Search Results (1,765)

Society is becoming more demanding with aquaculture’s environmental footprint and animal wellbeing. In order to potentially mitigate these concerns, feed formulations could be based on eco-efficient (circular economy-driven) or organic ingredients. This study aimed to investigate the growth performance, feed utilization, and health status of juvenile Nile tilapia (Oreochromis niloticus) when fed with such feeds. The growth trial lasted for 8 weeks, and fish had an initial weight of 31.0 ± 0.5 g (mean ± SD). Fish were fed until visual satiation, in quadruplicate, with one of three isonitrogenous and isoenergetic experimental feeds: a commercial-like feed without fishmeal (PD), a diet based on ingredients compatible with organic certification (ORG), or a feed formulated using circular economy-driven subproducts and emergent ingredients (ECO). Fish fed ECO showed a tendency for decreased feed intake, while ORG fish significantly reduced their intake compared to those fed PD. Consequently, fish fed ECO (62.7 ± 5.4 g) exhibited almost half the growth than those fed PD (107.8 ± 6.1 g), while ORG fish almost did not increase their weight (32.7 ± 1.3 g). ECO and ORG diets had a lower digestibility for protein, lipid, and energy when compared to PD. Feed utilization of fish fed ECO or ORG was also lower than those fed PD. From the health-related genes analyzed, only glutathione reductase (gsr) showed statistically significant differences, being more expressed in fish-fed ECO than those fed PD. Thus, even when such novel formulations induced extreme effects on voluntary feed intake, their impact was noted only in fish growth, but not in robustness. Full article

Lonicerae japonicae flos (LJF) and Lonicerae flos (LF) are traditional Chinese herbs that are commonly used and widely known for their medicinal properties and edibility. Although they may have a similar appearance and vary slightly in chemical composition, their effectiveness as medicine and their use in clinical settings vary significantly, making them unsuitable for substitution. In this study, a novel 2 × 3 six-channel fluorescent sensor array is proposed that uses machine learning algorithms in combination with the indicator displacement assay (IDA) method to quickly identify LJF and LF. This array comprises two coumarin-based fluorescent indicators (ES and MS) and three diboronic acid-substituted 4,4′-bipyridinium cation quenchers (Q1–Q3), forming six dynamic complexes (C1–C6). When these complexes react with the ortho-dihydroxy groups of phenolic acid compounds in LJF and LF, they release different fluorescent indicators, which in turn causes distinct fluorescence recovery. By optimizing eight machine learning algorithms, the model achieved 100% and 98.21% accuracy rates in the testing set and the cross-validation predictions, respectively, in distinguishing between LJF and LF using Linear Discriminant Analysis (LDA). The integration of machine learning with this fluorescent sensor array shows great potential in analyzing and detecting foods and pharmaceuticals that contain polyphenols. Full article

Biotechnological active peptides are gaining interest in the cosmetics industry due to their antimicrobial, anti-inflammatory, antioxidant, and anti-collagenase (ACE) effects, as well as wound healing properties, making them suitable for cosmetic formulations. The antimicrobial activity of peptides (2–10 kDa) secreted by Saccharomyces cerevisiae Ethanol-Red was evaluated against dermal pathogens using broth microdilution and challenge tests. ACE was assessed using a collagenase activity colorimetric assay, antioxidant activity via spectrophotometric monitoring of nitrotetrazolium blue chloride (NBT) reduction, and anti-inflammatory effects by quantifying TNF-α mRNA in lipopolysaccharides (LPS)-exposed dermal fibroblasts. Wound healing assays involved human fibroblasts, endothelial cells, and dermal keratinocytes. The peptides (2–10 kDa) exhibited antimicrobial activity against 10 dermal pathogens, with the Minimum Inhibitory Concentrations (MICs) ranging from 125 µg/mL for Staphylococcus aureus to 1000 µg/mL for Candida albicans and Streptococcus pyogenes. In the challenge test, peptides at their MICs reduced microbial counts significantly, fulfilling ISO 11930:2019 standards, except against Aspergillus brasiliensis. The peptides combined with Microcare^Ⓡ SB showed synergy, particularly against C. albicans and A. brasilensis. In vitro, the peptides inhibited collagenase activity by 41.8% and 94.5% at 250 and 1000 µg/mL, respectively, and demonstrated antioxidant capacity. Pre-incubation with peptides decreased TNF-α expression in fibroblasts, indicating anti-inflammatory effects. The peptides do not show to promote or inhibit the angiogenesis of endothelial cells, but are able to attenuate fibrosis, scar formation, and chronic inflammation during the final phases of the wound healing process. The peptides showed antimicrobial, antioxidant, ACE, and anti-inflammatory properties, highlighting their potential as multifunctional bioactive ingredients in skincare, warranting further optimization and exploration in cosmetic applications. Full article

Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are found in urine samples from patients with RCC. In this study, we propose to investigate the use of Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) on dried urine samples for distinguishing RCC. We analyzed dried urine samples from 49 patients with RCC, confirmed by histopathology, and 39 healthy donors using ATR-FTIR spectroscopy. The vibrational bands of the dried urine were identified by comparing them with spectra from dried artificial urine, individual urine components, and dried artificial urine spiked with urine components. Urea dominated all spectra, but smaller intensity peaks, corresponding to creatinine, phosphate, and uric acid, were also identified. Statistically significant differences between the FTIR spectra of the two groups were obtained only for creatinine, with lower intensities for RCC cases. The discrimination of RCC was performed through Principal Component Analysis combined with Linear Discriminant Analysis (PCA–LDA) and Support Vector Machine (SVM). Using PCA–LDA, we achieved a higher discrimination accuracy (82%) (using only six Principal Components to avoid overfitting), as compared to SVM (76%). Our results demonstrate the potential of urine ATR-FTIR combined with machine learning techniques for RCC discrimination. However, further studies, especially of other urological diseases, must validate this approach. Full article

As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m³/m³) and the highest coefficient of determination (R², above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices. Full article

Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (>30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods. Full article

In this study, an electronic tongue (E-tongue) and electronic nose (E-nose) systems were applied to detect pesticide residues, specifically Preza, Daconil, Curzate, Bricol, Accros, Amistar, and Funlate, in fruits such as cape gooseberries, apples, plums, and strawberries. These advanced systems present several advantages over conventional methods (e.g., GC-MS and others), including faster analysis, lower costs, ease of use, and portability. Additionally, they enable non-destructive testing and real-time monitoring, making them ideal for routine screenings and on-site analyses where effective detection is crucial. The collected data underwent rigorous analysis through multivariate techniques, specifically principal component analysis (PCA) and linear discriminant analysis (LDA). The application of machine learning (ML) algorithms resulted in a good outcome, achieving high accuracies in identifying fruits contaminated with pesticides and accurately determining the concentrations of those pesticides. This level of precision underscores the robustness and reliability of the methodologies employed, highlighting their potential as alternative tools for pesticide residue detection in agricultural products. Full article

The Internet of Things (IoT) is a significant technological advancement that allows for seamless device integration and data flow. The development of the IoT has led to the emergence of several solutions in various sectors. However, rapid popularization also has its challenges, and one of the most serious challenges is the security of the IoT. Security is a major concern, particularly routing attacks in the core network, which may cause severe damage due to information loss. Routing Protocol for Low-Power and Lossy Networks (RPL), a routing protocol used for IoT devices, is faced with selective forwarding attacks. In this paper, we present a federated learning-based detection technique for detecting selective forwarding attacks, termed FL-DSFA. A lightweight model involving the IoT Routing Attack Dataset (IRAD), which comprises Hello Flood (HF), Decreased Rank (DR), and Version Number (VN), is used in this technique to increase the detection efficiency. The attacks on IoT threaten the security of the IoT system since they mainly focus on essential elements of RPL. The components include control messages, routing topologies, repair procedures, and resources within sensor networks. Binary classification approaches have been used to assess the training efficiency of the proposed model. The training step includes the implementation of machine learning algorithms, including logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB). The comparative analysis illustrates that this study, with SVM and KNN classifiers, exhibits the highest accuracy during training and achieves the most efficient runtime performance. The proposed system demonstrates exceptional performance, achieving a prediction precision of 97.50%, an accuracy of 95%, a recall rate of 98.33%, and an F1 score of 97.01%. It outperforms the current leading research in this field, with its classification results, scalability, and enhanced privacy. Full article

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach’s ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification. Full article

Phaseolus lunatus L. is a species of landrace bean widely cultivated in Northeast Brazil. The integration of new technologies in the agricultural sector has highlighted the significance of seed images analysis as a valuable asset in the characterization process. The objective was to assess the morphology of 18 P. lunatus varieties gathered from four states in the Brazilian Northeast. To achieve this, 100 seeds from each variety were utilized, and their images were acquired using a flatbed scanner with a digital resolution of 400 dpi. Subsequently, the images were processed using the ImageJ software package for analyzing seed size, shape and color characteristics. Statistical analyses were performed with SPSS software applying stepwise Linear Discriminant Analysis (LDA). The overall accuracy rate for correct identification was 80.5%. Among the varieties, the lowest classification percentage was attributed to the ‘Coquinho Vermelha’ variety (39%), while the highest rates were observed for ‘Fava Roxa’ and ‘Fava de Moita’ (98%). The morpho-colorimetric classification system successfully discriminated the varieties of P. lunatus produced in the northeastern region of Brazil, highlighting the -+*/high degree of diversity within them. In particular, seeds with uniform coloring or clearly defined secondary color patterns were easier to classify. The varieties showed low correlation, forming distinct groups based on background color, secondary color, or seed size. Full article

Since ancient times, thermal spring water has been proven to be beneficial to the skin and to improving dermatologic disorders, explaining its incorporation into cosmetic formulations as an active ingredient. Chaves thermal spring water, from northern Portugal, has been used as a local spa since Roman times, and its customers are satisfied with its medicinal quality. Despite the lack of published evidence on its specific effects on the skin, this study evaluates the potential of using Chaves thermal water as a cosmetic ingredient. The physiochemical composition demonstrated that Chaves thermal spring water is low-mineralized water, and its major components are sodium, potassium, silicon, and calcium. In vitro experiments demonstrated that this low mineralization might explain the absence of antioxidant and antiaging potential, and the maintenance of collagen and fibronectin levels. The quantification of the IL-6 levels showed that Chaves thermal spring water could be used as an anti-inflammatory product, suggesting its use by individuals with skin diseases. In agreement with this result, in vivo experiments revealed that Chaves thermal spring water improved the integrity of the skin barrier and preserved the skin microbial community. Overall, the present work suggests that Chaves thermal spring water might be used as a cosmetic product. Full article

User-generated data can reflect various viewpoints and experiences derived from people’s perception outcomes. The perceptual results can be obtained, often by combining subjective public perceptions of the landscape with physiological monitoring data. Accessing people’s perceptions of the landscape through text is a common method. It is hard to fully render nuances, emotions, and complexities depending only on text by superficial emotional tendencies alone. Numerical representations may lead to misleading conclusions and undermine public participation. In addition, the use of physiological test data does not reflect the subjective reasons for the comments made. Therefore, it is essential to deeply parse the text and distinguish between segments with different semantic differences. In this study, we propose a perceptual psychology-based workflow to extract and visualize multifaceted views from user-generated data. The analysis methods of FCN, LDA, and LSTM were incorporated into the workflow. Six areas in Fuzhou City, China, with 12 city parks, were selected as the study object. Firstly, 9987 review data and 1747 pictures with corresponding visitor trajectories were crawled separately on the Dianping and Liangbulu websites. For in-depth analysis of comment texts and making relevant heat maps. Secondly, the process of clauses was added to get a more accurate representation of the sentiment of things based on the LSTM sentiment analysis model. Thirdly, various factors affecting the perception of landscapes were explored. Based on such, the overall people’s perception of urban parks in Fuzhou was finally obtained. The study results show that (1) the texts in terms of ‘wind’, ‘temperature’, ‘structures’, ‘edge space (spatial boundaries)’, and ‘passed space’ are the five most representative factors of the urban parks in Fuzhou; (2) the textual analyses further confirmed the influence of spatial factors on perception in the temporal dimension; and (3) environmental factors influence people’s sense of urban parks concerning specificity, clocking behavior, and comfort feelings. These research results provide indispensable references for optimizing and transforming urban environments using user-generated data. Full article

Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed. Full article

Coatings can be created using various technologies and serve different roles, including protection, functionality, and decorative purposes. Among these technologies, electrodeposition has emerged as a low-cost, versatile, and straightforward process with remarkable scalability and manufacturability. Nickel, extensively studied in the context of electrodeposition, has many applications ranging from decorative to functional. The main objective of the present work is the electrodeposition of double-layer nickel coatings, consisting of a bright nickel pre-coating followed by a black nickel layer with enhanced properties, onto steel substrates. The influence of deposition parameters on colour, morphology, adhesion, roughness, and coefficient of friction was studied. The effects of cetyltrimethylammonium bromide (CTAB) and WS₂ nanoparticles on the coatings’ properties and performance were also investigated. Additionally, the influence of the steel substrate’s pre-treatment, consisting of immersion in an HCl solution, prior to the electrodeposition, to etch the surface and activate it, was evaluated and optimized. The characterization of the pre-coating revealed a homogeneous surface with a medium superficial feature of 2.56 μm. Energy dispersive X-ray spectroscopy (EDS) results showed a high content of Ni, and X-ray diffraction (XRD) confirmed its crystallinity. In contrast, the black films’ characterization revealed their amorphous nature. The BN10 sample, which corresponds to a black nickel layer with a deposition time of 10 min, showed the best results for colour and roughness, presenting the lowest brightness (L*) value (closest to absolute black) and the most homogeneous roughness. EDS analysis confirmed the incorporation of WS₂, but all samples with CTAB exhibited signs of corrosion and cracks, along with higher coefficient of friction (COF) values. Full article

The left displacement of the abomasum (LDA) is a common condition in dairy cows that can significantly impact their welfare, productivity, and fertility. This study was carried out in Greek dairy farms over a period of 3 years. To ensure early detection, the farmers were trained to accurately identify the disease. The reproductive performance and milk production of 306 cows was assessed by considering the time to the first estrus, the calving-to-conception interval, and the number of artificial inseminations required for the establishment of pregnancy. Uterine health status, the timing of disease diagnosis, and the season of the year were also evaluated. In a separate study, the outcomes of 26 cases where cows suffered LDA and underwent surgical treatment with a delay of at least one week from disease onset, were compared to those of cases promptly treated. The results indicate that even early identification and treatment of LDA affects fertility and milk yield; these impacts worsen with the co-existence of uterine infections of affected. However, in late-treated cases, all reproductive and production indices show significant deterioration. Our findings suggest that timely diagnosis of the disease, preferably by the farmer, ensures minimal losses in cows affected by LDA. Full article