirical evidence supporting the effectiveness of this approach, laying the foundation for further theoretical analysis and exploration. This work contributes to the broader understanding of feature transferability across domains and has significant implications for the development of accurate and efficient models for medical image analysis, even in resource-constrained settings. Full article

While digitalization offers new opportunities for small- and medium-sized enterprises (SMEs), it also introduces the phenomenon of the “digitalization paradox”. This paper develops a theoretical model comprising digitalization, digital technology–business alignment, external social capital, and SMEs’ performance, rooted in strategic alignment theory (SAT) and social capital theory (SCT). The necessary data for the study were obtained by distributing questionnaires to 352 small and medium-sized enterprises engaged in digital practices in China, and hierarchical regression analysis was employed to investigate the impact of digitalization on the performance of SMEs and its boundaries of influence. The results indicate an inverted U-shaped relationship between digitalization and SME performance, with both digital technology–business alignment and external social capital serving as positive moderators. Specifically, digital technology–business alignment and external social capital both enhance the positive impact of digitalization on the performance of SMEs and mitigate its negative effects. The findings enhance comprehension of the “digitalization paradox” and offer new insights and solutions for SMEs to navigate the opportunities and challenges of digitalization. Full article

Starch extraction from tubers and roots has long been an essential process, playing a crucial role in diverse industries ranging from alimentary to pharmacology. This review explores the different methods employed in starch extraction, including traditional techniques and the most innovative mechanical strategies. The methods show a good improvement in many aspects, such as an improvement in the efficiency of the process and an improvement in the yield, showing a value of 10.0–65.0% depending on the starch source. On the other hand, solvents such as NaOH are used in many mechanical processes for alkaline digestion to improve the extraction time. Ethanol and K₂S₂O₅ concentrations of 0.5% and 0.8% were used to prevent oxidation and modify some properties of the extracted starch. The use of many solvents has improved the optimization of the processes, providing the final extracted starch with more advantages and better quality. However, using enzymes such as cellulase in new and innovative ways has provided more advantages and a better efficiency and yield than the other methods. Each method has its advantages and challenges, highlighting the importance of understanding the diversity of different approaches and their impact on the yield, sustainability, environmental considerations, and quality of the extracted starch. As the world looks for more ecological approaches, this review shows the importance of critically evaluating the yield, efficiency, and environmental implications of the extraction methods, providing us with more ways of evaluating the methods used for starch extraction. The ecological impact is a crucial point when evaluating the innovation of a new extraction process, which is why methods such as ultrasound and pulsed electric-field-assisted techniques have been proposed. These methods have been presented as sustainable techniques called green technologies, offering more approaches and different advantages than the other methods. This review intends to investigate the complexities and considerations of starch extraction, providing a solid basis for decision-making regarding starch extraction. In a time where sustainability and product quality are crucial elements of industrial strategy formulation, an in-depth understanding of these methods becomes imperative to the development of responsible practices and efficiency in starch extraction. Full article

Cytoreductive surgery (CRS) combined with hyperthermic intrathoracic chemoperfusion (HITOC) is a promising treatment strategy for pleural mesothelioma (PM). The aim of this study was to evaluate the impacts of this multimodal approach in combination with systemic treatment on disease-free survival (DFS) and overall survival (OS). In this retrospective multicenter study, clinical data from patients after CRS and HITOC for PM at four high-volume thoracic surgery departments in Germany were analyzed. A total of 260 patients with MPM (220 epithelioid, 40 non-epithelioid) underwent CRS and HITOC as part of a multimodal treatment approach. HITOC was administered with cisplatin alone (58.5%) or cisplatin and doxorubicin (41.5%). In addition, 52.1% of patients received neoadjuvant and/or adjuvant chemotherapy. The median follow-up was 48 months (IQR = 38 to 58 months). In-hospital mortality was 3.5%. Both the resection status (macroscopic complete vs. incomplete resection) and histologic subtype (epithelioid vs. non-epithelioid) had significant impacts on DFS and OS. In addition, adjuvant chemotherapy (neoadjuvant/adjuvant) significantly increased DFS (p = 0.003). CRS and HITOC within a multimodal treatment approach had positive impacts on the survival of patients with epithelioid PM after macroscopic complete resection. The addition of chemotherapy significantly prolonged the time to tumor recurrence or progression. Full article

While the opioid crisis has justifiably occupied news headlines, emergency rooms are seeing many thousands of visits for another cause: cannabinoid toxicity. This is partly due to the spread of cheap and extremely potent synthetic cannabinoids that can cause serious neurological and cardiovascular complications—and deaths—every year. While an opioid overdose can be reversed by naloxone, there is no analogous treatment for cannabis toxicity. Without an antidote, doctors rely on sedatives, with their own risks, or ‘waiting it out’ to treat these patients. We have shown that the canonical synthetic ‘designer’ cannabinoids are highly potent CB1 receptor agonists and, as a result, competitive antagonists may struggle to rapidly reverse an overdose due to synthetic cannabinoids. Negative allosteric modulators (NAMs) have the potential to attenuate the effects of synthetic cannabinoids without having to directly compete for binding. We tested a group of CB1 NAMs for their ability to reverse the effects of the canonical synthetic designer cannabinoid JWH018 in vitro in a neuronal model of endogenous cannabinoid signaling and also in vivo. We tested ABD1085, RTICBM189, and PSNCBAM1 in autaptic hippocampal neurons that endogenously express a retrograde CB1-dependent circuit that inhibits neurotransmission. We found that all of these compounds blocked/reversed JWH018, though some proved more potent than others. We then tested whether these compounds could block the effects of JWH018 in vivo, using a test of nociception in mice. We found that only two of these compounds—RTICBM189 and PSNCBAM1—blocked JWH018 when applied in advance. The in vitro potency of a compound did not predict its in vivo potency. PSNCBAM1 proved to be the more potent of the compounds and also reversed the effects of JWH018 when applied afterward, a condition that more closely mimics an overdose situation. Lastly, we found that PSNCBAM1 did not elicit withdrawal after chronic JWH018 treatment. In summary, CB1 NAMs can, in principle, reverse the effects of the canonical synthetic designer cannabinoid JWH018 both in vitro and in vivo, without inducing withdrawal. These findings suggest a novel pharmacological approach to at last provide a tool to counter cannabinoid toxicity. Full article

Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and availability of expert radiologists, as well as limitations of the reverse-transcription polymerase chain reaction (RT-PCR) method. Deep learning, a sophisticated machine learning technique, leverages radiological imaging modalities for disease diagnosis and image classification tasks. Previous research on COVID-19 classification has encountered several limitations, including binary classification methods, single-feature modalities, small public datasets, and reliance on CT diagnostic processes. Additionally, studies have often utilized a flat structure, disregarding the hierarchical structure of pneumonia classification. This study aims to overcome these limitations by identifying pneumonia caused by COVID-19, distinguishing it from other types of pneumonia and healthy lungs using chest X-ray (CXR) images and related tabular medical data, and demonstrate the value of incorporating tabular medical data in achieving more accurate diagnoses. Resnet-based and VGG-based pre-trained convolutional neural network (CNN) models were employed to extract features, which were then combined using early fusion for the classification of eight distinct classes. We leveraged the hierarchal structure of pneumonia classification within our approach to achieve improved classification outcomes. Since an imbalanced dataset is common in this field, a variety of versions of generative adversarial networks (GANs) were used to generate synthetic data. The proposed approach tested in our private datasets of 4523 patients achieved a macro-avg F1-score of 95.9% and an F1-score of 87.5% for COVID-19 identification using a Resnet-based structure. In conclusion, in this study, we were able to create an accurate deep learning multi-modal to diagnose COVID-19 and differentiate it from other kinds of pneumonia and normal lungs, which will enhance the radiological diagnostic process. Full article

The curative treatment of multiple solid tumors, including head and neck squamous cell carcinoma (HNSCC), utilizes radiation. The outcomes for HPV/p16-negative HNSCC are significantly worse than HPV/p16-positive tumors, with increased radiation resistance leading to worse locoregional recurrence (LRR) and ultimately death. This study analyzed the relationship between immune function and outcomes following radiation in HPV/p16-negative tumors to identify mechanisms of radiation resistance and prognostic immune biomarkers. A discovery cohort of 94 patients with HNSCC treated uniformly with surgery and adjuvant radiation and a validation cohort of 97 similarly treated patients were utilized. Tumor immune infiltrates were derived from RNAseq gene expression. The immune cell types significantly associated with outcomes in the discovery cohort were examined in the independent validation cohort. A positive association between high Th2 infiltration and LRR was identified in the discovery cohort and validated in the validation cohort. Tumor mutations in CREBBP/EP300 and CASP8 were significantly associated with Th2 infiltration. A pathway analysis of genes correlated with Th2 cells revealed the potential repression of the antitumor immune response and the activation of BRCA1-associated DNA damage repair in multiple cohorts. The Th2 infiltrates were enriched in the HPV/p16-negative HNSCC tumors and associated with LRR and mutations in CASP8, CREBBP/EP300, and pathways previously shown to impact the response to radiation. Full article

Neonates with a new diagnosis of anorectal malformation (ARM) present a unique challenge to the clinical team. ARM is strongly associated with additional midline malformations, such as those observed in the VACTERL sequence, including vertebral, cardiac, and renal malformations. Timely assessment is necessary to identify anomalies requiring intervention and to prevent undue stress and delayed treatment. We utilized a multidisciplinary team to develop an algorithm guiding the midline workup of patients newly diagnosed with ARM. Patients were included if born in or transferred to our neonatal intensive care unit (NICU), or if seen in clinic within one month of life. Complete imaging was defined as an echocardiogram, renal ultrasound, and spinal magnetic resonance imaging or ultrasound within the first month of life. We compared three periods: prior to implementation (2010–2014), adoption period (2015), and delayed implementation (2022); p ≤ 0.05 was considered significant. Rates of complete imaging significantly improved from pre-implementation to delayed implementation (65.2% vs. 50.0% vs. 97.0%, p = 0.0003); the most growth was observed in spinal imaging (71.0% vs. 90.0% vs. 100.0%, p = 0.001). While there were no differences in the rates of identified anomalies, there were fewer missed diagnoses with the algorithm (10.0% vs. 47.6%, p = 0.05). We demonstrate that the implementation of a standardized algorithm can significantly increase appropriate screening for anomalies associated with a new diagnosis of ARM and can decrease delayed diagnosis. Further qualitative studies will help to refine and optimize the algorithm moving forward. Full article

Aircraft wake vortices are serious threats to aviation safety. The Pulsed Coherent Doppler Lidar (PCDL) has been widely used in the observation of aircraft wake vortices due to its advantages of high spatial-temporal resolution and high precision. However, the post-processing algorithms require significant computing resources, which cannot achieve the real-time detection of a wake vortex (WV). This paper presents an improved Convolutional Neural Network (CNN) method for WV locating and grading based on PCDL data to avoid the influence of unstable ambient wind fields on the localization and classification results of WV. Typical WV cases are selected for analysis, and the WV locating and grading models are validated on different test sets. The consistency of the analytical algorithm and the CNN algorithm is verified. The results indicate that the improved CNN method achieves satisfactory recognition accuracy with higher efficiency and better robustness, especially in the case of strong turbulence, where the CNN method recognizes the wake vortex while the analytical method cannot. The improved CNN method is expected to be applied to optimize the current aircraft spacing criteria, which is promising in terms of aviation safety and economic benefit improvement. Full article

The catalytic decomposition of CH₄ to H₂ and carbon nanotubes (CNTs) was investigated regarding Pt(1)-Fe(30)/MCM-41 and Fe(30)/MCM-41 using a fixed-bed flow reactor under an atmosphere. X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), transmission electron microscope (TEM), and Raman spectroscopy were used to characterize the behavior of Pt(1)-Fe(30)/MCM-41 and Fe(30)/MCM-41. The hydrogen yield of Pt(1)-Fe(30)/MCM-41 was 3.2 times higher than that of Fe(30)/MCM-41. When 1 wt% of Pt was added to Fe(30)/MCM-41(Mobil Composition of Matter No. 41), the atomic percentage of Fe2p increased from 13.39% to 16.14% and the core Fe2p_1/2 electron levels of Fe⁰ and Fe²⁺ chemically shifted to lower energies (0.2 eV and 0.1 eV, respectively) than those of Fe(30)/MCM-41. The Fe, Pt, Si, and O nanoparticles were uniformly distributed on the catalyst surface, and the average iron particle sizes of the Pt(1)-Fe(30)/MCM-41 and Fe(30)/MCM-41 were about 33.4 nm and 58.5 nm, respectively. This is attributed to the uniform distribution of the nano-sized iron particles on the MCM-41 surface, which was due to the suitable metal-carrier interaction (SMCI) between Fe, Pt, and MCM-41 and the high reduction degree of Fe due to the spillover effect of H₂ from Pt to Fe. Pt(1)-Fe(30)/MCM-41 produced multiwalled CNTs and bamboo-shaped CNTs with high crystallinity and graphitization degree using the tip-growth mechanism, with an I_D/I_G ratio of 0.93 and a C(101)/C(002) ratio of 0.64. Full article

Microheaters are used in several applications, including medical diagnostics, synthesis, environmental monitoring, and actuation. Conventional microheaters rely on thin-film electrodes microfabricated in a clean-room environment. However, low-cost alternatives based on conductive paste electrodes fabricated using printing techniques have started to emerge over the years. Here, we report a surprising effect that leads to significant electrode performance improvement as confirmed by the thorough characterization of bulk, processed, and conditioned samples. Mixing silver ink and PVA results in the solubilization of performance-hindering organic compounds. These compounds evaporate during heating cycles. The new electrodes, which reach a temperature of 80 °C within 5 min using a current of 7.0 A, display an overall 42% and 35% improvement in the mechanical (hardness) and electrical (resistivity) properties compared to pristine silver ink electrodes. To validate our results, we use the composite heater to amplify and detect parasite DNA from Trypanosoma brucei, associated with African sleeping sickness. Our LAMP test compares well with commercially available systems, confirming the excellent performance of our nanocomposite heaters. Since their fabrication relies on well-established techniques, we anticipate they will find use in a range of applications. Full article

Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs. Full article

Fertilizer can improve the yield of crops per unit area, and uniform fertilizer discharge can improve the fertilizer utilization rate. Therefore, it is meaningful to improve the performance of fertilizer-discharge devices in order to improve the modernization level of crop field fertilizer management. To address the problems of operational smoothness, stability and poor uniformity of fertilizer discharge, and other difficult problems encountered with strip fertilizer-discharge devices, this study designs a disturbed fertilizer strip-discharge device with a vertical pendulum. The main factors affecting the performance of fertilizer discharge were the wedge angle of the push-disturbing main pendulum bar (PMPB), the inclination angle of the aided-stirring pendulum pick (APP), the flow gap of the pendulum bar (FGPB), and the operation frequency of the swing-rod combination (SRC). The discrete element method (DEM) was used to establish a simulation model of the fertilizer device to explore the influence of the main factors on the performance of fertilizer discharge, with the coefficient of variation (CV) of fertilizer discharge uniformity and fertilizer discharge accuracy (FDA) used as the evaluation indices. The results show that the factors affecting the CV of fertilizer discharge uniformity and FDA were, in order of priority, the operation frequency of the SRC, the FGPB, the wedge angle of the PMPB, and the inclination angle of the APP. The optimal parameters after rounding were as follows: the wedge angle of the PMPB was 45°, the inclination angle of the APP was 46°, the operation frequency of the SRC was 188 times/min, and the FGPB was 4.5 mm. At this point, the model predicted that the CV of fertilizer discharge uniformity would be 10.53%, and that the FDA would be 3.19%. Using the optimal parameters for bench test verification, it was found that the wedge angle of the PMPB was 45°, the inclination angle of the APP was 46°, the operation frequency of the SRC was 188 times/min, the FGPB was 4.5 mm, the CV of the uniformity of the fertilizer discharge was 11.06%, and the FDA was 3.51%. In the test, the fertilizer-discharge device was stable and had good adaptability to different fertilizers. The results of this study can provide a theoretical reference for the development of precision strip-fertilizer application devices. Full article

The relationship between cerebral rhythms and early sensorimotor development is not clear. In recent decades, evidence revealed a rhythmic modulation involving sensorimotor processing. A widely corroborated functional role of oscillatory activity is to coordinate the information flow across sensorimotor networks. Their activity is coordinated by event-related synchronisation and desynchronisation in different sensorimotor rhythms, which indicate parallel processes may be occurring in the neuronal network during movement. To date, the dynamics of these brain oscillations and early sensorimotor development are unexplored. Our study investigates the relationship between the cerebral rhythms using EEG and a typical rhythmic movement of infants, the non-nutritive sucking (NNS) behaviour. NNS is an endogenous behaviour that originates from the suck central pattern generator in the brainstem. We find, in 17 infants, that sucking frequency correlates with beta synchronisation within the sensorimotor area in two phases: one strongly anticipating (~3 s) and the other encompassing the start of the motion. These findings suggest that a beta synchronisation of the sensorimotor cortex may influence the sensorimotor dynamics of NNS activity. Our results reveal the importance of rapid brain oscillations in infants and the role of beta synchronisation and their possible role in the communication between cortical and deep generators. Full article

Recent studies suggest that the dietary intake of human milk oligosaccharides (HMOs) provides health benefits from infancy up to adulthood. Thus far, beneficial changes in the adult gut microbiome have been observed at oral doses of 5–20 g/day of HMOs. Efficacy of lower doses has rarely been tested. We assessed four HMO molecular species—2′Fucosyllactose (2′FL), Lacto-N-neotetraose (LNnT), 3′Sialyllactose (3′SL), and 6′Sialyllactose (6′SL)—at predicted doses from 0.3 to 5 g/day for 6-year-old children and adults (n = 6 each), using ex vivo SIFR^® technology (Cryptobiotix, Ghent, Belgium). This technology employing bioreactor fermentation on fecal samples enables us to investigate microbial fermentation products that are intractable in vivo given their rapid absorption/consumption in the human gut. We found that HMOs significantly increased short-chain fatty acids (SCFAs), acetate, propionate (in children/adults), and butyrate (in adults) from predicted doses of 0.3–0.5 g/day onwards, with stronger effects as dosing increased. The fermentation of 6′SL had the greatest effect on propionate, LNnT most strongly increased butyrate, and 2′FL and 3′SL most strongly increased acetate. An untargeted metabolomic analysis revealed that HMOs enhanced immune-related metabolites beyond SCFAs, such as aromatic lactic acids (indole-3-lactic acid/3-phenyllactic acid) and 2-hydroxyisocaproic acid, as well as gut–brain-axis-related metabolites (γ-aminobutyric acid/3-hydroxybutyric acid/acetylcholine) and vitamins. The effects of low doses of HMOs potentially originate from the highly specific stimulation of keystone species belonging to, for example, the Bifidobacteriaceae family, which had already significantly increased at doses of only 0.5 g/day LNnT (adults) and 1 g/day 2′FL (children/adults). Full article

The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection. Full article

Squid (Loligo vulgaris) is commonly prone to spoilage, leading to a short shelf-life. High-pressure processing (HPP) can play a role in maintaining the quality and freshness of squid. Along with HPP, food preservatives from natural sources such as mint extract (ME), which are effective, safe, available, and cost-effective, are required. The present study aimed to investigate the combined effect of ME and HPP on the quality of refrigerated squid mantle cuts (SMC) over a period of 15 days. The time-kill profiles of ME and planktonic cell inactivation by HPP were assessed. ME (400 mg/L) inhibited bacterial growth, while planktonic cells treated with HPP (400 MPa) exhibited a reduction at 5 min. Physicochemical and microbial qualities of SMC treated with ME (0, 200, 400 mg/L) followed by HPP (0.1, 200, 400 MPa) for 5 min were monitored during refrigerated storage. Samples treated with ME (400 mg/L) and HPP (400 MPa) exhibited lower weight loss, cooking loss, pH changes, volatile base content, microbial counts, and higher textural properties than other samples. Based on next-generation sequencing results, Brochothrix campestris from family Listeriaceae was the predominant spoilage bacteria in treated sample after 12 days of storage. Therefore, ME and HPP combined treatments exhibited effectiveness in extending the shelf-life of refrigerated SMC. Full article