Detailed Trajectory Research Articles

Trajectories of Change, Illness Understanding, and Parental Worries in Children and Adolescents Undergoing Internet-Delivered Cognitive-Behavioral Therapy for Functional Abdominal Pain Disorders: Protocol for a Single-Case Design and Explorative Pilot Study.

Functional abdominal pain disorders (FAPDs) are common in young people and are characterized by persistent or recurrent abdominal symptoms without apparent structural or biochemical abnormalities. FAPDs are associated with diminished quality of life, school absence, increased health care use, and comorbid anxiety and depression. Exposure-based internet-delivered cognitive behavioral therapy (ICBT) has demonstrated efficacy in alleviating abdominal symptoms and improving quality of life. However, a deeper understanding of effect mechanisms and identification of possible additional treatment targets could refine treatment. This protocol paper aims to describe a study focusing on children and adolescents undergoing ICBT for FAPDs, aiming to further investigate the underlying mechanisms of effect. Children (8-12 years), adolescents (13-17 years) with FAPDs, and their respective parents will be included for 10 weeks for ICBT. First, detailed trajectories of effect are examined through a randomized single-case design study involving 6 children and 6 adolescents (substudy 1). Following this, an open-ended explorative pilot study with 30 children and 30 adolescents explores potential illness-related cognitive biases and interoceptive accuracy before and after treatment (substudy 2). Finally, spanning across these 2 substudies, including all parents from substudies 1 and 2, we will assess parental distress and illness worries before and after treatment, and how these factors impact the treatment adherence and outcomes of the child or adolescent (substudy 3). Recruitment of participants began in June 2022 and is finalized for substudy 1 and ongoing for substudies 2 and 3. Recruitment is expected to be completed by January 2025, with final data collection during April 2025. The findings have the potential to contribute to the ongoing improvement of specialized psychological treatment for FAPDs in young people. ClinicalTrials.gov NCT05237882; https://clinicaltrials.gov/study/NCT05237882; ClinicalTrials.gov NCT05486585; https://clinicaltrials.gov/study/NCT05486585; OSF Registries osf.io/c49k7; https://osf.io/c49k7. DERR1-10.2196/58563.

Gender Incongruence in Danish Youth (GenDa): A Protocol for a Retrospective Cohort Study of Danish Children and Adolescents Referred to a National Gender Identity Service.

Background/Objectives In recent years, the national Gender Identity Service for individuals under 18 years of age in Denmark has seen a considerable increase in referrals of youngsters during puberty. Given this development, it is important to deepen our understanding of the characteristics of contemporary youngsters seeking help for gender incongruence. This understanding can serve as the foundation for improving current treatment regimens by ensuring optimal individual assessment and care. In this study, we aim to describe the sociodemographic characteristics, health profiles, and treatment trajectories in detail, as well as any changes in these characteristics, of all transgender and gender-diverse youngsters referred to the Gender Identity Service in Denmark from 2016 through 2022. Methods: This is a retrospective observational study of a national cohort comprising all individuals under 18 years of age referred to the Danish Gender Identity Service from 1 January 2016 to 1 January 2023. We will use data from medical records obtained at routine visits from the first assessment through repeated visits. Data on demographics, physical and mental health profiles, and information regarding gender identity will be collected and analyzed. The characteristics of those individuals who progressed to hormone therapy will be compared to those who did not. Results: This study aims to enhance our understanding of the characteristics and needs of contemporary youngsters with gender identity issues. Conclusions: The scientific evidence for the assessment and treatment of gender incongruence in youngsters is limited. The characteristics of youngsters seeking healthcare for gender incongruence today may differ from earlier.

Integrating approaches to the role of metaphor in the evolutionary dynamics of language

Abstract Metaphor occupies a central role not only in language use, but also in language change and evolution. Specifically, semantic extension motivated by metaphor plays an important role in extending the lexicon of languages. It is this process that enables the emergence of one of the key properties of modern languages, namely that they are open-ended, systematic, polysemous, structured semiotic systems. Here, we review results from three approaches whose integration presents an important cornerstone for an interdisciplinary account of the role of metaphor in the evolutionary dynamics of language: (1) Historical linguistics and diachronic semantics (2) Computational approaches and natural language processing, and (3) Experimental semiotics. Research in historical linguistics has shown that metaphor is a major mechanism of semantic change. Diachronic semantic analyses have not only mapped detailed historical trajectories of semantic extension motivated by metaphor, but also identified common metaphoric pathways of change as well as shared cognitive principles underlying them. Computational approaches and natural language processing have used findings and data from historical linguistics in attempts to automate the detection of metaphoric semantic change and to build data-driven models models of the principles underlying it. Experimental semiotics is a paradigm in which participants have to create novel communication systems in the absence of language. It represents an experimental design that can investigate cultural linguistic evolution and the emergence of metaphors and metaphorical extensions under controlled laboratory settings to shed light on the interactional and cognitive principles involved in it. Combining results from these approaches represents an important first step towards an interdisciplinary, integrative account of the role of metaphor, and processes of polysemous meaning extension more generally, in the evolutionary dynamics of language.

Molecular dynamics simulations of nanoparticle sintering in additive nanomanufacturing: insights from sintering mechanisms

The sintering behavior of nanoparticles (NPs), which determines the quality of additively nanomanufactured products, differs from conventional understanding established for microparticles. As NPs have a high surface-to-volume ratio, they are subjected to a higher influence from surface tension and a lower melting point than microparticles, resulting in variations in both crystallographic defect-mediated and surface diffusion mechanisms. Meanwhile, the interplay between these controlling mechanisms in NPs has not been well understood, primarily because sintering occurs on the nanosecond timescale, making it an exceptionally transient process. In this work, sintering of both equal and unequal sized Ag and Cu NP doublets with and without misorientation (both tilt and twist) is modeled through molecular dynamics (MD) simulations. The formation and evolution of crystallographic defects, such as vacancies, dislocations, stacking faults, twin boundaries, and grain boundaries, during sintering are investigated. The influence of these defects on plastic deformation and diffusion mechanisms, such as volume diffusion and grain boundary (GB) diffusion, is discussed to elucidate the responsible sintering mechanisms. The surface diffusion mechanism is visualized by using detailed atomic trajectories generated during the sintering process. Finally, the overall effectiveness of all diffusion sintering mechanisms is quantified. This study provides first insights into the complexity and dynamics of NP sintering mechanisms which can aid in the development of accurate predictive models.

Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.

Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.

A case study on the dust storm that occurred on March 13–18, 2022, over the Algerian Sahara, using satellite remote sensing

This study investigates the dynamics of a significant dust storm that occurred in Algeria in March 2022, employing data derived from the Sentinel-5P and CALIPSO satellite instruments. We examine the Aerosol Absorbing Index (AAI) to detect n absorbing aerosols, with a focus on desert dust, and analyze the attenuation coefficient. Additionally, we employ the HYSPLIT trajectory analyze to study dust transport and MERRA-2 to examine wind patterns wind. The key findings unveil a detailed trajectory of a prominent dust storm in Algeria in March 2022. The Aerosol Absorbing Index (AAI) effectively identifies absorbing aerosols, particularly desert dust, through thorough analyses of dust trajectory and wind patterns; augmenting these findings, CALIPSO satellite data has provided a detailed vertical profile of aerosols within the dust plume, emphasizing spatial and altitudinal extents. This research significantly contributes to advancing scientific discussions on atmospheric dynamics in arid regions and enhances our understanding and forecasting capabilities related to Saharan dust storm initiation and trajectory.

Spatial transcriptome analysis reveals de novo regeneration of poplar roots.

Propagation through cuttings is a well-established and effective technique for plant multiplication. This study explores the regeneration of poplar roots using spatial transcriptomics to map a detailed developmental trajectory. Mapping of the time-series transcriptome data revealed notable alterations in gene expression during root development, particularly in the activation of cytokinin-responsive genes. Our analysis identified six distinct clusters during the second and third stages, each corresponding to specific anatomical regions with unique gene expression profiles. Auxin response cis-elements (AuxREs) were prevalent in the promoters of these cytokinin-responsive genes, indicating a regulatory interplay between auxin and cytokinin. Pseudo-temporal trajectory analysis mapped the differentiation from cambium cells to root primordium cells, revealing a complex pattern of cell differentiation. SAC56 and LOS1 emerged as potential novel biomarkers for enhancing root regeneration, with distinct spatial expression patterns confirmed by in situ hybridization. This comprehensive spatial analysis enhances our understanding of the molecular interactions driving root regeneration and provides insights for improving plant propagation techniques.

The gradual establishment of complex coumarin biosynthetic pathway in Apiaceae

Complex coumarins (CCs) represent characteristic metabolites found in Apiaceae plants, possessing significant medical value. Their essential functional role is likely as protectants against pathogens and regulators responding to environmental stimuli. Utilizing genomes and transcriptomes from 34 Apiaceae plants, including our recently sequenced Peucedanum praeruptorum, we conduct comprehensive phylogenetic analyses to reconstruct the detailed evolutionary process of the CC biosynthetic pathway in Apiaceae. Our results show that three key enzymes – p-coumaroyl CoA 2’-hydroxylase (C2’H), C-prenyltransferase (C-PT), and cyclase – originated successively at different evolutionary nodes within Apiaceae through various means of gene duplications: ectopic and tandem duplications. Neofunctionalization endows these enzymes with novel functions necessary for CC biosynthesis, thus completing the pathway. Candidate genes are cloned for heterologous expression and subjected to in vitro enzymatic assays to test our hypothesis regarding the origins of the key enzymes, and the results precisely validate our evolutionary inferences. Among the three enzymes, C-PTs are likely the primary determinant of the structural diversity of CCs (linear/angular), due to divergent activities evolved to target different positions (C-6 or C-8) of umbelliferone. A key amino acid variation (Ala161/Thr161) is identified and proven to play a crucial role in the alteration of enzymatic activity, possibly resulting in distinct binding forms between enzymes and substrates, thereby leading to different products. In conclusion, this study provides a detailed trajectory for the establishment and evolution of the CC biosynthetic pathway in Apiaceae. It explains why only a portion, not all, of Apiaceae plants can produce CCs and reveals the mechanisms of CC structural diversity among different Apiaceae plants.

Disease Progression of Data-Driven Subtypes of Parkinson's Disease: 5-Year Longitudinal Study from the Early Parkinson's Disease Longitudinal Singapore (PALS) Cohort.

The detailed trajectory of data-driven subtypes in Parkinson's disease (PD) within Asian cohorts remains undisclosed. To evaluate the motor, non-motor symptom (NMS) progression among the data-driven PD clusters. In this 5-year longitudinal study, NMS scale (NMSS), Hospital Anxiety Depression Scale (HADS), and Epworth sleepiness scale (ESS) were carried out annually to monitor NMS progression. H& Y staging scale, MDS-UPDRS part III motor score, and postural instability gait difficulty (PIGD) score were assessed annually to evaluate disease severity and motor progression. Five cognitive standardized scores were used to assess detailed cognitive progression. Linear mixed model was performed to assess the annual progression rates of the longitudinal outcomes. Two hundred and six early PD patients, consisting of 43 patients in cluster A, 98 patients in cluster B and 65 subjects in cluster C. Cluster A (severe subtype) had significantly faster progression slope in NMSS Domain 3 (mood/apathy) score (p = 0.01), NMSS Domain 4 (perceptual problems) score (p = 0.02), NMSS Domain 7 (urinary) score (p = 0.03), and ESS Total Score (p = 0.04) than the other two clusters. Cluster A also progressed significantly in PIGD score (p = 0.04). For cognitive outcomes, cluster A deteriorated significantly in visuospatial domain (p = 0.002), while cluster C (mild subtype) deteriorated significantly in executive domain (p = 0.04). The severe cluster had significantly faster progression, particularly in mood and perceptual NMS domains, visuospatial cognitive performances, and postural instability gait scores. Our findings will be helpful for clinicians to stratify and pre-emptively manage PD patients by developing intervention strategies to counter the progression of these domains.

Absolute rate coefficient measurements of the reactions of vibrationally cold HD+ and H3+ ions with neutral C atoms

Ion-neutral reactions are driving the formation of small molecules in the gas phase of interstellar clouds, where hydrogen molecules and their ions are by far the most important collision partners for any species in the astrochemical network. Here we present absolute rate coefficient measurements for the reactions HD++C→CH+/CD++D/H and H3++C→CH+/CH2++H2/H obtained using a recently commissioned ion-neutral collision setup at the Cryogenic Storage Ring. Our measurements with vibrationally cold ions result in significantly higher rate coefficients when compared with previous studies using internally excited ions, bringing them in better agreement with classical capture theories. Moreover, we have performed detailed quasiclassical trajectory (QCT) calculations for the HD++C reaction, using new potential energy surfaces. Our experimental results and the QCT calculations show very good agreement for the absolute cross section of the reactions, as well as for the isotope effect. These results have great potential relevance for the chemistry of the interstellar medium and the onset of organic chemistry in space. Published by the American Physical Society 2024

ChoreoVis: Planning and Assessing Formations in Dance Choreographies

AbstractSports visualization has developed into an active research field over the last decades. Many approaches focus on analyzing movement data recorded from unstructured situations, such as soccer. For the analysis of choreographed activities like formation dancing, however, the goal differs, as dancers follow specific formations in coordinated movement trajectories. To date, little work exists on how visual analytics methods can support such choreographed performances. To fill this gap, we introduce a new visual approach for planning and assessing dance choreographies. In terms of planning choreographies, we contribute a web application with interactive authoring tools and views for the dancers' positions and orientations, movement trajectories, poses, dance floor utilization, and movement distances. For assessing dancers' real‐world movement trajectories, extracted by manual bounding box annotations, we developed a timeline showing aggregated trajectory deviations and a dance floor view for detailed trajectory comparison. Our approach was developed and evaluated in collaboration with dance instructors, showing that introducing visual analytics into this domain promises improvements in training efficiency for the future.

A time-varying shockwave speed model for reconstructing trajectories on freeways using Lagrangian and Eulerian observations

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.

In vitro fertilization outcome based on the detailed early luteal phase trajectory of hormones: a prospective cohort study

BackgroundOvarian stimulation and the use of human chorionic gonadotropin (hCG) for triggering oocyte maturation in women undergoing in vitro fertilisation (IVF) introduces several differences in luteal phase hormone levels compared with natural cycles that may negatively impact on endometrial receptivity and pregnancy rates after fresh embryo transfer. Exogenous luteal phase support is given to overcome these issues. The suitability of a pragmatic approach to luteal phase support is not known due to a lack of data on early phase luteal hormone levels and their association with fertility outcomes during IVF with fresh embryo transfer. This study determined early luteal phase profiles of serum progesterone, 17-hydroxyprogesterone and hCG, and associations between hormone levels/hormone level profile after hCG trigger and the live birth rate in women undergoing IVF with fresh embryo transfer.MethodsThis prospective single center, cohort study was conducted in Vietnam from January 2021 to December 2022. Women aged 18–38 years with normal ovarian reserve and undergoing controlled ovarian stimulation using a gonadotropin-releasing hormone antagonist protocol were included. Serum hormone levels were determined before trigger, at 12, 24 and 36 h after hCG, and daily from 1 to 6 days after oocyte pick-up. Serum hormone level profiles were classified as lower or upper. The primary outcome was live birth rate based on early luteal phase hormone level profile.ResultsNinety-five women were enrolled. Live birth occurred in 19/69 women (27.5%) with a lower progesterone profile and 13/22 (59.1%) with an upper progesterone profile (risk ratio [RR] 2.15; 95% confidence interval [CI] 1.28–3.60), and in 6/31 (19.4%) versus 26/60 (43.3%) with a lower versus upper serum 17-hydroxyprogesterone profile (RR 2.24; 95% CI 1.03–4.86). Nearly 20% of women had peak progesterone concentration on or before day 3 after oocyte pick-up, and this was associated with significantly lower chances of having a life birth.ConclusionsThese data show the importance of proper corpus luteum function with sufficient progesterone/17-hydroxyprogesterone production for achievement of pregnancy and to maximize the chance of live birth during IVF.Trial RegistrationNCT04693624 (www.clinicaltrials.gov).

Space-Charge Effects on Transverse Emittance in the Extraction Region of the RIKEN 28-GHz ECRIS

To confirm the significance of space-charge effects (SCEs) on the emittance growth in the “extraction region” of the RIKEN 28-GHz electron-cyclotron-resonance ion source (ECRIS), we reconstructed the phase-space distribution of argon (Ar) beams traveling through high-intensity nitrogen (N) beams and reaching at the end of “extraction region”. Here, the “extraction region” is defined as the low-energy beam transport region between the ECRIS puller electrode and a magnetic analyzer. The transverse phase-space distribution (PSD) of the Ar beams, which were exposed to the SCEs due to the high-intensity N beams, was measured after magnetic analysis using the pepper-pot emittance meter (PPEM). Because we suppressed each Ar beam current below 50 µA, the expansion in the transverse phase-space distribution due to the SCEs after the magnetic analyzer could be sufficiently reduced. Sufficient angular resolution was achieved by improving the image analysis of PPEM measurements with the “optical flow method”. Therefore, we obtained a transverse PSD at the endpoint of the extraction region of the ECRIS using the three dimensional backtracking. The obtained rms emittance of the Ar10,11,13+ were found to increas as a function of the extraction current I ext, while those of Ar16+ were found to be nearly constant. The reconstructed PSD at the end of the “extraction region” shows that the smaller the M/Q, in other words, the larger the charge state Q, the smaller both the positional and angular distribution for each I ext. To reveal the space charge effect on the beam trajectory passing though the “extraction region”, further experimental measurements and analyses are underway, including detailed trajectory simulation.

Dynamic genomic changes in methotrexate-resistant human cancer cell lines beyond DHFR amplification suggest potential new targets for preventing drug resistance.

Although DHFR gene amplification has long been known as a major mechanism for methotrexate (MTX) resistance in cancer, the early changes and detailed development of the resistance are not yet fully understood. We performed genomic, transcriptional and proteomic analyses of human colon cancer cells with sequentially increasing levels of MTX-resistance. The genomic amplification evolved in three phases (pre-amplification, homogenously staining region (HSR) and extrachromosomal DNA (ecDNA)). We confirm that genomic amplification and increased expression of DHFR, with formation of HSRs and especially ecDNAs, is the major driver of resistance. However, DHFR did not play a detectable role in the early phase. In the late phase (ecDNA), increase in FAM151B protein level may also have an important role by decreasing sensitivity to MTX. In addition, although MSH3 and ZFYVE16 may be subject to different posttranscriptional regulations and therefore protein expressions are decreased in ecDNA stages compared to HSR stages, they still play important roles in MTX resistance. The study provides a detailed evolutionary trajectory of MTX-resistance and identifies new targets, especially ecDNAs, which could help to prevent drug resistance. It also presents a proof-of-principal approach which could be applied to other cancer drug resistance studies.

CFD-DEM-based evaluation of main-channel sediment transport processes subject to supplement from a steep tributary

The accumulation of sediments within mountainous gullies serves as a significant source for the main channel. Understanding the transport characteristics is crucial for comprehending reach-scale sediment supply features and channel morphodynamics. This study addresses the previously unexplored challenges in accurately quantifying variations in sediment transport rates, particularly during critical transitions such as the mobilization of static sediment pulses into continuous motion. To accomplish this objective, a combined Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) computational framework is employed to directly simulate the detailed trajectories of all particles. After validating the CFD-DEM model, we examine the effects of three major influential factors (particle size, channel slope and channel width) on sediment transport characteristics. The results indicate that the sediment transport rate exhibits rapid rise to peak followed by gradual attenuation process, described by a long-tail effect. The cross-sectional averaged sediment transport rate positively correlates with the width of sediment transport belt. The downstream propagation of sediment is enhanced by steeper channel slope and narrower channel width. In addition, we find that cross-sectional averaged sediment transport rate controlled by these factors exhibits inherent self-similarity. The temporal variation of dimensionless sediment transport rate for a certain section can be modeled by a modified two-branch Weibull function encompasses three model parameters dependent on influential factors and longitudinal location. The parameter variation of the functional model, combined with linear variation of the three main influencing factors, provides support for explaining sediment transport processes in channels of dimensions different from those studied. These findings enhance the understanding of the formation and evolution mechanism of sediment transport induced by upstream loose deposit destruction.

Development of disease-specific growth charts for Korean children with Beckwith-Wiedemann syndrome.

Beckwith-Wiedemann syndrome (BWS) is an epigenetic overgrowth syndrome. Despite its distinctive growth pattern, the detailed growth trajectories of children with BWS remain largely unknown. We retrospectively analyzed 413 anthropometric measurements over an average of 4.4 years of follow-up in 51 children with BWS. We constructed sex-specific percentile curves for height, weight, and head circumference using a generalized additive model for location, scale, and shape. Males with BWS exhibited greater height at all ages evaluated, weight before the age of 10, and head circumference before the age of 9 than those of the general population. Females with BWS showed greater height before the age of 7, weight before the age of 4.5, and head circumference before the age of 7 than those of the general population. At the latest follow-up visit at a mean 8.4 years of age, bone age was significantly higher than chronological age. Compared to paternal uniparental disomy (pUPD), males with imprinting center region 2-loss of methylation (IC2-LOM) had higher standard deviation score (SDS) for height and weight, while females with IC2-LOM showed larger SDS for head circumference. These disease-specific growth charts can serve as valuable tools for clinical monitoring of children with BWS.

An ensemble docking-based virtual screening and molecular dynamics simulation of phytochemical compounds from Malaysian Kelulut Honey (KH) against SARS-CoV-2 target enzyme, human angiotensin-converting enzyme 2 (ACE-2)

The human angiotensin-converting enzyme 2 (ACE-2) receptor is a metalloenzyme that plays an important role in regulating blood pressure by modulating angiotensin II. This receptor facilitates SARS-CoV-2 entry into human cells via receptor-mediated endocytosis, causing the global COVID-19 pandemic and a major health crisis. Kelulut honey (KH), one of Malaysian honey recently gained attention for its distinct flavour and taste while having many nutritional and medicinal properties. Recent study demonstrates the antiviral potential of KH against SARS-CoV-2 by inhibiting ACE-2 in vitro, but the bioactive compound pertaining to the ACE-2 inhibition is yet unknown. An ensemble docking-based virtual screening was employed to screen the phytochemical compounds from KH with high binding affinity against the 10 best representative structures of ACE-2 that mostly formed from MD simulation. From 110 phytochemicals previously identified in KH, 27 compounds passed the ADMET analysis and proceeded to docking. Among the docked compound, SDC and FMN consistently exhibited strong binding to ACE-2's active site (-9.719 and −9.473 kcal/mol) and allosteric site (-7.305 and −7.464 kcal/mol) as compared to potent ACE-2 inhibitor, MLN 4760. Detailed trajectory analysis of MD simulation showed stable binding interaction towards active and allosteric sites of ACE-2. KH's compounds show promise in inhibiting SARS-CoV-2 binding to ACE-2 receptors, indicating potential for preventive use or as a supplement to other COVID-19 treatments. Additional research is needed to confirm KH's antiviral effects and its role in SARS-CoV-2 therapy, including prophylaxis and adjuvant treatment with vaccination. Communicated by Ramaswamy H. Sarma

Computer Aided Structure-Based Drug Design of Novel SARS-CoV-2 Main Protease Inhibitors: Molecular Docking and Molecular Dynamics Study

Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) virus syndrome caused the recent outbreak of COVID-19 disease, the most significant challenge to public health for decades. Despite the successful development of vaccines and promising therapies, the development of novel drugs is still in the interests of scientific society. SARS-CoV-2 main protease Mpro is one of the key proteins for the lifecycle of the virus and is considered an intriguing target. We used a structure-based drug design approach as a part of the search of new inhibitors for SARS-CoV-2 Mpro and hence new potential drugs for treating COVID-19. Four structures of potential inhibitors of (4S)-2-(2-(1H-imidazol-5-yl)ethyl)-4-amino-2-(1,3-dihydroxypropyl)-3-hydroxy-5-(1H-imidazol-5-yl)pentanal (L1), (2R,4S)-2-((1H-imidazol-4-yl)methyl)-4-chloro-8-hydroxy-7-(hydroxymethyl)octanoic acid (L2), 1,9-dihydroxy-6-(hydroxymethyl)-6-(((1S)-1,7,7-trimethylbicyclo [2.2.1]heptan-2-yl)amino)nonan-4-one (L3), and 2,4,6-tris((4H-1,2,4-triazol-3-yl)amino)benzonitrile (L4) were modeled. Three-dimensional structures of ligand–protein complexes were modeled and their potential binding efficiency proved. Docking and molecular dynamic simulations were performed for these compounds. Detailed trajectory analysis of the ligands’ binding conformation was carried out. Binding free energies were estimated by the MM/PBSA approach. Results suggest a high potential efficiency of the studied inhibitors.

A Predictive Out-of-Step Identification Method Based on Wide-Area Velocity-Acceleration Trajectory

Online identification of the transient instability is essential in power system analysis, as it facilitates the grid operator to decide and coordinate system failure correction control actions. The rapid technological development of phasor measurement units (PMUs) provides an opportunity for online transient out-of-step identification in real-time. This paper analyzes the characteristics of angular velocity-acceleration trajectories in detail, and then proposes a predictive transient out-of-step identification method among coherent groups of generators based on the velocity-acceleration trajectory sampled by PMUs. To prove that the method can distinguish the transient out-of-step event following disturbance reliably, a mathematical proof of the criterion extended to multi-machine system is made, and the influences of the exciter and emergency control are taken into consideration to a certain extent. Finally, the effectiveness and predictability of the proposed method are demonstrated on a 39-bus New England test system and two large-scale power system simulation models of China by numerical studies.