Search Results (5,614)

The objectives of this review are to identify the nutritional challenges faced by modern sows and present potential solutions to mitigate excessive maternal tissue loss and reproductive failure as it relates to recent genetic improvements. Current feeding programs have limitations to support the rapid genetic improvements in reproductive performance for modern sows. Since 2012, both litter size at birth and fetal weight have increased by 2.26 pigs per litter and 0.22 kg per piglet, respectively, thereby increasing the nutrient needs for sows during gestation and lactation. Prediction models generated in this review predict that modern sows would need 31% more lysine during gestation when compared with current feeding programs. Physiological challenges facing modern sows are also addressed in this review. High oxidative stress, pelvic organ prolapse, and lameness can directly affect the sow, whereas these physiological challenges can have negative impacts on colostrum and milk quality. In response, there is growing interest in investigating the functional roles of select bioactive compounds as feed additives to mitigate the severity of these challenges. Selenium sources, catechins, and select plant extracts have been utilized to reduce oxidative stress, calcium chloride and phytase have been used to mitigate pelvic organ prolapse and lameness, algae and yeast derivatives have been used to improve colostrum and milk quality, and fiber sources and probiotics have been commonly utilized to improve sow intestinal health. Collectively, this review demonstrates the unique challenges associated with managing the feeding programs for modern sows and the opportunities for revision of the amino acid requirements as well as the use of select bioactive compounds to improve reproductive performance. Full article

Chronic lymphocytic leukemia (CLL) is a genetically and clinically diverse hematological cancer affecting middle-aged and elderly individuals. Novel targeted therapy options are needed for patients who relapse following initial responses or who are intrinsically resistant to current treatments. There is a growing body of investigation currently underway on MDM2 inhibitors in clinical trials, reflecting the increasing interest in including these drugs in cancer treatment regimens. One of the developed compounds, idasanutlin (RG7388), has shown promise in early-stage clinical trials. It is a second-generation MDM2–p53-binding antagonist with enhanced potency, selectivity, and bioavailability. In addition to the TP53 status, which is an important determinant of the response, we have shown in our previous studies that the SF3B1 mutational status is also an independent predictive biomarker of the ex vivo CLL patient sample treatment response to RG7388. The objective of this study was to identify novel biomarkers associated with resistance to RG7388. Gene set enrichment analysis of differentially expressed genes (DEGs) between RG7388-sensitive and -resistant CLL samples showed that the increased p53 activity led to upregulation of pro-apoptosis pathway genes while DNA damage response pathway genes were additionally upregulated in resistant samples. Furthermore, differential expression of certain genes was detected, which could serve as the backbone for novel combination treatment approaches. This research provides preclinical data to guide the exploration of drug combination strategies with MDM2 inhibitors, leading to future clinical trials and associated biomarkers that may improve outcomes for CLL patients. Full article

Recently, there has been a renewed interest in the fermentation of kefir grains using fruit-based substrates, such as coconut water. Kefir grains contain a mixture of lactic acid bacteria (LAB), acetic acid bacteria (AAB), and yeast, which have important probiotic capacity and play a vital role in improving the nutritional and functional properties of the new product being developed. The principal objective of this study was to determine the functional properties of the microorganisms identified and characterized from kefir, CWK, and sourdough fermented with coconut water kefir (CWKS), such as Limosilactobacillus fermentum, Lactiplantibacillus plantarum, L. fusant, L. reuteri, L. kunkeei, Acetobacter aceti, A. lovaniensis, A. pasteurianus, Candida kefyr, Rhodotorula mucilaginosa, Saccharomyces cerevisiae, C. guilliermondii, and C. colliculosa. In addition to identifying functional properties, such as glutamic acid production, phytase production, phytic acid degradation, and exopolysaccharide production, from this study, it was found that significantly high quantities of glutamic acid, exopolysaccharide, and phytase enzyme were detected in two LAB isolates, Limosilactobacillus fermentum and Lactiplantibacillus plantarum. Full article

The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic Skin Responses (GSRs) together with self-reported questionnaires from two groups of students who practiced a deep breathing exercise either with a social robot or a laptop. From GSR signals, we obtained the change in participants’ arousal level throughout the intervention, and from the EEG signals, we extracted the change in their emotional valence using the neurometric of Frontal Alpha Asymmetry (FAA). While subjective perceptions of stress and user experience did not differ significantly between the two groups, the physiological signals revealed differences in their emotional responses as evaluated by the arousal–valence model. The Laptop group tended to show a decrease in arousal level which, in some cases, was accompanied by negative valence indicative of boredom or lack of interest. On the other hand, the Robot group displayed two patterns; some demonstrated a decrease in arousal with positive valence indicative of calmness and relaxation, and others showed an increase in arousal together with positive valence interpreted as excitement. These findings provide interesting insights into the impact of social robots as mental well-being coaches on students’ emotions particularly in the presence of the novelty effect. Additionally, they provide evidence for the efficacy of physiological signals as an objective and reliable measure of user experience in HRI settings. Full article

Weed infestation of cereal crops in organic farming is becoming a serious problem in agriculture. Sustainable agriculture requires the search for and implementation of crop management techniques that will reduce weeds without negatively impacting the environment. This research refers to the principles of integrated plant protection in sustainable agriculture, allowing the use of chemical plant protection products to be limited to the absolute minimum. Technology for growing spring barley based on the use of bacterial consortia in combination with living mulch (LM) can be an interesting approach to this problem. The aim of this three-year field research was to determine the effects of bacterial consortia and LM on the level of weed infestation in the organic spring barley crop. Two factors were tested in the experiment: bacterial consortia factors: control (without bacterial consortia); 1—Bacillus megaterium var. phosphaticum and Arthrobacter agilis; 2—Bacillus subtilis, Bacillus amyloliquefaciens, and Pseudomonas fluorescens; and LM: control (without LM); red clover; red clover + Italian ryegrass; and Italian ryegrass. This research demonstrated that the bacterial consortia tested significantly reduced both the biomass and number of weeds, including the following dominant weeds: Chenopodium album, Sinapis arvensis, Elymus repens, and Tripleurospermum inodorum. The use of LM also significantly reduced the weed infestation of spring barley stands. The lowest biomass and number of weeds, with the exception of Elymus repens, were recorded on objects with LM Italian ryegrass in spring barley in combination with bacterial consortium 2. The introduction of cultivation with LM Italian ryegrass or its mixture with red clover and the use of bacteria should be recommended for the practice of sustainable agriculture, which will reduce weeds through an ecological method. Full article

Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object’s optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object’s depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking. Full article

Subpixel object detection presents a significant challenge within the domain of hyperspectral image (HSI) processing, primarily due to the inherently limited spatial resolution of imaging spectrometers. For subpixel object detection, the dimensional extent of the object of interest is smaller than an individual pixel, which significantly diminishes the utility of spatial information pertaining to the object. Therefore, the efficacy of detection algorithms depends heavily on the spectral data inherent in the image. The detection of subpixel objects in hyperspectral imagery primarily relies on the suppression of the background and the enhancement of the object of interest. Hence, acquiring accurate background information from HSI images is a crucial step. In this study, an adaptive background endmember extraction for hyperspectral subpixel object detection is proposed. An adaptive scale constraint is incorporated into the background spectral endmember learning process to improve the adaptability of background endmember extraction, thus further enhancing the algorithm’s generalizability and applicability in diverse analytical scenarios. Experimental results demonstrate that the adaptive endmember extraction-based subpixel object detection algorithm consistently outperforms existing state-of-the-art algorithms in terms of detection efficacy on both simulated and real-world datasets. Full article

Water resource allocation systems typically involve multi-level decision-making, with each level having distinct goals and interests, while being influenced by various factors such as social, economic, environmental, and policy planning. The decision-making in water resource allocation systems is characterized by complex uncertainty factors and dynamic changes. In light of this, this study integrates stochastic chance-constrained programming, dynamic programming, bi-level programming, goal programming, and water rights trading to construct a bi-level objective programming model of water resource uncertainty based on water rights trading. The model not only effectively represents the random uncertainty, dynamic characteristics, interests of decision-making levels, and planning requirements of policies in water resource allocation systems but also utilizes market mechanisms to enable compensated transfer of water rights, fully leveraging the role of water rights marketization in water resource allocation. Taking the Yehe River Irrigation District in Hebei Province of China as an illustrative case study, the specific allocation scheme of each stage under the guaranteed rate of 50% in 2025 and the water rights trading results of each sub-region are obtained. Compared with the bi-level objective programming model of water resources uncertainty without water rights trading, the results show that the water consumption per CNY ten thousand GDP(WG)of the irrigation district decreased by 3.42%, and the economic benefits of Luquan District, Jingxing County, Pingshan County, and Yuanshi County in each sub-region increased by 19.17%, 7.19%, 15.11%, and 4.94%, respectively. This improves regional water use efficiency and economic benefits and provides a scientific basis for regional water resource allocation. Full article

Distributed grid-connected photovoltaic (PV) generation explores several methods that produce energy at or near the point of consumption, with the aim of reducing electricity losses among transmission networks. Consequently, home on-grid PV applications have garnered increased interest from both scientific researchers and industry professionals over the last decade. Nevertheless, the growing installation of intermittent nature residential PV systems (R-PV) in low-voltage distribution networks is leading to more cautious considerations of technology limitations and PV design challenges. This conservative perspective arises from the standpoint of grid quality and security, ultimately resulting in the revocation of PV connection authorization. Hence, the design of R-PV systems should consider not only the specifications of the PV panels and load profiles but also the characteristics and requirements of the connected power grid. This project therefore seeks to enhance the design considerations of grid-connected PV systems, in order to help the end-users meet the grid codes set out by the Saudi Electricity Regulatory Authority (SERA). Since the maximum amount of generated power is essential for PV system optimization, the ratio of grid strength to maximum transmitted power was employed to ascertain the suitable capacity of the PV system, while the assessment of PV power output was utilized to specify the system size. Furthermore, a battery energy storage system (BESS) with a small size (~10% of the PV capacity) is employed to enhance the PV power quality for a dependable grid interconnection. The BESS is equipped with a versatile power controller in order to achieve the designed objectives. The obtained results show an essential advancement in terms of power quality and reliability at the customer’s connection point. Moreover, with the design assessment process, the low-voltage ride-through (LVRT) and power factor requirements can be met, in addition to the total harmonic distortion (THD) and frequency transient limitations. The proposed solution assists end-users in efficiently designing their own R-PV systems while ensuring quality and sustainability for authorized grid interconnection. Full article

With the increasing penetration of digital technologies into health management, digital health interventions in workplaces have been subject to substantial interest. These interventions aim to enhance employee well-being, minimize absenteeism and presenteeism, and augment organizational productivity. This paper carries out a systematic review focusing on the key characteristics of effective digital health interventions designed to enhance health-related outcomes within workplace settings and evaluates their implications for prospective implementation in the workplace. According to PRISMA guidelines, the current systematic review adopted the most appropriate methods to retrieve studies from PubMed, covering interventions that included cognitive-behavioral therapy apps, software that reduces sedentary behaviors, virtual reality for well-being, and comprehensive health programs. The studies’ quality was assessed through standardized tools with a preference for randomized control trials and mixed-methods research. It was found that digital health interventions positively impact mental health, physical activity, and well-being. However, limitations were found due to self-reported data and potential biases. This review identified long-term effectiveness, objective outcome measures, and cost-effectiveness as areas for future research. Digital health interventions hold promise in enhancing workplace health strategies, as they offer scalable, personalized, cost-effective solutions. However, critically relevant research gaps have to be faced to integrate these successfully and exploit their real potential in organizational health strategies. Full article

Consumers often cite cognitive improvements as reasons for making dietary changes or using dietary supplements, a motivation that if leveraged could greatly enhance public health. However, rarely is it considered whether standardized cognitive tests that are used in nutrition research are aligned to outcomes of interest to the consumer. This knowledge gap presents a challenge to the scientific substantiation of nutrition-based cognitive health benefits. Here we combined focus group transcript review using reflexive thematic analysis and a multidisciplinary expert panel exercise to evaluate the applicability of cognitive performance tools/tasks for substantiating the specific cognitive benefits articulated by consumers with the objectives to (1) understand how consumers comprehend the potential benefits of nutrition for brain health, and (2) determine the alignment between consumers desired brain benefits and validated tests and tools. We derived a ‘Consumer Taxonomy of Cognitive and Affective Health in Nutrition Research’ which describes the cognitive and affective structure from the consumers perspective. Experts agreed that validated tests exist for some consumer benefits including focused attention, sustained attention, episodic memory, energy levels, and anxiety. Prospective memory, flow, and presence represented novel benefits that require the development and validation of new tests and tools. Closing the gap between science and consumers and fostering co-creative approaches to nutrition research are critical to the development of products and dietary recommendations that support realizable cognitive benefits that benefit public health. Full article

Background/Objectives: Myokines have been demonstrated to be associated with cardiovascular diseases; however, they have not been studied as biomarkers for peripheral artery disease (PAD). We identified interleukin-7 (IL-7) as a prognostic biomarker for PAD from a panel of myokines and developed predictive models for 2-year major adverse limb events (MALEs) using clinical features and plasma IL-7 levels. Methods: A prognostic study was conducted with a cohort of 476 patients (312 with PAD and 164 without PAD) that were recruited prospectively. Their plasma concentrations of five circulating myokines were measured at recruitment, and the patients were followed for two years. The outcome of interest was two-year MALEs (composite of major amputation, vascular intervention, or acute limb ischemia). Cox proportional hazards analysis was performed to identify IL-7 as the only myokine that was associated with 2-year MALEs. The data were randomly divided into training (70%) and test sets (30%). A random forest model was trained using clinical characteristics (demographics, comorbidities, and medications) and plasma IL-7 levels with 10-fold cross-validation. The primary model evaluation metric was the F1 score. The prognostic model was used to classify patients into low vs. high risk of developing adverse limb events based on the Youden Index. Freedom from MALEs over 2 years was compared between the risk-stratified groups using Cox proportional hazards analysis. Results: Two-year MALEs occurred in 28 (9%) of patients with PAD. IL-7 was the only myokine that was statistically significantly correlated with two-year MALE (HR 1.56 [95% CI 1.12–1.88], p = 0.007). For the prognosis of 2-year MALEs, our model achieved an F1 score of 0.829 using plasma IL-7 levels in combination with clinical features. Patients classified as high-risk by the predictive model were significantly more likely to develop MALEs over a 2-year period (HR 1.66 [95% CI 1.22–1.98], p = 0.006). Conclusions: From a panel of myokines, IL-7 was identified as a prognostic biomarker for PAD. Using a combination of clinical characteristics and plasma IL-7 levels, we propose an accurate predictive model for 2-year MALEs in patients with PAD. Our model may support PAD risk stratification, guiding clinical decisions on additional vascular evaluation, specialist referrals, and medical/surgical management, thereby improving outcomes. Full article

Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection’s robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources. To facilitate effective fusion, the YOLOv5 and NLNNs, K-means+, and spatial pyramid pooling fast+ (SPPF+) modules are integrated within a unified framework. The brain tumor dataset is used to refine the YOLOv5 model through the application of transfer learning techniques, adapting it specifically to the task of tumor detection. The results indicate that the combination of YOLOv5 and other modules results in enhanced detection capabilities in comparison to the utilization of YOLOv5 exclusively, proving recall rates of 86% and 83% respectively. Moreover, the research explores the interpretability aspect of the combined model. By visualizing the attention maps generated by the NLNNs module, the regions of interest associated with tumor presence are highlighted, aiding in the understanding and validation of the decision-making procedure of the methodology. Additionally, the impact of hyperparameters, such as NLNNs kernel size, fusion strategy, and training data augmentation, is investigated to optimize the performance of the combined model. Full article

Injuries represent a serious concern for football players, with a significant loss in terms of sport participation and long periods of rehabilitation. According to the 2019/20 UEFA Élite Club Injury Report, the average incidence of injuries during training is 2.8 per 1000 h of training, with an average absence from training of 20 days. In addition, injured athletes are 4 to 7 times more likely to relapse than uninjured athletes. High workloads and reduced recovery periods represent two of the most important modifiable risk factors. In this context, prevention and an adequate rehabilitation protocol are vital in managing injuries, reducing their incidence, and improving the return to competition. In recent years, technological development has provided new tools in rehabilitation, and Virtual reality (VR) has shown interesting results in treating neurologic and orthopedic pathologies. Virtual Reality (VR) technology finds application in the sports industry as a tool to examine athletes’ technical movements. The primary objective is to detect the biomechanical risk factors associated with anterior cruciate ligament injury. Additionally, VR can be used to train athletes in field-specific techniques and create safe and controlled therapeutic environments for post-injury recovery. Moreover, VR offers a customizable approach to treatment based on individual player data. It can be employed for both prevention and rehabilitation, tailoring the rehabilitation and training protocols according to the athletes’ specific needs. Full article

Application of lineament analysis in structural geology gained renewed interest when remote sensing data and technology became available through dedicated Earth observation satellites like Landsat in 1972. Lineament data have since been widely used in general structural investigations and resource and geohazard studies. The present contribution argues that lineament analysis remains a useful tool in structural geology research both at the regional and local scales. However, the traditional “lineament study” is only one of several methods. It is argued here that structural and lineament remote sensing studies can be separated into four distinct strategies or approaches. The general analyzing approach includes general structural analysis and identification of foliation patterns and composite structural units (mega-units). The general approach is routinely used by most geologists in preparation for field work, and it is argued that at least parts of this should be performed manually by staff who will participate in the field activity. We argue that this approach should be a cyclic process so that the lineament database is continuously revised by the integration of data acquired by field data and supplementary data sets, like geophysical geochronological data. To ensure that general geological (field) knowledge is not neglected, it is our experience that at least a part of this type of analysis should be performed manually. The statistical approach conforms with what most geologists would regard as “lineament analysis” and is based on statistical scrutiny of the available lineament data with the aim of identifying zones of an enhanced (or subdued) lineament density. It would commonly predict the general geometric characteristics and classification of individual lineaments or groups of lineaments. Due to efficiency, capacity, consistency of interpretation methods, interpretation and statistical handling, this interpretative approach may most conveniently be performed through the use of automatized methods, namely by applying algorithms for pattern recognition and machine learning. The focused and dynamic approaches focus on specified lineaments or faults and commonly include a full structural geological analysis and data acquired from field work. It is emphasized that geophysical (potential field) data should be utilized in lineament analysis wherever available in all approaches. Furthermore, great care should be taken in the construction of the database, which should be tailored for this kind of study. The database should have a 3D or even 4D capacity and be object-oriented and designed to absorb different (and even unforeseen) data types on all scales. It should also be designed to interface with shifting modeling tools and other databases. Studies of the Norwegian mainland have utilized most of these strategies in lineament studies on different scales. It is concluded that lineament studies have revealed fracture and fault systems and the geometric relations between them, which would have remained unknown without application of remote sensing data and lineament analysis. Full article