Temporal Index Research Articles

Differentiating Multiple Sclerosis and Glaucoma With Sectoral Pattern Analysis of Peripapillary Nerve Fiber Layer.

To distinguish between multiple sclerosis (MS) and glaucoma by nerve fiber layer (NFL) thinning patterns. MS patients were diagnosed by the 2017 McDonald Criteria; glaucoma patients had disc rim thinning or an NFL defect, with or without perimetric defect. The peripapillary NFL thickness was divided into eight sectors, and percentage reduction (% reduction) was calculated relative to normative reference values. The MS and glaucoma eyes were grouped based on the severity of NFL thinning in the worst sector: significant reduction (<1 percentile of normal reference), borderline reduction (1%∼5%), and no reduction (>5%). We devised four diagnostic indexes, and the area under the curve of receiver operating characteristics (AROC) and accuracy were used to evaluate the indexes. We enrolled 58 control subjects (58 eyes), 56 MS subjects (112 eyes), and 92 glaucoma subjects (92 eyes) at two centers. The most pronounced percent reduction in MS eyes occurred in the temporal-upper and temporal-lower sectors. In glaucoma eyes, this occurred in the inferior-temporal, inferior-nasal, and superior-temporal sectors. The temporal pattern index had the best AROC (0.96, 0.91-1.00) and accuracy (92.6%) in the significant reduction group. It had good AROC (0.88, 0.78-0.99) and accuracy (76.7%) in the borderline reduction group. Normalizing NFL reduction as a percentage of normal reference accentuated patterns characteristic of MS and glaucoma. Quantitative pattern indexes were effective in differentiating the two diseases. The utility of optical coherence tomography in the differential diagnosis of optic neuropathies is enhanced by analyzing the retinal nerve fiber layer percentage reduction pattern.

Theta oscillations in observers’ temporal cortex index postural instability of point-light displays

This study investigates whether postural equilibration follows the same principles of motor resonance as goal-oriented actions, namely, whether an individual activates the same neuronal substrates when experiencing postural perturbation as when observing another individual in this condition. To address this question, we examined electroencephalographic dynamics while subjects observed point-light displays featuring an unstable human display, a stable human display, and their respective scrambled counterparts lacking shape information and biological motion. We focused on theta band (4–7 Hz), which is a fundamental frequency for modulating brain activity during challenging balance tasks and reflects postural stability monitoring. Rather than mirroring activity, our findings suggest an inhibitory response to postural instability. Theta event-related synchronization in the left temporal cortex was dampened for the unstable display as compared to its scramble counterpart and to the stable display. This low theta response coincided with an increase in left temporal-prefrontal connectivity, compatible with top-down inhibitory mechanisms. By contrast, the stronger theta response to the stable display as compared to the unstable one could be due to the difficulty of recognizing low-motion biological stimuli, or alternatively, to a facilitation of stimulus processing and strengthening of the mirroring response. The response facilitation for stable posture, coupled with a diminished response to the unstable display, could contribute to a broader mechanism mitigating postural threats and ensuring stable balance. Future investigations should leverage these findings to explore how posture-related responses correlate with perceptual and motor expertise, and to more clearly define these mechanisms during dynamic social interactions.

Synchrotron self-Compton in a radiative-adiabatic fireball scenario: Modelling the multiwavelength observations in some Fermi/LAT bursts

Abstract Energetic GeV photons expected from the closest and the most energetic Gamma-ray bursts (GRBs) provide an unique opportunity to study the very-high-energy emission as well as the possible correlations with lower energy bands in realistic GRB afterglow models. In the standard GRB afterglow model, the relativistic homogeneous shock is usually considered to be fully adiabatic, however, it could be partially radiative. Based on the external forward-shock scenario in both stellar wind and constant-density medium. We present a radiative-adiabatic analytical model of the synchrotron self-Compton (SSC) and synchrotron processes considering an electron energy distribution with a power-law index of 1 &amp;lt; p &amp;lt; 2 and 2 ≤ p. We show that the SSC scenario plays a relevant role in the radiative parameter ε, leading to a prolonged evolution during the slow cooling regime. In a particular case, we derive the Fermi/LAT light curves together with the photons with energies ≥100 MeV in a sample of nine bursts from the second Fermi/LAT GRB catalog that exhibited temporal and spectral indices with ≳ 1.5 and ≈2, respectively. These events can hardly be described with closure relations of the standard synchrotron afterglow model, and also exhibit energetic photons above the synchrotron limit. We have modeled the multi-wavelength observations of our sample to constrain the microphysical parameters, the circumburst density, the bulk Lorentz factor and the mechanism responsible for explaining the energetic GeV photons.

Expanding Short-Term Pedestrian Volume Counts to Average Annual Daily Counts at Intersections: Temporal Pattern Indices and a Comparison of Statistical Land Use and Simple Threshold Methods

Quantifying pedestrian exposure for intersections is important for developing pedestrian-centric strategies. Exposure is typically expressed as the annual-average daily pedestrian traffic (AADPT). AADPT can be directly calculated when continuous counts (CCs) are available. However, CCs are rarely available for all intersections, requiring estimation methods. This work explores the steps involved in expanding pedestrian short-term counts (STCs) to AADPT using factor groups. Day-of-week-of-month expansion factors were used to expand 8-h STCs. The first step consists of grouping sites with similar pedestrian activity patterns (factor groups). Five factor groups were identified, and three pedestrian temporal pattern indicators were proposed to characterize them. The thresholds of these indicators for grouping sites with CCs are available in this work. The second step involves the challenge of associating sites where only STCs are available (unknown traffic patterns) with a particular factor group. Multinomial logistic regression (MLR) was developed to link factor groups to land use, socioeconomic, and operational attributes. Explanatory variables that represent commercial and industrial areas and the number of schools and transit stops were found to be significant. Simpler methods were also introduced to identify factor groups strongly affected by schools. The last step assesses the expansion of STCs to AADPT using the proposed approaches. The results showed improved performance when compared with averaging expansion factors across all sites. It was also demonstrated that, in certain cases, simple approaches that rely exclusively on the identification of sites that are highly affected by schools present similar performance to more complex approaches, such as MLR.

Investigating the off-axis GRB afterglow scenario for extragalactic fast X-ray transients

Context. Extragalactic fast X-ray transients (FXTs) are short-duration (∼ks) X-ray flashes of unknown origin, potentially arising from binary neutron star (BNS) mergers, tidal disruption events, or supernova shock breakouts. Aims. In the context of the BNS scenario, we investigate the possible link between FXTs and the afterglows of off-axis merger-induced gamma-ray bursts (GRBs). Methods. By modelling well-sampled broadband afterglows of 13 merger-induced GRBs, we make predictions for their X-ray light curve behaviour had they been observed off-axis, considering both a uniform jet with core angle θC and a Gaussian-structured jet whose edge lies at an angle θW = 2θC. We compare their peak X-ray luminosity, duration, and temporal indices α (where F ∝ tα) with those of the currently known extragalactic FXTs. Results. Our analysis reveals that a slightly off-axis observing angle of θobs ≈ (2.2 − 3)θC and a structured jet are required to explain the shallow (|α|≲0.3) temporal indices of the FXT light curves, which cannot be reproduced in the uniform-jet case at any viewing angle. In the case of a structured jet with truncation angle θW = 2θC, the distributions of the duration of the FXTs are consistent with those of the off-axis afterglows for the same range of observing angles, θobs ≈ (2.2 − 3)θC. While the distributions of the off-axis peak X-ray luminosity are consistent only for θobs = 2.2θC, focussing on individual events with different intrinsic luminosities reveals that the match of all three properties (peak X-ray luminosity, duration and temporal indices) of the FXTs at the same viewing angle is possible in the range θobs ∼ (2.2 − 2.6)θC. Despite the small sample of GRBs analysed, these results show that there is a region of the parameter space – although quite limited – where the observational properties of off-axis GRB afterglow can be consistent with those of the newly discovered FXTs. Future observations of FXTs discovered by the recently launched Einstein Probe mission and GRB population studies combined with more complex afterglow models will shed light on this possible GRB-FXT connection, and eventually unveil the progenitors of some FXTs.

Coral responses to a catastrophic marine heatwave are decoupled from changes in total coral cover at a continental scale.

The services provided by the world's coral reefs are threatened by increasingly frequent and severe marine heatwaves. Heatwave-induced degradation of reefs has often been inferred from the extent of the decline in total coral cover, which overlooks extreme variation among coral taxa in their susceptibility and responses to thermal stress. Here, we provide a continental-scale assessment of coral cover changes at 262 shallow tropical reef sites around Australia, using ecological survey data on 404 coral taxa before and after the 2016 mass bleaching event. A strong spatial structure in coral community composition along large-scale environmental gradients largely dictated how coral communities responded to heat stress. While heat stress variables were the best predictors of change in total coral cover, the pre-heatwave community composition best predicted the temporal beta-diversity index (an indicator of change in community composition over time). Indicator taxa in each coral community differed before and after the heatwave, highlighting potential winners and losers of climate-driven coral bleaching. Our results demonstrate how assessment of change in total cover alone may conceal very different responses in community structure, some of which showed strong regional consistency, and may provide a telling outlook of how coral reefs may reorganize in a warmer future.

Moments of parton distribution functions of any order from lattice QCD

We describe a procedure to determine moments of parton distribution functions of any order in lattice quantum chromodynamics (QCD). The procedure is based on the gradient flow for fermion and gauge fields. The flowed matrix elements of twist-2 operators renormalize multiplicatively, and the matching with the physical matrix elements can be obtained using continuum symmetries and the irreducible representations of Euclidean 4-dimensional rotations. We calculate the matching coefficients at one-loop in perturbation theory for moments of any order in the flavor nonsinglet case. We also give specific examples of operators that could be used in lattice QCD computations. It turns out that it is possible to choose operators with identical Lorentz indices and still have a multiplicative matching. One can thus use twist-2 operators exclusively with temporal indices, thus substantially improving the signal-to-noise ratio in the computation of the hadronic matrix elements. Published by the American Physical Society 2024

Unravelling the relationship between language anxiety and foreign language speech fluency in a monologue production

ABSTRACT This study investigated the interplay between language anxiety of advanced foreign language learners and their speech fluency measured with temporal indices. Language anxiety levels of 59 university level students of English were identified with the Input, Processing and Output Anxiety Scale (the IPOAS; MacIntyre & Gardner, 1994. The effects of induced anxiety on three stages of cognitive processing in computerized vocabulary learning.’ Studies in Second Language Acquisition 16 (1): 1–17. doi:10.1017/S0272263100012560) and the Post-Session Survey on Anxiety. The former informed about the levels of anxiety experienced at three stages of linguistic processing, hence, input anxiety, internal processing anxiety, and output anxiety. The latter was applied to identify language anxiety levels exhibited during a monologue production, reported as task-specific language anxiety. Speech samples from the performance were analyzed for fluency breakdown (the number of filled and silent pauses), speed of speech (articulation rate), and composite measures (speech rate). The findings revealed that the advanced foreign language learners with higher levels of internal processing anxiety and output anxiety used filled pauses more frequently. Furthermore, the levels of output anxiety and task-specific language anxiety were inversely related to speech and articulation rate. The study also showed statistically significant differences in the production of filled pauses and speech rate between advanced foreign language learners with low and high levels of language anxiety as measured by the IPOAS.

Simulating time delays and space–time memory interactions: An analytical approach

This study introduces a novel analytical framework to explore the effects of Caputo spatial and temporal memory indices combined with a proportional time delay on (non)linear (1+2)-dimensional evolutionary models. The solution is expressed as a Cauchy product of an absolutely convergent series that effectively captures the dynamics of these parameters. By extending the differential transform method into higher-dimensional fractional space, we reformulate the evolution equation as a (non)linear higher-order recurrence relation, which enables the precise determination of fractional series coefficients. Our findings show that Caputo derivatives and time delay significantly impact the system’s behavior, with graphical analysis revealing a continuous transition from a stationary to an integer state solution. The study also identifies a quantitative analogy between the Caputo-time fractional derivative and proportional time delay that highlights the role of Caputo derivatives as memory indices. This method has proven highly effective in deriving solutions for fractional higher-dimensional extensions of evolutionary equations.

Temporal subgraph matching method for multi-connected temporal graph

Temporal graph matching is used to find a matching subgraph set that satisfies temporal and topological information on the temporal graph. It is widely used in real scenarios and provides important support for big data mining and analysis. Most existing matching methods take the single-connected graph as the matching graph, which has a high computational cost. Simultaneously, the construction and matching complexity of its index on multi-connected temporal subgraph matching is high. To solve the above problems, we propose a new temporal subgraph matching problem for multi-connected temporal graphs and proposes the corresponding efficient filtering-selecting-matching three-stage multi-connected temporal subgraph matching method. In the filtering stage, a vertex-based filter is proposed to generate a candidate temporal subgraph set. In the select phase, a set of temporal subgraphs satisfying the vertex mapping is found. In the matching stage, the single-filter temporal subgraph matching method and temporal subgraph matching based on the temporal information tree index method are proposed to solve the problems that the edges of multi-connected temporal subgraphs cannot be mapped and the single graph matching efficiency is low, respectively. The experimental results show that the proposed method improves the matching accuracy, and the corresponding index size is reduced.

Multifractal analysis of heart rate variability in pregnancy during sleep.

Understanding the complex dynamics of heart rate variability (HRV) during pregnancy is crucial for monitoring both maternal well-being and fetal health. In this study, we use the Multifractal Detrended Fluctuations Analysis approach to investigate HRV patterns in pregnant individuals during sleep based on RR interval maxima (MM fluctuations). In addition, we study the type of multifractality within MM fluctuations, that is, if it arises from a broad probability density function or from varying long-range correlations. Furthermore, to provide a comprehensive view of HRV changes during sleep in pregnancy, classical temporal and spectral HRV indices were calculated at quarterly intervals during sleep. Our study population consists of 21 recordings from nonpregnant women, 18 from the first trimester (early-pregnancy) and 18 from the second trimester (middle-pregnancy) of pregnancy. Results. There are statistically significant differences ( -value < 0.05) in mean heart rate, rms heart rate, mean MM fluctuations, and standard deviation of MM fluctuations, particularly in the third and fourth quarter of sleep between pregnant and non-pregnant states. In addition, the early-pregnancy group shows significant differences ( -value < 0.05) in spectral indices during the first and fourth quarter of sleep compared to the non-pregnancy group. Furthermore, the results of our research show striking similarities in the average multifractal structure of MM fluctuations between pregnant and non-pregnant states during normal sleep. These results highlight the influence of different long-range correlations within the MM fluctuations, which could be primarily associated with the emergence of sleep cycles on multifractality during sleep. Finally, we performed a separability analysis between groups using temporal and spectral HRV indices as features per sleep quarter. Employing only three features after Principal Component Analysis (PCA) to the original feature set, achieving complete separability among all groups appears feasible. Using multifractal analysis, our study provides a comprehensive understanding of the complex HRV patterns during pregnancy, which holds promise for maternal and fetal health monitoring. The separability analysis also provides valuable insights into the potential for group differentiation using simple measures such as mean heart rate, rms heart rate, and mean MM fluctuations or in the transformed feature space based on PCA.

Probing Thermal Electrons in Gamma-Ray Burst Afterglows

Particle-in-cell simulations have unveiled that shock-accelerated electrons do not follow a pure power-law distribution, but have an additional low-energy “thermal” part, which owns a considerable portion of the total energy of the electrons. Investigating the effects of these thermal electrons on gamma-ray burst (GRB) afterglows may provide valuable insights into the particle acceleration mechanisms. We solve the continuity equation of electrons in energy space, from which multiwavelength afterglows are derived by incorporating processes including synchrotron radiation, synchrotron self-absorption, synchrotron self-Compton scattering, and γ–γ annihilation. First, there is an underlying positive correlation between the temporal and spectral indices due to the cooling of electrons. Moreover, thermal electrons result in simultaneous nonmonotonic variations of both the spectral and temporal indices at multiple wavelengths, which could be individually recorded by the 2.5 m Wide Field Survey Telescope and Vera Rubin Observatory Legacy Survey of Space and Time (LSST). The thermal electrons could also be diagnosed using afterglow spectra from synergistic observations in the optical (with LSST) and X-ray (with the Microchannel X-ray Telescope on board the Space Variable Objects Monitor) bands. Finally, we use Monte Carlo simulations to obtain the distribution of the peak flux ratio (R X) between the soft and hard X-rays, and of the time delay (Δt) between the peak times of the soft X-ray and optical light curves. The thermal electrons significantly raise the upper limits of both R X and Δt. Thus, the distribution of GRB afterglows with thermal electrons is more scattered in the R X−Δt plane.

Optimizing Database Performance in Complex Event Processing through Indexing Strategies

Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data continue to grow, optimizing the performance of CEP systems becomes a critical challenge. This paper investigates the impact of indexing strategies on the performance of databases handling complex event processing. We propose a novel indexing technique, called Hierarchical Temporal Indexing (HTI), specifically designed for the efficient processing of complex event queries. HTI leverages the temporal nature of event data and employs a multi-level indexing approach to optimize query execution. By combining temporal indexing with spatial- and attribute-based indexing, HTI aims to accelerate the retrieval and processing of relevant events, thereby improving overall query performance. In this study, we evaluate the effectiveness of HTI by implementing complex event queries on various CEP systems with different indexing strategies. We conduct a comprehensive performance analysis, measuring the query execution times and resource utilization (CPU, memory, etc.), and analyzing the execution plans and query optimization techniques employed by each system. Our experimental results demonstrate that the proposed HTI indexing strategy outperforms traditional indexing approaches, particularly for complex event queries involving temporal constraints and multi-dimensional event attributes. We provide insights into the strengths and weaknesses of each indexing strategy, identifying the factors that influence performance, such as data volume, query complexity, and event characteristics. Furthermore, we discuss the implications of our findings for the design and optimization of CEP systems, offering recommendations for indexing strategy selection based on the specific requirements and workload characteristics. Finally, we outline the potential limitations of our study and suggest future research directions in this domain.

A novel spectro-temporal index NDTeI combined with machine learning algorithm for Sentinel-2 cloud detection

ABSTRACT There are challenges for cloud detection over bright surfaces, especially for Sentinel-2 image without thermal band. In this paper, we developed a new cloud detection algorithm for Sentinel-2 data designed to tackle the challenges of cloud detection on bright targets. Our algorithm (called RF-NDTeI-SNIC) was built through the integration of Random Forest (RF) model, Normalized Difference Temporal Index (NDTeI) cloud index and Simple Non-Iterative Clustering (SNIC) image segmentation algorithm. First, RF and NDTeI were used to derive the initial and second stage’s cloud detection results, respectively. Then, the RF- and NDTeI-based results were fused to remove the bright surfaces and land cover changes. Finally, the fusion results were further refined using SNIC for morphological processing. The proposed RF-NDTeI-SNIC algorithm was evaluated using nine challenging cloud-covered Sentinel-2 scenes with substantial bright surfaces. Results indicated that the average overall accuracy of our algorithm was 95.01%, with average commission rate of 10.19% for cloud. In addition, five commonly used Sentinel-2 cloud mask algorithms including s2cloudless, QA, Fmask, CDI and Tmask were selected for comparative analysis. Results suggested that our algorithm outperformed others with overall accuracies of 89.60–94.17% and cloud commission rate of 9.46–37.50%, and have significant advantages in terms of bright surfaces. In summary, the RF-NDTeI-SNIC algorithm we developed was capable of yielding accurate Sentinel-2 cloud masks, and the novel NDTeI cloud index we proposed gave a new and effective approach for improving the separability of clouds and bright surfaces.

SUBTLE: An Unsupervised Platform with Temporal Link Embedding that Maps Animal Behavior

While huge strides have recently been made in language-based machine learning, the ability of artificial systems to comprehend the sequences that comprise animal behavior has been lagging behind. In contrast, humans instinctively recognize behaviors by finding similarities in behavioral sequences. Here, we develop an unsupervised behavior-mapping framework, SUBTLE (spectrogram-UMAP-based temporal-link embedding), to capture comparable behavioral repertoires from 3D action skeletons. To find the best embedding method, we devise a temporal proximity index (TPI) as a new metric to gauge temporal representation in the behavioral embedding space. The method achieves the best TPI score compared to current embedding strategies. Its spectrogram-based UMAP clustering not only identifies subtle inter-group differences but also matches human-annotated labels. SUBTLE framework automates the tasks of both identifying behavioral repertoires like walking, grooming, standing, and rearing, and profiling individual behavior signatures like subtle inter-group differences by age. SUBTLE highlights the importance of temporal representation in the behavioral embedding space for human-like behavioral categorization.

Static and Temporal Dynamic in Functional Connectivity of Large-scale Brain Networks During Acute Stress Regulate Stress Resilience Differently: The Promotion Role of Trait Resilience

Stress resilience has been largely regarded as a process in which individuals actively cope with and recover from stress. Over the past decade, the emergence of large-scale brain networks has provided a new perspective for the study of the neural mechanisms of stress. However, the role of inter-network functional-connectivity (FC) and its temporal fluctuations in stress resilience is still unclear. To bridge this knowledge gap, seventy-seven participants (age, 17–22 years, 37 women) were recruited for a ScanSTRESS brain imaging study. A static perspective was initially adopted, using changes in FC that obtained from stress vs. control condition during the entire stress induction phase as a static indicator. Further, changes in FC between different stress runs were analyzed as an index of temporal dynamics. Stress resilience was gauged using salivary cortisol levels, while trait resilience was measured via behavioral-activation-system (BAS) sensitivity. Results found that, for the static index, enhanced FC between the salience-network (SN), default-mode-network (DMN) and limbic-network (LBN) during acute stress could negatively signal stress resilience. For the temporal dynamics index, FC among the dorsal-attention-network (DAN), central-executive-network (CEN) and visual-network (VN) decreased significantly during repeated stress induction. Moreover, the decline of FC positively signaled stress resilience, and this relationship only exist in people with high BAS. The current research elucidates the intricate neural underpinnings of stress resilience, offering insights into the adaptive mechanisms underlying effective stress responses.

Meet JEANIE: A Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment

Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects’ poses, leading to temporal-viewpoint misalignment when comparing two sets of frames or evaluating the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE) for sequence pairs. In particular, we focus on 3D skeleton sequences whose camera and subjects’ poses can be easily manipulated in 3D. We evaluate JEANIE on skeletal Few-shot Action Recognition (FSAR), where matching well temporal blocks (temporal chunks that make up a sequence) of support-query sequence pairs (by factoring out nuisance variations) is essential due to limited samples of novel classes. Given a query sequence, we create its several views by simulating several camera locations. For a support sequence, we match it with view-simulated query sequences, as in the popular Dynamic Time Warping (DTW). Specifically, each support temporal block can be matched to the query temporal block with the same or adjacent (next) temporal index, and adjacent camera views to achieve joint local temporal-viewpoint warping. JEANIE selects the smallest distance among matching paths with different temporal-viewpoint warping patterns, an advantage over DTW which only performs temporal alignment. We also propose an unsupervised FSAR akin to clustering of sequences with JEANIE as a distance measure. JEANIE achieves state-of-the-art results on NTU-60, NTU-120, Kinetics-skeleton and UWA3D Multiview Activity II on supervised and unsupervised FSAR, and their meta-learning inspired fusion.

Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer‘s disease

Understanding the psychiatric symptoms of Alzheimer s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is not well-known. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored.This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory and composite scores for memory, executive function, and language, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated, controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define “asymmetry-extreme” groups (individuals with laterality indices greater than two standard deviations from the mean).695 individuals (age = 73.9 ± 7.6 years, 372 (53.5 %) females) were included, comprising 281 (40%) cognitively unimpaired (CU) amyloid negative, 185 (27%) CU amyloid positive, and 229 (33%) impaired (CI) amyloid positive participants. In the full cohort analysis, right temporal tau was associated with worse behavior (B = 8.14, p-value = 0.007), and left temporal tau was associated with worse language (B = 1.4, p-value < 0.001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, there was additional heterogeneity along the anterior-posterior dimension.Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Wide multi-cultural implementation of social cognition measures is needed to understand right-sided asymmetry in AD.

Temporal beta diversity and plant metacommunity structure dynamics over a 43-year period in an inter-tropical mexican arid region

Understanding the dynamics of metacommunity structure and temporal beta diversity is vital for comprehending ecological dynamics at local and regional scales; it is essential to evaluate impacts of factors such as climate change, dispersal, and biotic interactions. We examined changes in the composition and abundance of 199 perennial plant species in the semiarid Zapotitlán Valley, Mexico, over a 43-year period. We studied three local community types, scrubland, cardonal, and tetechera, with surveys conducted in 1980, 2003, 2018, and 2023. Shifts in species composition and abundance over time were analysed using the Temporal Beta Diversity Index distinguishing between species gains or losses. We investigated the metacommunity structure elements—coherence, turnover, and boundary clumping—in each survey. We observed pronounced temporal changes in species’ composition and abundance across all community types. These temporal changes stem from the losses and gains in species and individuals. Notably, 2023 showed significant losses in species occurrence and abundance, potentially attributable to an extended drought likely exacerbated by climate change. Surprisingly, the metacommunity structure remained Clementsian throughout our study, emphasising the stability of the cardonal in maintaining distinctions between local community types. This suggests that the metacommunity spatial structure is resistant to changes in local community compositions.

A Case Report of Temporal Macular Thinning Associated with Alport Syndrome

Purpose: To report a case of severe of severe temporal macular thinning in a patient with Alport syndrome. Bilateral temporal thinning is rarely associated with any other systemic conditions, so it is important that ophthalmologists be aware of the significance of this finding and refer for further internal medicine evaluation appropriately. Methods: Clinical examination and multimodal imaging are provided from a clinic visit one month after inpatient treatment for a hypertensive crisis associated with renal failure. Results: The 19-year-old patient had resolving hypertensive retinopathy. High-resolution optical coherence tomography (OCT) confirmed that he had temporal thinning indices (TTIs) consistent with his underlying clinical diagnosis of Alport syndrome. Conclusion: This case report emphasizes the important role of OCT as a commonly available noninvasive retinal imaging modality that can readily demonstrate the temporal macular thinning that is characteristic of Alport syndrome and just a few other conditions. OCT can be an attractive alternative to the much more invasive renal biopsy testing for Alport syndrome when genetic testing is ambiguous or when renal biopsy is considered too risky for the patient.