Wide Spectrum Research Articles

N-Stearoylethanolamine Exerts Cardioprotective Effects in Old Rats.

Aging is associated with the slowing down of metabolic processes, diminished physiological processes, changes in hormonal activity and increasing exposure to oxidative stress factors and chronic inflammation. The endocannabinoid system (ECS) is a major signaling network that plays a pro-homeostatic role in the central and peripheral organs of the human body. A class of minor lipids, N-acylethanolamines (NAEs), which do not activate cannabinoid receptors, except for anandamide, but can potentiate the action of endocannabinoids and have a wide spectrum of biological activity and significant adaptogenic potential, belongs to ECS. The results of different studies over the past decades have established the protective effect of NAE on many pathological conditions. This study aimed to investigate the cardioprotective effects of C18:0 NAE- N-stearoylethanolamine (NSE) in aged rats. In this study, we focused on investigating the effects of C18:0 NAE- N-stearoylethanolamine (NSE) on the intensity of oxidative/ nitrosative stress, antioxidant potential, lipoprotein profile and inflammation markers of blood plasma, phospholipid composition and age-related morphological changes of old rat heart tissues. The study was conducted on Sprague Dawley male laboratory rats. The three groups of rats were involved in the study design. The first group consisted of young rats aged 4 months (n=10). The second (n=10) and third (n=10) groups included old rats aged of 18 months. Rats from the third group were administered a per os aqueous suspension of NSE at a dose of 50 mg/kg of body weight daily for 10 days. All groups of rats were kept on a standard vivarium diet. The blood plasma, serum, and heart of rats were used for biochemical and histological analysis. The cardioprotective effect of N-stearoylethanolamine in old rats was established, which was expressed in the normalization of the antioxidant system condition and the level of proinflammatory cytokines, positive modulation of blood plasma and lipoprotein profile, normalization of heart tissue lipid composition, and significant reduction in age-related myocardium morphological changes. The revealed effects of N-stearoylethanolamine can become the basis for developing a new drug for use in complex therapy to improve the quality of life of older people.

Association of cortical gyrification, white matter microstructure, and phenotypic profile in medication-naïve obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is thought to arise from dysconnectivity among interlinked brain regions resulting in a wide spectrum of clinical manifestations. Cortical gyrification, a key morphological feature of human cerebral cortex, has been considered associated with developmental connectivity in early life. Monitoring cortical gyrification alterations may provide new insights into the developmental pathogenesis of OCD. Sixty-two medication-naive patients with OCD and 59 healthy controls (HCs) were included in this study. Local gyrification index (LGI) was extracted from T1-weighted MRI data to identify the gyrification changes in OCD. Total distortion (splay, bend, or twist of fibers) was calculated using diffusion-weighted MRI data to examine the changes in white matter microstructure in patients with OCD. Compared with HCs, patients with OCD showed significantly increased LGI in bilateral medial frontal gyrus and the right precuneus, where the mean LGI was positively correlated with anxiety score. Patients with OCD also showed significantly decreased total distortion in the body, genu, and splenium of the corpus callosum (CC), where the average distortion was negatively correlated with anxiety scores. Intriguingly, the mean LGI of the affected cortical regions was significantly correlated with the mean distortion of the affected white matter tracts in patients with OCD. We demonstrated associations among increased LGI, aberrant white matter geometry, and higher anxiety in patients with OCD. Our findings indicate that developmental dysconnectivity-driven alterations in cortical folding are one of the neural substrates underlying the clinical manifestations of OCD.

Multi‐azo Photoswitches for Improved Molecular Solar Thermal Energy Storage

AbstractMolecular solar thermal energy storage (MOST) based on photoisomerization represents a novel approach for the capture, conversion and storage of solar energy. Azo photoswitches can store energy by isomerization from their thermodynamically stable E isomers to higher energy metastable Z isomers. The energy density of azo photoswitches is crucial as it directly determines the storage capacity, but it is relatively low (160 J g−1). Therefore, enhancing the energy density through molecular structural design represents a central research focus in the field of MOST. A straightforward approach to enhance the energy density is to design multi‐azo photoswitches. This allows multiple azobenzene units to share a common framework while keeping the molecular weight as small as possible. In particular, when two azobenzene units are connected via a phenyl ring in a meta orientation, it facilitates efficient isomerization, thereby maximizing the energy density of the azo photoswitches (392 J g−1). This paper provides a brief overview of the development of multi‐azo photoswitches and highlights their outstanding performance as a MOST system. It also offers prospects for their future advancements in the field. We propose that, to further improve the energy density of multi‐azo photoswitches, one approach is to design wide spectrum of light photoisomerization of multi‐azo photoswitches. Additionally, introducing photo‐induced phase changes to multi‐azo photoswitches enables the simultaneous storage of both photon energy and ambient heat.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

First report of Tunisian patients with CDKL5-related encephalopathy.

Mutations in the cyclin-dependent kinase-like 5 gene (CDKL5) are associated with a wide spectrum of clinical presentations. Early-onset epileptic encephalopathy (EOEE) is the most recognized phenotype. Here we describe phenotypic features in eight Tunisian patients with CDKL5-related encephalopathy. We included all cases with clinical features consistent with CDKL5-related encephalopathy: infantile epileptic spasm, acquired microcephaly, movement disorders and visual impairment. We collected data about seizure types, electroencephalogram, magnetic resonance imaging, and metabolic analysis. The diagnosis of CDKL5 mutation was made thanks to Sanger sequencing with an ABI PRISM 3100-Avant automated DNA sequencer using a Big Dye Terminator Cycle Sequencing Reaction Kit v1.1. and Next Generation Sequencing (NGS) since the development of a gene panel responsible for DEE within the framework of "Strengthening the Sfax University Expertise for diagnosis and management of epileptic encephalopathies". We collected four boys and four girls aged meanly 6 years old with confirmed mutation on CDKL5 gene. Overall, we identified five de novo CDKL5 mutations including three Frame-shift mutations, one missense mutation, and a splicing variant. The mean age at first seizure onset was 4 months. The first seizure type was infantile epileptic spasm (4/8) followed by tonic (2/8) and myoclonic seizures (2/8). Out of eight cases, four exhibited two stages epileptic course while epilepsy in three other patients progressed on three stages. Regarding development, most cases (6/8) had psychomotor retardation from the start whilst the two others showed psychomotor regression with the onset of seizures. Additional clinical features included visual impairment (7/8), tone abnormalities (7/8), stereotypies (7/8), and acquired microcephaly (6/8). Our present report delineates an unusual phenotype of CDKL5-related encephalopathy with male gender predominance and delayed onset epilepsy. It interestingly described new phenotypic features and uncommon benign developmental profiles in boys, different patterns of CDKL5-epilepsy, neuroimaging findings, and CDKL5 mutational spectrum.

Machine learning models reveal distinct disease subgroups and improve diagnostic and prognostic accuracy for individuals with pathogenic SCN8A gain-of-function variants.

Distinguishing clinical subgroups for patients suffering with diseases characterized by a wide phenotypic spectrum is essential for developing precision therapies. Patients with gain-of-function (GOF) variants in the SCN8A gene exhibit substantial clinical heterogeneity, viewed historically as a linear spectrum ranging from mild to severe. To test for hidden clinical subgroups, we applied two machine-learning algorithms to analyze a dataset of patient features collected by the International SCN8A Patient Registry. We used two research methodologies: a supervised approach that incorporated feature severity cutoffs based on clinical conventions, and an unsupervised approach employing an entirely data-driven strategy. Both approaches found statistical support for three distinct subgroups and were validated by correlation analyses using external variables. However, distinguishing features of the three subgroups within each approach were not concordant, suggesting a more complex phenotypic landscape. The unsupervised approach yielded strong support for a model involving three partially ordered subgroups rather than a linear spectrum. Application of these machine-learning approaches may lead to improved prognosis and clinical management of individuals with SCN8A GOF variants and provide insights into the underlying mechanisms of the disease.

Reinforcement Learning for Solving Multiple Vehicle Routing Problem with Time Window

Vehicle routing problem with time window (VRPTW) is of great importance for a wide spectrum of services and real-life applications, such as online take-out and car-hailing platforms. A promising method should generate high-qualified solutions within limited inference time, and there are three major challenges: (a) directly optimizing the goal with several practical constraints; (b) efficiently handling individual time-window limits; and (c) modeling the cooperation among the vehicle fleet. In this article, we present an end-to-end reinforcement learning framework to solve VRPTW. First, we propose an agent model that encodes constraints into features as the input and conducts harsh policy on the output when generating deterministic results. Second, we design a time penalty augmented reward to model the time-window limits during gradient propagation. Third, we design a task handler to enable the cooperation among different vehicles. We perform extensive experiments on two real-world datasets and one public benchmark dataset. Results demonstrate that our solution improves the performance by up to 11.7% compared to other RL baselines and could generate solutions for instances within seconds, while existing heuristic baselines take for minutes as well as maintain the quality of solutions. Moreover, our solution is thoroughly analyzed with meaningful implications due to the real-time response ability.

Classification of Blood Cells with Convolutional Neural Network Model

Among the blood cells, white blood cells (WBC), which play a crucial role in forming our body's defense system, are essential components. Originating in the bone marrow, these cells serve as the fundamental components of the immune system, shouldering the responsibility of safeguarding the body against foreign microbes and diseases. Insufficient WBC counts may compromise the body's skill to resist infections, a status known as leukopenia. White blood cell counting is a specialty procedure that is usually carried out by qualified physicians and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network (CNN) models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed CNN model.

Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation

RNA-binding proteins are central for many biological processes and their characterization has demonstrated a broad range of functions as well as a wide spectrum of target structures. RNA G-quadruplexes are important regulatory elements occurring in both coding and non-coding transcripts, yet our knowledge of their structure-based interactions is at present limited. Here, using theoretical predictions and experimental approaches, we show that many chromatin-binding proteins bind to RNA G-quadruplexes, and we classify them based on their RNA G-quadruplex-binding potential. Combining experimental identification of nuclear RNA G-quadruplex-binding proteins with computational approaches, we build a prediction tool that assigns probability score for a nuclear protein to bind RNA G-quadruplexes. We show that predicted G-quadruplex RNA-binding proteins exhibit a high degree of protein disorder and hydrophilicity and suggest involvement in both transcription and phase-separation into membrane-less organelles. Finally, we present the G4-Folded/UNfolded Nuclear Interaction Explorer System (G4-FUNNIES) for estimating RNA G4-binding propensities at http://service.tartaglialab.com/new_submission/G4FUNNIES.

Abstract 2044: A live cell PRMT5 NanoBRET Target Engagement Assay querying competitive and uncompetitive modes of inhibition

Abstract PRMT5 is an essential arginine methyltransferase that regulates a wide spectrum of cellular processes through the methylation of histone and non-histone substrates. In PRMT5 catalysis, SAM serves as the cofactor and methyl group donor, generating a methylated guanidinium moiety on the target substrate. In normal cells, the SAM pools are maintained through the methionine salvage pathway. In 15% of cancers, a key enzyme in this pathway (MTAP) is deleted, leading to an accumulation of the intermediate MTA, which inhibits PRMT5 activity. This genetic loss of function has therefore been pursued as a collateral vulnerability in MTAP deleted cancers. To exploit this synthetic lethality, a number of novel inhibitors have been developed that bind cooperatively at the PRMT5/MTA complex, offering a compelling pathway to precision medicine. Here we describe a novel, live cell NanoBRET Target Engagement assay that enables mechanistic studies on a variety of PRMT5 inhibitors. Using a cell-permeable NanoBRET probe directed to the substrate pocket of PRMT5, both substrate- and cofactor-competitive engagement can be quantified in cells. Moreover, this method can be used to quantify MTA-uncompetitive target engagement in cells, providing a platform to measure the potency of PRMT5-MTA-drug ternary complex formation. This method serves as a first-in-class method to quantify uncompetitive target engagement in live cells, which can be applied to other model systems. Citation Format: Ani Michaud, Elisabeth Rothweiler, Cesear Corona, Michael Beck, Jennifer Wilkinson, James Vasta, Kilian Huber, Matthew Robers. A live cell PRMT5 NanoBRET Target Engagement Assay querying competitive and uncompetitive modes of inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2044.

ATP1A2-related epileptic encephalopathy and movement disorder: Clinical features of three novel patients.

Variants in the ATP1A2 gene exhibit a wide clinical spectrum, ranging from familial hemiplegic migraine to childhood epilepsies and early infantile developmental epileptic encephalopathy (EIDEE) with movement disorders. This study aims to describe the epileptology of three unpublished cases and summarize epilepsy features of the other 17 published cases with ATP1A2 variants and EIDEE. Medical records of three novel patients with pathogenic ATP1A2 variants were retrospectively reviewed. Additionally, the PUBMED, EMBASE, and Cochrane databases were searched until December 2023 for articles on EIDEE with ATP1A2 variants, without language or publication year restrictions. Three female patients, aged 6 months-10 years, were investigated. Epilepsy onset occurred between 5 days and 2 years, accompanied by severe developmental delay, intellectual disability, drug-resistant epilepsy, severe movement disorder, and recurrent status epilepticus. All individuals had pathogenic variants of the ATP1A2 gene (ATP1A2 c.720_721del (p.Ile240MetfsTer9), ATP1A2c.3022C > T (p.Arg1008Trp), ATP1A2 c.1096G > T (p.Gly366Cys), according to ACMG criteria. Memantine was p) rescribed to three patients, one with a reduction in ictal frequency, one with improvement in gait pattern, coordination, and attention span, and another one in alertness without significant side effects. This study reinforces the association between ATP1A2 variants and a severe phenotype. All patients had de novo variants, focal motor seizures with impaired awareness as the primary type of seizure; of the 11 EEGs recorded, 10 presented a slow background rhythm, 7 multifocal interictal epileptiform discharges (IED), predominantly temporal IEDs, followed by frontal IED, as well as ten ictal recordings, which showed ictal onset from the same regions mentioned above. Treatment with antiseizure medication was generally ineffective, but memantine showed moderate improvement. Prospective studies are needed to enlarge the phenotype and assess the efficacy of NMDA receptor antagonist therapies in reducing seizure frequency and improving quality of life.

Emotional Intelligence and Attachment style Among Adolescents: A Correlational Study

Emotional intelligence aids adolescents in the establishment of robust connections, enables them to reach sound judgments, and equips them to effectively navigate challenging circumstances. A perspective through which we can comprehend EQ is as an integral facet of possessing the ability to effectively interact with individuals, thereby enhancing our prospects of triumph in virtually every aspect of our existence. Comprehension and harmonious coexistence with fellow human beings not only contribute to our personal growth but also serve as a catalyst for attaining success across a wide spectrum of domains.
 Psychologists, in their rigorous exploration of human behavior, have identified a constellation of personality traits that are commonly referred to as the "dark triad." This triumvirate of psychological characteristics consists of a manipulative attitude, narcissism, and a startling lack of empathy. When confronted with the disconcerting possibility that one may have an evil child, it is only natural to seek clarity and understanding. However, discerning whether a child is truly malevolent or simply mischievous is no easy task. Fortunately, psychologists have developed a nuanced framework that allows them to distinguish between the merely naughty and the genuinely sinister. By meticulously examining the behavioral patterns and subtle indicators, these experts can shed light on the enigmatic signs that may suggest the presence of an evil child.

The enhanced visible-light-driven photocatalytic performance of nanocrystalline TiO2 decorated by quinazolinone-photosensitizer toward photocatalytic treatment of simulated wastewater

Semiconductor-assisted photocatalysis is an efficient, green, low-cost technology employed for wastewater treatment. TiO2-based photocatalysts are the common photocatalysts; however, their large bandgap energy excludes their photocatalytic activities under UV irradiation. Hence, this work investigates the synthesis of quinazolinone photosensitizer to improve the photocatalytic activity of TiO2 in the visible region and efficiently maximize the utilization of solar irradiation. Quinazolinone photosensitizer is a heterocyclic compound exhibits a wide spectrum in the visible region. The prepared samples are characterized by XRD, FE-SEM, FTIR, DRS, and PL. The photocatalytic activity of the Quinazolinone photosensitized-TiO2 catalyst is evaluated toward the degradation of rhodamine B (RB) as a model of organic pollutants in wastewater under UV-A and visible light irradiations. Quinazolinone photosensitized-TiO2 catalyst exhibited a wider absorption spectrum range and lower bandgap of 2.65 eV extending its activity to the visible region compared to bare TiO2 (3.00 eV). Control tests and factors affecting the photocatalytic degradation rate of RB dye such as pH of the medium, dye concentration and catalyst dose are investigated. Degradation of RB follows 1st-order kinetics. The mechanism of degradation is described based on trapping experiments.

Anion Hybridization for Enhanced Broadband Optical Response in Bi‐Doped Photonic Glass and Fiber

AbstractGiven the ever‐increasing demand for transmission capacity in optical fiber communication systems, there is a growing focus on developing active fibers and devices with an optical response across a wider spectrum. Bismuth (Bi)‐doped photonic glasses have attracted significant interest for their exceptional ultra‐broadband optical response, particularly within telecommunication bands. However, developing advanced Bi‐activated materials with robust optical response, high compatibility with commercial transmission systems, and low processing temperature remains a huge challenge. Herein, an anion hybridization strategy is proposed to overcome the aforementioned limitations. The anion hybridization significantly enhances the broadband optical response (≈10 times) of the Bi center following the introduction of fluorine ions, and reduces the transition temperature of the photonic glass (≈200 °C). Moreover, the near‐infrared optical response bandwidth experiences significant broadening, a result attributed to the larger inhomogeneous broadening. Furthermore, successful fabrication of the anion‐hybridized Bi‐activated glass fiber is achieved, demonstrating excellent compatibility with commercial transmission fibers. Additionally, the fabricated fiber exhibits an amplified spontaneous emission and on‐off gain across a broad waveband, further validating its efficacy. These results indicate that anion hybridization offers an effective strategy to optimize the optical and thermal properties of photonic materials to develop novel advanced photonic devices.

Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to represent entities and relations of knowledge graphs into low-dimensional semantic spaces for a wide spectrum of applications such as link prediction, knowledge reasoning and knowledge completion. In this article, we provide a systematic review of existing KGE techniques based on representation spaces. Particularly, we build a fine-grained classification to categorise the models based on three mathematical perspectives of the representation spaces: (1) algebraic perspective, (2) geometric perspective and (3) analytical perspective. We introduce the rigorous definitions of fundamental mathematical spaces before diving into KGE models and their mathematical properties. We further discuss different KGE methods over the three categories, as well as summarise how spatial advantages work over different embedding needs. By collating the experimental results from downstream tasks, we also explore the advantages of mathematical space in different scenarios and the reasons behind them. We further state some promising research directions from a representation space perspective, with which we hope to inspire researchers to design their KGE models as well as their related applications with more consideration of their mathematical space properties.

Lactiplantibacillus plantarum and Lactiplantibacillus paraplantarum postbiotics: Assessment of the biotic-derived metabolites with cytocompatibility and antitumoral potential

In this study, the postbiotics production ability of Lactiplantibacillus plantarum MIUG BL21 (Lpb.21) and Lactiplantibacillus paraplantarum MIUG BL74 (Lpb.74), was demonstrated through the fermentation of different media based on agri-foods by-products, gluten free flours, and bovine colostrum. Three categories of postbiotics were identified, such as bioactive peptides, a wide spectrum of polyphenols with antioxidant activity and short chain fatty acids. After fermentation, the polypeptides' profile showed different patterns, with more intense bands at about 30 kDa in gluten-free flour based fermentation product (FP) and between 18 and 34 kDa in the colostrum-based FP. The main synthetized organic acids were acetic acid, followed by isovaleric and valeric acid, respectively. The postbiotics production was highly depended on the medium composition and LAB strains, evidencing the presence of isovaleric, valeric acids, isobutyric acid, isorhamnetin, gallic acid, naringin, galangin, syringic acid, p-coumaric acid, ferulic acid, apigenin, abscisic acid, pinocembrin, pinostrobin, hyperoside, caffeic acid, catechin, ellagic acid and hyperoside. The FP derived by agri-food byproducts fermentation highlighted a better antimicrobial activity when compared to the FP obtained through bovine colostrum-based media' fermentation. All the fermented medium showed good cytocompatibility and antitumoral potential, correlated with both the strains fermentative potential and biotechnological conditions, especially with the medium composition.

Numerical Analysis of Convergent-Divergent Angles and Operating Conditions Impact on Rocket Nozzle Performance Parameters

Comprehensive numerical analysis was conducted to elucidate the exhaust performance of rocket engine nozzles. The study focused on unravelling the intricate relationship between convergence and divergence angles and their impact on the exhaust performance parameters, including velocity coefficient (cv), angularity coefficient (Ca), and gross thrust coefficient (Cfg). In contrast to conventional studies that focus mainly on the divergent section, this research delved into both convergent and divergent aspects of nozzle geometry. For the convergent section, a range of angles from 20° to 45° was systematically examined. For the divergent section, a wide spectrum of angles was explored, ranging from small (10°-13°), medium (14°-19°) and large (20°-25°) divergent angles. Further, we venture beyond geometry, investigating the influence of nozzle pressure ratio (NPR) on these key metrics. Realisable 𝑘𝑘−𝜀𝜀, enhanced wall traitement was used to simulate nozzle flow. The study identified the optimal convergent angle at 37.5°. The 15° diverging angle provides good overall performance, while the 23° angle strikes the ideal compromise: maximizing thrust and efficiency while minimizing weight and maintaining optimal performance.

Defining the Shape of the Mylohyoid Muscle: A Morphometric Imaging Study.

Implantable hypoglossal nerve stimulation (HNS) therapy is an evolving therapeutic alternative for patients with refractory obstructive sleep apnea (OSA). The muscular anatomy of this region has implications for surgical access through this zone as well as positioning and anchoring of hardware in this area. The purpose of this study was to radiologically describe the topography of the mylohyoid muscle and adjacent structures across a wide age spectrum. We retrospectively evaluated computed tomography scans of the neck in 102 patients who were imaged for reasons unrelated to the floor of mouth or submental space. Patients with prior surgery or pathology in the area of interest were excluded. Fourteen relevant muscle measurements were made on a midline sagittal image and a coronal image positioned at the midpoint between the hyoid bone and the mandible. We included 49 men and 53 women with an average age of 44 years (range 19-70). The average mylohyoid length was 42 mm; the average distance between the anterior digastric bellies was 17 mm. The average angle of the central mylohyoid was 174° in the sagittal plane and 164° in the coronal plane. Several measurements were significantly correlated with patient age, including the angle measurements and the distance between the digastric muscles. Aberrant digastric anatomy was common. The mylohyoid muscle has multiple radiologically distinct segments with predictable curvatures. An understanding of submental muscular anatomy, along with its variability between patients, may be beneficial to the development of bilateral implantable neurostimulation technology for the treatment of refractory OSA. N/A Laryngoscope, 134:2970-2975, 2024.

Synthesis, Structure, and Biological Activity of the Germanium Dioxide Complex Compound with 2-Amino-3-Hydroxybutanoic Acid

Currently, a promising direction of study is the use of biologically active coordination compounds in the pharmacopoeia and the creation of effective bactericidal drugs, biomaterials, and enzyme modulators on that basis. The paper considers a coordination germanium compound with 2-amino-3-hydroxybutanoic acid. The prospects for the use of the compound in medicine are outlined. This work is aimed at solving the problems regarding the synthesis of biologically active compounds with a wide spectrum of actions. The structure and composition of the coordination compound have been established through calculation and experimental methods. The biocidal (bactericidal and fungicidal) activity of germanium-containing compounds against a number of bacteria and microscopic fungi has been studied. Using the quantum-chemical method with density functional theory (DFT, B3LYP/6–311++G(2d,2p)), the theoretical IR spectrum of the compound was calculated. The structure of the coordination compound and the structure of the intermediates at all stages of the synthesis process were established by calculation.

Introduction to the Special Issue on Perspectives on Recommender Systems Evaluation

Evaluation plays a vital role in recommender systems—in research and practice—whether for confirming algorithmic concepts or assessing the operational validity of designs and applications. It may span the evaluation of early ideas and approaches up to elaborate implementations of systems integrated into everyday product settings; it may target a wide spectrum of different factors being evaluated. In this special issue, we explore recommender systems evaluation—theory and practice—while considering a diverse set of perspectives. These include recommender systems purposes, stakeholders, methodological approaches, and consequences. The collection of articles in this special issue offers insightful analyses of current recommender system evaluation practices, acknowledging their limitations, and setting out future research directions. As recommender systems evolve, the need for adequate evaluation methods and approaches increases. This special issue sheds light on areas undergoing development or requiring added attention from the research and practitioner communities in recommender systems. The compilation serves as a call to the recommender systems research community, motivating continued research and exploration of evaluation metrics, methods, and strategies.