Light-induced insulator-metal transition in Sr2IrO4 reveals the nature of the insulating ground state.

Sr2IrO4 has attracted considerable attention due to its structural and electronic similarities to La2CuO4, the parent compound of high-Tc superconducting cuprates. It was proposed as a strong spin-orbit-coupled Jeff = 1/2 Mott insulator, but the Mott nature of its insulating ground state has not been conclusively established. Here, we use ultrafast laser pulses to realize an insulator-metal transition in Sr2IrO4 and probe the resulting dynamics using time- and angle-resolved photoemission spectroscopy. We observe a gap closure and the formation of weakly renormalized electronic bands in the gap region. Comparing these observations to the expected temperature and doping evolution of Mott gaps and Hubbard bands provides clear evidence that the insulating state does not originate from Mott correlations. We instead propose a correlated band insulator picture, where antiferromagnetic correlations play a key role in the gap opening. More broadly, our results demonstrate that energy-momentum-resolved nonequilibrium dynamics can be used to clarify the nature of equilibrium states in correlated materials.

Rehabilitation Based on BCI: An Innovative Enhancement for Sensorimotor Cortex Rhythms Systemization.

The research proposes a novel strategy for categorizing electroencephalograms (EEG) in real-time brain-computer interfaces that have rehabilitation applications. The methodology utilizes Five Cross-Common Spatial Patterns (FCCSP) to develop a motor movement/imagery systemization model that extracts multi-domain characteristics with excellent performance. The goal is to eliminate the impact caused by EEG's nonstationarity. The article highlights the findings of a real-time technique that is incorporated into a comprehensive prediction system, and it offers an innovative method to boost accuracy in real-time Sensory-Motor cortex Rhythms (SMR). The accuracy increased from 57.14% using raw EEG to 85.71% after preprocessing, and from 58.08% to 97.94% in public domain SMR. The proposed Butterworth bandpass filter is optimized using the FCCSP to determine the ideal bandwidth that incorporates the whole EEG features in beta waves. The Hybrid Systemization of the Correlated Feature Removal classifier is then integrated with the FCCSP method to create improved predictive models. As a consequence, while applied to real-time and PhysioNet datasets, the outcome system achieved outstanding accuracy values of 85.71% and 97.94%, respectively. This demonstrates the robustness of the strategy to increase SMR prediction efficiency.

Multiple-trait genomic prediction for swine meat quality traits using gut microbiome features as a correlated trait.

Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple-trait indirect selection with cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross-validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive-to-record traits in crossbred individuals. Initial steps involved variance components estimation using multiple-trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple-trait models incorporating different numbers of OTU with single-trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high-dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly-to-record traits like meat quality by incorporating gut microbiome information.

Crystal structure via fluctuation scattering.

Crystallography is a quintessential method for determining the atomic structure of crystals. The most common implementation of crystallography uses single crystals that must be of sufficient size, typically tens of micrometres or larger, depending on the complexity of the crystal structure. The emergence of serial data-collection methods in crystallography, particularly for time-resolved experiments, opens up opportunities to develop new routes to structure determination for nanocrystals and ensembles of crystals. Fluctuation X-ray scattering is a correlation-based approach for single-particle imaging from ensembles of identical particles, but has yet to be applied to crystal structure determination. Here, an iterative algorithm is presented that recovers crystal structure-factor intensities from fluctuation X-ray scattering correlations. The capabilities of this algorithm are demonstrated by recovering the structure of three small-molecule crystals and a protein crystal from simulated fluctuation X-ray scattering correlations. This method could facilitate the recovery of structure-factor intensities from crystals in serial crystallography experiments and relax sample requirements for crystallography experiments.

Incorporating body measurement traits to increase genetic gain of feed efficiency and carcass traits in Japanese Black steers.

The objective of the present study was to comprehensively evaluate whether body measurement traits, including BW and body size, could be used as indicators of genetic selection for feed efficiency and carcass traits in Japanese Black steers. First, we estimated the genetic parameters for body measurements, feed efficiency, and carcass traits. Second, we estimated the correlated responses in feed efficiency and carcass traits when selection was applied to one or multiple body measurement traits. In total, 4,578 Japanese Black steers with phenotypic values of residual feed intake (RFI) and residual BW gain (RG) as feed efficiency traits and carcass weight (CWT) and beef marbling standard (BMS) as carcass traits were used. Eleven body measurement traits were measured at the start and finish of the fattening periods (BMT1 and BMT2, respectively), and their growth during the fattening period (BMT3) was used for genetic analyses. The results of genetic parameters showed that the heritability estimates were low to moderate (0.10-0.66), and the genetic correlations among body measurement traits were also estimated to be positively moderate to high in each measuring point (0.23-0.99). The genetic correlations of body measurement traits with RFI and BMS were estimated to be low (-0.14-0.30 and -0.17-0.35, respectively), but those with CWT were positively low to high (0.12-0.97). The genetic correlation estimates between BMT3 and RG were moderate to high (0.38-0.78). Second, correlated responses were estimated under positive selection for body measurement traits. Positive selection for BMT2 and BMT3 increased CWT and RG; however, positive selection for body measurement traits resulted in no change in RFI and BMS. Favorable directions of genetic gains, which were positive for RG, CWT, and BMS and negative for RFI, were obtained by selection indices, including multiple traits in BMT1. Our results suggest that using only one body measurement trait as an indicator of genetic selection for RFI is difficult. However, body measurement traits can be indirect indicators of improved RG. Our results also suggest that genetic improvement of both RFI and RG without reducing CWT and BMS could be achieved using selection indices that account for a balance of body conformation using multiple body measurement traits in Japanese Black cattle.

Potential negative effects of genomic selection.

Initial findings on genomic selection (GS) indicated substantial improvement for major traits, such as performance, and even successful selection for antagonistic traits. However, recent unofficial reports indicate an increased frequency of deterioration of secondary traits. This phenomenon may arise due to the mismatch between the accelerated selection process and resource allocation. Traits explicitly or implicitly accounted for by a selection index move toward the desired direction, whereas neglected traits change according to the genetic correlations with selected traits. Historically, the first stage of commercial genetic selection focused on production traits. After long-term selection, production traits improved, whereas fitness traits deteriorated, although this deterioration was partially compensated for by constantly improving management. Adding these fitness traits to the breeding objective and the used selection index also helped offset their decline while promoting long-term gains. Subsequently, the trend in observed fitness traits was a combination of a negative response due to genetic antagonism, positive response from inclusion in the selection index, and a positive effect of improving management. Under GS, the genetic trends accelerate, especially for well-recorded higher heritability traits, magnifying the negatively correlated responses for fitness traits. Then, the observed trend for fitness traits can become negative, especially because management modifications do not accelerate under GS. Additional deterioration can occur due to the rapid turnover of GS, as heritabilities for production traits can decline and the genetic antagonism between production and fitness traits can intensify. If the genetic parameters are not updated, the selection index will be inaccurate, and the intended gains will not occur. While the deterioration can accelerate for unrecorded or sparsely recorded fitness traits, GS can lead to an improvement for widely recorded fitness traits. In the context of GS, it is crucial to look for unexpected changes in relevant traits and take rapid steps to prevent further declines, especially in secondary traits. Changes can be anticipated by investigating the temporal dynamics of genetic parameters, especially genetic correlations. However, new methods are needed to estimate genetic parameters for the last generation with large amounts of genomic data.

Initial findings on genomic selection indicated substantial improvement for major traits such as growth or milk yield and even successful selection for secondary traits such as fertility or survival. However, recent unofficial reports indicate an increased frequency of problems in several secondary traits. This study looks at potential sources of those problems and mitigation strategies. Under selection initially carried out for production traits, production improved, but fertility (i.e., a secondary trait) declined, with the decline partially compensated for by improving management. Later, also because the observed deteriorations were becoming too strong, these traits became part of the breeding objectives, and used selection indexes were modified to include secondary traits, halting the deterioration. Under genomic selection, genetic gains accelerate, especially for higher heritability production traits, potentially magnifying the negative responses for secondary traits, and management modifications may not be fast enough to alleviate the decline. The responses can especially decline for unrecorded or sparsely recorded fitness traits. While the decline may be slow and hard to see, it may be serious in the long term and hard to reverse. Changes under genomic selection may be monitored by recalculating genetic parameters every generation. Secondary traits that become more antagonistic with production traits will likely deteriorate more and will need special attention.

Local Gate Enhanced Correlated Phases in Twisted Monolayer-Bilayer Graphene.

Manipulating the flat band degeneracy and thus getting the correlated insulating phases has been an ideal thread for realizing the exotic quantum phenomenon in the moiré system. To achieve this goal, the delicately tuned twist angle and a substantial displacement field (D) are rigorously requested. Here, we report our scanning tunneling microscope (STM) work on reaching these correlated insulating states in twisted monolayer-bilayer graphene through a decorated tip. It acts as a local top gate, leading to an enhanced local D, and enables us to fully lift the 8-fold degeneracy of the flat bands. With the aid of this technique, we further expand the correlated insulating states into a more tolerant twist angle that is down to 0.92°. Moreover, the correlated insulating phases in the hole-doping regime are realized. Our tip decoration method allows us to integrate the STM study with the high displacement field for the correlated phases in the twisted moiré systems.

Variational Monte Carlo study of stripes as a function of doping in thet-t'Hubbard model.

We perform variational Monte Carlo simulations of the single-band Hubbard model on the square lattice with both nearest (t) and next-nearest (t') neighbor hoppings. Our work investigates the consequences of increasing hole doping on the instauration of stripes and the behavior of the superconducting order parameter, with a discussion on how the two phenomena affect each other. We consider two different values of the next-nearest neighbor hopping parameter, that are appropriate for describing cuprate superconductors. We observe that stripes are the optimal state in a wide doping range; the stripe wavelength reduces at increasing doping, until stripes melt into a uniform state for large values of doping. Superconducting pair-pair correlations, indicating the presence of superconductivity, are always suppressed in the presence of stripes. Our results suggest that the phase diagram for the single-band Hubbard model is dominated by stripes, with superconductivity being possible only in a narrow doping range between striped states and a nonsuperconducting metal.

Dimensionality Engineering of Magnetic Anisotropy from the Anomalous Hall Effect in Synthetic SrRuO3 Crystals.

Magnetic anisotropy in atomically thin correlated heterostructures is essential for exploring quantum magnetic phases for next-generation spintronics. Whereas previous studies have mostly focused on van der Waals systems, here we investigate the impact of dimensionality of epitaxially grown correlated oxides down to the monolayer limit on structural, magnetic, and orbital anisotropies. By designing oxide superlattices with a correlated ferromagnetic SrRuO3 and nonmagnetic SrTiO3 layers, we observed modulated ferromagnetic behavior with the change of the SrRuO3 thickness. Especially, for three-unit-cell-thick layers, we observe a significant 1500% improvement of the coercive field in the anomalous Hall effect, which cannot be solely attributed to the dimensional crossover in ferromagnetism. The atomic-scale heterostructures further reveal the systematic modulation of anisotropy for the lattice structure and orbital hybridization, explaining the enhanced magnetic anisotropy. Our findings provide valuable insights into engineering the anisotropic hybridization of synthetic magnetic crystals, offering a tunable spin order for various applications.

Modeling correlated uncertainties in stochastic compartmental models.

We consider compartmental models of communicable disease with uncertain contact rates. Stochastic fluctuations are often added to the contact rate to account for uncertainties. White noise, which is the typical choice for the fluctuations, leads to significant underestimation of the disease severity. Here, starting from reasonable assumptions on the social behavior of individuals, we model the contacts as a Markov process which takes into account the temporal correlations present in human social activities. Consequently, we show that the mean-reverting Ornstein-Uhlenbeck (OU) process is the correct model for the stochastic contact rate. We demonstrate the implication of our model on two examples: a Susceptibles-Infected-Susceptibles (SIS) model and a Susceptibles-Exposed-Infected-Removed (SEIR) model of the COVID-19 pandemic and compare the results to the available US data from the Johns Hopkins University database. In particular, we observe that both compartmental models with white noise uncertainties undergo transitions that lead to the systematic underestimation of the spread of the disease. In contrast, modeling the contact rate with the OU process significantly hinders such unrealistic noise-induced transitions. For the SIS model, we derive its stationary probability density analytically, for both white and correlated noise. This allows us to give a complete description of the model's asymptotic behavior as a function of its bifurcation parameters, i.e., the basic reproduction number, noise intensity, and correlation time. For the SEIR model, where the probability density is not available in closed form, we study the transitions using Monte Carlo simulations. Our modeling approach can be used to quantify uncertain parameters in a broad range of biological systems.

Assessing renal interstitial fibrosis using compartmental, non-compartmental, and model-free diffusion MRI approaches.

OBJECTIVE: To assess renal interstitial fibrosis (IF) using diffusion MRI approaches, and explore whether corticomedullary difference (CMD) of diffusion parameters, combination among MRI parameters, or combination with estimated glomerular filtration rate (eGFR) benefit IF evaluation. METHODS: Forty-two patients with chronic kidney disease were included, undergoing MRI examinations. MRI parameters from apparent diffusion coefficient (ADC), intra-voxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion-relaxation correlated spectrum imaging (DR-CSI) were obtained both for renal cortex and medulla. CMD of these parameters was calculated. Pathological IF scores (1-3) were obtained by biopsy. Patients were divided into mild (IF = 1, n = 23) and moderate-severe fibrosis (IF = 2-3, n = 19) groups. Group comparisons for MRI parameters were performed. Diagnostic performances were assessed by the receiver operator's curve analysis for discriminating mild from moderate-severe IF patients. RESULTS: Significant inter-group differences existed for cortical ADC, IVIM-D, IVIM-f, DKI-MD, DR-CSI VB, and DR-CSI VC. Significant inter-group differences existed in ΔADC, ΔMD, ΔVB, ΔVC, ΔQB, and ΔQC. Among the cortical MRI parameters, VB displayed the highest AUC = 0.849, while ADC, f, and MD also showed AUC > 0.8. After combining cortical value and CMD, the diagnostic performances of the MRI parameters were slightly improved except for IVIM-D. Combining VB with f brings the best performance (AUC = 0.903) among MRI bi-variant models. A combination of cortical VB, ΔADC, and eGFR brought obvious improvement in diagnostic performance (AUC 0.963 vs 0.879, specificity 0.826 vs 0.896, and sensitivity 1.000 vs 0.842) than eGFR alone. CONCLUSION: Our study shows promising results for the assessment of renal IF using diffusion MRI approaches. CRITICAL RELEVANCE STATEMENT: Our study explores the non-invasive assessment of renal IF, an independent and effective predictor of renal outcomes, by comparing and combining diffusion MRI approaches including compartmental, non-compartmental, and model-free approaches. KEY POINTS: Significant difference exists for diffusion parameters between mild and moderate-severe IF. Generally, cortical parameters show better performance than corresponding CMD. Bi-variant model lifts the diagnostic performance for assessing IF.

Analyzing injury severity of three-wheeler motorized rickshaws: A correlated random parameters approach with heterogeneity in means.

Traffic crashes involving three-wheeler motorized rickshaw (3-WMR) are alarming public health and socioeconomic concerns in developing countries. While most of the earlier studies have dealt with safety analysis of four- and two-wheelers, there is a noticeable gap in understanding the safety dynamics, especially the risk factors affecting the crashes involving 3-WMR. The present study aims to address this gap by exploring potential risk factors influencing 3-WMR crashes, utilizing a correlated random parameters multinomial logit model with heterogeneity in means (CRPMNLMHM). This modeling framework advances the classic random parameters model by capturing associations among random parameters, providing a more comprehensive understanding of crash risks associated with 3-WMR. The empirical analysis draws on three years of traffic crash records (2017-2019) maintained by RESCUE 1122 in Rawalpindi city, Pakistan. A comparative assessment between the modeling frameworks demonstrated that CRPMNLMHM outperformed its counterparts. Model assessment for heterogeneity in the means identifies two significant variables, i.e., young age and nighttime, which yield statistically significant random parameters. In addition, the model's results suggest that fatal and severe injury outcomes in 3-WMR crashes are affected by several attributes related to temporal characteristics (weekend, nighttime, and off-peak indicators), driver profiles (young, older aged, and speeding), posted speed limits (>70 kmph), weather conditions (raining), and crash characteristics (collision with pedestrians, trucks, or 3-WMR overturning). The present study's findings offer invaluable insights, emphasizing the significance of considering for unobserved heterogeneity in variables contributing to the injury severity of 3-WMR crashes. Moreover, in light of the findings, a set of policy implications are suggested, which will guide safety practitioners to develop more effective countermeasures to address safety issues associated with 3-WMRs.

Pollination efficiency and the pollen-ovule ratio.

Pollination presents a risky journey for pollen grains. Pollen loss is sometimes thought to favour greater pollen investment to compensate for the inefficiency of transport. Sex allocation theory, to the contrary, has consistently concluded that postdispersal loss should have no selective effect on investment in either sex function. But the intuitively appealing compensation idea continues to be raised despite the lack of theoretical endorsement. We address the theoretical issue with a model that directly represents pollen loss (and ovule loss through floral demise or loss of receptivity) as rate-dependent dynamical processes. These loss rates can be varied to examine the effect of pollination efficiency on optimal sex allocation. Pollen-ovule ratios follow from the sex allocation based on the resource costs of pollen and ovule production. This model confirms conventional findings that pollen loss should have essentially no effect on sexual resource allocation in large, panmictic populations. Pollen limitation of seed set does not alter this conclusion. These results force us to rethink the empirical association of pollination efficiency with low pollen-ovule ratios. This pattern could arise if efficient pollen transport commonly results in stigmatic deposition of cohorts of related pollen. Empirical evidence of correlated paternity supports this explanation.

Stereochemically Active Lone Pairs Stabilizing Intrinsic Vacancy Defects in Thermoelectric InTe.

Harvesting waste heat efficiently with thermoelectric energy conversion requires materials with low thermal conductivity. Recently, it was demonstrated how dynamic lone pair expression in thermoelectric InTe is responsible for giant anharmonicity leading to a very low lattice thermal conductivity. InTe also contains correlated disorder of intrinsic defects due to vacancies, and this contributes to additional lowering of the thermal conductivity. Here we use the three-dimensional difference pair distribution (3D-ΔPDF) to analyze 25 K single crystal diffuse X-ray scattering from InTe to unravel the local defect structure, and propose a microscopic structural model. Extended off-centering of In+ ions induced by vacancies allows for the local expression of stereochemically active lone pairs. The associated electronic stabilization is proposed to be a driving force for the formation of In+ vacancy defects in InTe.

Relative importance of intensity and spectrum of artificial light at night in disrupting behavior of a nocturnal rodent.

The influence of light spectral properties on circadian rhythms is of substantial interest to laboratory-based investigation of the circadian system and to field-based understanding of the effects of artificial light at night. The tradeoffs between intensity and spectrum regarding masking behaviors are largely unknown, even for well-studied organisms. We used a custom LED illumination system to document the response of wild type house mice (Mus musculus) to 1-hr nocturnal exposure of all combinations of four intensity levels (0.01, 0.5, 5, and 50 lx) and three correlated color temperatures (CCT; 1750, 1950, and 3000 K). Higher intensities of light (50 lx) suppressed cage activity substantially, and consistently more for the higher CCT light (91% for 3000 K; 53% for 1750 K). At the lower intensities (0.01 lx), mean activity was increased, with the greatest increases for the lowest CCT (12.3% increase at 1750 K; 3% increase at 3000 K). Multiple linear regression confirmed the influence of both CCT (p<0.001) and intensity (p<0.001) on changes in activity (r2=0.66, F9,171=3.33; p<0.001) with the scaled effect size of intensity 3.6 times greater than CCT. Activity suppression was significantly lower for male than female mice (p<0.0001). Assessment of light-evoked cFos expression in the suprachiasmatic nucleus at 50 lx showed no significant difference between high and low CCT exposure. The significant differences by spectral composition illustrate a need to account for light spectrum in circadian studies of behavior and confirm that spectral controls can mitigate some, but certainly not all, of the effects of light pollution on species in the wild.

Modified correlated measurement errors model for estimation of population mean utilizing auxiliary information.

The existence of measurement errors cannot be avoided in practice. It is a prominent fact that the existence of measurement errors diminishes conventional properties of the estimators. A modified correlated measurement errors model has been proposed. Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566-5593. 10.1080/03610918.2016.1165845, 2017) correlated measurement errors model is a particular member of the suggested modified model. In this article, we have tackled the estimation of population mean utilizing auxiliary information under modified correlated measurement errors model. We have developed ratio and product estimators and studied their properties in case of simple random sampling without replacement (SRSWOR) up to first order of approximation. It has been illustrated that suggested ratio and product estimators are more efficient than the conventional unbiased estimator as well as Shalabh and Tsai (Commun Stat Simul Comput 46(7):5566-5593. 10.1080/03610918.2016.1165845, 2017) ratio and product estimators under very realistic situations. An empirical study has also been performed to demonstrate the merits of the recommended estimators over other estimators.

Memory Corrections to Markovian Langevin Dynamics.

Analysis of non-Markovian systems and memory-induced phenomena poses an everlasting challenge in the realm of physics. As a paradigmatic example, we consider a classical Brownian particle of mass M subjected to an external force and exposed to correlated thermal fluctuations. We show that the recently developed approach to this system, in which its non-Markovian dynamics given by the Generalized Langevin Equation is approximated by its memoryless counterpart but with the effective particle mass M∗

Testing for marginal covariate effect when the subgroup size induced by the covariate is informative.

In many cluster-correlated data analyses, informative cluster size poses a challenge that can potentially introduce bias in statistical analyses. Different methodologies have been introduced in statistical literature to address this bias. In this study, we consider a complex form of informativeness where the number of observations corresponding to latent levels of a unit-level continuous covariate within a cluster is associated with the response variable. This type of informativeness has not been explored in prior research. We present a novel test statistic designed to evaluate the effect of the continuous covariate while accounting for the presence of informativeness. The covariate induces a continuum of latent subgroups within the clusters, and our test statistic is formulated by aggregating values from an established statistic that accounts for informative subgroup sizes when comparing group-specific marginal distributions. Through carefully designed simulations, we compare our test with four traditional methods commonly employed in the analysis of cluster-correlated data. Only our test maintains the size across all data-generating scenarios with informativeness. We illustrate the proposed method to test for marginal associations in periodontal data with this distinctive form of informativeness.

High correlated color temperature artificial lighting impairs retinal pigment epithelium integrity and chloride ion transport: A potential mechanism for choroidal thinning.

Among the environmental factors contributing to myopia, the role of correlated color temperature (CCT) of ambient light emerges as a key element warranting in-depth investigation. The choroid, a highly vascularized and dynamic structure, often undergoes thinning during the progression of myopia, though the precise mechanism remains elusive. The retinal pigment epithelium (RPE), the outermost layer of the retina, plays a pivotal role in regulating the transport of ion and fluid between the subretinal space and the choroid. A hypothesis suggests that variations in choroidal thickness (ChT) may be modulated by transepithelial fluid movement across the RPE. Our experimental results demonstrate that high CCT illumination significantly compromised the integrity of tight junctions in the RPE and disrupted chloride ion transport. This functional impairment of the RPE may lead to a reduction in fluid transfer across the RPE, consequently resulting in choroidal thinning and potentially accelerating axial elongation. Our findings provide support for the crucial role of the RPE in regulating ChT. Furthermore, we emphasize the potential hazards posed by high CCT artificial illumination on the RPE, the choroid, and refractive development, underscoring the importance of developing eye-friendly artificial light sources to aid in the prevention and control of myopia.

U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis.

Introduction: The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data. Methods: In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics. Results: We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement. Conclusion: Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.