Search

One of Jung's most significant contributions concerns the mysterious, inexplicable and always out‐of‐reach nature of the self. In this paper, I will focus on the borders of the self and their nature, location and dynamics of maintenance and change in geographically, historically, and culturally situated subjects. Reflecting on the refugee experience, I intend to gain more insights into our psychic functioning and the dynamics of the self in relation to itself, the other and groups. The experiences of some refugees, marked bysignificant trauma and migration, shed light on how the boundaries of the self are frequently contested and perpetually negotiated with others, and how our subjectivity is shaped by ongoing dynamics of occupation, dispute and/or negotiation, conducted at various levels of our social and individual existence. My argument is that these processes occur at a specific site: the boundaries of the self, involving intrapsychic, interpersonal and group psychological dynamics, with reverberations in the socio‐political and cultural spheres, and reciprocal influences between all these levels. This paper aims to concentrate on the shifts in these boundaries, illustrated through clinical vignettes. [ABSTRACT FROM AUTHOR]

The realization of the Self, although the absolute basis of Jungian psychology, has internal inconsistencies, is difficult to understand, and offers a promise of realization that cannot be fulfilled. This has weakened it, and its existence is now one of speculation. If this is the case, what is to be done with it in the consulting room? How should it be introduced and accounted for, if at all? This paper suggests some alternatives but concludes that the myth of the Self has gravitas in other established traditions, and it is those that offer a more coherent framework for the emergence of the Self. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Analytical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Analytical Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Selenium (Se) is one of the essential trace elements for humans. Plants are the main source of Se for humans, while soil Se is the primary source of Se for plants. Biofortification, which involves the transfer of Se from soil to plants and animals, is currently recognized as the safest and most effective approach for Se supplementation for humans. However, Se in soil primarily exists in forms that plants cannot easily utilize, so enhancing Se transfer from soil to plants is crucial for optimal Se utilization. In this paper, we provided a comprehensive analysis of Se forms in soil. Then we summarized the strategies for enhancing Se transfer from soil to plants. These strategies include adjusting redox potential, managing soil moisture, modulating pH value, improving organic matter, optimizing ion competition, promoting beneficial microbes, and considering the synergy between plant rhizosphere and soil. Furthermore, we reviewed Se forms and metabolism after uptake into plants to better understand its role in human health. Finally, we came up with the challenges and perspectives, to provide new insights for further study in this area. This work also offers potential solutions for enhancing Se transformation from soil to plants and utilizing soil Se to produce naturally Se-rich products. [ABSTRACT FROM AUTHOR]

Wearing safety helmets at construction sites is a major measure to prevent safety accidents, so it is essential to supervise and ensure that workers wear safety helmets. This requires a high degree of real-time performance. We improved the network structure based on YOLOv7. To enhance real-time performance, we introduced GhostModule after comparing various modules to create a new efficient structure that generates more feature mappings with fewer linear operations. SE blocks were introduced after comparing several attention mechanisms to highlight important information in the image. The EIOU loss function was introduced to speed up the convergence of the model. Eventually, we constructed the efficient model EGS-YOLO. EGS-YOLO achieves a mAP of 91.1%, 0.2% higher than YOLOv7, and the inference time is 13.3% faster than YOLOv7 at 3.9 ms (RTX 3090). The parameters and computational complexity are reduced by 37.3% and 33.8%, respectively. The enhanced real-time performance while maintaining the original high precision can meet actual detection requirements. [ABSTRACT FROM AUTHOR]

Bimetallic nanoparticles (BNPs) have garnered a great interest rather than monometallic nanoparticles (NPs) in terms of biotechnological applications due to their enhanced properties. They have a growing interest as promising biomedical agents for drug delivery, antibacterial, and anticancer treatments due to their significant permeability and retention effects. Both of nano-size copper (Cu) and selenium (Se) exhibited a significant activity in biological treatment and drug delivery systems. Consequently, the present work aimed to focus on the novel biogenic synthesis of Cu-Se BNPs using cell-free extract of a marine bacterium, and studying their anticancer and antioxidant activities. Based on 16S rDNA sequencing, the isolated marine bacterium was identified Bacillus amyloliquefaciens by phylogenetic analysis. Biogenic synthesis of Cu-Se BNPs was optimized by studying the effect of some physiological factors such as reaction time, pH, and temperature. Based on the reaction conditions, the biologically synthesized Cu-Se NPs was obtained with different particle size that ranged from 15.7-106.0 nm. The spherical small sized (15.7 nm) bimetallic Cu-Se BNPs displayed anticancer activity against HepG2 and MDA cell lines, while the cell viability was reduced by 87 % and 81 %, respectively. The estimated IC50 values were 696.4 µg/ ml for HepG2 and 273.9 µg/ ml for MDA, while scavenging capacity (SC) of Cu-Se BNPs (SC50 = 305.3 µM) showed lower ability to 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) scavenging compared to ascorbic acid (SC50 = 95.9 µM).

Abstract Selenium (Se) is one of the essential trace elements for humans. Plants are the main source of Se for humans, while soil Se is the primary source of Se for plants. Biofortification, which involves the transfer of Se from soil to plants and animals, is currently recognized as the safest and most effective approach for Se supplementation for humans. However, Se in soil primarily exists in forms that plants cannot easily utilize, so enhancing Se transfer from soil to plants is crucial for optimal Se utilization. In this paper, we provided a comprehensive analysis of Se forms in soil. Then we summarized the strategies for enhancing Se transfer from soil to plants. These strategies include adjusting redox potential, managing soil moisture, modulating pH value, improving organic matter, optimizing ion competition, promoting beneficial microbes, and considering the synergy between plant rhizosphere and soil. Furthermore, we reviewed Se forms and metabolism after uptake into plants to better understand its role in human health. Finally, we came up with the challenges and perspectives, to provide new insights for further study in this area. This work also offers potential solutions for enhancing Se transformation from soil to plants and utilizing soil Se to produce naturally Se-rich products. Graphical Abstract

Nanotechnology has enormous promise for a wide range of applications in biology. Nanoparticles (NPs) have the benefit of improving bioactivity, decreasing toxicity, allowing for precision targeting, and modulating the release profile of encapsulated compounds. Nanomaterials' unique qualities, such as their tiny size, biocompatibility, and ability to cross cell membranes for drug administration, make them useful in a variety of biological applications. Selenium (Se), a critical trace element, stands out among these nanoparticles due to its specific bioactivities in nano forms. Selenium is incorporated into Selenoproteins such as selenocysteine (Sec), which play an important role in maintaining physiological redox balance via oxidoreductase activity, a critical enzymatic process. In the field of medication delivery, selenium-based devices have been designed to transport pharmaceuticals to specific locations. Selenium nanoparticles (SeNPs) appear to be a suitable platform for delivering medications to their desired sites. Selenium's medicinal potential has been thoroughly investigated, including its efficacy against various cancer cells, microbial pathogens, viral infections, neuroprotective properties, diabetic control, oxidative stress, and inflammation-mediated illnesses such as rheumatoid arthritis. Notably, due to selenium's extraordinary involvement in immune system regulation, SeNPs have an edge over other nanoparticles. SeNPs phytosynthesis offers an appealing alternative to standard physical and chemical processes, featuring biocompatibility and environmental friendliness. This paper gives an overview of SeNPs' biological uses and emphasizes recent advances in the field.

Flooding is the most frequent phenomenon that leads to social and economic disruption worldwide. Effective flood management necessitates an understanding of the spatial distribution of flood-susceptible areas. Currently, the delineation of flood-susceptible areas and their effective management are a significant theme of flood research. However, in order to ensure sustainable flood management, the precise identification of flood-susceptible areas is yet to be explored. The Hooghly basin is one of the flood-affected areas of West Bengal in India, where the dense river system, topography, and geographic location make it more susceptible to flooding. The major aim of this study is to examine the flood susceptibility of the Hooghly basin using two significant methods of multicriteria decision making models (MCDM) i.e., Shannon entropy (SE) and fuzzy analytical hierarchy process (FAHP) models. Multicriteria decision-making models have been found to be effective in evaluating factors for decision making. These two models were applied to twelve flood conditioning factors such as drainage density, elevation, LULC, normalized difference built up index (NDBI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), population density, rainfall, distance to rivers, slope, stream power index (SPI) and topographic wetness index (TWI). Binary logistic regression was also performed to identify the confounders of susceptibility in the basin. Findings indicated that nearly 56–58% area of the basin susceptible to floods. Drainage density, elevation, rainfall, and distance to rivers were identified as the major determinants of flood susceptibility. Validation of the models through the area under ROC curve (AUC) showed good predictability for SE (0.70) and FAHP (0.71) based flood susceptibility prediction. Findings of this study may provide a base for stakeholders and planners in managing and minimizing flood susceptibility in the basin. [ABSTRACT FROM AUTHOR]

Peanut is an economically important crop, but it is susceptible to Cr contamination. In this study, we used peanut as experimental material to investigate the effects of exogenous P, Se interacting with Cr on the nutrient growth and antioxidant system of peanut seedlings by simulating Cr (0 μM, 50 μM, and 100 μM) stress environment. The results showed that exogenous P, Se supply could mitigate irreversible damage to peanut seedlings by altering the distribution of Cr in roots and aboveground, changing root conformation, and repairing damaged cells to promote growth. When the Cr concentration is 100 μM, it exhibits the highest toxicity. Compared to the control group P and Se (0 MM), the treatment with simultaneous addition of P + Se (0.5 + 6.0) resulted in a significant increase in root length and root tip number by 248.7% and 127.4%, respectively. Additionally, there was a 46.9% increase in chlorophyll content, a 190.2% increase in total surface area of the seedlings, and a respective increase of 149.1% and 180.3% in soluble protein content in the shoot and roots. In addition, by restricting the absorption of Cr and reducing the synthesis of superoxide dismutase SOD (Superoxide dismutase), CAT (Catalase), POD (Peroxidase), and MDA (Malonaldehyde), it effectively alleviates the oxidative stress on the antioxidant system. Therefore, the exogenous addition of P (0.5 MM) and Se (6.0 MM) prevented the optimal concentration of chromium toxicity to peanuts. Our research provides strong evidence that the exogenous combination of P and Se reduces the risk of peanut poisoning by Cr, while also exploring the optimal concentration of exogenous P and Se under laboratory conditions, providing a basis for further field experiments. [ABSTRACT FROM AUTHOR]

Introduction: The precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YOLOv7 based on an improved YOLOv7 architecture was proposed to identify five types of weeds in complex wheat fields. Methods: First, a dataset was constructed for five weeds that are commonly found, namely, Descurainia sophia, thistle, golden saxifrage, shepherd's purse herb, and Artemisia argyi. Second, a wheat weed detection model called CSCWYOLOv7 was proposed to achieve the accurate identification and classification of wheat weeds. In the CSCW-YOLOv7, the CARAFE operator was introduced as an up-sampling algorithm to improve the recognition of small targets. Then, the Squeeze-and-Excitation (SE) network was added to the Extended Latent Attention Networks (ELAN) module in the backbone network and the concatenation layer in the feature fusion module to enhance important weed features and suppress irrelevant features. In addition, the contextual transformer (CoT) module, a transformer-based architectural design, was used to capture global information and enhance self-attention by mining contextual information between neighboring keys. Finally, the Wise Intersection over Union (WIoU) loss function introducing a dynamic nonmonotonic focusing mechanism was employed to better predict the bounding boxes of the occluded weed. Results and discussion: The ablation experiment results showed that the CSCWYOLOv7 achieved the best performance among the other models. The accuracy, recall, and mean average precision (mAP) values of the CSCW-YOLOv7 were 97.7%, 98%, and 94.4%, respectively. Compared with the baseline YOLOv7, the improved CSCW-YOLOv7 obtained precision, recall, and mAP increases of 1.8%, 1%, and 2.1%, respectively. Meanwhile, the parameters were compressed by 10.7% with a 3.8-MB reduction, resulting in a 10% decrease in floating-point operations per second (FLOPs). The Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method suggested that the CSCW-YOLOv7 can learn a more representative set of features that can help better locate the weeds of different scales in complex field environments. In addition, the performance of the CSCW-YOLOv7 was compared to the widely used deep learning models, and results indicated that the CSCW-YOLOv7 exhibits a better ability to distinguish the overlapped weeds and small-scale weeds. The overall results suggest that the CSCW-YOLOv7 is a promising tool for the detection of weeds and has great potential for field applications. [ABSTRACT FROM AUTHOR]

• This study supports the utility of differentiating between periodic discharges (PDs) during and after Status Epilepticus (SE) • Interictal PDs during SE are not related to outcome; PDs occurring 24–72 h from SE resolution strongly correlate with poor prognosis. • Considering only PDs after SE cessation improves EMSE (Epidemiology-based mortality score in SE) overall accuracy. Periodic Discharges (PDs) in Status Epilepticus (SE) are historically related to negative outcome, and the Epidemiology-based Mortality Score in SE (EMSE) identifies PDs as an EEG feature associated with unfavorable prognosis. However, supportive evidence is conflicting. This study aims to evaluate the prognostic significance of interictal PDs during and following SE. All 2020–2023 non-hypoxic-ischemic SE patients with available EEG during SE were retrospectively assessed. Interictal PDs during SE (SE-PDs) and PDs occurring 24–72 h after SE resolution (post-SE-PDs) were examined. In-hospital death was defined as the primary outcome. 189 SE patients were finally included. SE-PDs were not related to outcome, while post-SE-PDs were related to poor prognosis confirmed after multiple regression analysis. EMSE global AUC was 0.751 (95%CI:0.680–0.823) and for EMSE-64 cutoff sensitivity was 0.85, specificity 0.52, accuracy 63%. We recalculated EMSE score including only post-SE-PDs. Modified EMSE (mEMSE) global AUC was 0.803 (95%CI:0.734–0.872) and for mEMSE-64 cutoff sensitivity was 0.84, specificity 0.68, accuracy 73%. Interictal PDs during SE were not related to outcome whereas PDs persisting or appearing > 24 h after SE resolution were strongly associated to unfavorable prognosis. EMSE performed well in our cohort but considering only post-SE-PDs raised specificity and accuracy for mEMSE64 cutoff. This study supports the utility of differentiating between interictal PDs during and after SE for prognostic assessment. [ABSTRACT FROM AUTHOR]

Physical Attractiveness (PA) is an essential factor affecting consumer response (CR) in service encounters (SE). Using an experimental design, the purpose of the study is to demonstrate the physical attractiveness (PA) of a service representative's effects (CR) in the context of consumer satisfaction, service quality perception, and likability of the service representative. The study investigated the complex underlying mechanism between these two variables by analyzing the mediating role of social distance perceptions (SDP), and the moderating role of consumer’s physical attractiveness (CPA) and gender. The context of the study is the Airline Industry of Pakistan. The data of the study was collected through scenario based self-administered questionnaires from in-flight 385 passengers using non-probability sampling (voluntary response sampling) from. Analyzing the results of the study it is predicted that high PA has a positive effect on CR. Hence it is also important to take the consumer’s perspective into consideration, as this perspective gives rise to situations under which the beauty premium notion might not hold. The results of the study revealed that a consumer’s gender and his/her own level of PA is an important determinants of how he/she reacts to the attractiveness of the representative. Results can provide significant insights to Airline companies to strategize the recruitment and deployment of cabin crew members in accordance with the level of the customer’s gender and his/her own (PA). [ABSTRACT FROM AUTHOR]

Nanoparticles have drawn significant interest in a range of applications, ranging from biomedical to environmental sciences, due to their distinctive physicochemical characteristics. In this study, it was reported that simple biological production of Ag, Se, and bimetallic Ag2Se nanoparticles (NPs) with Pseudomonas aeruginosa is a promising, low-cost, and environmentally friendly method. For the first time in the scientific literature, Ag2Se nanoparticles have been generated via green bacterial biosynthesis. UV-vis spectroscopy, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and EDX were used to characterize the produced NPs. Biosynthesized NPs were examined for antibacterial, antibiofilm, and photocatalytic properties, and it was determined that the effects of NPs were dose dependent. The biosynthesized AgNPs, SeNPs, and Ag2Se NPs showed anti-microbial activity against Escherichia coli and Staphylococcus aureus. Minimal inhibitory concentrations (MICs) of E. coli and S. aureus were between 150 and 250 µg/mL. The NPs showed antibiofilm activity against E. coli and S. aureus at sub-MIC levels and reduced biofilm formation by at least 80% at a concentration of 200 µg/mL of each NPs. To photocatalyze the breakdown of Congo red, Ag, Se, and Ag2Se NPs were utilized, and their photocatalytic activity was tested at various concentrations and intervals. A minor decrease of photocatalytic degradation was detected throughout the NPs reuse operation (five cycles). Based on the encouraging findings, the synthesized NPs demonstrated antibacterial, antibiofilm, and photocatalytic properties, suggesting that they might be used in pharmaceutical, medical, environmental, and other applications. [ABSTRACT FROM AUTHOR]

Selenium (Se) is an essential trace element for human physiological metabolism. The application of organic Se as a source to cultivate Se-rich plants for micronutrient supplementation has been receiving increasing attention. In our study, a bacterial strain named H1 was isolated from the soil in Heilongjiang Province, China, and under optimal culture conditions, the unit Se content could reach 3000 μg·g−1 and its 16S ribosomal DNA sequence seemed to be a new molecular record of an Enterobacter species. After the domestication of Se tolerance and Se-rich experiments, H1 can be used as a Se source for cultivation of Se-rich Auricularia auricula. The results showed that soluble protein, soluble sugar, free amino acid and vitamin C contents in Auricularia auricula were notably increased by 28.7%, 21.8%, 32.5% and 39.2% under the treatment of Se concentration of 0.24 mg·kg−1, respectively. These findings enhance our understanding that H1 is more conducive to Se uptake and nutrient accumulation. [ABSTRACT FROM AUTHOR]

AbstractAgricultural modernization urgently requires precise control of pear tree leaf diseases, with accurate identification and segmentation of disease spots becoming crucial aspects for ensuring crop health. Addressing potential issues such as missed segmentation, missegmentation, and low segmentation accuracy in the traditional DeepLabv3+ model for this task, this study proposes an innovative approach based on an improved DeepLabv3+ network model. To enhance computational efficiency, MobileNetV2 is introduced as the backbone network. This reduces the model’s computational load and significantly improves segmentation speed, making it more suitable for real-time applications. Secondly, after the ASPP (Atrous Spatial Pyramid Pooling) convolution, the Squeeze-and-Excitation (SE) attention mechanism is introduced, integrating the features of pear tree leaf diseases to make the network focus more on the critical components of disease spots, thereby enhancing segmentation accuracy. Finally, the loss function is optimized by employing a linear combination of the cross-entropy loss function and Dice loss, encompassing comprehensive overall image loss and accurate loss calculation for the target area. Experimental validation demonstrates a significant improvement in the enhanced DeepLabv3+ across metrics such as MIoU, mPA, mPr, and mRecall, reaching 86.32%, 88.97%, 91.10%, and 88.97%, respectively. The improved model excels in pear tree leaf disease segmentation compared to traditional methods like DeepLabv3+, SegNet, FCN, and PSPNet. This confirms the enhanced model’s outstanding performance and generalization ability in segmenting pear tree leaf lesions, highlighting its potential value in practical applications.

Selenium (Se) is a crucial micronutrient for human health. Plants are the primary source of Se for humans. Selenium in the soil serves as the primary source of Se for plants. The soil contains high total Se content in large areas in Guangxi, China. However, the available Se is low, hindering Se uptake by plants. Microorganisms play a pivotal role in the activation of Se in the soil, thereby enhancing its uptake by plants. In this study, selenobacteria were isolated from Se-rich soils in Guangxi. Then two selenobacteria strains, YLB1-6 and YLB2-1, representing the highest (30,000 mg/mL) and lowest (10,000 mg/mL) Se tolerance levels among the Se-tolerant bacteria, were selected for subsequent analysis. Although the two selenobacteria exhibited distinct effects, they can significantly transform Se species, resulting in a decrease in the soil residual Se (RES-Se) content while concurrently increasing the available Se (AVA-Se) content. Selenobacteria also enhance the transformation of Se valencies, with a significant increase observed in soluble Se6+ (SOL-Se6+). Additionally, selenobacteria can elevate the pH of acidic soil. Selenobacteria also promote the uptake of Se into plants. After treatment with YLB1-6 and YLB2-1, the Se content in the aboveground part of Chinese flowering cabbage increased by 1.96 times and 1.77 times, respectively, while the Se accumulation in the aboveground part of the plant significantly increased by 104.36% and 81.69%, respectively, compared to the control. Further whole-genome sequencing revealed the genetic difference between the two selenobacteria. Additionally, 46 and 38 candidate genes related to selenium utilization were identified from YLB1-6 and YLB2-1, respectively. This work accelerates our understanding of the potential molecular mechanism of Se biofortification by selenobacteria. It also provides microorganisms and gene targets for improving crop varieties or microorganisms to exploit the rich Se source in soil. [ABSTRACT FROM AUTHOR]

• Selenium (Se) application showed no significant effect on maize biomass and yield. • Foliar and soil application of Se enhanced grain Se content. • Grain Se recovery was maximum with foliar Se application. • Foliar Se application surpasses soil Se application in terms of grain Se content. Selenium (Se) is a vital mineral nutrient for animal and human health, which can be supplemented through seed biofortification techniques. Present study determined the potential of improving Se content in maize grain via several Se fertilizer application methods to enhance the nutritional status of native populations. Field trials were conducted for two growing years with different Se application methods and application rates to evaluate grain Se content in maize, Se recovery, grain macro- and micro-nutrients and grain yield under rain-fed conditions. Results demonstrated that foliar and soil Se applications showed no significant impacts on maize biomass and grain yield as well as maize grain N, P, K, Ca, Mg, Fe, Mn, Cu and Zn contents. However, both foliar and soil Se application significantly enhanced the maize grain Se content. Foliar application of Se exhibited greater Se recoveries of 54 % and 108 % in the grain over soil Se fertilizer for the first and second growing seasons while the later was recorded with only 1.71 % and 0.97 % increase as compared to control. In conclusion, foliar Se application at silking stage at the rate of 8.85 µM enhanced the grain Se content in maize at minimum costs as compared to soil Se application. [ABSTRACT FROM AUTHOR]

Salt toxicity is one of the foremost environmental stresses that declines nutrient uptake, photosynthetic activity and growth of plants resulting in a decrease in crop yield and quality. Seed priming has become an emergent strategy to alleviate abiotic stress and improve plant growth. During the current study, turnip seed priming with sodium selenite (Na2SeO3) was investigated for its ability to mitigate salt stress. Turnip (Brassica rapa L. var. Purple Top White Globe) seeds primed with 75, 100, and 125 μML−1 of Se were subjected to 200 mM salt stress under field conditions. Findings of the current field research demonstrated that salt toxicity declined seed germination, chlorophyll content, and gas exchange characteristics of B. rapa seedling. Whereas, Se-primed seeds showed higher germination rate and plant growth which may be attributed to the decreased level of hydrogen peroxide (H2O2) and malondialdehyde (MDA) decreased synthesis of proline (36%) and besides increased total chlorophyll (46%) in applied turnip plants. Higher expression levels of genes encoding antioxidative activities (CAT, POD, SO,D and APX) mitigated oxidative stress induced by the salt toxicity. Additionally, Se treatment decreased Na+ content and enhanced K+ content resulting in elevated K+/Na+ ratio in the treated plants. The in-silico assessment revealed the interactive superiority of Se with antioxidant enzymes including CAT, POD, SOD, and APX as compared to sodium chloride (NaCl). Computational study of enzymes-Se and enzymes-NaCl molecules also revealed the stress ameliorative potential of Se through the presence of more Ramachandran-favored regions (94%) and higher docking affinities of Se (-6.3). The in-silico studies through molecular docking of Na2SeO3, NaCl, and ROS synthesizing enzymes (receptors) including cytochrome P450 (CYP), lipoxygenase (LOX), and xanthine oxidase (XO), also confirmed the salt stress ameliorative potential of Se in B. rapa. The increased Ca, P, Mg, and Zn nutrients uptake nutrients uptake in 100 μML−1 Se primed seedlings helped to adjust the stomatal conductivity (35%) intercellular CO2 concentration (32%), and photosynthetic activity (41%) resulting in enhancement of the yield attributes. More number of seeds per plant (6%), increased turnip weight (115 gm) root length (17.24 cm), root diameter (12 cm) as well as turnip yield increased by (9%tons ha−1) were recorded for 100 μML−1 Se treatment under salinity stress. Findings of the current research judiciously advocate the potential of Se seed priming for salt stress alleviation and growth improvement in B. rapa. According to our best of knowledge, it is the first time that seed primed with Selenium have been evaluated regarding NaCl stress mitigation in turnip. Salinity toxicity negatively affected physiochemical activities and growth of B.rapa. Seed priming with Selenium (Na2SeO3) mitigated salinity stress. Selenium (Se) enhanced nutrition, photosynthetic and antioxidant activity of applied plants. Selenium treated plants exhibited improved growth and reduced salinity content. [ABSTRACT FROM AUTHOR]

The deployment of cooperative communication inside a cell-Free mMIMO system can improve both the spectral efficiency SE performance as well as the energy efficiency EE performance. This increase is due to the increase in the signal to interference plus noise ratio SINR. In fact, the SINR can be greatly improved when the cognitive radio theory is applied due to its capability of interference mitigation. In this manuscript, the cognitive intelligent reflecting surfaces IRSs are distributed inside a cell-Free mMIMO system in order to improve the SE as well as the EE performance. Moreover, the cognitive radio can raise each user SINR. In this manuscript, the authors try to carry out the suitable mathematical model for the fore-mentioned system. Moreover, the system will be simulated. Subsequently, the simulation results will be explained. It is obvious that the implantation of cognitive IRSs inside, cell-Free mMIMO systems can greatly increase the system performance. [ABSTRACT FROM AUTHOR]

In this study, we investigated the effects of different types of selenium (Se) (sodium selenite [SS], yeast selenium [YS], and nano-selenium [NS]) on the toxicity, growth, Se accumulation, and transformation of Lucilia sericata maggots (LSMs). We found that the 50% lethal concentration of LSMs exposed to SS was 2.18 and 1.96 times that of YS and NS, respectively. LSM growth was significantly promoted at exposure concentrations of 10–50 mg kg−1 in group SS and 10–30 mg kg−1 in group YS, whereas NS inhibited LSMs growth at all concentrations (p < 0.05). Total Se content in LSMs, conversion efficiency to organic and other forms of Se, and bioaccumulation factor of Se were the highest in the SS group when exposed to 50 mg kg−1 (81.6 mg kg−1, 94.6%, and 1.63, respectively). Transcriptomic results revealed that LSMs significantly upregulated the amino acid (alanine, aspartate, glutamic, and tyrosine) and tricarboxylic acid cycle signaling pathways (p < 0.05) on exposure to Se, resulting in a significant increase in LSMs biomass and quality. In conclusion, our study indicates that LSMs exhibit good tolerance to SS and can convert it into bioorganic or other forms of Se. [ABSTRACT FROM AUTHOR]

An analytical method to accurately determine trace levels of Se and Te is essential to meet the growing importance of these elements to Earth science research. Herein, an in‐house assembled photochemical vapour generation (PVG) unit, instead of normal sample nebulisation, with inductively coupled plasma‐mass spectrometry (ICP‐MS), was developed for the detection of Se and Te in geological samples. The major parameters that might potentially influence the PVG efficiencies of Se(IV) and Te(IV) were investigated. The unit could fully generate volatile species under 30 s of ultraviolet irradiation in the presence of 15% v/v formic acid, 15% v/v acetic acid and 50 mg l‐1 of Co2+. Under optimised conditions, the limit of detections (LODs, 3s, n = 11) of the proposed method were 0.5 ng l‐1 for Se and 0.6 ng l‐1 for Te. The RSDs of Se and Te were 2.0% and 2.3% (1 μg l‐1, n = 7), respectively. Interference experiments showed that Fe, Ti, V and Cu have certain negative effects, so the standard addition method was used for real sample analysis. Measurement results for eleven CRMs, including soils (GSS‐4, GSS‐7), sediments (GSD‐9, GSD‐10) and rocks (GSR‐1, GSR‐2, GSR‐3, GSR‐5, AGV‐2, GSP‐2 and W‐2a), were consistent with literature values, and showed better precision, indicating the feasibility of the proposed method for determination of trace Se and Te in geological samples. [ABSTRACT FROM AUTHOR]

Crop diseases and pests cause significant economic losses to agriculture every year, making accurate identification crucial. Traditional pest and disease detection relies on farm experts, which is often time-consuming. Computer vision technology and artificial intelligence can provide automated disease detection, enabling real-time precise control of crop diseases and timely prevention measures. To accurately identify plant diseases under complex natural conditions, we developed an improved crop pest and disease recognition model based on the original YOLOv5 network. First, we integrated the Squeeze-and-Excitation (SE) module into YOLOv5, allowing our proposed model to better distinguish leaf features of different crops and accurately identify disease types. Second, to enhance the model’s feature extraction capability for diseased areas and reduce the loss of disease feature information, we replaced the original Up-sample module in YOLOv5 with a lightweight up-sampling operator, the CARAFE module. Third, we improved the original loss function using the EIoU loss function to increase the model’s detection accuracy. Lastly, to reduce model complexity and meet real-time detection requirements, we introduced the Ghost Convolution module into the backbone network. During the experimental phase, to validate the model’s effectiveness, we randomly divided sample images from the constructed crop pest and disease database into training, validation, and test sets. Experimental results showed that the improved YOLOv5 model achieved an accuracy of 90.0%, a recall rate of 91.4%, mAP@.50 of 92.1%, and mAP@.5:.95 of 64%. The parameter count and computational load were reduced by 23.9% and 31.2%, respectively, outperforming popular methods including YOLOv5, YOLOv7, and YOLOv8. The improved model can accurately identify crop pests and diseases under natural conditions and is suitable for deployment in real-world applications, providing a technical reference for crop pest and disease management.

Wearing safety helmets at construction sites is a major measure to prevent safety accidents, so it is essential to supervise and ensure that workers wear safety helmets. This requires a high degree of real-time performance. We improved the network structure based on YOLOv7. To enhance real-time performance, we introduced GhostModule after comparing various modules to create a new efficient structure that generates more feature mappings with fewer linear operations. SE blocks were introduced after comparing several attention mechanisms to highlight important information in the image. The EIOU loss function was introduced to speed up the convergence of the model. Eventually, we constructed the efficient model EGS-YOLO. EGS-YOLO achieves a mAP of 91.1%, 0.2% higher than YOLOv7, and the inference time is 13.3% faster than YOLOv7 at 3.9 ms (RTX 3090). The parameters and computational complexity are reduced by 37.3% and 33.8%, respectively. The enhanced real-time performance while maintaining the original high precision can meet actual detection requirements.

The ability of brassicas to accumulate selenium is crucial for their positive effects on health. Selenium improves the immune system and the antioxidant defenses. Selenium biofortification of brassicas has therefore been explored to increase dietary selenium intake in humans. However, the effects of selenium biofortification on bioactive compounds, mainly phenolic compounds, are not clear. So, this systematic review and meta‐analysis aimed to answer the question 'What are effects of the biofortification of brassicas with selenium on total phenolic compounds?' Ten studies, which assessed the effect of selenium biofortification on total phenolic compounds, were selected for qualitative synthesis and four studies were included in the meta‐analysis after a thorough literature review of the PubMed, Science Direct, and Web of Knowledge databases. The quality of the evidence ranged from high to moderate. The meta‐analysis results indicated that the total phenolic compound content was significantly higher (P = 0.002) in the supplemented group but the results showed considerable heterogeneity (P < 0.00001, I2 = 97%) between studies. This systematic review and meta‐analysis summarizes the effect of Se biofortification on the increase in the content of total phenolic compounds and it suggests that several factors can affect this relationship. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Current semantic segmentation methods have high accuracy. However, it has the disadvantage of high computational complexity and time consumption, which makes it difficult to meet the application requirements in complex environments. To achieve fast and accurate semantic segmentation of images, we propose a lightweight semantic segmentation method called Light-Deeplabv3+. First, a MobileNetV2Lite-SE architecture with SE module is proposed as the backbone network of the model, which can reduce the number of model parameters and improve the segmentation speed. Second, we propose an ACsc-ASPP module based on asymmetric dilated convolution block (ADCB) and scSE module to solve the semantic information loss during feature extraction. Our improvements can obtain more semantic features and improve segmentation accuracy. Finally, we propose a DSC-Blaze module to replace the original 3 × 3 standard convolution. It consists of depthwise separable convolution (DSC) and Blaze module, which can improve the model segmentation speed while maintaining the receptive field. The experimental results prove that the Mean Intersection over Union (MIoU) of Light-Deeplabv3+ on the PASCAL VOC2012 dataset is 73.15 % , and the parameter size is only 6.291MB. Its calculation amount is only 20.883G, and the speed on the 3060Ti platform is 37.66 frames per second (FPS). Compared with traditional Deeplabv3+, Light-Deeplabv3+ can achieve efficient and accurate image segmentation results with less computational overhead, and its performance is comparable to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

To analyze the enrichment characteristics and correlation of Se and heavy metals in soil-blueberry fruit systems in different geological background areas of Guizhou Province, 86 soil samples and 58 blueberry fruit samples were collected from blueberry planting areas in the Majiang, Ziyun, and Huangping counties of Guizhou Province. The contents of Se and 9 other heavy metals in soil and blueberry fruit were determined using an inductively coupled plasma mass spectrometer (ICP-MS) and inductively coupled plasma emission spectrometer (ICP-AES). The results showed that the Se contents of the soils in the dolomite and limestone distribution areas are higher than the background values of topsoil in Guizhou, and both met the Se-rich soil standard. The Se content of the soil in the study areas exhibits the characteristics of limestone area>dolomite area>sandstone area, indicating that the geological background controls the Se content of the soil. In dolomite soil, Se is negatively correlated with As, Cu, Hg, Ni, Pb, and Zn, while in limestone soil, Se is positively correlated with As, Cr, Ni, and Zn. In dolomite and limestone areas, the Se in soil is negatively correlated with Hg and Pb in blueberry fruit. There is no significant correlation in the sandstone area. The blueberries in each growing area have a high enrichment capacity for Se and have all reached the selenium-rich blueberry standard, which has the potential for development into selenium-rich blueberry agriculture. [ABSTRACT FROM AUTHOR]

Selenium (Se) is an essential trace element that plays a crucial role in maintaining the health of humans, animals, and certain plants. It is extensively present throughout the Earth's crust and is absorbed by crops in the form of selenates and selenite, eventually entering the food chain. Se biofortification is an agricultural process that employs agronomic and genetic strategies. Its goal is to enhance the mechanisms of crop uptake and the accumulation of exogenous Se, resulting in the production of crops enriched with Se. This process ultimately contributes to promoting human health. Agronomic strategies in Se biofortification aim to enhance the availability of exogenous Se in crops. Concurrently, genetic strategies focus on improving a crop's capacity to uptake, transport, and accumulate Se. Early research primarily concentrated on optimizing Se biofortification methods, improving Se fertilizer efficiency, and enhancing Se content in crops. In recent years, there has been a growing realization that Se can effectively enhance crop growth and increase crop yield, thereby contributing to alleviating food shortages. Additionally, Se has been found to promote the accumulation of macro-nutrients, antioxidants, and beneficial mineral elements in crops. The supplementation of Se biofortified foods is gradually emerging as an effective approach for promoting human dietary health and alleviating hidden hunger. Therefore, in this paper, we provide a comprehensive summary of the Se biofortification conducted over the past decade, mainly focusing on Se accumulation in crops and its impact on crop quality. We discuss various Se biofortification strategies, with an emphasis on the impact of Se fertilizer strategies on crop Se accumulation and their underlying mechanisms. Furthermore, we highlight Se's role in enhancing crop quality and offer perspective on Se biofortification in crop improvement, guiding future mechanistic explorations and applications of Se biofortification. [ABSTRACT FROM AUTHOR]

Shiitake mushrooms are an important edible fungus, and their nutrient content is related to their quality. With the acceleration of urbanization, there has been a serious loss of population and shortage of labor in rural areas. The problem of harvesting agricultural products after maturity is becoming more and more prominent. In recent years, deep learning techniques have performed well in classification tasks using image data. These techniques can replace the manual labor needed to classify the quality of shiitake mushrooms quickly and accurately. Therefore, in this paper, a MobileNetV3_large deep convolutional network is improved, and a mushroom quality classification model using images with complex backgrounds is proposed. First, captured image data of shiitake mushrooms are divided into three categories based on the appearance characteristics related to shiitake quality. By constructing a hybrid data set, the model's focus on shiitake mushrooms in images with complex backgrounds is improved. And the constructed data set is expanded using data enhancement methods to improve the generalization ability of the model. The total number of images after expansion is 10,991. Among them, the number of primary mushroom images is 3758, the number of secondary mushroom images is 3678, and the number of tertiary mushroom images is 3555. Subsequently, the SE module in MobileNetV3_large network is improved and processed to enhance the model recognition accuracy while reducing the network size. Finally, PolyFocalLoss and migration learning strategies are introduced to train the model and accelerate model convergence. In this paper, the recognition performance of the improved MobileNetV3_large model is evaluated by using the confusion matrix evaluation tool. It is also compared with other deep convolutional network models such as VGG16, GoogLeNet, ResNet50, MobileNet, ShuffleNet, and EfficientNet using the same experimental conditions. The results show that the improved MobileNetV3_large network has a recognition accuracy of 99.91%, a model size of 11.9 M, and a recognition error rate of 0.09% by the above methods. Compared to the original model, the recognition accuracy of the improved model is increased by 18.81% and the size is reduced by 26.54%. The improved MobileNetV3_large network model in this paper has better comprehensive performance, and it can provide a reference for the development of quality recognition and classification technologies for shiitake mushrooms cultivated in greenhouse environments. [ABSTRACT FROM AUTHOR]

The elements in blood normally reflect the levels in prey, indicating a recent exposure. Laughing gulls (Leucophaes atricilla) eat mainly horseshoe crab eggs (Limulus polyphemus) in the spring in Delaware Bay, New Jersey. The levels of arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), mercury (Hg), and selenium (Se) in the blood of laughing gulls foraging on crab eggs were examined in Delaware Bay to provide information on a species that is normally a generalist, and to determine if the levels of these elements were similar in 2019 and 2022/2023, were intercorrelated, and were related to those in crab eggs. Hg increased from 2019 (136 ± 31 ng/g) to 2022/2023 (473 ± 75 ng/g), while Cd and Se decreased. There were some significant correlations among elements and a close relationship between the element levels in blood and those in crab eggs collected in the same month (except for As). The levels differed between laughing gulls and three species of shorebirds for As and Cd. The elements in the blood of gulls and shorebirds should be similar because they eat mainly the same eggs in the same places. A significant proportion of laughing gull blood samples had levels of Hg and Se that were above the levels associated with adverse effects, which requires further examination. [ABSTRACT FROM AUTHOR]

Purpose – This study explores the relationship between corruption and shadow economy (SE) by examining the potential links and interactions between these two phenomena to see whether it is a one-way or two-way relationship and a complementarity or substitution linkage. Design/methodology/approach – Using a dataset comprised of 145 countries all over the world between 1996 and 2015, the authors apply the simultaneous two-step system generalized method of moments approach to address the research question. Findings – The study findings support a positive bidirectional relationship between corruption and SE. As such, this study has provided evidence supporting the complementarity association. In the authors' further analyses, they point out that several factors can moderate this positive bidirectional linkage. In particular, while Foreign Direct Investment (FDI) inflows strengthen it, it is weakened by other institutional factors such as civil liberties and political rights. Finally, by splitting the full sample into three different subsamples and then examining countries at varying stages of economic development, the authors can gain valuable insights into the evolving dynamics of the relationship between corruption and SE. Specifically, while the authors observe that the positive direction of corruption to SE remains unchanged across different nations, they observe that the positive influence of SE on corruption is strongest among developed economies only. Practical implications – The study findings provide an important policy implication. This study highlights the synergistic relationship between SE and corruption, indicating that reducing corruption will reduce the size of the SE. Consequently, this reduction in the SE can mitigate the adverse effects of corruption on economic development. Originality/value – This paper is among the first empirical studies that critically investigate the interrelationship between SE and corruption. It then explores how this two-way linkage is conditional on some factors, such as economic development levels and institutional quality indicators.

Abstract As the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making.

Selenium (Se) is an essential trace element for human physiological metabolism. The application of organic Se as a source to cultivate Se-rich plants for micronutrient supplementation has been receiving increasing attention. In our study, a bacterial strain named H1 was isolated from the soil in Heilongjiang Province, China, and under optimal culture conditions, the unit Se content could reach 3000 μg·g−1 and its 16S ribosomal DNA sequence seemed to be a new molecular record of an Enterobacter species. After the domestication of Se tolerance and Se-rich experiments, H1 can be used as a Se source for cultivation of Se-rich Auricularia auricula. The results showed that soluble protein, soluble sugar, free amino acid and vitamin C contents in Auricularia auricula were notably increased by 28.7%, 21.8%, 32.5% and 39.2% under the treatment of Se concentration of 0.24 mg·kg−1, respectively. These findings enhance our understanding that H1 is more conducive to Se uptake and nutrient accumulation.

ABSTRACT The first reported bla GMA-1 gene encoding gammaproteobacterial mobile class A β-lactamase (GMA-1) was identified in a recently defined mobile DNA element, a strand-biased circularizing integrative element (SE). Increased genomic data revealed the presence of bla GMA-1 in marine bacteria, including pathogenic species such as Vibrio parahaemolyticus and Photobacterium damselae. Herein, we address the substrate range of GMA-1 and how frequently bla GMA-1 was acquired by the chromosomes or plasmids via SEs using sequences in a publicly available database. An Escherichia coli strain carrying bla GMA-1 exhibited resistance to amoxicillin, piperacillin, and carbenicillin, but it remained susceptible to cephalosporins, monobactam, and carbapenems, indicating that GMA-1 belongs to functional group 2c, narrow-spectrum β-lactamases. bla GMA-1-flanking sequence analysis for sequences in the RefSeq/GenBank database revealed a total of eight distinct SE-mediated bla GMA-1 acquisition events and six SE-independent bla GMA-1 acquisition events, including bla GMA-1 alone translocation, without involving a specific insertion sequence, or integron. Thus, this study shows that GMA-1 is specialized for penicillin degradation and is mainly disseminated by SEs; however, SE is not the only genetic mechanism transmitting bla GMA-1. IMPORTANCE Despite increasing reports, class A β-lactamases of environmental bacteria remain very poorly characterized, with limited understanding of their transmission patterns. To address this knowledge gap, we focused on a recently designated GMA family β-lactamase gene, bla GMA-1, found in marine bacterial genera such as Vibrio. This study shows that gammaproteobacterial mobile class A β-lactamase is specialized for penicillin degradation, and bla GMA-1 is frequently linked to strand-biased circularizing integrative elements (SEs) in sequences in the RefSeq/GenBank database. Evidence for the implication of SEs in β-lactamase environmental transmission provides insights for future surveillance studies of antimicrobial resistance genes in human clinical settings.

Status epilepticus (SE) is a life-threatening disorder that can result in death or severe brain damage, and there is a substantial body of evidence suggesting a strong association between pyroptosis and SE. Sterol regulatory element binding protein 1 (SREBP1) is a significant transcription factor participating in both lipid homeostasis and glucose metabolism. However, the function of SREBP1 in pyroptosis during SE remains unknown. In this study, we established a SE rat model by intraperitoneal injection of lithium chloride and pilocarpine in vivo. Additionally, we treated HT22 hippocampal cells with glutamate to create neuronal injury models in vitro. Our results demonstrated a significant induction of SREBP1, inflammasomes, and pyroptosis in the hippocampus of SE rats and glutamate-treated HT22 cells. Moreover, we found that SREBP1 is regulated by the mTOR signaling pathway, and inhibiting mTOR signaling contributed to the amelioration of SE-induced hippocampal neuron pyroptosis, accompanied by a reduction in SREBP1 expression. Furthermore, we conducted siRNA-mediated knockdown of SREBP1 in HT22 cells and observed a significant reversal of glutamate-induced cell death, activation of inflammasomes, and pyroptosis. Importantly, our confocal immunofluorescence analysis revealed the co-localization of SREBP1 and NLRP1. In conclusion, our findings suggest that deficiency of SREBP1 attenuates glutamate-induced HT22 cell injury and hippocampal neuronal pyroptosis in rats following SE. Targeting SREBP1 may hold promise as a therapeutic strategy for SE.

The multiple input - multiple output (MIMO) systems have sparked a lot of research interest because of their ability to increase the system capacity without using much bandwidth. The High-Speed Train (HST) communication system's capacity was calculated by assuming that the SNR is unaffected by variations in attenuation and channel losses. Here, considering this optimization problem, a power allocation algorithm using the Water-Filling technique is proposed for the MIMO system with known Channel Side Information (CSI) at the transmitter and receiver. The presented algorithm may be efficiently optimized for improving channel capacity to achieve spectral efficiency (SE).

AbstractAn efficient bank is more robust to shocks, fosters competitiveness, and promotes stability of the financial system. This study estimates Ethiopia’s commercial banks’ level of efficiency and its determinants during the period 2014–2020. Data Envelopment Analysis (DEA), Malmquist DEA, and Tobit regression were employed to analyze the data. The result indicated that the average efficiency score of banks in the constant returns to scale (CRS), variable returns to scale (VRS), and scale efficiency (SE) models were 95.5%, 99.85%, and 96.95% , respectively. Furthermore, in the VRS model, a state bank is more efficient than private banks. During the study period, the Total Factor Productivity (TFP) of Banks improved by 1%. According to the Tobit model, the efficiency of banks grows with an increment in the number of branches, bank size, and credit risk. However, when, liquidity risk and the log of the fixed asset increase, bank efficiency will decrease. The level of capitalization, log of GDP, and inflation, on the other hand, do not influence bank efficiency. Therefore, banks should pay close attention to aspects that influence technical efficiency.

As a deep learning network model, ResNet50 can effectively recognize facial expressions to a certain extent, but there are still problems such as insufficient extraction of local effective feature information and a large number of parameters. In this paper, we take ResNet50 as the basic framework to optimize and improve this network. Firstly, by analyzing the influence mechanism of the attention mechanism module on the network feature information circulation, the optimal embedding position of CBAM (Convolutional Block Attention Module) and SE modules in the ResNet50 network is thus determined to effectively extract local key information, and then the number of model parameters is effectively reduced by embedding the depth separable module. To validate the performance of the improved ResNet50 model, the recognition accuracy reached 71.72% and 95.72% by ablation experiments using Fer2013 and CK+ datasets, respectively. We then used the trained model to classify the homemade dataset, and the recognition accuracy reached 92.86%. In addition, compared with the current more advanced methods, the improved ResNet50 network model proposed in this paper can maintain a balance between model complexity and recognition ability and can provide a technical reference for facial expression recognition research. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to investigate the impact of two different machine milking vacuum levels on the quantitative and qualitative characteristics of milk and the hygiene of mammary glands in ewes, when administered vitamin E and selenium (Se). The results indicated that the chemical composition of milk was not affected by the vacuum level, but the supplementation of vitamin E and Se had a positive influence on milk quality. Milk yield was notably increased with the 46 kPa vacuum level (high vacuum level). Furthermore, the supplementation of vitamin E and Se reduced the somatic cell count (SCC), which indicated better mammary gland health status at both vacuum levels. Significant differences in total bacterial count (TBC) between the vacuum levels were not observed, regardless of vitamin E and Se administration. In conclusion, the study suggests that increased vacuum level can improve milk yields and the supplementation of animal diets with vitamin E and Se has a positive impact on milk quality as well as on reducing SCC and improving the hygiene of machine milked ewes' mammary glands. The aim of this research was to study the effect of two machine milking vacuum levels on the quantitative and qualitative characteristics of milk and mammary gland hygiene of ewes, when vitamin E and Se were administrated supplementarily. The experiment was conducted at the Vlasti Research Station in the Greek province of West Macedonia. Ninety-six ewes of the Mountainous Greek sheep breed were used. Animals were separated in four equal groups of 24 ewes per group. A 2 × 2 factorial design was applied, with two milking vacuum levels (38 kPa and 46 kPa) and two rations, one supplemented with vitamin E (300 I.U.) and Se (3 mg/kg DM feed) and one without any vitamin E and Se supplementation. Six test days were assigned (evening and morning milkings) at 14-day intervals, from April to July. Following milk yield control, milk samples were collected for chemical composition and somatic cell count (SCC) determination. At the end of milking of each lot, the milk from the terminal receiver of the milking machine was received for the evaluation of total bacterial count (TBC). The results revealed that milk yield was improved considerably in the case of 46 kPa vacuum level. Moreover, the chemical composition of milk was not influenced by vacuum level; however, the administration of vitamin E and Se appeared to have a positive effect. Moreover, the addition of vitamin E and Se decreased somatic cell counts (number and log10) at the two assessed machine milking vacuum levels. In reference to TBC and their log10, significant differences were not observed at both milking vacuum levels, regardless of vitamin E and Se administration. Statistical analysis did not indicate any interactions between the factors that were studied. Therefore, it is concluded that the quantity of vitamin E and Se supplemented to the ration has a positive effect on decreasing SCC and consequent positive action in the hygiene of the mammary glands of machine milked ewes. [ABSTRACT FROM AUTHOR]

As the lifeline of various industries, crude oil is frequently considered a pillar of economic development. The accurate and reliable prediction of crude oil price can provide investors and decision makers with valuable guidance for formulating their strategies. However, the complexity of the crude oil market and the volatility of oil prices pose significant challenges to forecasting crude oil price. To achieve higher prediction accuracy, this paper proposes a new model in which a new modal component reconstruction rule based on sample entropy (SE) is innovatively proposed and includes an improved particle swarm optimization model to avoid getting trapped in a local optimal solution. Two real crude oil price series are selected for experimentation, and the results demonstrate that the proposed model exhibits superior performance in predicting crude oil price. The developed model outperforms the compared models in terms of mean absolute error, root‐mean‐square error, mean absolute percentage error, and coefficient of determination. This result indicates that Complete Ensemble Empirical Mode Decomposition with Adaptive Noise exhibits better decomposition performance. SE can effectively reduce error accumulation, and particle swarm optimization with an adaptive learning strategy (ALS‐PSO) achieves better optimization results for a long short‐term memory network compared with PSO only. Therefore, the proposed model is an effective crude oil price prediction tool that can be applied in practice for investment and policy‐making. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Plant Production is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The precise control of potato diseases is an urgent demand in smart agriculture, with one of the key aspects being the accurate identification and segmentation of potato leaf diseases. Some disease spots on potato leaves are relatively small, and to address issues such as information loss and low segmentation accuracy in the process of potato leaf disease image segmentation, a novel approach based on an improved UNet network model is proposed. Firstly, the incorporation of ResNet50 as the backbone network is introduced to deepen the network structure, effectively addressing problems like gradient vanishing and degradation. Secondly, the unique characteristics of the UNet network are fully utilized, using UNet as the decoder to ingeniously integrate the characteristics of potatoes with the network. Finally, to better enable the network to learn disease spot features, the SE (squeeze and excitation) attention mechanism is introduced on top of ResNet50, further optimizing the network structure. This design allows the network to selectively emphasize useful information features and suppress irrelevant ones during the learning process, significantly enhancing the accuracy of potato disease segmentation and identification. The experimental results demonstrate that compared with the traditional UNet algorithm, the improved RS-UNet network model achieves values of 79.8% and 88.86% for the MIoU and Dice metrics, respectively, which represent improvements of 8.96% and 6.33% over UNet. These results provide strong evidence for the outstanding performance and generalization ability of the RS-UNet model in potato leaf disease spot segmentation, as well as its practical application value in the task of potato leaf disease segmentation. [ABSTRACT FROM AUTHOR]

Introduction: Mediterranean diets (MedDiets) are linked to substantial health benefits. However, there is also growing evidence that the intensification of food production over the last 60 years has resulted in nutritionally relevant changes in the composition of foods that may augment the health benefits of MedDiets. Objective: To synthesize, summarize, and critically evaluate the currently available evidence for changes in food composition resulting from agricultural intensification practices and their potential impact on the health benefits of MedDiets. Methods: We summarized/synthesized information from (i) systematic literature reviews/meta-analyses and more recently published articles on composition differences between conventional and organic foods, (ii) desk studies which compared food composition data from before and after agricultural intensification, (iii) recent retail and farm surveys and/or factorial field experiments that identified specific agronomic practices responsible for nutritionally relevant changes in food composition, and (iv) a recent systematic literature review and a small number of subsequently published observational and dietary intervention studies that investigated the potential health impacts of changes in food composition resulting from agricultural intensification. Results and Discussion: There has been growing evidence that the intensification of food production has resulted in (i) lower concentrations of nutritionally desirable compounds (e.g., phenolics, certain vitamins, mineral micronutrients including Se, Zn, and omega-3 fatty acids, α-tocopherol) and/or (ii) higher concentrations of nutritionally undesirable or toxic compounds (pesticide residues, cadmium, omega-6 fatty acids) in many of the foods (including wholegrain cereals, fruit and vegetables, olive oil, dairy products and meat from small ruminants, and fish) that are thought to contribute to the health benefits associated with MedDiets. The evidence for negative health impacts of consuming foods from intensified conventional production systems has also increased but is still limited and based primarily on evidence from observational studies. Limitations and gaps in the current evidence base are discussed. Conclusions: There is now substantial evidence that the intensification of agricultural food production has resulted in a decline in the nutritional quality of many of the foods that are recognized to contribute to the positive health impacts associated with adhering to traditional MedDiets. Further research is needed to quantify to what extent this decline augments the positive health impacts of adhering to a traditional MedDiet. [ABSTRACT FROM AUTHOR]

In the field of aerial remote sensing, detecting small objects in aerial images is challenging. Their subtle presence against broad backgrounds, combined with environmental complexities and low image resolution, complicates identification. While their detection is crucial for urban planning, traffic monitoring, and military reconnaissance, many deep learning approaches demand significant computational resources, hindering real-time applications. To elevate the accuracy of small object detection in aerial imagery and cater to real-time requirements, we introduce SenseLite, a lightweight and efficient model tailored for aerial image object detection. First, we innovatively structured the YOLOv5 model for a more streamlined structure. In the backbone, we replaced the original structure with cutting-edge lightweight neural operator Involution, enhancing contextual semantics and weight distribution. For the neck, we incorporated GSConv and slim-Neck, striking a balance between reduced computational complexity and performance, which is ideal for rapid predictions. Additionally, to enhance detection accuracy, we integrated a squeeze-and-excitation (SE) mechanism to amplify channel communication and improve detection accuracy. Finally, the Soft-NMS strategy was employed to manage overlapping targets, ensuring precise concurrent detections. Performance-wise, SenseLite reduces parameters by 30.5%, from 7.05 M to 4.9 M, as well as computational demands, with GFLOPs decreasing from 15.9 to 11.2. It surpasses the original YOLOv5, showing a 5.5% mAP0.5 improvement, 0.9% higher precision, and 1.4% better recall on the DOTA dataset. Compared to other leading methods, SenseLite stands out in terms of performance. [ABSTRACT FROM AUTHOR]