OBJECTIVE: To reconstruct age-structured case counts of COVID-19 using sentinel reporting, which replaced universal reporting of COVID-19 from May 2023 in Japan.
METHODS: Using COVID-19 sentinel data stratified by discrete age groups in selected prefectures and referring to universal case count data up to May 8, 2023, we fitted a statistical model to handle weekly growth rates as a function of age and time so as to convert sentinel data to case counts after cessation of universal reporting.
RESULTS: The age distribution of cases in sentinel reporting was significantly biased toward younger age groups compared to universal reporting. When comparing the epidemic size of the 9th wave (May 8 to September 18, 2023) to the 8th wave (October 3, 2022 to April 10, 2023), using the wave-on-wave ratio of total cumulative sentinel cases led to a significant underestimation of the wave-on-wave in Tokyo (0.975, vs 1.461 by universal reporting) and Okinawa (1.299, vs 1.472). The estimates of growth rates, scaling factors between universal and sentinel cases, and expected universal case count showed robustness to changes in the ending week of the data period.
CONCLUSIONS: Our model quantified COVID-19 dynamics, comparably to universal reporting that ended in May 2023, enabling detailed and up-to-date health burden analysis using sentinel reports. The cumulative incidence was greater than that suggested from sentinel data in Tokyo, Nara, and Okinawa. Per-population burdens among children were particularly high in Osaka and Nara, indicating a strong bias in sentinel reporting toward pediatric cases.

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence are often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this \"paradox of predictability.\" We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative-genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding about how evolution works within and across timescales.

UNASSIGNED: Because Alzheimer\'s disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD.
UNASSIGNED: This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging.
UNASSIGNED: We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging.
UNASSIGNED: The representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.

Since the early 2000\'s, the Atlas of Variations in Medical Practice in the Spanish National Health System (namely, Atlas VPM) has been analysing and informing unwarranted variations in health care provision and outcomes in the Spanish Health System.Atlas VPM covers a two-fold perspective: a geographic one, where unwarranted variations would reflect the uneven exposure of the population to health care as a consequence of the place of residence; and, a provider-specific approach, where unwarranted variations would reflect differences in utilisation and outcomes that are at provider-level.Building on routine data (hospital and primary care electronic records, administrative data, geographic information, etc.) Atlas VPM has adapted the classical small area methods and has included a large panoply of techniques, such as Bayesian methods, hierarchical modelling or time-series forecasting.Led by the Data Science for Health Services and Policy Research group at the Institute for Health Sciences in Aragon, Atlas VPM implies a linkage and exchange process with the 17 Departments of Health of the Spanish regions where the research agenda is shared and research outcomes are translated into profiling and benchmarking interactive tools meant to facilitate clinical and policy decision-making.

OBJECTIVE: The allergic airway disease, such as allergic rhinitis, chronic rhinosinusitis, asthma, is a general term of a range of inflammatory disorders affecting the upper and lower airways and lung parenchyma. This study aimed to investigate the short-term effects of air pollutants and meteorological factors on AAD-related daily outpatient visits.
METHODS: An ecological study.
METHODS: Data on outpatient visits due to AAD (n = 4,554,404) were collected from the platform of the Ningbo Health Information from January 1, 2015 to December 31, 2021. A Quasi-Poisson generalized additive regression model was established to analyze the lag effects of air pollution on daily outpatient visits for AAD. Restricted cubic spline functions were used to explore the potential non-linear relationships between air pollutants and meteorological and daily outpatient visits for AAD.
RESULTS: PM2.5, PM10, SO2, NO2, or CO were associated with daily outpatient visits for AAD, and there was a significant increasing trend in the cumulative lag effects. SO2 had the largest effect at Lag07, with a 25.3% (95% CI: 21.6%-29.0%) increase in AAD for every 10 μg/m3 increase in exposure concentration. Subgroup analysis showed that the 0-18 years old age group had the strongest effects, especially for AR, and all effects were stronger in the cold season.
CONCLUSIONS: Given that patients aged 0-18 are more susceptible to environmental changes, protective measures specifically for children should be taken during dry and cold weather conditions with poor air quality.

UNASSIGNED: Both high- and low-income countries reported increased antibiotic consumption among COVID-19 patients during the first months of the pandemic. To date, however, no studies have examined changes in antibiotic consumption during the COVID-19 pandemic within humanitarian emergency contexts.
UNASSIGNED: Data was collected by Médecins Sans Frontières (MSF) for the years 2018-2021 across the following humanitarian settings: Afghanistan (Lashkar Gah), Bangladesh (Kutupalong), the Democratic Republic of Congo (Mweso and Baraka), and South Sudan (Bentiu). Inpatient and outpatient antibiotic consumption was calculated as Daily Defined Dose (DDD) per 1000 inhabitants per day, as per the World Health Organisation\'s (WHO) Collaborating Centre for Drug Statistics Methodology. Interrupted time series (ITS) analysis, using an autoregressive integrated moving average (ARIMA) model was used to analyse retrospective monthly antibiotic consumption. The impact of COVID-19 pandemic was evaluated as total antibiotic consumption and according to WHO Access, Watch, Reserve (AWaRe) group classifications within each humanitarian setting.
UNASSIGNED: The COVID-19 pandemic had no statistically significant impact on total antibiotic consumption in South Sudan (Bentiu) and Bangladesh (Kutupalong). Similarly, the pandemic had no impact on total antibiotic consumption in DR Congo (Baraka), despite an initial 0.27% (estimate=.274, p-value=0.006) increase in March 2020 driven by Access group antibiotics. Meanwhile, total antibiotic consumption in DR Congo (Mweso) and Afghanistan (Lashkar Gah) declined by 0.74% (estimate = -.744, p = 0.003) and 0.26% (estimate = -.26, p < 0.001), respectively with the COVID-19 pandemic.
UNASSIGNED: Further studies are required to investigate what may have contributed to these results.

Forest disturbance regimes across biomes are being altered by interactive effects of global change. Establishing baselines for assessing change requires detailed quantitative data on past disturbance events, but such data are scarce and difficult to obtain over large spatial and temporal scales. The integration of remote sensing with dense time series analysis and cloud computing platforms is enhancing the ability to monitor historical disturbances, and especially non-stand replacing events along climatic gradients. Since the integration of such tools is still scarce in Mediterranean regions, here, we combine dense Landsat time series and the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) method to monitor forest disturbance in continental Spain from 1985 to 2023. We adapted the CCDC-SMA method for improved disturbance detection creating new spectral libraries representative of the study region, and quantified the year, month, severity, return interval, and type of disturbance (stand replacing, non-stand replacing) at a 30 m resolution. In addition, we characterised forest disturbance regimes and trends (patch size and severity, and frequency of events) of events larger than 0.5 ha at the national scale by biome (Mediterranean and temperate) and forest type (broadleaf, needleleaf and mixed). We quantified more than 2.9 million patches of disturbed forest, covering 4.6 Mha over the region and period studied. Forest disturbances were on average larger but less severe in the Mediterranean than in the temperate biome, and significantly larger and more severe in needleleaf than in mixed and broadleaf forests. Since the late 1980s, forest disturbances have decreased in size and severity while increasing in frequency across all biomes and forest types. These results have important implications as they confirm that disturbance regimes in continental Spain are changing and should therefore be considered in forest strategic planning for policy development and implementation.

Complex systems are typically characterized by intricate internal dynamics that are often hard to elucidate. Ideally, this requires methods that allow to detect and classify in an unsupervised way the microscopic dynamical events occurring in the system. However, decoupling statistically relevant fluctuations from the internal noise remains most often nontrivial. Here, we describe \"Onion Clustering\": a simple, iterative unsupervised clustering method that efficiently detects and classifies statistically relevant fluctuations in noisy time-series data. We demonstrate its efficiency by analyzing simulation and experimental trajectories of various systems with complex internal dynamics, ranging from the atomic- to the microscopic-scale, in- and out-of-equilibrium. The method is based on an iterative detect-classify-archive approach. In a similar way as peeling the external (evident) layer of an onion reveals the internal hidden ones, the method performs a first detection/classification of the most populated dynamical environment in the system and of its characteristic noise. The signal of such dynamical cluster is then removed from the time-series data and the remaining part, cleared-out from its noise, is analyzed again. At every iteration, the detection of hidden dynamical subdomains is facilitated by an increasing (and adaptive) relevance-to-noise ratio. The process iterates until no new dynamical domains can be uncovered, revealing, as an output, the number of clusters that can be effectively distinguished/classified in a statistically robust way as a function of the time-resolution of the analysis. Onion Clustering is general and benefits from clear-cut physical interpretability. We expect that it will help analyzing a variety of complex dynamical systems and time-series data.

BACKGROUND: Hypoxic-ischemic injury of neurons is a pathological process observed in several neurological conditions, including ischemic stroke and neonatal hypoxic-ischemic brain injury (HIBI). An optimal treatment strategy for these conditions remains elusive. The present study delved deeper into the molecular alterations occurring during the injury process in order to identify potential therapeutic targets.
METHODS: Oxygen-glucose deprivation/reperfusion (OGD/R) serves as an established in vitro model for the simulation of HIBI. This study utilized RNA sequencing to analyze rat primary hippocampal neurons that were subjected to either 0.5 or 2 h of OGD, followed by 0, 9, or 18 h of reperfusion. Differential expression analysis was conducted to identify genes dysregulated during OGD/R. Time-series analysis was used to identify genes exhibiting similar expression patterns over time. Additionally, functional enrichment analysis was conducted to explore their biological functions, and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Quantitative real-time polymerase chain reaction (qRT-PCR) was used for validation of hub-gene expression.
RESULTS: The study included a total of 24 samples. Analysis revealed distinct transcriptomic alterations after OGD/R processes, with significant dysregulation of genes such as Txnip, Btg2, Egr1 and Egr2. In the OGD process, 76 genes, in two identified clusters, showed a consistent increase in expression; functional analysis showed involvement of inflammatory responses and signaling pathways like tumor necrosis factor (TNF), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and interleukin 17 (IL-17). PPI network analysis suggested that Ccl2, Jun, Cxcl1, Ptprc, and Atf3 were potential hub genes. In the reperfusion process, 274 genes, in three clusters, showed initial upregulation followed by downregulation; functional analysis suggested association with apoptotic processes and neuronal death regulation. PPI network analysis identified Esr1, Igf-1, Edn1, Hmox1, Serpine1, and Spp1 as key hub genes. qRT-PCR validated these trends.
CONCLUSIONS: The present study provides a comprehensive transcriptomic profile of an in vitro OGD/R process. Key hub genes and pathways were identified, offering potential targets for neuroprotection after hypoxic ischemia.

BACKGROUND: The interplay between root-knot nematode (RKN) parasitism and the complex web of host-associated microbiota has been recognized as pivotal for effective management of the pest. However, studies assessing this relationship have focussed on the bacterial and fungal communities, neglecting the unicellular eukaryotic members. Here, we employed amplicon sequencing analysis of the bacterial 16S rRNA, fungal ITS and eukaryotic 18S rRNA genes, and comprehensively examined how the microbiome composition, diversity and networking developed with time in the rhizospheres and roots of RKN-inoculated and non-inoculated tomato plants.
RESULTS: As expected, infection with the RKN Meloidogyne incognita decreased plant growth. At individual timepoints, we found distinct bacterial, fungal and eukaryote community structures in the RKN-inoculated and non-inoculated rhizospheres and roots, and RKN inoculation affected several taxa in the root-associated microbiome differentially. Correlation analysis revealed several bacterial and fungal and few protist taxa that correlated negatively or positively with M. incognita. Moreover, network analysis using bacterial, fungal and eukaryotic data revealed more dynamic networks with higher robustness to disturbances in the RKN-inoculated than in the non-inoculated rhizospheres/roots. Hub taxa displayed a noticeable successional pattern that coincided with different phases of M. incognita parasitism. We found that fungal hubs had strong negative correlations with bacteria and eukaryotes, while positive correlations characterized hub members within individual kingdoms.
CONCLUSIONS: Our results reveal dynamic tomato-associated microbiomes that develop along different trajectories in plants suffering M. incognita infestation and non-infested plants. Overall, the results identify stronger associations between RKN and bacterial and fungal taxa than between eukaryotic taxa and RKN, suggesting that fungal and bacterial communities could play a larger role in the regulation of RKN. The study identifies several putative RKN-antagonistic bacterial and fungal taxa and confirms the antagonistic potential previously identified in other taxa.