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The effect of external application of palm pollen grains extracts on phoenix dactylifera cv. zaghloul fruits quality
Abstract This study was carried out to examine the effect of aqueous or ethanolic date palm pollen grain extract on Phoenix dactylifera , cv. Zaghloul fruits. Pollen grain extracts were prepared from male date palm trees cv. Barhi. The experiment was conducted in a completely randomized block design during two consecutive seasons with three spraying treatments, i.e. control (distilled water), aqueous or ethanolic date palm pollen grain extract. Each treatment was sprayed twice, i.e. at the Hababouk (fruit’ cell division) and Kimri (fruit’ cell elongation) stages. The results indicated that spraying of 700 ppm aqueous or ethanolic date palm pollen grain extract significantly improved the productivity and quality of ‘Zaghloul’ fruits by increasing the dry matter, crude fiber, ash, total soluble solids, reducing, non-reducing, and total soluble sugars, total carbohydrates, protein, and mineral nutrients concentrations as well as the peroxidase (POD) and catalase (CAT) activities as compared to the control. Also, the external application of aqueous or ethanolic extracts enhanced fruit amino acids acquisition, and total phenols, whereas decreased moisture percentage, titratable acidity, and tannins concentrations in treated date palm fruits. Evidences might indicate that ethanolic date pollen grains extract followed by aqueous extract improve ‘Zaghloul’ fruit yield as compared to control by regulating the nutrients acquisition, sugar accumulation, amino acids profile, and antioxidant response. These findings could support the use of ethanolic or aqueous date pollen grain extract as a bio-stimulant to improve date palm fruit yield and quality.
Cost-effective laser metal deposition of 304L stainless steel for repairing and enhancing 316L and mild steel engineering components
Abstract This study presents a cost-effective additive manufacturing (AM) approach using Laser Metal Deposition (LMD) to enhance the durability and repair of 316L stainless steel and mild steel engineering components. By depositing a protective 304L stainless steel layer, this method extends the components’ life cycle in harsh environments while offering significant cost savings, as 304L powder is less expensive than 316L. The research optimized the LMD process by exploring high scan speeds (up to 8000 mm/min) and powder feed rates (up to 50 g/min) to enhance productivity and ensure an economically viable repair solution. Defect-free layers with strong metallurgical bonding were successfully deposited on both substrates using an optimal energy density of 100–200 J/mm $$^2$$ , an interaction time of 0.5–1.6 seconds, and a powder feed rate of 10–30 g/min. The resulting 304L layers demonstrated enhanced microhardness (around 200 HV) compared to both the 316L and mild steel substrates and corrosion resistance comparable to 316L (and superior to mild steel), with a low corrosion rate of 0.002 mpy in a 3.5% NaCl solution. These results confirm that LMD is a viable and economical solution for repairing and protecting engineering components in various industries such as automotive, pharmaceutical, and marine. The study also highlights the necessary precautions for high-power LMD processes.
Nerdy and easy to pronounce: why we chose Apheros as the name for our technology start-up firm
Comprehensive characterization of the molecular feature of acetylation in colorectal cancer using integrated single-cell and bulk RNA sequencing
The meaning behind the moniker: how to choose a science-company name that delights
The effect of C60 fullerene on the musculus gastrocnemius contraction in chronically alcohol-exposed rats and the potential mechanism of its action
Abstract Over 90% of consumed alcohol is metabolized through oxidative and non-oxidative pathways, producing highly reactive compounds capable of generating reactive oxygen species (ROS). ROS induce increased oxidative stress and lipid peroxidation, thereby disrupting the structural integrity of myocytes and the functions of skeletal muscles in general. It is hypothesized that biocompatible and bioavailable C60 fullerenes, as potent antioxidants, can effectively absorb ROS, normalizing the functional state of the muscular system during the chronic alcoholic myopathy (CAM). Here, for the first time, the effect of C60 fullerenes (oral daily dose of 1 mg/kg) administered together with alcohol (40% ethanol in drinking water) on the contractile activity of the musculus gastrocnemius in male Wistar rats (age 1 month, weight 170 ± 10 g; n = 36) during the development of CAM over 3, 6, and 9 months was analyzed using tensometry. Biochemical analysis was used to evaluate pro- and antioxidant balance indicators in the blood of alcohol-exposed animals under the influence of C60 fullerenes. Finally, the potential mechanism of action of C60 fullerenes under chronic alcohol intoxication in rats was analyzed using a computer simulation technique. The data obtained indicate an improvement in the studied biomechanical markers of contraction in alcohol-exposed musculus gastrocnemius with a range of 16–50 ± 3%. Additionally, significant biochemical changes were observed in the pro- and antioxidant balance in the blood of experimental rats, showing an improvement of 22–39 ± 2% due to the action of C60 fullerenes, compared to the alcohol-exposed group. It is demonstrated that C60 fullerene nanoparticles can bind ethanol molecules, thereby reducing the negative effects of alcohol on the functioning of the muscular system.
Daily briefing: No strong evidence backs up Trump’s claims about Tylenol and autism
Amylopectin xerogel with onion based sulfur nitrogen doped carbon quantum dots as a chemosensor for chromium and biosensor for microbial spoilage in tomatoes
Abstract This study presents the development of a multifunctional, biodegradable xerogel film based on amylopectin and poly(N-isopropylacrylamide) (poly(NIPAm)) incorporating sulfur and nitrogen-doped carbon quantum dots (S, N–CQDs) derived from red onion peels (ROP). The formation mechanism and stability of the composite film were investigated using DFT calculations, revealing enhanced interactions and stability in the S, N–CQDs-containing film (amylopectin-S, N–CQDs15). FTIR and SEM analyses confirmed the successful incorporation of S, N–CQDs and revealed a tighter pore structure in the composite film, leading to increased surface area. The amylopectin-S, N–CQDs15 film exhibited significantly improved antibacterial activity, with inhibition rates of 95.25% against Escherichia coli, 99.12% against Staphylococcus aureus, and 99.23% against Candida albicans. These findings were supported by molecular docking studies indicating strong binding affinities. Furthermore, the film demonstrated its potential as a smart sensor through distinct fluorescence responses to these microorganisms: it showed mixed green and red fluorescence with E. coli, blue dots with S. aureus, and a change from large red regions to numerous green dots with C. albicans. The film also exhibited a fluorescence shift from red to blue upon exposure to Cr(VI). Notably, the film displayed pH-responsive color transitions relevant to monitoring tomato spoilage. These findings highlight the potential of this bio-based composite film, prepared from a waste resource, as a sustainable and effective solution for active food packaging, offering antimicrobial properties and detection of spoilage and contamination.
Analysis of epidemiology, etiology and injury patterns in 2,179 digit amputations
Abstract As digit amputations can profoundly affect hand function and quality of life, insight into their anatomical distribution, etiology, and epidemiology is fundamental to improving treatment and prevention. This retrospective study investigates 2,179 digit amputations in 1,768 patients treated between April 2005 and December 2021 at a German Level I trauma center, excluding successful replantations. The cohort was predominantly male (89.1%) with a median age of 49 years (IQR: 34–61) and age peaks at 20–30 and 40–60 years. Occupational injuries accounted for 38.7% of cases, more frequent among males and those under 40. Temporal trends showed seasonal peaks in July and September and increased incidence on Fridays and Saturdays. Sharp injuries were the leading cause, followed by blunt trauma and avulsion. The index finger was most frequently affected, with the distal interphalangeal joint being the most common amputation level among Long fingers. Multiple digit amputations occurred in 17.5% of cases and predominantly in patients suffering from leisure trauma. This study provides a detailed epidemiological and etiological analysis of digit amputations, revealing a young, male-dominated cohort with a significant proportion of occupational trauma. The findings highlight the need for targeted prevention strategies and informed planning of trauma care resources.
Learning based prediction of cuttings concentration for enhancing hole cleaning efficiency in eccentric and deviated wells
Abstract Directional drilling often encounters challenges such as eccentric annulus conditions caused by the weight of the drill string and oscillations, compounded by gravity-induced cuttings accumulation that obstructs flow and impedes drilling processes due to inefficient hole cleaning. This study focuses on addressing these issues by developing machine learning (ML) models to predict cuttings concentration (CA) in eccentric deviated wells, aiming to enhance predictive accuracy and optimize hole-cleaning operations. The research employs multiple ML algorithms including back propagation neural network (BPNN), radial basis function network (RBFN), and support vector machine (SVM). Models are trained using comprehensive field data from six deviated wells in the Gulf of Suez, Egypt, with inputs comprising rheological properties, drilling operation parameters, cutting transport velocity ratio (VTR), and carrying capacity index (CCI). The models undergo rigorous validation to ensure robustness and accuracy, employing both internal validation techniques to avoid overfitting and extensive testing across varying degrees of eccentricity. The developed RBFN model demonstrated superior performance compared to existing empirical and fuzzy logic models, achieving a relation coefficient (R) of 0.993 and an average absolute error (AAE) of 1.18 at an eccentricity degree (ε) of 0.5. In further validation within neighboring test wells, the RBFN model accurately predicted CA across different eccentricities, showing high reliability with R-values of 0.984, 0.978 and 0.971 and AAE-values of 1.1, 1.4 and 1.7 for = 0, 0.4 and 0.8, respectively. Sensitivity analyses confirmed the critical influence of VTR and CCI, with their impact most pronounced at the highest eccentricity tested. This study presents a significant advancement in drilling technology by integrating advanced ML methodologies to improve the monitoring and optimization of hole-cleaning efficiency in deviated wells. The novel application of these sophisticated models offers a promising solution to real-time challenges in drilling operations, enhancing efficiency and reducing operational risks associated with eccentric deviated wells. Incorporating ML models into routine drilling operations can potentially transform standard practices, making this approach a valuable asset in the field of petroleum engineering.
Predictive robot eyes enhance attentional guidance in cooperative human–robot interaction
Abstract A key factor in successful human–robot interaction (HRI) is the predictability of a robot’s actions. Visual cues, such as eyes or arrows, can serve as directional indicators to enhance predictability, potentially improving performance and increasing trust. This study investigated the effects of predictive cues on performance, trust, and visual attention allocation in an industrial HRI setting. Using a 3 (predictive cues: abstract anthropomorphic eyes, directional arrows, no cue) $$\times$$ 3 (experience in three experimental blocks) mixed design, 42 participants were tasked with predicting a robot’s movement target as quickly as possible. Contrary to our expectations, predictive cues did not significantly affect trust or prediction performance. However, eye-tracking revealed that participants exposed to anthropomorphic eyes identified the target earlier than those without cues. Interestingly, participant’s self-reports showed infrequent use of the cues as directional guidance. Still, greater cue usage, as indicated by fixation data, was associated with faster predictions, suggesting that predictive cues, particularly anthropomorphic ones, guide visual attention and may improve efficiency. These findings highlight the nuanced role of predictive cues in HRI: even when not heavily relied on or reflected in performance, they can subtly guide attention and support interaction.
Behavioural predictability in chickens in response to anxiogenic stimuli is influenced by maternal corticosterone levels during egg formation
Abstract Across species, prenatal maternal stress has been shown to create heterogeneity in behavioural phenotypes. Research has recently highlighted that individuals vary in how predictable they are in their behavioural responses. This within-individual variation in behaviour is likely to be of biological importance, since individuals interact with the world not only through their mean behavioural phenotype, but also through their full range of behavioural variation. Yet, the underlying mechanisms that create and constrain between-individual variation in behavioural predictability remain largely unexplored. Here, we estimate whether experimental elevation of maternal corticosterone during egg laying (to model prenatal maternal stress) can cause variation in behavioural predictability in a population of chickens. Offspring’s behavioural predictability was quantified by testing them repeatedly (16 times) in a standard anxiety test (open-field test). Elevated maternal corticosterone resulted in less anxious and more predictable offspring compared to control offspring. These findings provide the first evidence that maternal corticosterone levels, via prenatal pathways, may influence multi-hierarchical behavioural plasticity by affecting both the magnitude and the predictability of behavioural responses. These results not only expand our current knowledge about the ways maternal stress can affect offspring’s behavioural phenotypes but also suggest a possible proximate mechanism underlying within-population variation in individual behavioural predictability.
Dual ACE2 epitope-based biomimetic receptors for selective sensing of SARS-CoV variants
Abstract We report a combinatorial approach to design peptide-based biomimetic sensors for detecting β-type coronaviruses with high sensitivity and selectivity. We selected three peptide epitopes from key regions of the ACE2 receptor that are involved in viral binding to different variants and immobilized them individually or in binary combinations on gold sensor chips. Using Surface Plasmon Resonance (SPR), we found that single-epitope sensors displayed nanomolar dissociation constants to three RBD variants (SARS-CoV-2 Delta < SARS-CoV-2 Alpha < SARS-CoV-1) and a KD = 1.2 ± 0.4 nM to the full SARS-CoV-2 Alpha spike protein, with negligible binding to the a-coronavirus hCoV-NL63 spike protein. Molecular dynamics simulations revealed that the tightest binding epitope closely mimics ACE2 interactions with β-coronaviruses, explaining its superior performance. In contrast, dual epitope systems exhibited a reversed variant preference, with a pronounced affinity enhancement for SARS-CoV-1 (KD = 6 ± 2 nM). This was attributed to cooperative epitope interactions that could restrict the conformational flexibility of the longer epitope, favoring the effective intermolecular contacts that strengthen the interaction with the RBD. These findings suggest a time-saving approach for developing sensitive and selective sensors for rapidly mutating viruses.
GTEx pro enables accurate multi-tissue gene expression analysis using robust normalization and batch correction
Abstract The Genotype-Tissue Expression (GTEx) project provides a valuable resource for investigating gene regulation across various human tissues. However, its cross-sectional design introduces technical artifacts and batch effects related to donor demographics and tissue processing. These confounders obscure biological signals and distort multi-tissue analyses. We present GTEx_Pro, a Nextflow-based pipeline for preprocessing GTEx v8 transcriptomic data, enhancing multi-tissue comparability. It integrates TMM + CPM normalization and SVA batch effect correction to improve biological signal recovery while reducing systematic variations across 54 GTEx tissues. Designed for scalability and reproducibility, GTEx_Pro facilitates accurate multi-tissue transcriptomic analysis, and a similar framework can be adapted to other large-scale transcriptome datasets.
Plasticity of interhemispheric motor cortex connectivity induced by brain state-dependent cortico-cortical paired-associative stimulation
Abstract Transcallosal connectivity between the hand areas of the two primary motor cortices (M1) is important for coordination of unimanual and bimanual hand motor function. Effective connectivity of this M1-M1 pathway can be tested in the form of short-interval interhemispheric inhibition (SIHI) using dual-coil transcranial magnetic stimulation (TMS). Recently, we and others have demonstrated that the phase of the ongoing sensorimotor µ-rhythm has significant impact on corticospinal excitability as measured by motor evoked potential (MEP) amplitude, and repetitive TMS of the high-excitability state (trough of the µ-rhythm) but not other states resulted in long-term potentiation-like MEP increase. Here, we tested to what extent the phase of the ongoing µ-rhythm in the two M1 affects long-term change in SIHI. In healthy subjects we applied cortico-cortical paired associative stimulation (ccPAS) in four different µ-phase conditions in the left conditioning M1 and right test M1 (trough-trough, trough-positive peak, positive peak-trough, random phase). We found long-term strengthening of SIHI but no differential effect of phase conditions. Findings point to a distinct regulation of plasticity of corticospinal versus M1-M1 connectivity. The observed ccPAS-induced strengthening of effective M1-M1 connectivity (SIHI) may be utilized for therapeutic applications that potentially benefit from modification of interhemispheric excitation/inhibition balance.
Population-specific calibration and validation of an open-source bone age AI
Abstract Assessing skeletal maturity through bone age (BA) evaluation is crucial for monitoring children’s growth and guiding treatments, such as hormonal therapy and orthopedic interventions. In recent years, artificial intelligence (AI) methods have been developed to automate BA assessment. However, bone growth patterns may vary by ancestry, and many AI models are trained on limited population datasets, raising concerns about their applicability to populations not included in the training process. To address this shortcoming for the case of the Georgian population, we retrospectively collected 381 pediatric hand X-rays and established a manual BA reference rating from seven local pediatric radiologists and endocrinologists. We then used a subset of 121 images to perform a sex-specific linear calibration of the open-source AI, Deeplasia, creating Deeplasia-GE. On the held-out test set (n = 260), the default version of Deeplasia achieved a mean absolute difference (MAD) of 6.57 months, which improved to 5.69 months after calibration. We observed that the default Deeplasia overestimates the BA in the Georgian cohort with a signed mean difference (SMD) of + 2.85 and + 5.35 months for females and males respectively, which after calibration is significantly reduced to -0.03 and + 0.58 months for females and males, respectively. We find that Deeplasia-GE has a smaller error than all the raters and, by design, Deeplasia-GE inherits the high test-retest reliability from Deeplasia. These findings suggest that Deeplasia-GE is a reliable AI-based BA assessment method for Georgian children.
Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection
Abstract This study compares various preprocessing techniques for hyperspectral deep learning–based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as well varying levels of blood and light reflections removal. We also examine how the size of the patches extracted from the hyperspectral data affects the models’ performance. We additionally explore various strategies to mitigate our dataset’s imbalance (where cancerous tissues are underrepresented). Our results indicate that. Scaling: Standardization significantly improves both sensitivity and specificity compared to Normalization. Larger input patch sizes enhance performance by capturing more spatial context. Noise reduction unexpectedly degrades performance. Blood filtering is more effective than filtering reflected light pixels, although neither approach produces significant results. By carefully maintaining consistent testing conditions, we ensure a fair comparison across preprocessing methods and reproducibility. Our findings highlight the necessity of careful preprocessing selection to maximize deep learning performance in medical imaging applications.
MCC950 targets the ROS-NEK7-NLRP3 axis to improve type 2 diabetic retinopathy
Abstract 1 mM of MCC950 targets the ROS-NEK7-NLRP3 axis to ameliorate T2DM in rats and exhibits peak efficacy in improving retinopathy. It has been found that the specific inhibitor MCC950 can alleviate diabetic retinopathy by inhibiting NLRP3 inflammasome, but its concentration-dependent efficacy on retinal pathology needs to be explored. The aim of this study was to quantify the effects of intravitreal injection of graded concentrations of MCC950 (0.01, 0.1, 1,10 mM) on retinal structure and NLRP3 inflammasome signalling in type 2 diabetic male rats, and to reveal that 1 mM MCC950 may exert optimal retinoprotective effects by down-regulating the NEK7-NLRP3 pathway. Type 2 diabetic male rats induced by streptozotocin were administered intravitreal injections of MCC950 at varying concentrations (0.01, 0.1, 1, 10 mM). Quantitative assessments revealed that a concentration of 1 mM MCC950 markedly improved retinal histopathological alterations ( p < 0.05) and modulated retinal apoptosis and oxidative stress to a considerable degree ( p < 0.05). On a mechanistic level, MCC950 suppressed NLRP3 inflammasome activation by disrupting the interaction between NEK7 and NLRP3 (manifested by the down-regulation of pathway-associated protein expression, p <0.05) and a strong positive correlation between NEK7 and NLRP3 protein expression ( r = 0.62, p = 0.19); inhibited the activation of the NLRP3 inflammasome (manifested by reduced levels of Cleaved Caspase-1, IL-1β, and IL-18, p < 0.001). There was a positive correlation between the intensity of ROS fluorescence and the fluorescence expression of NEK7 ( r = 0.8857, p < 0.05), with MCC950 treatment significantly lowering retinal ROS levels at the 1 mM concentration. In conclusion, MCC950 inhibits ROS-mediated NEK7 upregulation, NLRP3 activation, and attenuates pathological damage, oxidative stress, retinal inflammation, and apoptosis in type 2 diabetic retina via ROS-NEK7-NLRP3 pathway.