Browse Articles

Deadly Myanmar earthquake was probably a rare rupture, scientists say

Sorting rare earth magnets motors for recycling without opening the motors

Abstract A novel approach for sorting electric motors without dismantling them is reported in this article. This approach increases the likelihood that a motor selected from a mixture for recycling would contain rare earth elements (REEs), which are pivotal for numerous advanced green technologies. The challenge of inadvertently dismantling motors without REEs during recycling is addressed by this proposed innovative method. It relies on cogging interactions and power density for sorting motors with REEs. The process uses an algorithm introduced in this work to sort motors in two steps by (a) distinguishing induction motors from permanent magnet motors, and (b) distinguishing motors with critical REEs (e.g., Nd, Pr, Dy, Tb) from ferrite-based motors, all without opening the motors. The former was accomplished with 100% accuracy while the latter was accomplished with ~ 78% accuracy. Such high accuracy improves the recycling efficiency of critical REEs from motors, alleviating concern about supply disruption and supporting resource conservation and sustainability.

DeepATsers: a deep learning framework for one-pot SERS biosensor to detect SARS-CoV-2 virus

Abstract The integration of Artificial Intelligence (AI) techniques with medical kits has revolutionized disease diagnosis, enabling rapid and accurate identification of various conditions. We developed a novel deep learning model, namely DeepATsers based on a combination of CNN and GAN to employ a one-pot SERS biosensor to rapidly detect COVID-19 infection. The model accurately identifies each SARS-CoV-2 protein (S protein, N protein, VLP protein, Streptavidin protein, and blank signal) from its experimental fingerprint-like spectral data introduced in this study. Several augmentation techniques such as EMSA, Gaussian-noise, GAN, and K-fold cross-validation, and their combinations were utilized for the SERS spectral dataset generalization and prevented model overfitting. The original experimental dataset of 126 spectra was augmented to 780 spectra that resembled the original set by using GAN with a low KL divergence value of 0.02. This significantly improves the average accuracy of protein classification from 0.6000 to 0.9750. The deep learning model deployed optimal hyperparameters and outperformed in most measurements comparing supervised machine learning methods such as RF, GBM, SVM, and KNN, both with and without augmented spectral datasets. For model training, a whole range of spectra wavenumbers ( $$320 \hbox { cm}^{-1}$$ to $$1650 \hbox { cm}^{-1}$$ ) as well as wavenumbers ( $$1078 \hbox { cm}^{-1}$$ and $$1582 \hbox { cm}^{-1}$$ ) only for fingerprint peak spectra were employed. The former led to highly accurate 0.9750 predictions in comparison to 0.4318 for the latter one. Finally, independent experimental spectra of SARS-CoV-2 Omicron variant were used in the model verification. Thus, DeepATsers can be considered a robust, generalized, and generative deep learning framework for 1D SERS spectral datasets of SARS-CoV-2.

Involvement of circadian clock protein PER2 in controlling sleep deprivation induced HMGB1 up-regulation by targeting p300 in the cortex

AMF inoculation reduces yield losses in rice exposed to alternate wetting and drying and low fertilization

‘One of the darkest days’: NIH purges agency leadership amid mass lay-offs

Development and validation of a highly sensitive HPLC method for quantifying cardiovascular drugs in human plasma using dual detection

Abstract Cardiovascular diseases are the major cause of global mortality, and often require the concomitant use of a number of drugs to prevent and reduce these deaths. The challenge is to find effective and accurate methods for analyzing these drugs in plasma. This research introduces an innovative, sustainable HPLC-FLD method for the concurrent determination of bisoprolol (BIS), amlodipine besylate (AML), telmisartan (TEL), and atorvastatin (ATV) within human plasma. Chromatographic separation was achieved using an isocratic elution mode on a Thermo Hypersil BDS C18 column (150 × 4.6 mm, 5.0 μm), while the mobile phase comprised of ethanol and 0.03 M potassium phosphate buffer (pH 5.2) in a 40:60 ratio, with a flow rate of 0.6 mL/min. The eluate was analyzed using UV detection within the wavelength range of 210–260 nm to confirm effective separation of the four cardiovascular drugs. For enhanced specificity, a fluorescence detector was set to 227ex/298em for BIS, 294ex/365em for TEL, 274ex/378em for ATV, and 361ex/442em for amlodipine. The method was validated following the International Council for Harmonisation (ICH) guidelines. Linearity was established within the ranges of 5–100 ng/mL for BIS and AML, 0.1–5 ng/mL for TEL, and 10–200 ng/mL for ATV, ensuring accuracy and precision. The significant of the current work represented in introduction of a highly sensitive, and selective analytical method, utilizing an economical sample preparation strategy, for the simultaneous determination of four different cardiovascular drugs (bisoprolol, amlodipine, telmisartan, and atorvastatin) in spiked human plasma. The extraction of sample was carried by liquid-liquid extraction (LLE) and analyzed by LC-fluorescence detector. The chromatographic run was short (less than10 min) which is a greet economical value.

Investigation of the allelopathic effect of two medicinal plant in agroforestry system

Seasonal changes in the force velocity sprint profile of Spanish youth football players across age categories

Abstract The purpose of this study was to investigate the changes in the force-velocity (F-V) profile and 30-metres sprint times of youth football players over a competitive season and across different age groups. Sixty-four players were categorized into five age groups (Under-10 (U10), Under-12 (U12), Under-14 (U14), Under-16 (U16), Under-18 (U18)) and assessed at three time points during the season: the pre-season (P1), mid-season (P2), and end season (P3) using GNSS/GPS technology. Results showed that the theoretical maximum force (F0) increased by the end of the season compared to the baseline in U14, U16, and U18 (p < 0.05; ES: 1.92–5.19) and was also higher at the end compared to mid-season in U14 players (p < 0.05; ES: 3.01). The theoretical maximum velocity (V0) was significantly higher at the end of the season compared to the baseline in U12 (p < 0.05; ES: 2.61) and mid-season in U12 and U16 players (p < 0.05; ES: 1.70–2.37). U10 and U12 showed lower F0 and V0 values compared to older players across all periods. The study concludes that both the timing of the season and the age influence the F-V profile, with older age groups showing greater improvements. These findings provide valuable insights for optimizing training programs tailored to the seasonal changes and age of football.

CircSCD1 inhibits ferroptosis in breast Cancer through stabilizing SCD1 protein via deubiquitinase OTUB1

Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI

Relationship between De Ritis and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage: Insights from the LongTEAM registry

Digital protection scheme based on Durbin Watson and Pearson similarity indices for current signals practically applied to power transformers

Abstract Electrical faults can change the power quality parameters of power systems. A numerical protection technique for fault detection and imbalance assessment based the Durbin-Watson (DW) factors for three phase currents is proposed in this paper. The approach integrates two protection functions based on the Durbin-Watson and Pearson similarity algorithms into one protection scheme. This strategy can figure out online faults located on the three-phase power transformer windings, such as turn-to-turn, winding-to-neutral, and winding-to-winding. Moreover, it can distinguish between balanced and imbalanced currents. To assess the validity of the protection scheme, it is practically examined on a three-phase power transformer with tapped windings connected to a three-phase load. Comprehensive tests are conducted to investigate the efficacy and efficiency of the suggested scheme. The analog-to-digital converter is integrated with LABVIEW software to process and analyze the two algorithms of the suggested scheme. The results of the experiments reveal that the security, dependability and precision ratios of the developed protection are almost 99%. Additionally, the protection system can immediately identify electrical faults, triggering a tripping signal to both the annunciator panel and the circuit breaker trip coil of the equipment, but it remains inactive under normal operating conditions and acceptable current unbalance. In the fault events, the numerical approach can respond quickly using a limited data set within a single cycle of the foundation frequency, and operate effectively using a pair of algorithms based on DW and Pearson similarity. It is also robust against the condition of sound transformer windings. Besides, it can determine and estimate the severity rate of perturbation and unbalance in power transformer currents, and it has a protection redundancy. Furthermore, the scheme is extremely sensitive to light fault currents, and has a unique set of tripping curves.

Brain implant translates thoughts to speech in an instant

Single-cell RNA sequencing reveals important role of monocytes and macrophages during mucopolysaccharidosis treatment

Incredible close-up of colourful crab spiders — March’s best science images

Feature fusion with attributed deepwalk for protein–protein interaction prediction

Fully printed doped vanadium dioxide (M) nanoparticles-based temperature sensor with enhanced sensitivity for reliable environmental monitoring using packaging strategy

Contrast and luminance dependence of target choice and visual orientation in walking stick insects

Abstract When presented with static images, animals show robust preferences for particular visual features, and reliably turn towards and approach selected visual landmarks. In target choice paradigms, stick insects tend to approach edges with high image contrast, but also show robust orientation based on luminance alone. To better understand which stimulus features actually govern turning towards static visual targets, this study tests the relative importance of two elementary cues of spatial vision – luminance and contrast. We do so in a large open-field arena, using luminance-modulated, static 360° patterns with and without high-contrast edges. We show that target choice strongly depends on image contrast, though with a bias towards areas of low luminance. Comparison of heading directions during approach with terminal locations at the arena wall suggests an early, coarse orientation based on luminance, with subsequent steering towards high-contrast regions. When walking towards a target with high-contrast edges, the likelihood to turn away towards a Gaussian distractor image increases with decreasing edge contrast of the original target. Subjective equality of the two images occurs for an approximate 2:1 weighing of contrast and luminance, indicating that a stronger contrast-dependent edge-orientation mechanism acts in parallel with a weaker luminance-dependent phototaxis mechanism. Given the significance of stick insects as study organisms for the control of legged locomotion, future research may now test whether the two visual orientation mechanisms lead to distinct turning responses at the level of step pattern or leg movement variables.

Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India