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In this study, we comprehensively investigate thermal lens (TL) spectroscopy, known for its ultra-sensitivity in probing molecular properties through nonlinear heating responses to femtosecond lasers. Using time-resolved TL spectroscopy and numerical simulations, we focus on the influence of convection on heat generation and the resulting phase shift in the probe beam. We examined single-beam, dual-beam same wavelength, and dual-beam different wavelength scenarios, systematically investigating power dependence, pump beam spot size, and sample length limitations. Our findings reveal a direct relationship between the TL effect and pump power, resulting in decreased probe beam transmittance with increasing convection. Additionally, the TL strength grows within the Rayleigh regime as the sample length increases. Utilizing the same wavelength for the probe beam enhances the TL effect in dual-beam setups. Notably, tight focusing of the pump beam substantially reduces the lag between convection and conduction. Our empirical results closely match the experimental data, providing a thorough explanation of the TL process and its underlying principles. These insights can be applied to design and optimize TL-based optical devices and systems for higher sensitivity, highlighting the potential of TL spectroscopy in advanced molecular property probing. [ABSTRACT FROM AUTHOR]

In 1926, Freud famously conjectured that the human ego defense of repression against an internal instinctual threat evolved from the animal motor defense of flight from an external predatory threat. Studies over the past 50 years mainly in rodents have investigated the neurobiology of the fight-or-flight reflex to external threats, which activates the emergency alarm system in the dorsal periaqueductal gray (dPAG), the malfunction of which appears likely in panic and post-traumatic stress disorders, but perhaps also in some "non-emergent" conditions like social anxiety and "hysterical" conversion disorder. Computational neuroscience studies in mice by Reis and colleagues have revealed unprecedented insights into the dPAG-related neural mechanisms underlying these evolutionarily honed emergency vertebrate defensive functions (e.g., explore, risk assessment, escape, freeze). A psychoanalytic interpretation of the Reis studies demonstrates that Freud's 1926 conjecture is confirmed, and that internal instinctual threats alone can also set off the dPAG emergency alarm system, which is regulated by 5-HT1A and CRF-1 receptors. Consistent with current psychoanalytic and neurobiologic theories of panic, several other of the primitive components of the dPAG alarm system may also have relevance for understanding of the unconscious determinants of impaired object relationships (e.g., avoidance distance). These dPAG findings reveal (1) a process of "evolution in situ ," whereby a more sophisticated dPAG ego defense is seen evolving out of a more primitive dPAG motor defense, (2) a dPAG location for the phylogenetically ancient kernel of Freud's Ego and Id, and (3) a Conscious Id theory that has been conclusively invalidated. [ABSTRACT FROM AUTHOR]

This paper provides a detailed historical review of the cavity flow phenomena in fluid mechanics, from recorded studies in the late 19th century to more recent work. Research has been reviewed, independently and in culmination with other studies, to summarise the major and minor governing parameters of the flow. Outlined are influences of technology, regarding numerical models, experimental methods, analysis, and control techniques. All Mach regimes are assessed; low incompressible, sub-, trans-, super- and hypersonic where substantial research was available. A large variety of cavity geometry was presented, mostly rectangular, with more complex features akin to industry application, and where available, assessment of the boundary layer structure is also included. Conclusions on present understanding, and requirements for future work are given, with an aligned set of available data. Cavity flow-field initialisation and development is dependent on; upstream (U/S) flow conditions of; airspeed M ∞ , boundary layer (BL) disturbance (δ), displacement (δ ∗) and momentum (θ) thickness, either laminar or turbulent, and cavity geometry; length (L), depth (D) and width (W), with ratios L / D , L / W , δ / D and L / θ defining cavity response. I.e., a narrow cavity with a thin BL U/S tends toward a periodic 3D flow-field, with 3D effects and periodicity decreasing as W and δ increase. Control is achievable through SL stabilisation via spanwise disturbance from the leading edge (LE), or thickening the BL, thus shear layer (SL). Experiments are preferred over numerical models, due to the inefficiency and high cost of required models (Colonius, 2001; Rowley and Williams, 2006; Lawson and Barakos, 2011). We understand effects of L , D , L / D , and M ∞ , thus future work should focus on W , BL and how they impact mode switching and stream/spanwise flow propagation. Also introducing more complex geometry, realistic to application, to observe additional 3D effects and U/S momentum change, in contribution to a scaling parameter and determination of criteria for activation of material displacement. [ABSTRACT FROM AUTHOR]

Pim-1 kinase, a serine/threonine kinase, is often overexpressed in various cancers, contributing to disease progression and poor prognosis. In this study, we explored the potential of flavonoids as inhibitors of Pim-1 kinase using a combination of molecular docking and steered molecular dynamics (SMD) simulations. Our docking studies revealed two main binding orientations for the flavonoid molecules. The SMD simulations showed that the binding mode with higher pulling forces was linked to stronger inhibitory activity, with a strong positive correlation (R2 = 0.92) between pulling forces and IC50 values. Quercetin stood out as the most potent inhibitor, showing a pulling force of about 820 pN and an IC_(5) 0 of less than 6 µM. Further dynamic simulations indicated that quercetin's hydroxyl groups at the C3, C-5 and C-7 positions formed stable hydrogen bonds with key residues GLU-121, Leu-44 and Val-126, respectively enhancing its binding stability and effectiveness. Our results emphasized the critical role of the hydroxyl group at the C-3 position, which plays a pivotal function in effectively anchoring these molecules in the active site of Pim-1 kinase. Principal component analysis (PCA) of Pim-1 kinase's conformational changes revealed that potent inhibitors like quercetin, galangin, and kaempferol significantly restricted the enzyme's flexibility, suggesting potential inhibitory effect. These findings provide insights into the structural interactions between flavonoids and Pim-1 kinase, offering a foundation for future experimental investigations. However, further studies, including in vitro and in vivo validation, are necessary to assess the pharmacological relevance and specificity of flavonoids in cancer therapy. [ABSTRACT FROM AUTHOR]

This review provides a survey of computational methods generally used in the evaluation of the corrosion inhibition evaluation of metallic materials in various environments. Herein a general overview of corrosion inhibition is given, thereafter, a discussion of the most widely used computational methods is given which include; molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, density functional theory (DFT), Semi-empirical methods and first-principles calculations and Ab initio (Hartree Fock) calculations. A comparison of comparative analysis of experimental and computational approaches in corrosion inhibition studies was given after which the prospects of the use of computational methods in corrosion science were discussed. Overall, each of these methods has its strengths and limitations, but when used in combination, they are capable of providing a holistic understanding of corrosion inhibition mechanisms. [ABSTRACT FROM AUTHOR]

The present work illustrates the synthesis of some new mono‐azo dyes, which include different amines such as 2‐amino thiazole, 2‐amino‐5‐methyl‐1,3,4‐thiadiazole, and 2‐amino‐5‐methyl thiazole with 3‐diethylamino phenol as a common coupling component. The prepared azo moieties were structurally confirmed by spectroscopic techniques like NMR (1H and 13C), FT‐IR, UV –absorption, and emission properties, and HR‐MS spectrometry. Significant variations in the absorption and emission wavelengths of the azo dyes were shown by the solvatochromic behavior, which also provided an important insight into the formation of azo and hydrazone tautomeric forms. The density functional theory of the dyes was carried out to know the HOMO‐LUMO energy gaps, molecular electrostatic potential, and reduced density gradient. Further, they were screened for their in vitro antibacterial and antioxidant activities, and the results showed that the compounds exhibited excellent potentialities against various strains. [ABSTRACT FROM AUTHOR]

In the cerebellum, granule cells make parallel fibre contact on (and excite) Golgi cells and Golgi cells inhibit granule cells, forming an open feedback loop. Parallel fibres excite Golgi cells synaptically, each making a single contact. Golgi cells inhibit granule cells in a structure called a glomerulus almost exclusively by GABA spillover acting through extrasynaptic GABAA receptors. Golgi cells are connected dendritically by gap junctions. It has long been suspected that feedback contributes to homeostatic regulation of parallel fibre signals activity, causing the fraction of the population that are active to be maintained at a low level. We present a detailed neurophysiological and computationally-rendered model of functionally grouped Golgi cells which can infer the density of parallel fibre signals activity and convert it into proportional modulation of inhibition of granule cells. The conversion is unlearned and not actively computed; rather, output is simply the computational effect of cell morphology and network architecture. Unexpectedly, the conversion becomes more precise at low density, suggesting that self-regulation is attracted to sparse code, because it is stable. A computational function of gap junctions may not be confined to the cerebellum. [ABSTRACT FROM AUTHOR]

ABSTRACT The study examines the binding affinities and pharmacological potential of synthesized metal compounds with PfNDH2 in Plasmodium falciparum. So as to identify the combatting agent for malaria, six new organotellurium (IV) complexes (8a–8f) were prepared through a condensation reaction of 3‐methyl‐2‐thiophene carboxaldehyde and p‐nitroaniline. The synthesized compounds were inclusively characterized through physical and spectral techniques signifying a distorted octahedral geometry. The reported complexes were screened for antimalarial and antioxidant activities and analyzed using micro assays and DPPH assays, respectively. Using docking interactions, the research evaluates the binding energies, hydrogen bonds, and steric interactions of various compounds with PfNDH2, aiming to identify promising candidates for antimalarial drug development. Among the compounds, 8f showed the strongest binding affinity, with the lowest binding energy of −135.626 kcal/mol, and diverse hydrogen bonds with the target enzyme. Structure‐based pharmacophore modeling revealed critical interaction profiles and selectivity scores for the compounds, with 8f emerging as a promising candidate. Molecular electrostatic potential (MESP) analysis offered insights into the distribution of electron density within the compounds, influencing their reactivity and potential as antimalarial agents. ADMET analysis highlighted the pharmacokinetic and toxicological profiles of the compounds, revealing critical aspects for optimizing safety and efficacy. Overall, the findings provide valuable information for further development and optimization of potential antimalarial drugs. [ABSTRACT FROM AUTHOR]

In the current paper, we describe computational molecular modeling to investigate the quantum chemical, spectroscopic, MESP and in-silico Anticancer, ADME and toxicity study of (E)-2-(2-(3,4-dihydronaphthalen-1(2H)-ylidene)hydrazineyl)-4-(4-methoxyphenyl)thiazole by one pot three component reaction. DFT method with B3LYP/6-311G(d,p) level was used to optimize the geometry, molecular electrostatic potential (MESP), Mulliken charges and vibrational assignments. The UV-Vis assignments, frontier molecular orbital (FMO), electronic parameters, and global descriptor were studied using the TD-DFT method with CAM-B3LYP/6-311G(d,p) level. In-silico anticancer study revealed favorable binding interaction with an excellent docking score comparable to the anticancer drug Dasatinib. The molecular docking study found that, target 4xt2 receptor's amino acid residues VAL422B, LEU407D, MET426D, TYR327A, GLN421B, SER424B, and LEU423D play a vital role. The title compound has the requisite toxicity and ADME profiles, and it has the potential to be therapeutic. RESEARCH HIGHLIGHTS: One pot synthesis of (E)-2-(2-(3,4-dihydronaphthalen-1(2H)-ylidene)hydrazineyl)-4-(4-methoxyphenyl)thiazole Computational insights into structural analysis, HOMO-LUMO, electronic parameters, spectroscopic (UV-Vis and IR), MESP, and Mulliken charges. In silico anticancer study revealed favorable binding interaction with a docking score of -65.34 Favorable ADME and toxicity profile of the studied compound. [ABSTRACT FROM AUTHOR]

The DMSO/I2 mediated cyclization reaction of (E)-3-(3,4-dimethoxyphenyl)-1-(2-hydroxyphenyl)prop-2-en-1-one afforded the 2-(3,4-dimethoxyphenyl)-4H-chromen-4-one (DMPC). DMPC was characterized by FT-IR, 1H NMR, 13C NMR and HRMS techniques. DMPC was screened for their in vitro antibacterial activity against four bacterial strains namely E. coli, B. subtilis, S. aureus, and Streptococcus spp. and antifungal activity against R. oryzae, P. chrysogenum, A. niger, and C. albicans fungal strains. The synthesized compounds exhibited significant antibacterial and antifungal properties. DMPC possesed potent antibacterial activity against Streptococcus spp. while the powerful antifungal action was found against P. chrysogenum. Besides % free radical scavenging activity was accessed using DPPH and OH assays. DMPC was found to be more active DPPH radical scavenger than OH radical. Theoretical investigation of DMPC was carried out by density functional theory (DFT) method at the B3LYP/6-311++G(d,p) level of theory. Computational investigation of molecular structure, MESP, and HOMO–LUMO was performed to study DMPC. The calculated energy gap of FMOs was found to be 4.25 eV for DMPC. In the MESP plot, the most electronegative potential region found around the oxygen atom. The theoretical and experimental NMR was correlated and correct NMR assignments have been made. Based on the physicochemical features, lipophilicity, water solubility, pharmacokinetic characteristics, and drug similarity matching investigations, the DMPC was revealed to have favorable biological qualities. Highlights: DMSO/I2 mediated cyclization reaction of (E)-3-(3,4-dimethoxyphenyl)-1-(2-hydroxyphenyl)prop-2-en-1-one afforded the 2-(3,4-dimethoxyphenyl)-4H-chromen-4-one (DMPC). DMPC was screened for its in vitro antibacterial activity against four bacterial strains namely E. coli, B. subtilis, S. aureus, and Streptococcus spp. Antifungal activity of DMPC against R. oryzae, P. chrysogenum, A. niger, and C. albicans fungal strains. DMPC exhibited significant antibacterial and antifungal properties. Computational study on MESP, HOMO–LUMO, NMR, and IR. ADME study indicates good pharmacological profile. [ABSTRACT FROM AUTHOR]

The δ‐conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage‐gated sodium channels causing paralysis and other neurological responses, but difficulties in their isolation and synthesis have made structural characterization challenging. Taking advantage of recent breakthroughs in computational algorithms for structure prediction that have made modeling especially useful when experimental data is sparse, this work uses both the deep‐learning‐based algorithm AlphaFold and comparative modeling method RosettaCM to model and analyze 18 previously uncharacterized δ‐conotoxins derived from piscivorous, vermivorous, and molluscivorous cone snails. The models provide useful insights into the structural aspects of these peptides and suggest features likely to be significant in influencing their binding and different pharmacological activities against their targets, with implications for drug development. Additionally, the described protocol provides a roadmap for the modeling of similar disulfide‐rich peptides by these complementary methods. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa) is a staple food for billions of people across the globe, that feeds nearly three-quarters of the human population on Earth, particularly in Asian countries. Rice yield has been drastically reduced and severely affected by various biotic and abiotic stresses, especially pathogens. Controlling the attack of such pathogens is a matter of immediate concern as yield losses in rice crops could deprive millions of lives of nourishment worldwide. Pyricularia oryzae is one such pathogen that has been considered the major disease of rice because of its worldwide geographic distribution. P. oryzae belongs to the kingdom fungi, that causes rice blast ultimately adversely affecting the yield of the rice crop. Keeping in view this alarming scenario, the present study was designed so that the identifications of genome-encoded miRNAs of Oryza sativa were employed to target and silence the genome of P. oryzae. This study accomplished the computational analysis of algorithms related to miRNA target prediction. Four computational target prediction algorithms i.e., psRNATarget, RNA22, miRanda, and RNAhybrid were utilized in this investigation. The consensus among target prediction algorithms was created to discover six miRNAs from the O. sativa genome with the conservation of the target site fully evaluated on the genome of P. oryzae. The discovery of these novel six miRNAs in Oryza sativa paved a strong way toward the control of this disease in rice. It will open doors for further research in the field of gene silencing in rice. These miRNAs can be designed and employed in the future as experimentation to create constructs regarding the silencing of P. oryzae in rice crops. In the future, this research would be surely helpful for the development of P. oryzae resistant rice varieties. [ABSTRACT FROM AUTHOR]

Cosmetic surgery is ever more affordable and accessible, but carries physical and psychological risks. Yet, no study to date has directly examined risk-taking behaviour under controlled conditions, beyond self-report and in relation to cosmetic surgery attitudes. We used the Balloon Analogue Risk Task and advanced computational modelling to measure decision-making behaviour and identify the latent parameters driving behaviour associated with cosmetic surgery attitudes in women with no cosmetic surgery history (N = 265) and a subsample of women with a cosmetic surgery history (N = 24). Risk taking was higher in women with greater acceptance and history of cosmetic surgery. Computational modelling revealed increased risk taking in women with greater acceptance of cosmetic surgery when decisions were made with greater knowledge of loss (risk) and not when the likelihood of loss was unknown (uncertainty). When women with greater acceptance of cosmetic surgery made decisions, they also placed less emphasis on possible losses (reduced loss aversion). Our findings suggest that women seeking cosmetic procedures may be less sensitive to losses and thus make more risky decisions. Greater emphasis should be placed on communicating potential losses rather than just the associated risks to women considering cosmetic procedures. No Level Assigned This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Antimicrobial Resistance (AMR) due to non-responding viruses, fungi, bacteria and parasites leads to discovery of new antimicrobial medicines which can control the risk of disease spread, severe illness, disability and death. Heterocyclic chemistry has always been a continuous supplier of novel antimicrobial agents which are in great demand in pharma sector. Therefore, compounds such as 1-(Chloromethyl)-1H-Benzotriazole, 1; 1-((1-H-benzo[d][1,2,3]triazol-1-yl)methyl)phenyl hydrazine, 2; 1-((1-H-benzo[d][1,2,3]triazol-1-yl)methyl)hydrazine, 3; and N-(benzo[e][1,2,4]triazin-4(3-H)-ylmethylbenzenamine, 4 were designed, and synthesized through conventional and microwave-assisted methods. All of these novel benzotriazoles were explored through in-vitro antimicrobial studies and in silico studies. Antimicrobial activity was carried out against bacterial strains Escherichia coli, Bacillus subtilis, and fungal strains Aspergillus niger and Candida albicans at concentrations 5, 10 and 15 mg/ml. In silico studies was carried out with 4CAW: Aspergillus fumigatus N-myristoyl transferase in complex with myristoyl CoA and a pyrazole sulphonamide ligand. Our antimicrobial and molecular docking studies revealed that all of the derivatives showed promising activity, moreover molecular docking gave significant values of ligand posed energy and docking run elapsed time which further endorsed the astonishing characteristic of benzotriazole derivatives esp. N-(benzo[e]a[1,2,4] triazin-4(3-H)-ylmethylbenzenamine for biological and therapeutic leads. [ABSTRACT FROM AUTHOR]

In the complexity of the Fourth Industrial Revolution era, the importance of creative thinking is increasingly emphasized in the context of learning computing and algorithms. These skills are instrumental in inspiring innovative solutions, addressing complex challenges, and fostering the development of advanced technologies that characterize the transformative landscape of Industrial Revolution 4.0. This study aims to determine the effectiveness of the generative learning model based on cognitive conflict in improving the creative thinking skills (CTS) and learning outcomes of students in the computational physics and algorithms & programming courses. This research used mixed methods consisting of pretest-posttest control group design and snowballing technique. The research instruments consist of cognitive tests, psychomotor tests, affective tests, CTS tests, observation questionnaires, and interviews. The research sample consisted of 138 students taking computational physics and algorithms & programming courses. Quantitative data were analyzed using multivariate analysis of variance and qualitative data were analyzed using narrative analysis. The findings indicate that this model effectively improves students' CTS and learning outcomes. Furthermore, the cognitive conflict aspect encourages students to be creative in analyzing and solving problems. This model has the potential to be used to optimize students' potential in facing the demands of the fourth industrial revolution. [ABSTRACT FROM AUTHOR]

Abstract This review provides a survey of computational methods generally used in the evaluation of the corrosion inhibition evaluation of metallic materials in various environments. Herein a general overview of corrosion inhibition is given, thereafter, a discussion of the most widely used computational methods is given which include; molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, density functional theory (DFT), Semi-empirical methods and first-principles calculations and Ab initio (Hartree Fock) calculations. A comparison of comparative analysis of experimental and computational approaches in corrosion inhibition studies was given after which the prospects of the use of computational methods in corrosion science were discussed. Overall, each of these methods has its strengths and limitations, but when used in combination, they are capable of providing a holistic understanding of corrosion inhibition mechanisms.

Abstract Rice (Oryza sativa) is a staple food for billions of people across the globe, that feeds nearly three-quarters of the human population on Earth, particularly in Asian countries. Rice yield has been drastically reduced and severely affected by various biotic and abiotic stresses, especially pathogens. Controlling the attack of such pathogens is a matter of immediate concern as yield losses in rice crops could deprive millions of lives of nourishment worldwide. Pyricularia oryzae is one such pathogen that has been considered the major disease of rice because of its worldwide geographic distribution. P. oryzae belongs to the kingdom fungi, that causes rice blast ultimately adversely affecting the yield of the rice crop. Keeping in view this alarming scenario, the present study was designed so that the identifications of genome-encoded miRNAs of Oryza sativa were employed to target and silence the genome of P. oryzae. This study accomplished the computational analysis of algorithms related to miRNA target prediction. Four computational target prediction algorithms i.e., psRNATarget, RNA22, miRanda, and RNAhybrid were utilized in this investigation. The consensus among target prediction algorithms was created to discover six miRNAs from the O. sativa genome with the conservation of the target site fully evaluated on the genome of P. oryzae. The discovery of these novel six miRNAs in Oryza sativa paved a strong way toward the control of this disease in rice. It will open doors for further research in the field of gene silencing in rice. These miRNAs can be designed and employed in the future as experimentation to create constructs regarding the silencing of P. oryzae in rice crops. In the future, this research would be surely helpful for the development of P. oryzae resistant rice varieties.

Increasingly, influencers are employed to market not only products but also ideas and beliefs. The far right has recognized the strategic potential of influencer communication to tap into new target groups and mobilize supporters. This paper provides insights into the little-explored field of far-right influencers. We conceptualize them as individual actors characterized by far-right ideology, positioned as political influencers, actively advocating for their ideological aims. Employing a multi-layered computational approach to explore communication practices and networking structures of 243 German-speaking far-right influencers on Telegram, we derive a typology and observe the emergence of a functionally differentiated influencer collective. In this collective, each community has specific functions and characteristics that emphasize different ideological aspects, mobilization modes, and influencer practices. Despite the decentralized organization, we find high efficiency in information dissemination. The results corroborate the assumed potential of far-right influencers as disseminators of ideological content who can be particularly persuasive through their role as parasocial opinion leaders. [ABSTRACT FROM AUTHOR]

Objective: This study was undertaken to validate a set of candidate biomarkers of seizure susceptibility in a retrospective, multisite case–control study, and to determine the robustness of these biomarkers derived from routinely collected electroencephalography (EEG) within a large cohort (both epilepsy and common alternative conditions such as nonepileptic attack disorder). Methods: The database consisted of 814 EEG recordings from 648 subjects, collected from eight National Health Service sites across the UK. Clinically noncontributory EEG recordings were identified by an experienced clinical scientist (N = 281; 152 alternative conditions, 129 epilepsy). Eight computational markers (spectral [n = 2], network‐based [n = 4], and model‐based [n = 2]) were calculated within each recording. Ensemble‐based classifiers were developed using a two‐tier cross‐validation approach. We used standard regression methods to assess whether potential confounding variables (e.g., age, gender, treatment status, comorbidity) impacted model performance. Results: We found levels of balanced accuracy of 68% across the cohort with clinically noncontributory normal EEGs (sensitivity =61%, specificity =75%, positive predictive value =55%, negative predictive value =79%, diagnostic odds ratio =4.64, area under receiver operated characteristics curve =.72). Group level analysis found no evidence suggesting any of the potential confounding variables significantly impacted the overall performance. Significance: These results provide evidence that the set of biomarkers could provide additional value to clinical decision‐making, providing the foundation for a decision support tool that could reduce diagnostic delay and misdiagnosis rates. Future work should therefore assess the change in diagnostic yield and time to diagnosis when utilizing these biomarkers in carefully designed prospective studies. [ABSTRACT FROM AUTHOR]

Surface grinding is critical for producing precise workpiece dimensions and surface finishes, especially for materials like cast iron and steel. Optimal grinding materials must reliably cling to magnetic chucks and resist melting or fouling the cutting wheel. Our investigation seeks to optimize the static and dynamic performance of surface grinder machines by evaluating their structural integrity and dynamics using finite element analysis (FEA). Modal and static studies are done under various tool post and column configurations to simulate real-world machining circumstances. Modal analysis reveals key resonant modes, with lowest frequencies 55, 61, and 85 Hz, respectively, seen when the column and tool post are centrally positioned. Modal analysis elucidates the natural frequencies affecting the machine's structure, aiding in resonance abatement during machining, and increasing machining precision. Static obtains a −500 N force along the Z-axis direction at the tooltip, alongside a gravity force of 9.8 m/s affecting all components. Investigation indicated substantial distortion at the junction of the grinding stone and primary tool, with a maximum deformation of 18.5 μm. Harmonic analysis, done within a frequency range of 55–235 Hz, corroborated the mode shape analysis results by exerting a −500 N force along the Z-axis. Spatial analysis was done to investigate the movement of the machine column and tool post during milling. Position C displayed the highest modal frequency 61, 67, and 95, respectively, due to the tool post's proximity to the working surface. This emphasizes the necessity of spatial optimization in machining. Experimental validation utilizing the predictive diagnostic performance system (PDPS) validates the differentiation between healthy and unhealthy machine signals during grinding, facilitating real-time issue identification. Predictive maintenance predicts machine issues, cuts downtime, boosts quality, and saves costs. This study attempts to find maximum deformation in machine tool structures under different configurations, ultimately increasing the structural properties and stability of surface grinding machines. [ABSTRACT FROM AUTHOR]

The monitoring of antioxidants is crucial to prevent damage caused by reactive oxygen species (ROS). In this study, we introduce an innovative electrochemical sensor tailored for detecting riboflavin (RF), a powerful antioxidant. The sensor was developed by modifying a gold electrode (AuE) with cobalt oxide (Co3O4) and reduced graphene oxide (rGO). The resulting nanocomposite‐modified electrode (AuE/Co3O4‐rGO) exhibited a substantial surface area of 0.41 cm2 in the redox probe, leading to an enhanced RF peak characterized by remarkably low charge transfer resistance (1.61 KΩ) and a high exchange current density (18.6 μA/cm2). Under optimized conditions, the sensor achieved a limit of detection (LOD) for RF at 1.30 μM, over a concentration range of 6.5–42.2 μM. These results highlight the sensor's potential applicability in real‐world scenarios, including the analysis of milk and pharmaceutical samples. A kinetics study revealed that the electrochemical reaction involving RF is adsorption‐controlled, emphasising the critical role of surface interactions. The modified electrode's interaction with RF significantly influences overall reaction kinetics. These findings were further supported by density functional theory (DFT) calculations and molecular simulations. Our nanocomposite‐modified electrode provides valuable insights into the atomistic interactions governing sensor performance, advancing the field of electrochemical sensing for antioxidants like riboflavin. [ABSTRACT FROM AUTHOR]

Investors, practitioners, and stock researchers highly need data related to financial performance to predict a company's financial health condition, which is used as a basis to consider investing in it. The Indonesia Stock Exchange (IDX) website provides reports on the company's financial performance. Unfortunately, the company’s financial data found on the IDX website are in PDF format, and researchers must download them one by one, which takes a long time. This study presents a website-based application, named Indonesia Company Performance (INDCOMP), built using the R programming language and involving various R packages and frameworks to assist investors, practitioners, and stock researchers in studying the financial performance of companies. This application can help users quickly access the financial performance data of various companies, present financial performance data in data tables, and perform data visualizations as well as statistical analyses [ABSTRACT FROM AUTHOR]

Images are today at the centre of multiple social and technological tensions as a consequence of the adoption of digital coding, of the massive diffusion of social networks and of the algorithmic processing to which they are subject, resulting in new opportunities for developing analytical inquiries and meaning-producing social actions. In this introduction, the authors intend to reconstruct the broad context that makes images one of the most important resources of the digital era and to focus on some of the research tracks that characterize it. In the first part, they begin by focusing on the relationship between images and the digital, which they retrace in accordance with the selection of three key moments: the transition from ontology to the epistemology of digital media; the opening, by social networks and portable devices, of a field for the computational study of contemporary cultures; and, finally, the analytical potential arising from the encounter between digital archives and computer algorithms. In the second part, they present the three axes around which this issue is structured: archives, identity and algorithms. They first of all discuss the concept of the archive, by presenting four different understandings it has come to bear in conjunction with digital encoding – the archive as heritage, resource, effect and as database. They go on to address the relation between images and identities, arguing that social platforms and visual apps are a new domain for identity experimentation and social aggregation. Finally, they discuss the issue of algorithms and more generally of the new computational economy that associates large amounts of data with their mobilization as operational images. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS), an intricate neurological disorder, continues to challenge our understanding of the pivotal interplay between the immune system and the central nervous system (CNS). This condition arises from the immune system's misdirected attack on nerve fiber protection, known as myelin sheath, alongside nerve fibers themselves. This enigmatic condition, characterized by demyelination and varied clinical manifestations, prompts exploration into its multifaceted etiology and potential therapeutic avenues. Research has revealed a potential connection between Epstein Barr virus (EBV), specifically Epstein Barr Nuclear Antigen 1 (EBNA-1), and MS. The immune response to EBNA-1 antigen triggers the production of anti-EBNA-1 molecules, including IgG that identify a similar amino acid sequence to EBNA-1 in myelin, inadvertently targeting myelin sheath and contributing to MS progression. Currently, no treatment exists for EBNA-1-induced MS apart from symptom management. Addressing this, a novel potential therapeutic avenue utilizing small interference RNAs (siRNA) has been designed. By targeting the conserved EBNA-1 gene sequences in EBV types 1 and 2, five potential siRNAs were identified in our analysis. Thorough evaluations encompassing off-target binding, thermodynamics and secondary structure elucidation, efficacy prediction, siRNA-mRNA sequence binding affinity exploration, melting temperature, and docking of siRNAs with human argonaute protein 2 (AGO2) were conducted to elucidate the siRNAs efficiency. These designed siRNA molecules harnessed promising silencing activity in the EBNA-1 gene encoding the EBNA-1 antigen protein and thus have the potential to mitigate the severity of this dangerous virus. [ABSTRACT FROM AUTHOR]

Myelinated axons conduct action potentials, or spikes, in a saltatory manner. Inward current caused by a spike occurring at one node of Ranvier spreads axially to the next node, which regenerates the spike when depolarized enough for voltage-gated sodium channels to activate, and so on. The rate at which this process progresses dictates the velocity at which the spike is conducted and depends on several factors including axial resistivity and axon diameter that directly affect axial current. Here we show through computational simulations in modified double-cable axon models that conduction velocity also depends on extracellular factors whose effects can be explained by their indirect influence on axial current. Specifically, we show that a conventional double-cable model, with its outside layer connected to ground, transmits less axial current than a model whose outside layer is less absorptive. A more resistive barrier exists when an axon is packed tightly between other myelinated fibers, for example. We show that realistically resistive boundary conditions can significantly increase the velocity and energy efficiency of spike propagation, while also protecting against propagation failure. Certain factors like myelin thickness may be less important than typically thought if extracellular conditions are more resistive than normally considered. We also show how realistically resistive boundary conditions affect ephaptic interactions. Overall, these results highlight the unappreciated importance of extracellular conditions for axon function. [ABSTRACT FROM AUTHOR]

Dialysis membranes are not hemocompatible with human blood, as the patients are suffering from the blood–membrane interactions' side effects. Zwitterionic structures have shown improved hemocompatibility; however, their complicated synthesis hinders their commercialization. The goal of the study is to achieve fast functionalization for carboxybetaine and sulfobetaine zwitterionic immobilization on PES membranes while comparing the stability and the targeted hemocompatibility. The chemical modification approach is based on an aminolysis reaction. Characterization, computational simulations, and clinical analysis were conducted to study the modified membranes. Atomic force microscopy (AFM) patterns showed a lower mean roughness for carboxybetaine-modified (6.3 nm) and sulfobetaine-modified (7.7 nm) membranes compared to the neat membrane (52.61 nm). The pore size of the membranes was reduced from values above 50 nm for the neat PES to values between 2 and 50 nm for zwitterionized membranes, using Brunauer–Emmett–Teller (BET) analysis. More hydrophilic surfaces led to a growth equilibrium water content (EWC) of nearly 6% for carboxybetaine and 10% for sulfobetaine-modified membranes. Differential scanning calorimetry (DSC) measurements were 12% and 16% stable water for carboxybetaine- and sulfobetaine-modified membranes, respectively. Sulfobetaine membranes showed better compatibility with blood with respect to C5a, IL-1a, and IL-6 biomarkers. Aminolysis-based zwitterionization was found to be suitable for the improvement of hemodialysis membranes. The approach introduced in this paper could be used to modify the current dialysis membranes with minimal change in the production facilities. [ABSTRACT FROM AUTHOR]

There are many applications, such as fruits, paddy rice drying, and so on, where air heaters are comprehensively used. The provision of ribs in solar energy‐based air heaters has been accredited to be a beneficial approach for escalating its thermal performance. This investigation reveals the performance evaluation of a rectangular sectioned solar air heater duct placed with the curved type of ribs organized in noncontinuous and truncation patterns. Influence of variation of pitch ratio (4.0–12.0), block‐age ratio (0.06–0.1), the ratio of rib height to the radius of rib curvature (0.75–1), and the ratio of rib truncation gap to its height (0–0.75) at different values of Reynolds's numbers (Re) in the range 12,000–37,000 on the performance revealing factors explicitly heat transfer augmentation factor, the ratio of friction factor, as well as overall thermal performance enhancement (OTPE) have been discussed. The experimentation was performed on a similar rectangular air heater as that of numerical simulation with the same boundary conditions. It is figured out from numerical simulation that the provision of a central truncation gap in the arrangement of ribs helped in the development of vortices with minimal pressure loss due to friction. The greatest Nusselt number augmentation ratio between ribbed and plain plates that was attained is of the order of 2.01 for a pitch ratio of 9.0, blockage ratio of 0.08, the ratio of rib height to the radius of rib curvature 0.25 at the highest value of Re. The obtained friction factor ratio between the ribbed plate and the plain plate is of the order of 2.34 for p/y = 9.0 at maximum Re. The highest value of overall thermal performance enhancement (OTPE) is attained as 1.52 at the pitch ratio of 9, at the maximum value of Re. The central truncation gap of the rib pattern promoted the creation of vortices with a decreased pressure loss penalty. The uppermost value of the factor presenting overall thermal performance enhancement in this work is far superior to unity. This suggests that adding curved ribs to the air heater's duct is a helpful way to improve its overall performance. Empirical correlations have been established between the Nusselt number and geometrical parameters, likewise the friction factor and geometrical parameters using the regression study. The values of the Nusselt number, and friction factor that come from experimental work and developed empirical correlations fall between the highest deviation limits of ±9% and ±8%, respectively. This work is certainly helpful in the context of solar air heaters used for applications such as drying paddy rice. [ABSTRACT FROM AUTHOR]

Here, we describe the efforts we dedicated to the challenge of modifying entrenched emotionally laden memories. In recent years, through a number of collaborations and using a combination of behavioral, molecular, and computational approaches, we: (a) developed novel approaches to fear attenuation that engage mechanisms that differ from those engaged during extinction (Monfils), (b) examined whether our approaches can generalize to other reinforcers (Lee, Gonzales, Chaudhri, Cofresi, and Monfils), (c) derived principled explanations for the differential outcomes of our approaches (Niv, Gershman, Song, and Monfils), (d) developed better assessment metrics to evaluate outcome success (Shumake and Monfils), (e) identified biomarkers that can explain significant variance in our outcomes of interest (Shumake and Monfils), and (f) developed better basic research assays and translated efforts to the clinic (Smits, Telch, Otto, Shumake, and Monfils). We briefly highlight each of these milestones and conclude with final remarks and extracted principles. [ABSTRACT FROM AUTHOR]

This paper describes a feasibility study for the computational classification of traditional three-voiced Georgian vocal music, based on characteristic chord sequences extracted from digital scores. We demonstrate that for this purpose the differences between Western 5-line staff notation and a more appropriate heptatonic system for traditional Georgian music can be adjusted for by a simple transformation. A corpus of about 500 digital scores, consisting of labeled ‘song classes’, i.e. subsets of folk songs from different regions and of liturgical songs in different styles, served as a testbed for the development of a classification procedure based on a higher-order Markov model that - in addition to the classification - yields chord progression sequences for each ‘song class’. Their capacity for interpretation was tested by one hundred cross-validation runs, in which randomly selected subsets of 3/4 of the size of each song class were used to train classifiers, which were then applied to the remaining1/4 subsets. The sizes of the intersections of the successfully classified songs in all cross-validations are interpreted as direct measures of the degree of representativeness of the songs for their respective ‘song classes’. Based on a second validation experiment, in which we split up the datasets into equally sized subsets of 1/2 and 1/4 of the original subsets, respectively, we estimate that the smallest subset size for an interpretation of the observed chord progression patterns as properties of a ‘song class’ is of the order of 50 songs. Currently, in our corpus, this requirement is only met by the subsets from Svaneti and Shemokmedi. [ABSTRACT FROM AUTHOR]

Purpose: The study is aimed at the quantitative analysis of Labetalol utilizing Box-Benken design, where higher order response surfaces are generated and optimized for a significant model using fewer runs than a usual factorial technique. Method: The kinetic spectrophotometric methods were established to compute labetalol in pharmaceutical formulations. Initial rate and fixed-time methods were developed to determine labetalol with the mixture of potassium iodate and potassium iodide at room temperature to produce a stable yellow-coloured product that absorbs at 351 nm. The optimization was reached using the Box-Behnken experimental design involving response surface methodology. Four significant factors were investigated: labetalol volume, reaction time, potassium iodate and potassium iodide volume against response absorbance. The two and three-dimensional response surface plots revealed the significance of each parameter on the response and the correlation between them. The importance of statistical analysis and polynomial equations on the model’s efficacy. The developed method was also validated according to International Conference on Harmonization guidelines, and outcomes were accurate and precise within the limit. Results: The linearity was in the range of 0.25–49.9 µg/mL. The analysis of the different calibrations suggests excellent linearity ranging from 0.9928-0.9988. The limit of detection (LOD) and limit of quantitation (LOQ) are in the range of 0.28-0.35 µg/mL and 0.92-1.16 µg/m, respectively. In comparing the proposed method with the reference one, the recovery ranges of the initial rate, fixed time and the reference methods were in the range of 99.34–99.57, 99.43-99.56, and 98.56-98.99, respectively. Conclusion: The proposed method is simple, environmentally friendly, and involves cost-effective instruments. The proposed method’s greenness report was compared to the reference-reported traditional methods using the analytical Eco-Scale evaluation tool. The reported approach was greener regarding using perilous reagents, energy consumption, and waste production. Therefore, the proposed strategies could be safely applicable in pharmaceutical formulations, biological fluids, and wastewater samples to quantify labetalol. [ABSTRACT FROM AUTHOR]

Advancements in next-generation sequencing (NGS) technologies and innovative bioinformatics tools have significantly accelerated virus discovery by analyzing of NGS data. This approach provides a cost-effective and efficient method for processing large datasets, allowing for rapid virus detection and identification. Researchers can comprehensively understand virus-host interactions by integrating data mining with other omics data, such as proteomics (the study of proteins) and metabolomics (the study of metabolic processes). Recent progress has significantly enhanced the efficiency and accuracy of virus identification by using a sophisticated NGS data mining approach. This study provides an in-depth discussion of these techniques, offering a detailed overview of workflows and applicable computational methods. Despite these advantages, the virus discovery process through data mining encounters obstacles such as ethical issues, the absence of standardized protocols for virus discovery procedures, and challenges in validation and interpretation. Addressing these obstacles is crucial for fully realizing the potential of NGS data mining in virus research. This review discusses current methodologies, recent advancements, and future directions to overcome these challenges, ultimately contributing to our understanding of viral diversity and virus-host dynamics.

Pim-1 kinase, a serine/threonine kinase, is often overexpressed in various cancers, contributing to disease progression and poor prognosis. In this study, we explored the potential of flavonoids as inhibitors of Pim-1 kinase using a combination of molecular docking and steered molecular dynamics (SMD) simulations. Our docking studies revealed two main binding orientations for the flavonoid molecules. The SMD simulations showed that the binding mode with higher pulling forces was linked to stronger inhibitory activity, with a strong positive correlation (R2 ≈ 0.92) between pulling forces and IC50 values. Quercetin stood out as the most potent inhibitor, showing a pulling force of about 820 pN and an IC_(5) 0 of less than 6 µM. Further dynamic simulations indicated that quercetin’s hydroxyl groups at the C3, C-5 and C-7 positions formed stable hydrogen bonds with key residues GLU-121, Leu-44 and Val-126, respectively enhancing its binding stability and effectiveness. Our results emphasized the critical role of the hydroxyl group at the C-3 position, which plays a pivotal function in effectively anchoring these molecules in the active site of Pim-1 kinase. Principal component analysis (PCA) of Pim-1 kinase’s conformational changes revealed that potent inhibitors like quercetin, galangin, and kaempferol significantly restricted the enzyme’s flexibility, suggesting potential inhibitory effect. These findings provide insights into the structural interactions between flavonoids and Pim-1 kinase, offering a foundation for future experimental investigations. However, further studies, including in vitro and in vivo validation, are necessary to assess the pharmacological relevance and specificity of flavonoids in cancer therapy.

Abstract The δ‐conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage‐gated sodium channels causing paralysis and other neurological responses, but difficulties in their isolation and synthesis have made structural characterization challenging. Taking advantage of recent breakthroughs in computational algorithms for structure prediction that have made modeling especially useful when experimental data is sparse, this work uses both the deep‐learning‐based algorithm AlphaFold and comparative modeling method RosettaCM to model and analyze 18 previously uncharacterized δ‐conotoxins derived from piscivorous, vermivorous, and molluscivorous cone snails. The models provide useful insights into the structural aspects of these peptides and suggest features likely to be significant in influencing their binding and different pharmacological activities against their targets, with implications for drug development. Additionally, the described protocol provides a roadmap for the modeling of similar disulfide‐rich peptides by these complementary methods.

Recent work on the diazonium ion metabolite of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNKDI) suggests that 5-(pyridin-3-yl)-3,4-dihydro-2H-furan-1-ium (NNKFI) may form from NNKDI via an intramolecular reaction. NNKDI is an important carcinogen whose role as an alkylating agent has received significant attention. While there is some experimental evidence supporting NNKFI’s production in vitro, it has not yet been directly observed. Little is known about NNKFI’s structure and reactivity. We report the first in silico examination of this ion. Our study utilized Kohn–Sham density functional theory (B3LYP/6-311G**) and coupled cluster theory (CCSD/6-31G*) to produce energy-optimized structures, vibrational normal modes and molecular orbitals for NNKFI. To gain insight into the chemical properties of this species, we calculated electrostatic potential surfaces, natural population analysis charges and local Fukui indices. We report data and results for NNKFI’s cis and trans conformers. Our work confirms C5 as the preferred site for nucleophilic attack in NNKFI. Stretching motions and predicted bond lengths near O1 are consistent with a somewhat weakened carbonyl structure in this ion. Partial charges, electrostatic potential surfaces and local Fukui indices reveal delocalization of cationic charge on the furanium moiety and notable carbocation character at C5.

The current study is focused on the synthesis and evaluation of 1-Hexadecyl-3-methylimidazolium tetrachloroindate [C16 mim][In Cl4] based ionic liquid (IL) as a corrosion inhibitor for mild steel in 1M HCl. Various advanced methods were employed in this research, such as potentiodynamic polarization (PDP), quantum chemical computations, molecular dynamics simulations, weight loss assessments, electrochemical impedance spectroscopy (EIS) and multivariate statistics via machine learning models. The ionic liquid (IL) under investigation demonstrated a notable corrosion inhibition efficiency (93.88 % weight loss, 94. % PDP, 75 % EIS). The combine electrochemical approach suggested a mechanism influenced by electron transfer, underscoring the IL's as a mixed-type inhibitor. The experimental data based on weight loss was optimized using response surface methodology (RSM). Maximum inhibition efficiency of 93.72 % was predicted by the RSM model. Also, the machine learning models based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) demonstrated good predictive power in analyzing the interactive effects affecting the inhibition process. The adsorption behavior of [C16 mim][In Cl4] on the mild steel surface further conformed to the Langmuir isotherm, demonstrating a monolayer adsorption process. The comprehensive nature of this approach facilitated a more in-depth adsorption process through computational modelling based on DFT and molecular dynamics. The machine learning models aligned credibly with the experimental findings with pronounced degree of accuracy. Thus, these integrated approaches unravel the potential of the studied IL as effective and sustainable corrosion inhibitor for severe acidic environments.

AbstractThe diverse forms of journalism adopted in the newsroom have redefined the news media landscape. This systematic review paper reviews fifty journal articles (2000 and 2021), including some of the new forms of journalism: data and network journalism, automated and virtual reality journalism, and augmented reality journalism. This paper fills the research gap by offering insights into these new forms of journalism cumulatively, contrary to the existing literature that discusses single forms. The findings reveal a shift in mainstream news media from traditional reporting methods. The adoption of new forms is part of the survival strategy in this competitive media market.

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem. [ABSTRACT FROM AUTHOR]

This research paper focuses on the challenges associated with learning classifiers from large-scale, highly imbalanced datasets prevalent in many real-world applications. Traditional algorithms learning often need better performance and high computational efficiency when dealing with imbalanced data. Factors such as class imbalance, noise, and class overlap make it demanding to learn effective classifiers. In this study, we propose a novel self-paced ensemble framework for classifying imbalanced data. The framework employs under-sampling to self-harmonize data hardness and build a robust ensemble. Extensive experimental testing demonstrates promising results in handling overlapping classes and skewed distributions while maintaining computational efficiency. The self-paced ensemble method addresses the challenges of high imbalance ratios, class overlap, and noise presence in large-scale imbalanced classification problems. By incorporating the knowledge of these challenges into our learning framework, we establish the concept of classification hardness distribution, and the self-paced ensemble is a revolutionary learning paradigm for massive imbalance categorization, capable of improving the performance of existing learning algorithms on imbalanced data and providing better results for future applications. [ABSTRACT FROM AUTHOR]

Simple Summary: The exact cellular origin of glioblastoma (GBM) remains uncertain, with prevalent theories suggesting emergence from transformed endogenous stem cells. These cells likely play pivotal roles in tumor initiation and progression. The impact of proximity of GBM to the brain's neurogenic zones on patient survival remains a lingering question. Our study investigated tumor infiltration into the subventricular zone (SVZ), the subgranular zone (SGZ) and the cortex alongside clinical variables, such as KPS score, multifocality and selected molecular markers. Utilizing a fully automated processing and segmentation pipeline, we objectively quantified these relationships in 177 IDH wild-type glioblastomas. Our findings support prior research indicating SVZ proximity as a predictor for poor survival, while SGZ proximity showed no significant impact. We also established new survival thresholds based on tumor mass fractions and minimal distances. Contrary to previous studies, we found no significant correlations between SVZ contact and multifocal growth pattern or MGMT promoter methylation. So far, the cellular origin of glioblastoma (GBM) needs to be determined, with prevalent theories suggesting emergence from transformed endogenous stem cells. Adult neurogenesis primarily occurs in two brain regions: the subventricular zone (SVZ) and the subgranular zone (SGZ) of the hippocampal dentate gyrus. Whether the proximity of GBM to these neurogenic niches affects patient outcome remains uncertain. Previous studies often rely on subjective assessments, limiting the reliability of those results. In this study, we assessed the impact of GBM's relationship with the cortex, SVZ and SGZ on clinical variables using fully automated segmentation methods. In 177 glioblastoma patients, we calculated optimal cutpoints of minimal distances to the SVZ and SGZ to distinguish poor from favorable survival. The impact of tumor contact with neurogenic zones on clinical parameters, such as overall survival, multifocality, MGMT promotor methylation, Ki-67 and KPS score was also examined by multivariable regression analysis, chi-square test and Mann–Whitney-U. The analysis confirmed shorter survival in tumors contacting the SVZ with an optimal cutpoint of 14 mm distance to the SVZ, separating poor from more favorable survival. In contrast, tumor contact with the SGZ did not negatively affect survival. We did not find significant correlations with multifocality or MGMT promotor methylation in tumors contacting the SVZ, as previous studies discussed. These findings suggest that the spatial relationship between GBM and neurogenic niches needs to be assessed differently. Objective measurements disprove prior assumptions, warranting further research on this topic. [ABSTRACT FROM AUTHOR]

In our pursuit of designing potent bioactive compounds, we synthesized six novel tellurium (IV) complexes through the condensation of 5‐methyl‐2‐thiophene carboxaldehyde and p‐nitroaniline. These compounds underwent rigorous investigation using various analytical techniques (TGA, SEM, EDAX, and powder XRD) and spectral analyses (NMR, mass spectrometry, UV–Vis, and FTIR). Spectroscopic analysis suggested an octahedral geometry for the complexes, revealing chelation through the thiophene sulfur and azomethine nitrogen atoms. Thermal analysis indicated a three‐step degradation process, ultimately leaving behind metal oxide as the end product. We conducted in‐vitro antimicrobial screening using the broth micro‐dilution method, highlighting the significant antimicrobial and antioxidant properties of complexes 3c and 3d, as well as the potent antimicrobial activity of 3b and 3f against various bacterial strains, including Candida albicans. To further substantiate our findings, we performed advanced computational analyses, including molecular docking, pharmacophore modeling, DFT, MESP, and ADMET studies. Molecular docking validated our antimicrobial results, whereas pharmacophore models enhanced our understanding of molecular interactions with proteins, potentially identifying novel bioactive compounds. Furthermore, DFT and MESP investigations underscored the superior biological efficacy of tellurium (IV) complexes over thiophene‐derived ligands, emphasizing their potential as therapeutic agents. ADMET analysis revealed their favorable profile as precursors for drug development with minimal adverse effects. In summary, our research seamlessly integrates experimental and theoretical aspects, offering innovative insights into drug design and potential applications in pharmaceuticals. This study represents a significant milestone, paving the way for future advancements in the field of pharmaceutical science. [ABSTRACT FROM AUTHOR]

In this work, a melamine-based covalent organic framework (COF) was synthesized. Then its performance as a potential adsorbent for the removal of tetracycline (TC) was scrutinized by density functional theory computations. The theoretical results showed that TC interaction with COF is experimentally possible, exothermic, spontaneous, and thermodynamically favorable. The adsorption mechanism was checked out by natural bond orbital computations, the results of which demonstrated that TC interaction with COF is a physisorption process. The influence of temperature and the presence of the solvent was also investigated computationally, and the results showed that these parameters do not affect the interactions. Afterward, all effective operational parameters, including the amount of COF, agitation time, and pH, were optimized by the one-factor-at-a-time method to achieve the highest efficiency. Under optimum conditions, the experimental data were fitted to Langmuir, Freundlich, and Sips models. The TC adsorption behavior was well-described by the Sips isotherm with a maximum adsorption capacity of 168.98 mg/g. The desorption process was also discussed, and a mixture of methanol and acetic acid was selected as the most suitable desorption solvent. [ABSTRACT FROM AUTHOR]

Background and Hypothesis Design Results Conclusion Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. “Oddities” in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis.Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first‐episode psychosis (FEP), 18 in a clinical high‐risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language‐based subgroups.We observed a three‐cluster solution. The largest cluster included most HC and the majority of patients, indicating a ‘typical linguistic profile (TLP)’. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP – early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP – stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness).The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of ‘formal thought disorder’. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common neurological disorders globally, affecting about 50 million people, with nearly 80% of those affected residing in low- and middle-income countries. It is characterized by recurrent seizures that result from abnormal electrical brain activity, with seizures varying widely in manifestation. The exploration of the biomechanical effects that seizures have on brain dynamics and stress levels is relevant for the development of more effective treatments and protective strategies. This study uses a blend of experimental data and computational simulations to assess the brain's physical response during seizures, particularly focusing on the behavior of cerebrospinal fluid and the resulting mechanical stresses on different brain regions. Notable findings show increases in stress, predominantly in the posterior gyri and brainstem, during seizures and an evidence of brain displacement relative to the skull. These observations suggest a dynamic and complex interaction between the brain and skull, with maximum shear stress regions demonstrating the limited yet essential protective role of the CSF. By providing a deeper understanding of the mechanical changes occurring during seizures, this research supports the goal of advancing diagnostic tools, informing more targeted treatment interventions, and guiding the creation of customized therapeutic strategies to enhance neurological care and protect against the adverse effects of seizures. [ABSTRACT FROM AUTHOR]

There is a growing focus on the computational aspects of psychiatric disorders in humans. This idea also is gaining traction in nonhuman animal studies. Commenting on a new comprehensive overview of the benefits of applying this approach in translational research by Neville et al. (Cognitive Affective & Behavioral Neuroscience 1–14, 2024), we discuss the implications for translational model validity within this framework. We argue that thinking computationally in translational psychiatry calls for a change in the way that we evaluate animal models of human psychiatric processes, with a shift in focus towards symptom-producing computations rather than the symptoms themselves. Further, in line with Neville et al.'s adoption of the reinforcement learning framework to model animal behaviour, we illustrate how this approach can be applied beyond simple decision-making paradigms to model more naturalistic behaviours. [ABSTRACT FROM AUTHOR]

The novel aromatic cyclic compound type of crystals such as AMPF as 2-amino-4-methylpyridinium fumarate are properly grown by slow evaporation method; the grown crystals are analyzed for single crystalline X-ray diffraction study with the lattice constants as a, b, c with 5.0234 Å; 20.0146 Å; 10.8852 Å and β as 95.767° and are grown at room temperature; the crystal is with volume as 1094.41Å3; crystal system is monoclinic and is of P21/n as space group. The computational study portrays and delineates the AMPF with unit cell and the Laue's pattern of both ways and also with that of the ORTEP – the thermal ellipsoidal plot for proper projection in the atomic scaling. The dielectric behavior of the material is analyzed for pure macro level scaling and is high dielectric constant – εr and Conductivity – σac with less dielectric loss factors prefer the use of AMPF crystals in the field of opto-electronic and µ-electronic engineering usage. The µ-influx of 4.9691 microns properly portrays for electronic filtering application of AMPF and is asserted and implemented as a better candidate in voltage regulation processing. [ABSTRACT FROM AUTHOR]

The last years have seen the application of Natural Language Processing, in particular, language models, to the study of the Semantics of ancient Greek, but only a little work has been done to create gold data for the evaluation of such models. In this contribution we introduce AGREE, the first benchmark for intrinsic evaluation of semantic models of ancient Greek created from expert judgements. In the absence of native speakers, eliciting expert judgements to create a gold standard is a way to leverage a competence that is the closest to that of natives. Moreover, this method allows for collecting data in a uniform way and giving precise instructions to participants. Human judgements about word relatedness were collected via two questionnaires: in the first, experts provided related lemmas to some proposed seeds, while in the second, they assigned relatedness judgements to pairs of lemmas. AGREE was built from a selection of the collected data. [ABSTRACT FROM AUTHOR]

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Copyright of Electronic Components & Materials is the property of Electronic Components & Materials 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.)

Targeting pro‐inflammatory cytokines and their production is found to be of therapeutic benefit for the regulation of inflammation in various chronic autoimmune diseases. Our continued efforts to discover small molecular‐weight pro‐inflammatory cytokine inhibitors resulted in identifying a novel natural lignan molecule named polonilignan, isolated from the culture broth extract of an endophytic fungus Penicillium polonicum. An in silico study (molecular docking, ADME predictions, binding free energy calculation and molecular dynamics simulation) of the polonilignan over the pro‐inflammatory cytokines proteins TNF‐α, IL‐6 and IL‐1β was performed using Schrodinger LLC software to understand the binding interactions, drug‐like properties, and stability of the ligand‐protein complex. Further, in‐vitro testing of inhibition of TNF‐α, IL‐6 and IL‐1β by polonilignan was carried out using ELISA and RT‐PCR on LPS‐induced RAW 264.7 cell lines along with the testing of nitrite production effect (Griess assay) and cytotoxicity (MTT) analysis. Under the computational study, polonilignan revealed good docking scores, binding interactions, and stability under MDS and desirable in silico ADME results over the proteins TNF‐α, IL‐1β and IL‐6. Poloniligan showed significant inhibition of IL‐1β, IL‐6 and TNF‐α with IC50 values of 2.01 μM, 6.59 μM and 42.10 μM, respectively. Also, it reduced the translocation of the NF‐κB subunit p65 to the nucleus (confocal microscopy). The mRNA expression levels of pro‐inflammatory markers IL‐1β, TNF‐α and IL‐6 levels were lowered significantly (p <.001) by the compound, and the diminution was higher with IL‐1β. Further, the lignan was non‐cytotoxic and effective in attenuating nitrite release (IC50 48.56 μM). Thus, polonilignan has been identified as a new pan‐cytokine and NO inhibitor, it is recommended to optimise a method for the synthesis of this small molecular weight lignan and explore its pharmacokinetic characteristics, toxicity and therapeutic effect under various chronic inflammatory disease models. [ABSTRACT FROM AUTHOR]

Tungsten trioxide (WO 3), an n-type semiconductor, has attracted attention due to its diversity of properties, such as catalytic activity, gas sensing capabilities, photochromic behavior, and its ability to absorb both ultraviolet and visible light. This study aimed to investigate the monoclinic phase of the synthesized WO 3 (m-WO 3) through a combination of computational and experimental studies. We present the results of a theoretical investigation on m-WO 3 using first-principles calculations (ab initio) and the pseudopotential model of plane waves. The Density Functional Theory (DFT) was employed, utilizing both the Local Density Approximation (LDA) and Generalized Gradient Approximation (GGA) to account for exchange potential and correlation effects. Structural properties, electron band structure, electron density of states, and optical properties (reflectivity and optical absorption) were computed for the optimized nanostructure at its lowest energy state. A comparison between the calculated lattice parameters and theoretical/experimental results revealed a good agreement. Furthermore, the thermodynamic potentials and specific heat at the constant volume of the nanostructure were calculated. Infrared (IR) and Raman spectra within the frequency range of 0–1000 cm−1 were estimated and assigned, and the results were compared with experimental data and demonstrated good agreement with the literature. [ABSTRACT FROM AUTHOR]