Quantum mechanics of particles constrained to spiral curves with application to polyene chains.

CONTEXT: Due to advances in synthesizing lower-dimensional materials, there is the challenge of finding the wave equation that effectively describes quantum particles moving on 1D and 2D domains. Jensen and Koppe and Da Costa independently introduced a confining potential formalism showing that the effective constrained dynamics is subjected to a scalar geometry-induced potential; for the confinement to a curve, the potential depends on the curve's curvature function. METHOD: To characterize the π electrons in polyenes, we follow two approaches. First, we utilize a weakened Coulomb potential associated with a spiral curve. The solution to the Schrödinger equation with Dirichlet boundary conditions yields Bessel functions, and the spectrum is obtained analytically. We employ the particle-in-a-box model in the second approach, incorporating effective mass corrections. The π - π ∗ transitions of polyenes were calculated in good experimental agreement with both approaches, although with different wave functions.

Testing predictions from the memory and identity theory of ICD-11 complex posttraumatic stress disorder: Measurement development and initial findings.

BACKGROUND: The 'Memory and Identity Theory' of ICD-11 Complex Posttraumatic Stress Disorder (CPTSD) was recently published but has not yet been subjected to empirical testing. The objective of this study was to evaluate newly developed measures of memory and identity disturbances and test hypothesized structural relations between these constructs and CPTSD symptoms. METHODS: Self-report data were collected from a nationwide sample of adults living in Ukraine (N = 2050) in September 2023. Exploratory factor analysis was used to assess the latent structure of the newly developed measures, and structural equation modeling was used to test the associations between memory and identity disturbances and CPTSD symptoms. RESULTS: 90 % of participants experienced a lifetime trauma, and 9.2 % screened positive for ICD-11 CPTSD. Results indicated that the newly developed measures of memory and identity problems possessed satisfactory psychometric properties, and all but one of the model-implied structural associations were observed. CONCLUSION: This study provides initial empirical support for the Memory and Identity theory of ICD-11 CPTSD, indicating that the basis of this disorder lies in distinct memory and identity processes. Several effects not predicted by the theory were observed, and these can provide the basis for further model testing and refinement.

RBM15 Protects From Myocardial Infarction by Stabilizing NAE1.

RNA-binding proteins play multiple roles in several biological processes. However, the roles of RBM15-an important RNA-binding protein and a significant regulator of RNA methylation-in cardiovascular diseases remain elusive. This study aimed to investigate the biological function of RBM15 and its fundamental mechanisms in myocardial infarction (MI). Methylated RNA immunoprecipitation sequencing was used to explore the N6-methyladenosine (m6A) difference between MI and normal tissues. Our findings showed the elevated level of m6A in MI, and its transcription profile in both MI and normal tissues. RBM15 was the main regulator and its overexpression attenuated apoptosis in cardiomyocytes and improved cardiac function in mice after MI. Then, we used one target NEDD8 activating enzyme E1 subunit and its inhibitor (MLN4924) to investigate the impact of RBM15 targets on cardiomyocytes. Finally, the enhanced m6A methylation in the presence of RBM15 overexpression led to the increased expression and stability of NEDD8 activating enzyme E1 subunit. Our findings suggest that the enhanced m6A level is a protective mechanism in MI, and RBM15 is significantly upregulated in MI and promotes cardiac function. This study showed that RBM15 affected MI by stabilizing its target on the cell apoptosis function, which might provide a new insight into MI therapy.

Unsupervised and semi-supervised domain adaptation networks considering both global knowledge and prototype-based local class information for Motor Imagery Classification.

The non-stationarity of EEG signals results in variability across sessions, impeding model building and data sharing. In this paper, we propose a domain adaptation method called GPL, which simultaneously considers global knowledge and prototype-based local class information to enhance the classification accuracy of motor imagery signals. Depending on the amount of labeled data available in the target domain, the method is implemented in both unsupervised and semi-supervised versions. Specifically, at the global level, we employ the maximum mean difference (MMD) loss to globally constrain the feature space, achieving comprehensive alignment. In the context of class-level operations, we propose two memory banks designed to accommodate class prototypes in each domain and constrain feature embeddings by applying two prototype-based contrastive losses. The source contrastive loss is used to organize source features spatially based on categories, thereby reconciling inter-class and intra-class relationships, while the interactive contrastive loss is employed to facilitate cross-domain information interaction. Simultaneously, in unsupervised scenarios, to mitigate the adverse effects of excessive pseudo-labels, we introduce an entropy-aware strategy that dynamically evaluates the confidence level of target data and personalized constraints on the participation of interactive contrastive loss. To validate our approach, extensive experiments were conducted on a highly regarded public EEG dataset, namely Dataset IIa of the BCI Competition IV, as well as a large-scale EEG dataset called GigaDB. The experiments yielded average classification accuracies of 86.03% and 84.22% respectively. These results demonstrate that our method is an effective EEG decoding model, conducive to advancing the development of motor imagery brain-computer interfaces. The architecture proposed in this study and the code for data partitioning can be found at https://github.com/zhangdx21/GPL.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups.

OBJECTIVE: Although Motor Imagery-based Brain-Computer Interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state EEG network differs between BCI-literacy and -illiteracy. APPROACH: To address the issues above, we analysed three large public EEG datasets using three functional connectivity (FC) and three effective connectivity (EC) metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for Multivariate Granger Causality (MVGC). The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found. SIGNIFICANCE: Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

A novel temporal-frequency combination pattern optimization approach based on information fusion for motor imagery BCIs.

Motor imagery (MI) stands as a powerful paradigm within Brain-Computer Interface (BCI) research due to its ability to induce changes in brain rhythms detectable through common spatial patterns (CSP). However, the raw feature sets captured often contain redundant and invalid information, potentially hindering CSP performance. Methodology-wise, we propose the Information Fusion for Optimizing Temporal-Frequency Combination Pattern (IFTFCP) algorithm to enhance raw feature optimization. Initially, preprocessed data undergoes simultaneous processing in both time and frequency domains via sliding overlapping time windows and filter banks. Subsequently, we introduce the Pearson-Fisher combinational method along with Discriminant Correlation Analysis (DCA) for joint feature selection and fusion. These steps aim to refine raw electroencephalogram (EEG) features. For precise classification of binary MI problems, an Radial Basis Function (RBF)-kernel Support Vector Machine classifier is trained. To validate the efficacy of IFTFCP and evaluate it against other techniques, we conducted experimental investigations using two EEG datasets. Results indicate a notably superior classification performance, boasting an average accuracy of 78.14% and 85.98% on dataset 1 and dataset 2, which is better than other methods outlined in this article. The study's findings suggest potential benefits for the advancement of MI-based BCI strategies, particularly in the domain of feature fusion.

Polarized running training adapted to versus contrary to the menstrual cycle phases has similar effects on endurance performance and cardiovascular parameters.

PURPOSE: This study compared the effects of polarized running training adapted to the menstrual cycle (MC) phases versus polarized training adapted contrary to the MC on endurance performance and cardiovascular parameters. METHODS: Thirty-three naturally menstruating, moderately trained females (age: 26 ± 4 years; BMI: 22.3 ± 3.2 kg/m2; V ˙ O2max/rel: 40.35 ± 4.61 ml/min/kg) were randomly assigned to a control (CON) and intervention (INT) group. Both groups participated in a load-matched eight-week running training intervention. In the INT, high-intensity sessions were aligned with the mid and late follicular phase, low-intensity sessions with the early and mid-luteal phase, and recovery with the late luteal and early follicular phase. In the CON, high-intensity sessions were matched to the late luteal and early follicular phase, and recovery to the mid and late follicular phase. Endurance performance and cardiovascular parameters were assessed at baseline and after the intervention. RESULTS: Twenty-six females completed the intervention. A repeated measures ANOVA determined no time × group interaction effect for any parameter. A significant time effect was found for maximal oxygen uptake (F(1,12) = 18.753, p = 0.005, ηp2 = 0.630), the velocity at the ventilatory threshold one (F(1,12) = 10.704, p = 0.007, ηp2 = 0.493) and two (F(1,12) = 7.746, p = .018, ηp2 = .413). CONCLUSION: The training intervention improved endurance performance in both groups, with no further benefit observed from the MC-adapted polarized training in a group-based analysis. Replications with an extended intervention period, a larger sample size, and a more reliable MC determination are warranted.

Silencing DOCK2 Attenuates Cardiac Fibrosis Following Myocardial Infarction in Mice Via Targeting PI3K/Akt and Wnt/ß-Catenin Pathways.

Cardiac fibrosis following myocardial infarction (MI) seriously affects the prognosis and survival rate of patients. This study aimed to determine the effect and regulation mechanism of the dedicator of cytokinesis 2 (DOCK2) during this process. Experiments were carried out in mice in vivo, and in Ang II treated cardiac fibroblasts (CFs) in vitro. DOCK2 was increased in mouse myocardial tissues after MI and Ang II-treated CFs. In MI mice, DOCK2 silencing improved cardiac function, and ameliorated cardiac fibrosis. DOCK2 knockdown suppressed the activation of CFs and decreased the expression of α-SMA, collagen I, and collagen III. Suppression of DOCK2 mitigated Ang II induced migration of CFs. DOCK2 inhibition reduced the activity of the PI3K/Akt and Wnt/ß-catenin pathways, while this change could be reversed by the pathway activators, SC79 and SKL2001. In summary, DOCK2 suppression improves cardiac dysfunction and attenuates cardiac fibrosis after MI via attenuating PI3K/Akt and Wnt/ß-catenin pathways.

AAPM WGTG51 Report 385: Addendum to the AAPM's TG-51 protocol for clinical reference dosimetry of high-energy electron beams.

An Addendum to the AAPM's TG-51 protocol for the determination of absorbed dose to water is presented for electron beams with energies between 4 MeV and 22 MeV ( 1.70 cm ≤ R 50 ≤ 8.70 cm $1.70\nobreakspace {\rm cm} \le R_{\text{50}} \le 8.70\nobreakspace {\rm cm}$ ). This updated formalism allows simplified calibration procedures, including the use of calibrated cylindrical ionization chambers in all electron beams without the use of a gradient correction. New k Q $k_{Q}$ data are provided for electron beams based on Monte Carlo simulations. Implementation guidance is provided. Components of the uncertainty budget in determining absorbed dose to water at the reference depth are discussed. Specifications for a reference-class chamber in electron beams include chamber stability, settling, ion recombination behavior, and polarity dependence. Progress in electron beam reference dosimetry is reviewed. Although this report introduces some major changes (e.g., gradient corrections are implicitly included in the electron beam quality conversion factors), they serve to simplify the calibration procedure. Results for absorbed dose per linac monitor unit are expected to be up to approximately 2 % higher using this Addendum compared to using the original TG-51 protocol.

A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia.

Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.

The impact of market integration on industrial green transformation: A case study of the yangtze river delta in China.

Regional integration plays an important role in dicarbon reduction and sustainability development. Based on dynamic panel model and spatial econometric model, this study analyzes the impact of market integration (MI) on industrial green transformation (IGT). The study finds that: (1) MI has a nonlinear relationship with the IGT. With the increasing of MI degree, the IGT shows the characteristic of first rising and then declining. (2) MI plays a role in the IGT mainly through industrial agglomeration, resource mismatch and green technological progress, respectively. (3) The impact of MI on IGT exhibits spatial heterogeneity. Although MI significantly promotes IGT in core cities, its impact on the IGT in peripheral cities is not significant. (4) The IGT has spatial spillover effect, and the improvement of MI degree in adjacent regions can also promote the IGT in the cities.

Rapid submillimeter QSM and R2* mapping using interleaved multishot 3D-EPI at 7 and 3 Tesla.

PURPOSE: To explore the high signal-to-noise ratio (SNR) efficiency of interleaved multishot 3D-EPI with standard image reconstruction for fast and robust high-resolution whole-brain quantitative susceptibility (QSM) and R 2 ∗ $$ {R}_2^{\ast } $$ mapping at 7 and 3T. METHODS: Single- and multi-TE segmented 3D-EPI is combined with conventional CAIPIRINHA undersampling for up to 72-fold effective gradient echo (GRE) imaging acceleration. Across multiple averages, scan parameters are varied (e.g., dual-polarity frequency-encoding) to additionally correct for B 0 $$ {\mathrm{B}}_0 $$ -induced artifacts, geometric distortions and motion retrospectively. A comparison to established GRE protocols is made. Resolutions range from 1.4 mm isotropic (1 multi-TE average in 36 s) up to 0.4 mm isotropic (2 single-TE averages in approximately 6 min) with whole-head coverage. RESULTS: Only 1-4 averages are needed for sufficient SNR with 3D-EPI, depending on resolution and field strength. Fast scanning and small voxels together with retrospective corrections result in substantially reduced image artifacts, which improves susceptibility and R 2 ∗ $$ {R}_2^{\ast } $$ mapping. Additionally, much finer details are obtained in susceptibility-weighted image projections through significantly reduced partial voluming. CONCLUSION: Using interleaved multishot 3D-EPI, single-TE and multi-TE data can readily be acquired 10 times faster than with conventional, accelerated GRE imaging. Even 0.4 mm isotropic whole-head QSM within 6 min becomes feasible at 7T. At 3T, motion-robust 0.8 mm isotropic whole-brain QSM and R 2 ∗ $$ {R}_2^{\ast } $$ mapping with no apparent distortion in less than 7 min becomes clinically feasible. Stronger gradient systems may allow for even higher effective acceleration rates through larger EPI factors while maintaining optimal contrast.

P2Y12 adenosine receptor inhibitors preloading among patients with non-ST-elevation acute coronary syndromes; insights from a nationwide multicenter registry.

BACKGROUND: The safety and efficacy of treatment with P2Y12 adenosine-diphosphate receptor inhibitors (P2Y12-RI) before coronary angiography among patients with non-ST-segment elevation acute coronary syndromes (NSTEACS) are questionable. AIMS: To assess the pretreatment rate with P2Y12-RI and its association with ischemic and bleeding risks among patients with NSTEACS. METHODS: The study comprised patients with NSTEACS referred for coronary angiography and included in the Acute Coronary Syndrome Israeli Surveys between 2013 and 2021. Patients were divided into two groups according to the timing of P2Y12-RI loading concerning coronary angiography: pretreatment and posttreatment. The primary endpoints were 30-day major adverse cardiovascular events (MACE; composite of cardiovascular mortality, myocardial infarction, stroke, stent thrombosis, and urgent revascularization) and 1-year all-cause mortality. RESULTS: Of 3076 patients, 2423 (78.8%) received pretreatment with a P2Y12-RI, and 653 (21.2%) received P2Y12-RI posttreatment. Prasugrel and ticagrelor were used more in the posttreatment group compared to the pretreatment group (16% vs. 6% and 38% vs. 25%, respectively, p < 0.001 for both). No difference was observed in the rate of 30-day MACE comparing pretreatment and posttreatment (5.3% vs. 2.2%, respectively, p = 0.62). A sensitivity analysis of 30-day MACE among patients from the 2021 survey demonstrated similar results (2.5% in the posttreatment group vs. 8.0% in the pretreatment group, p = 0.13). There were no differences in 1-year all-cause mortality rates between the pretreatment and posttreatment groups (4.8% vs. 3.8%, p = 0.31). CONCLUSIONS: Among patients with NSTEACS referred for an invasive strategy, the P2Y12-RI posttreatment strategy was associated with similar 30-day and 1-year MACE as the pretreatment strategy. These large-scale, multicenter, real-world data provide reassurance on the safety and efficacy of delaying P2Y12-IR until after coronary stratification to improve clinical decision-making.

Stability analysis of a metapopulation model for the dynamics of malaria's spread including climatic factors.

Eradicating malaria remains a big challenge for computer scientists, mathematicians, epidemiologists, entomologists, physicians and many others. Their approaches range from recovering patients to eradicating the disease. However, collaboration, not always efficient between all these scientists, leads to the implementation of incomplete prototypes or to an under-exploitation of their results. Environmental and climatic factors are part of these elements that are usually omitted by computer scientists and mathematicians in the modelling of the malaria spread dynamic. Tropical countries, most affected by the disease are also mostly underdeveloped or developing countries, and therefore, statistical data are often lacking or difficult to access. Populations are constantly in motion over ecosystems with different environmental and climatic conditions, from a region to another. In this paper, we analyse the global asymptotic stability at the disease-free equilibrium of a metapopulation model including climatic factors.

Decoding N400m Evoked Component: A Tutorial on Multivariate Pattern Analysis for OP-MEG Data.

Multivariate pattern analysis (MVPA) has played an extensive role in interpreting brain activity, which has been applied in studies with modalities such as functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG) and Electroencephalography (EEG). The advent of wearable MEG systems based on optically pumped magnetometers (OPMs), i.e., OP-MEG, has broadened the application of bio-magnetism in the realm of neuroscience. Nonetheless, it also raises challenges in temporal decoding analysis due to the unique attributes of OP-MEG itself. The efficacy of decoding performance utilizing multimodal fusion, such as MEG-EEG, also remains to be elucidated. In this regard, we investigated the impact of several factors, such as processing methods, models and modalities, on the decoding outcomes of OP-MEG. Our findings indicate that the number of averaged trials, dimensionality reduction (DR) methods, and the number of cross-validation folds significantly affect the decoding performance of OP-MEG data. Additionally, decoding results vary across modalities and fusion strategy. In contrast, decoder type, resampling frequency, and sliding window length exert marginal effects. Furthermore, we introduced mutual information (MI) to investigate how information loss due to OP-MEG data processing affect decoding accuracy. Our study offers insights for linear decoding research using OP-MEG and expand its application in the fields of cognitive neuroscience.

Study on influence of turbulence intensity on blade airfoil icing mass & aerodynamic performance.

Wind is an emerging renewable energy resource, but more useful in cold regions. With the increasing threat of climate change and global warming, the unpredictability of wind energy patterns has been affected. With continual threats from extremes and uncertainties, icing on wind turbines has been noted to grow affecting aerodynamic performance. The effect of turbulence intensity at its impact on aerodynamic performance was numerically done using ANSYS Fluent and FENSAP ICE software. Conditions considered for the study included turbulence intensities, median volume diameter (MVD), liquid water content (LWC), angle of attack, and ambient temperature for 180 min. The study's conditions aimed at providing a wide range of effects covering the in-cloud icing and freezing drizzle. The mass of ice increased with an increase in LWC when it increased from 0.05 g/m3 to 0.3 g/m3, and MVD with 1000 µm compared to 40 µm, but when temperature decreased led to an increase from -1 °C to -15 °C. Increasing the angle of attack led to reduced aerodynamic performance with stall angle occurring at α = 0-18°. An increase in the turbulence intensity from 0.01 % to 50 % resulted in decreased CL/CD.

Exact analytical soliton solutions of the M-fractional Akbota equation.

In this paper we explore the new analytical soliton solutions of the truncated M-fractional nonlinear ( 1 + 1 ) -dimensional Akbota equation by applying the exp a function technique, Sardar sub-equation and generalized kudryashov techniques. Akbota is an integrable equation which is Heisenberg ferromagnetic type equation and have much importance for the analysis of curve as well as surface geometry, in optics and in magnets. The obtained results are in the form of dark, bright, periodic and other soliton solutions. The gained results are verified as well as represented by two-dimensional, three-dimensional and contour graphs. The gained results are newer than the existing results in the literature due to the use of fractional derivative. The obtained results are very helpful in optical fibers, optics, telecommunications and other fields. Hence, the gained solutions are fruitful in the future study for these models. The used techniques provide the different variety of solutions. At the end, the applied techniques are simple, fruitful and reliable to solve the other models in mathematical physics.

Assessment of Thermal and Mechanical Indices as Acoustic Output Parameters Used in Obstetric Ultrasound in Saudi Arabia.

BACKGROUND: Patient safety is paramount in ultrasound procedures, particularly in obstetric ultrasounds involving both the mother and fetus. The thermal and mechanical indices (TI and MI) serve as crucial indicators of the acoustic output during ultrasound. Clinicians and specialists must know these indices and ensure they are within safe ranges. This study aimed to assess the parameters of acoustic output power employed in obstetric ultrasound (thermal and mechanical index). METHODOLOGY: A cross-sectional observational study conducted at Maternity and Children's Hospital in Al-Madina Al-Munawwarah, the data was collected from obstetric scanning of 411 pregnant females using a data collection sheet including gravida and women's age, gestational age, scan mode, scan time, and thermal and mechanical index (TI and MI) values. RESULTS: The study found that there were significant differences in safety indices measurement between different modes; in Pulsed Doppler, mean Thermal Index Bone (TIb) had the highest value (1.60±0.40), and the Mechanical Index (MI) was the lowest (0.68±0.33). There were insignificant differences in safety indices values in different modes in different trimesters. The thermal indices of soft tissue and bony structure (TIs and TIb) of brightness mode (B-mode) were constant in all trimesters, but the MI in the first trimester was lower than in the other trimesters. CONCLUSION: This study found significant differences in TIs, TIb, and MI in different modes of obstetric ultrasound. Pulsed Doppler ultrasonography had the highest TIb value and a lower MI value. The ultrasound acoustic exposure output parameters were within the standard's recommended limit.