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Chemical compatibility at the interface of garnet-type Ga-LLZO solid electrolyte and high-energy Li-rich layered oxide cathode for all-solid-state batteries

Zinc oxide nanoparticles foliar use and arbuscular mycorrhiza inoculation retrieved salinity tolerance in Dracocephalum moldavica L. by modulating growth responses and essential oil constituents

C–C bond coupling with sp3 C–H bond via active intermediates from CO2 hydrogenation

Artificial intelligence in dentistry: Assessing the informational quality of YouTube videos

Background and purpose The most widely used social media platform for video content is YouTubeTM. The present study evaluated the quality of information on YouTubeTM on artificial intelligence (AI) in dentistry. Methods This cross-sectional study used YouTubeTM (https://www.youtube.com) for searching videos. The terms used for the search were "artificial intelligence in dentistry," "machine learning in dental care," and "deep learning in dentistry." The accuracy and reliability of the information source were assessed using the DISCERN score. The quality of the videos was evaluated using the modified Global Quality Score (mGQS) and the Journal of the American Medical Association (JAMA) score. Results The analysis of 91 YouTube™ videos on AI in dentistry revealed insights into video characteristics, content, and quality. On average, videos were 22.45 minutes and received 1715.58 views and 23.79 likes. The topics were mainly centered on general dentistry (66%), with radiology (18%), orthodontics (9%), prosthodontics (4%), and implants (3%). DISCERN and mGQS scores were higher for videos uploaded by healthcare professionals and educational content videos(P<0.05). DISCERN exhibited a strong correlation (0.75) with the video source and with JAMA (0.77). The correlation of the video’s content and mGQS, was 0.66 indicated moderate correlation. Conclusion YouTube™ has informative and moderately reliable videos on AI in dentistry. Dental students, dentists and patients can use these videos to learn and educate about artificial intelligence in dentistry. Professionals should upload more videos to enhance the reliability of the content.

Nomophobia among nursing students: prevalence and associated factors

Effectiveness of movement representation techniques in non-specific shoulder pain: a systematic review and meta-analysis

TOPS-speed complex-valued convolutional accelerator for feature extraction and inference

AbstractComplex-valued neural networks process both amplitude and phase information, in contrast to conventional artificial neural networks, achieving additive capabilities in recognizing phase-sensitive data inherent in wave-related phenomena. The ever-increasing data capacity and network scale place substantial demands on underlying computing hardware. In parallel with the successes and extensive efforts made in electronics, optical neuromorphic hardware is promising to achieve ultra-high computing performances due to its inherent analog architecture and wide bandwidth. Here, we report a complex-valued optical convolution accelerator operating at over 2 Tera operations per second (TOPS). With appropriately designed phasors we demonstrate its performance in the recognition of synthetic aperture radar (SAR) images captured by the Sentinel-1 satellite, which are inherently complex-valued and more intricate than what optical neural networks have previously processed. Experimental tests with 500 images yield an 83.8% accuracy, close to in-silico results. This approach facilitates feature extraction of phase-sensitive information, and represents a pivotal advance in artificial intelligence towards real-time, high-dimensional data analysis of complex and dynamic environments.

A GPU-accelerated fuzzy method for real-time CT volume filtering

During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy images results in a higher radiation dose being administered to the patient. Filtering techniques can be utilized to reduce radiation without losing diagnosis capabilities. The objective in this work is to obtain an implementation of a filter capable of processing medical images in real-time. To achieve this we have developed several filter methods based on fuzzy logic, and their GPU implementations, to reduce mixed Gaussian-impulsive noise. These filters have been developed to work in attenuation coefficients so as to not lose any information from the CT scans. The testing volumes come from the Mayo clinic database and consist of CT volumes at full and at simulated low dose. The GPU parallelizations reach speedups of over 2700 and take less than 0.1 seconds to filter more than 300 slices. In terms of quality the filter is competitive with other state of the art algorithmic and AI filters. The proposed method obtains good performance in terms of quality and the parallelization results in real-time filtering.

A single vector system for tunable and homogeneous dual gene expression in Escherichia coli

Role of sclerostin in mastocytosis bone disease

AbstractMastocytosis is a heterogeneous group of disorders, characterized by accumulation of clonal mast cells which can infiltrate several organs, most often spine (70%). The pathogenesis of mastocytosis bone disease is poorly understood. The main aim of the study was to investigate whether neoplastic mast cells may be the source of sclerostin and whether there is an association between sclerostin and selected bone remodeling markers with mastocytosis related bone disease. We assessed sclerostin, bioactive sclerostin, and SOST gene expression in HMC-1.2 human mast cell culture supernatants and plasma of SM patients (n = 39). We showed that human mast cells can secrete sclerostin, and after their stimulation with IL-6, there is a significant increase in SOST gene expression. We observed significantly higher levels of sclerostin in patients diagnosed with more advanced disease. We observed a statistically significant correlation between concentations of sclerostin and its bioactive form and the concentration of alkaline phosphatase (ALP), and between sclerostin and interleukin-6 (IL-6). We observed that significantly higher sclerostin concentrations are present in patients with increased sclerosis of the spongy bone. Sclerostin may serve as a marker of more advanced disease and bone disease in mastocytosis. Further studies are justified to evaluate its role in mastocytosis.

In vivo spontaneous Ca2+ activity in the pre-hearing mammalian cochlea

AbstractThe refinement of neural circuits towards mature function is driven during development by patterned spontaneous calcium-dependent electrical activity. In the auditory system, this sensory-independent activity arises in the pre-hearing cochlea and regulates the survival and refinement of the auditory pathway. However, the origin and interplay of calcium signals during cochlear development is unknown in vivo. Here we show how calcium dynamics in the cochlear neuroepithelium of live pre-hearing mice shape the activity of the inner hair cells (IHCs) and their afferent synapses. Both IHCs and supporting cells (SCs) generate spontaneous calcium-dependent activity. Calcium waves from SCs synchronise the activity of nearby IHCs, which then spreads longitudinally recruiting several additional IHCs via a calcium wave-independent mechanism. This synchronised IHC activity in vivo increases the probability of afferent terminal recruitment. Moreover, the modiolar-to-pillar segregation in sound sensitivity of mature auditory nerve fibres appears to be primed at pre-hearing ages.

Correction: Moving towards a core measures set for patient safety in perioperative care: An e-Delphi consensus study

A comparative analysis of lumboperitoneal shunt outcomes in patients with post-hemorrhagic and post-traumatic hydrocephalus

Robust STAP with coprime sampling structure based on optimal singular value thresholding

Probing SARS-CoV-2 membrane binding peptide via single-molecule AFM-based force spectroscopy

AbstractThe SARS-CoV-2 spike protein’s membrane-binding domain bridges the viral and host cell membrane, a critical step in triggering membrane fusion. Here, we investigate how the SARS-CoV-2 spike protein interacts with host cell membranes, focusing on a membrane-binding peptide (MBP) located near the TMPRSS2 cleavage site. Through in vitro and computational studies, we examine both primed (TMPRSS2-cleaved) and unprimed versions of the MBP, as well as the influence of its conserved disulfide bridge on membrane binding. Our results show that the MBP preferentially associates with cholesterol-rich membranes, and we find that cholesterol depletion significantly reduces viral infectivity. Furthermore, we observe that the disulfide bridge stabilizes the MBP’s interaction with the membrane, suggesting a structural role in viral entry. Together, these findings highlight the importance of membrane composition and peptide structure in SARS-CoV-2 infectivity and suggest that targeting the disulfide bridge could provide a therapeutic strategy against infection.

An Extenics-TRIZ integrated RFPS model for different object of design requirements

Extending product life is one of the effective ways to reduce the waste of resources. However, many unsatisfactory products are scrapped because of a lack of adequate performance. The product should be improved and upgraded innovatively, and the existing upgradable products may create more economic benefits for the longer product life cycles. This paper proposed a product innovative design and product upgrade employing an Extenics-TRIZ Integrated requirement-function-principle-structure (RFPS) model, which aims at complex requirement flexibility with easy-to-use design process when the product needs a redesign. Here, the requirement flexibility refers to the ability of a design object to adapt its design levels. There are two design strategies: the extension analysis methods are utilized to map the top-level requirements to functions, principles, and structures requirements, and then the TRIZ is used to handle the design problems according to the objects on different levels. This design knowledge is summarized as RFPS, and it can be reused in computer-aided innovation further. A case study for a cutting table is illustrated to the innovation and upgrade, and it indicates the effectiveness for designers to implement the design methodology.

Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning

Impact of epinephrine on neurological outcomes in out-of-hospital cardiac arrest after automated external defibrillator use in Japan

Immunoassay detection of multiphosphorylated tau proteoforms as cerebrospinal fluid and plasma Alzheimer’s disease biomarkers

Predicting noncontact injuries of professional football players using machine learning

Noncontact injuries are prevalent among professional football players. Yet, most research on this topic is retrospective, focusing solely on statistical correlations between Global Positioning System (GPS) metrics and injury occurrence, overlooking the multifactorial nature of injuries. This study introduces an automated injury identification and prediction approach using machine learning, leveraging GPS data and player-specific parameters. A sample of 34 male professional players from a Portuguese first-division team was analyzed, combining GPS data from Catapult receivers with descriptive variables for machine learning models—Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), and Adaptive Boosting (AdaBoost)—to predict injuries. These models, particularly the SVMs with cost-sensitive learning, showed high accuracy in detecting injury events, achieving a sensitivity of 71.43%, specificity of 74.19%, and overall accuracy of 74.22%. Key predictive factors included the player’s position, session type, player load, velocity and acceleration. The developed models are notable for their balanced sensitivity and specificity, efficiency without extensive manual data collection, and capability to predict injuries for short time frames. These advancements will aid coaching staff in identifying high-risk players, optimizing team performance, and reducing rehabilitation costs.