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General-purpose financial statements prepared under GAAP are for a consolidated reporting entity--a collection of legal entities that includes the parent and any subsidiaries it controls. This reporting model results in a loss of information about the individual legal entities within the consolidated reporting entity. Our study examines the role of parent-only balance sheet information in assessing the credit risk of the parent when it is a bank holding company. We obtain evidence from three trading platforms: credit default swaps (CDS), outstanding bonds, and new bonds. We find that parent-only leverage is useful for debtholders to assess the parent's credit risk even after considering consolidated leverage. Moreover, in CDS markets, parent-only leverage is more useful for firms without downstream guarantee than for firms with guarantee and is less useful for firms with a stronger internal capital market. Our study raises the awareness of parent-only financial information for credit risk assessments. [ABSTRACT FROM AUTHOR]

Background: The development of effective inhibitors that can inhibit amyloid β (Aβ) peptides aggregation and promote neurite outgrowth is crucial for the possible treatment of Alzheimer's disease (AD). Lobaria (Schreb.) Hoffm., a traditional Chinese medicine used in Himalaya region for inflammatory diseases, contains depsides/depsidones (DEPs) such as gyrophoric acid, norstictic acid, and stictic acid known for their anti-cancer and antiinflammation properties. Methods: Lobaria extracts were analyzed using HPLC to identify DEPs and establish standards. The inhibitory effects of Lobaria on Aβ42 fibrillization and depolymerization were assessed using various approaches with biophysical and cellular methods. The neuroprotective activity of Lobaria extracts and its DEPs aganist Aβ-mediated cytotoxicity was also evaluated. Results: Norstictic and stictic acid were found in the water extract, while norstictic, stictic, and gyrophoric acid were detected in the ethanol extract of Lobaria. Both extracts, and their DEPs effectively inhibited Aβ42 fibrillation and disaggregate mature Aβ42 fibrils. Notably, the ethanol extract showed superior inhibitory effect compared to the water extract, with gyrophoric acid being the most effective DEPs. Additionally, herbal extract-treated Aβ42 aggregation species significantly protected neuronal cells from Aβ42-induced cell damage and promoted neurite outgrowth. Conclusion: This study is the first to investigate the effect of Lobaria on Aβ42 and neuronal cell in AD. Given that Lobaria is commonly used in ethnic medicine and food with good safety records, our findings propose that Lobaria extracts and DEPs have potential as neuroprotective and therapeutic agents for AD patients. [ABSTRACT FROM AUTHOR]

We present the results of probabilistic seismic hazard assessment for Iran based on a statistical procedure specifically developed to manage macroseismic intensity data. This method takes into careful consideration the specific features of such data, which are characterized as ordinal, discrete, and confined within a finite interval, ensuring a logically coherent approach throughout the analysis. The results of our assessment are then compared with hazard maps generated using a standard approach, putting in evidence significant differences both on a national scale and relative to individual cities. This comparative analysis will be useful in identifying areas of utmost concern, where further studies are strongly recommended to yield hazard estimates of greater robustness and reliability. By pinpointing these critical scenarios, we aim to guide future research endeavors towards providing more accurate and reliable seismic hazard estimates. Identifying these critical situations facilitates the prioritization of resources and interventions, ultimately enhancing seismic risk mitigation efforts across Iran. [ABSTRACT FROM AUTHOR]

In 2014, the Australian Government introduced the New Colombo Plan (NCP), whereby undergraduate students would become 'informal ambassadors' enhancing Australia's 'soft power' within the Asia-Pacific region. Participation by students, universities, host organisations and communities expanded rapidly before Australia's pandemic-driven closure of international borders. This hiatus has afforded a space for reflection upon the NCP, including the pivotal study tour supervisor role. Supervisors assume informal responsibilities beyond their formal roles, often in unfamiliar settings, with scarce recognition. Their assumption of responsibility is conceptualised here within a formal-informal spectrum, which underpins an evaluation of how the role might be accorded greater recognition. [ABSTRACT FROM AUTHOR]

Emerging Zoned Namespace (ZNS) SSDs, providing the coarse-grained zone abstraction, hold the potential to significantly enhance the cost efficiency of future storage infrastructure and mitigate performance unpredictability. However, existing ZNS SSDs have a static zoned interface, making them in-adaptable to workload runtime behavior, unscalable to underlying hardware capabilities, and interfering with co-located zones. Applications either under-provision the zone resources yielding unsatisfied throughput, create over-provisioned zones and incur costs, or experience unexpected I/O latencies. We propose eZNS, an elastic-ZNS interface that exposes an adaptive zone with predictable characteristics. eZNS comprises two major components: a zone arbiter that manages zone allocation and active resources on the control plane, and a hierarchical I/O scheduler with read congestion control and write admission control on the data plane. Together, eZNS enables the transparent use of a ZNS SSD and closes the gap between application requirements and zone interface properties. Our evaluations over RocksDB demonstrate that eZNS outperforms a static zoned interface by 17.7% and 80.3% in throughput and tail latency, respectively, at most. [ABSTRACT FROM AUTHOR]

We present earthquake scenarios developed to accompany the release of the 2023 update to the US Geological Survey National Seismic Hazard Model (NSHM). Scenarios can serve a range of local and regional needs, from developing proactive-targeted mitigation strategies for minimizing impending risk to aiding emergency management planning. These deterministic scenarios can also be used to communicate seismic hazard and risk to audiences who are not well versed in methods, such as probabilistic seismic hazard analyses. Specifically, we discuss the scenarios developed, challenges, and lessons learned in the development process, and how this work aided the development of the 2023 NSHM itself. In total, 28 scenarios were developed for Hawaii, Utah, Alaska, and Virginia considering the 2023 NSHM science, past scenario efforts, and input from local experts and stakeholders. Finally, we investigate how NSHM modeling decisions can change estimated impacts to Utah and Hawaii in more detail showing, for example, that a shallower dip of the Wasatch fault under Salt Lake City can increase predicted ground-motion intensities and therefore estimated losses and deaths. [ABSTRACT FROM AUTHOR]

Abstract\nIMPLICATIONS FOR REHABILITATIONPurpose: The world is approaching the sustainable development goals deadline, but many countries still do not produce the data required to track their indicators by disability. Integrating valid and relievable disability measurement tools into existing data platforms is key to ensuring that “no one is left behind.” In this paper, we aim to demonstrate that it is possible to gather valid data on disability for disaggregation using the WHO Functioning and Disability Disaggregation Tool.Materials and methods: Using representative data from India, Lao PDR, and Tajikistan collected through the Gallup World Poll, we estimated the likelihood of a positive sustainable development indicator by disability level. Logit regression was used, adjusted for age, sex, household size, number of children, marital status, urban or rural area, and country-fixed effects.Results: Our estimates showed a consistent disability gradient across all countries and indicators: the higher the level of disability, the lower the probability of having a positive outcome in barely any sustainable development goal.Conclusion: Our study demonstrates that it is not too late to generate sound and precise data about inequalities faced by persons with mild, moderate, or severe disability. This data is essential for reducing inequalities through evidence-based policymaking.The disability gradient observed across all countries and indicators included in this study showed that the higher the level of disability (mild, moderate, or severe), the lower the probability of having a positive outcome in any sustainable development goal.An underestimation of inequalities is observed when people with no, mild and moderate disability are combined into a group called “no severe disability” and used as the comparator to people with “severe disability.”Rehabilitation policy and planning must consider the disability gradient and respond to the different needs of persons with mild, moderate, and severe disability.The disability gradient observed across all countries and indicators included in this study showed that the higher the level of disability (mild, moderate, or severe), the lower the probability of having a positive outcome in any sustainable development goal.An underestimation of inequalities is observed when people with no, mild and moderate disability are combined into a group called “no severe disability” and used as the comparator to people with “severe disability.”Rehabilitation policy and planning must consider the disability gradient and respond to the different needs of persons with mild, moderate, and severe disability. [ABSTRACT FROM AUTHOR]

This study aimed to examine the mechanisms of the photoplethysmography (PPG) signal formation using Monte Carlo simulations of light transport in biological tissues and experimental observations. Based on a three-layer skin model in backscattering geometry, we sequentially simulated volumetric blood changes and the aggregation/disaggregation of erythrocytes in the dermal layer and estimated their contribution to the registered PPG signal. The calculations were conducted for two wavelengths: 525 nm and 810 nm. For green light, absorption predominates over scattering in the formation of a PPG signal, whereas, for near-infrared light, scattering prevails over absorption. This theoretical result was verified using the Modified Beer–Lambert law and clinical in vivo PPG data of seven healthy subjects. Changes in the size of the scatterers during erythrocyte aggregation and disaggregation can significantly contribute to the PPG signal at near-infrared light. Thus, for the green waveband, the classical volumetric model can be considered dominant in the PPG signal formation. In contrast, for the near-infrared range, both volumetric and aggregation effects must be considered as being approximately equal. [ABSTRACT FROM AUTHOR]

Cancer stem cells are pivotal players in tumors initiation, growth, and metastasis. While several markers have been identified, there remain challenges particularly in heterogeneous malignancies like adult soft tissue sarcomas, where conventional markers are inherently overexpressed. Here, we designed BODIPY scaffold fluorescence probes (BD‐IMC‐1, BD‐IMC‐2) that activate via disaggregation targeting for cyclooxygenase (COX), a potential marker for CSCs in sarcoma in clinical pathology. Based on their structures, BD‐IMC‐1 showcased higher susceptibility to disaggregation compared to BD‐IMC‐2, consistent with their selective interaction with COX. Notably, the BD‐IMC‐1 revealed positive cooperativity binding to COX‐2 at sub‐micromolar ranges. Both probes showed significant fluorescence turn‐on upon LPS or PMA triggered COX‐2 upregulation in live RAW264.7, HeLa, and human sarcoma cell line (Saos‐LM2) up to 2‐fold increase with negligible toxicity. More importantly, the BD‐IMC‐1 demonstrated their practical imaging for COX‐2 positive cells in paraffin‐fixed human sarcoma tissue. Considering the fixed tissues are most practiced pathological sample, our finding suggests a potential of disaggregation activated chemosensor for clinical applications. [ABSTRACT FROM AUTHOR]

Amyloidosis is characterized by the abnormal accumulation of amyloid proteins. The causative proteins aggregate from monomers to oligomers and fibrils, among which some intermediate oligomers are considered as major toxins. Cytotoxic oligomers are generated not only by aggregation but also via fibril disaggregation. However, little is known about the structural characteristics and generation conditions of cytotoxic oligomers produced during disaggregation. Herein, we summarized the structural commonalities of cytotoxic oligomers formed under various disaggregation conditions, including the addition of heat shock proteins or small compounds. In vitro experimental data demonstrated the presence of high-molecular-weight oligomers (protofibrils or protofilaments) that exhibited a fibrous morphology and β-sheet structure. Molecular dynamics simulations indicated that the distorted β-sheet structure contributed to their metastability. The tendency of these cytotoxic oligomers to appear under mild disaggregation conditions, implied formation during the early stages of disaggregation. This review will aid researchers in exploring the characteristics of highly cytotoxic oligomers and developing drugs that target amyloid aggregates. [ABSTRACT FROM AUTHOR]

This study investigates the disaggregated behavioral responses of mobile money users' willingness to pay for electronic levy payments in Ghana. We use cross‐sectional survey primary data and a probit regression estimation technique with its associated average marginal effects and check robustness with a linear probability model. The evidence from the study indicates that the disaggregated behavioral factors that drive willingness to pay for the e‐levy include individuals maintaining their current levels of electronic fund transfers, the intention to increase their electronic fund transactions, and intention to stop using electronic fund transfers that attract the e‐levy. Beyond the disaggregated behavioral responses, psychological (e.g., trust in government) and socioeconomic factors (individual income and gender) are identified as important drivers of willingness to pay for the e‐levy. We suggest that policies around e‐levies must not only focus on individual behavioral responses alone, as the drivers include behavioral, psychological, and socioeconomic factors. Related Articles: Adegboye, Alex, Kofo Adegboye, Uwalomwa Uwuigbe, Stephen Ojeka, and Eyitemi Fasanu. 2023. "Taxation, Democracy, and Inequality in Sub‐Saharan Africa: Relevant Linkages for Sustainable Development Goals." Politics & Policy 51(4): 696–722. https://doi.org/10.1111/polp.12547. Lachapelle, Erick, Thomas Bergeron, Richard Nadeau, Jean‐François Daoust, Ruth Dassonneville, and Éric Bélanger. 2021. "Citizens' Willingness to Support New Taxes for COVID‐19 Measures and the Role of Trust." Politics & Policy 49(3): 534–65. https://doi.org/10.1111/polp.12404. Shock, David R. 2013. "The Significance of Opposition Entrepreneurs on Local Sales Tax Referendum Outcomes." Politics & Policy 41(4): 588–614. https://doi.org/10.1111/polp.12028. [ABSTRACT FROM AUTHOR]

Ensuring the sustainable management of water and sanitation for all is the primary goal of Sustainable Development Goal 6 (SDG 6). SDG indicator 6.4.2 (level of water stress) is critical for monitoring the progress toward SDG 6. The assessment of the SDG indicator 6.4.2 is currently based on statistical data at the national scale, i.e., one value for one country, which cannot reflect spatial variability in water stress in a country. The lack of data at sub-national scales limits the assessment of water stress in sub-national regions. This study developed a method of disaggregating national statistical renewable water resources (TRWR) and freshwater withdrawals (TFWW) to estimate the SDG 6.4.2 water stress indicator at a sub-national scale by combining satellite remote sensing data and model simulated data. Remote sensing (RS)-based precipitation (P); the difference between precipitation and evapotranspiration (P-ET); and the difference between precipitation, evapotranspiration, terrestrial water storage change (P-ET-dS), and model-simulated naturized runoff and withdrawal water use were used as spatial and temporal surrogates to disaggregate the national-scale statistics of TRWR and TFWW to the grid scale. Gridded TRWR and TFWW can be used to calculate the water stress of any interest regions. Disaggregated TRWR, TFWW, and water stress estimation were validated at three different spatial scales, from major river basins and provinces to prefectures in China, by comparing the corresponding statistical data. The results show that the disaggregation for TRWR is generally better than for TFWW, and the overall accuracy for water stress estimation can reach up to 91%. The temporal evolution of disaggregated variables also showed good consistency with statistical time series data. The RS-based P-ET and P-ET-dS have great potential for disaggregating TRWR at different spatiotemporal scales, with no obvious differences with the results using the model simulation as a surrogate for the disaggregation of SDG indicator 6.4.2. The disaggregation accuracy can be further improved when the sub-regional statistical data of TRWR and TFWW are applied to the disaggregation approach. [ABSTRACT FROM AUTHOR]

Despite having significantly higher rates of atopic dermatitis, psoriasis, and pigmentary disorders compared to White patients, studies suggest that Asian Americans are underrepresented in outpatient dermatology clinics. In this study, we utilize the National Health Interview Survey (NHIS) and prioritize disaggregated analyses to evaluate differences between the most populous Asian American subgroups (Chinese, Filipino, Indian, and “Other”) in utilization of outpatient dermatologic care. We utilized multivariable logistic regression to compare outpatient dermatologic care use between each Asian American subgroup and Non-Hispanic Whites. Out of 96,559 adults, our study included 5264 self-identified Asian American and 91,295 non-Hispanic White adults. Most Asian participants were female, had health insurance, and had incomes > 2 times above the federal poverty line. We found that, compared to 21.4% for NH whites, lifetime prevalence of total body skin exam was highest among Filipino Americans (12.3%) and lowest among Indian Americans (7%). Additionally, all Asian American subgroups had a significantly lower odd than NH Whites of ever having a total body skin exam, with Indian Americans having the lowest odds. While the benefit of TBSEs in Indian Americans is unclear, it is possible that differing cultural perceptions about dermatologic needs, barriers to care, or immigration status may be contributing to the observed difference. Furthermore, the Indian diaspora encapsulates a range of skin tones, risk factors, and behaviors that may differentially influence dermatologic disease risk, similar to trends identified among Hispanic patients (Trepanowski et al. in J Am Acad Dermatol 88:1206–1209, 2023). Additional research utilizing the seven national databases that have been identified as providing disaggregated Asian racial information (Kamal et al. in J Am Acad Dermatol, 2023) may be useful to further illuminate avenues for intervention. [ABSTRACT FROM AUTHOR]

The amyloid aggregation of alpha-synuclein within the brain is associated with the pathogenesis of Parkinson's disease (PD) and other related synucleinopathies, including multiple system atrophy (MSA). Alpha-synuclein aggregates are a major therapeutic target for treatment of these diseases. We identify two small molecules capable of disassembling preformed alpha-synuclein fibrils. The compounds, termed CNS-11 and CNS-11g, disaggregate recombinant alpha-synuclein fibrils in vitro, prevent the intracellular seeded aggregation of alpha-synuclein fibrils, and mitigate alpha-synuclein fibril cytotoxicity in neuronal cells. Furthermore, we demonstrate that both compounds disassemble fibrils extracted from MSA patient brains and prevent their intracellular seeding. They also reduce in vivo alpha-synuclein aggregates in C. elegans. Both compounds also penetrate brain tissue in mice. A molecular dynamics-based computational model suggests the compounds may exert their disaggregating effects on the N terminus of the fibril core. These compounds appear to be promising therapeutic leads for targeting alpha-synuclein for the treatment of synucleinopathies.

Abstract Recognizing the diversity of preferences for, and values ascribed to, ecosystems in decision‐making can help to realize more sustainable and equitable policies for transformative change. The goal of this paper was to assess how rankings of ecosystem products (i.e. their relative importance in people's lives) relate to people's individual characteristics, their social–ecological context and the values they ascribe to each ecosystem product. In our case study in southwestern Ethiopia, we considered 11 ecosystem products and four value types (direct use, exchange, relational, intrinsic). We used descriptive statistics, hierarchical clustering and chi‐square tests of independence to analyse the data. On average, maize and teff were ranked as most important, and direct use and relational value were the most important value types. Beneficiaries often ascribed multiple values to each ecosystem product, and direct use and relational values better explained overall importance rankings than exchange or intrinsic values. Five groups of beneficiaries, who each prioritized a different set of ecosystem products, differed in their occupation, and in their social–ecological context, in terms of the villages they lived in and the ecosystem products they produced. Beneficiaries in each of the five groups ascribed different value types to their prioritized ecosystem products, and these did not always align with the value types that were generally judged most important by the group. We recommend that sustainable landscape management should reflect the diversity of people's value ascription, including non‐exchange values. Read the free Plain Language Summary for this article on the Journal blog.

Evapotranspiration (ET) is a crucial parameter in agriculture as it plays a vital role in managing water resources, monitoring droughts, and optimizing crop yields across different ecosystems. Given its significance in crop growth, it is essential to measure ET accurately and continuously to conduct precise analyses in agriculture. However, the continuous monitoring of ET changes is very challenging: while in-situ measurements are costly and not feasible for covering a wide geography, remote sensing-based ET products are typically dependent on optical satellites that cannot operate and transmit data under certain weather conditions, especially in the presence of clouds. In this article, we present the first comprehensive study on predicting ET from synthetic aperture radar (SAR) imagery, which we refer to as SAR2ET. Our work is motivated by the fact that SAR has the critical advantages of being all-weather available and sensitive to crop and soil changes. In handling the SAR2ET problem, we additionally incorporate nonoptical meteorological and topographical input data from auxiliary data sources. We approach SAR2ET as a multimodal image-to-image translation task, for which we train a UNet-shaped network. To evaluate the effectiveness of SAR-based ET predictions, we construct a benchmark dataset over a large geographical region with image samples covering a whole agriculture season. Our experimental findings on this dataset suggest that first, the proposed approach leads to strong results, second, valuable information can be extracted from both SAR and auxiliary data sources, and finally, SAR2ET is overall a promising research direction toward obtaining data-driven year-round ET estimates. The benchmark dataset will be shared publicly upon publication to stimulate future work.

Thanks to the frequent revisiting, satellite microwave radiometers have great potential for surface soil moisture (SM) monitoring. However, their spatial resolution is not sufficient for hydrological studies in small catchments as well as applications to precision farming. In this study, a disaggregation technique based on machine learning is proposed: the technique combines Sentinel-1 (S-1) SAR data with SM generated from advanced microwave scanning radiometer 2 by the IFACs HydroAlgo algorithm, with the aim of enhancing the SM spatial resolution from the original 10 km to about 30 m. To this scope, two machine-learning techniques have been considered for the implementation, namely artificial neural networks (ANNs) and random forests (RF). Training is carried out by aggregating and coregistering S-1 data with the HydroAlgo SM at 10-km resolution. After training, the ANN and RF algorithms are applied pixel-by-pixel to the S-1 images at full resolution for generating the enhanced SM maps. The method has been implemented and validated in two agricultural areas located in Central Italy, where a series of experiments has been carried out between 2019 and 2020 for collecting the main soil and vegetation parameters at the same time of satellite overpasses. To assess the actual resolution of the output SM, the validation against in situ measurements has been carried out by aggregating data at 10, 30, 50, and 70 m. The results confirmed the effectiveness of the proposed method: validation carried out at 30 m obtained R≃0.82 and RMSE≃0.05 m3/m3 that represent a noticeable improvement with respect to the results obtained by HydroAlgo at 10 km (R≃0.56 and RMSE >> 0.1 m3/m3). Validation results also pointed out the superior performances of the ANN based with respect to the RF-based disaggregation.

The aim of this article is to show the methodological principles that govern the interrelationship of the main ideas, concepts, and theories of modern biology. In recent years, several prominent biological researchers attempted to offer insights into the development of biological knowledge. However, a critical examination of these attempts indicates that a methodological analysis is a more fitting approach for achieving this goal. Indeed, the challenge of establishing inter-theoretical connections between biological theories and discoveries falls not within the purview of biology itself but rather within the methodology of biology. This paper explores the key research findings that determine the vertical order of biological knowledge, thereby deepening our cognitive understanding of this field. It also explores the theoretical advancements that expand the horizontal scope of this knowledge. The conceptual framework is illustrated through two diagrams Map I and Map II, which illustrate the methodological structure of biological ideas, concepts, and theories from the era of Linnaeus to the early 21st century. Map II further demonstrates how complex explanatory narratives can be constructed based on contemporary biological and evolutionary knowledge.

The Open Radio Access Network (RAN) emerges as a revolutionary architecture promising unprecedented levels of openness, flexibility, and intelligence within radio access networks. Central to this innovation is the integration of Machine Learning (ML) and Artificial Intelligence (AI) within the RAN Intelligent Controller (RIC), aimed at optimizing network operations and enhancing control mechanisms. This paper undertakes a thorough examination of Open RAN, particularly focusing on its energy consumption aspects, which are pivotal for ensuring the sustainability of future wireless networks. In this paper, we review and compare Open RAN architecture with previous network architectures. In particular we focus on O-RAN Alliance specifications. Additionally, we explore the deployment of ML across various facets of Open RAN and highlights how to estimate the energy consumption of ML models. Through constructing explicit energy consumption models for key O-RAN components, we provide a granular analysis of their energy profiles. Finally we compare the energy dynamics of O-RAN against traditional RAN architectures, delineating the impact of virtualization and disaggregation on energy efficiency.

One of the key parameters that affects the accuracy of land surface temperature (LST) disaggregation is the environmental variables that are fed to the disaggregation model. The aim of this article is to present a new strategy for the disaggregation of LST based on adjacency effects. To do this, a dataset obtained from satellite images and auxiliary information from five European cities was used. First, maps of environmental variables that affect LST were collected. Second, a map of effective environmental variables was produced by calculating and applying the influence of the adjacency effects of each environmental variable based on the proposed weighted inverse distance kernel. Finally, the datasets of environmental variables and effective environmental variables were used separately in the disaggregation process to convert LST at 990 m to disaggregated LST (DLST) at 90 m. The mean RMSEs between LST and DLST obtained without considering the adjacency effects approach for the built-up, agricultural, pasture, forest, and water lands in the cold (warm) season were 0.85 (1.55), 0.72 (1.31), 0.98 (1.63), 0.59 (1.2), and 0.40 (1.12) K, respectively. Taking into account the adjacency effects, the mean RMSE between LST and DLST on built-up, agricultural, pasture, forest, and water lands used in the cold season decreased by 0.35, 0.17, 0.13, 0.09, and 0.03 K, respectively. These values were 0.54, 0.36, 0.33, 0.34, and 0.07 K for the warm season, respectively. The result showed that considering adjacency effects increases the accuracy of LST disaggregation.

This work explores the potential of obtaining high-resolution thermal infrared (TIR) data provided by the Sentinel-2 (S2) & Sentinel-3 (S3) constellation in a typical semiarid agricultural environment. Maps of land surface temperature (LST) with 10–20 m spatial resolution were obtained from the synergy S2–S3 in the Barrax test site in Spain, for a set of 14 different dates in the summers of 2018–2019. Ground measurements of LST transects covering a variety of croplands and surface conditions were used for a ground validation of the disaggregation approaches. A cross validation of the LST products was also conducted using Landsat-8/TIRS images. Two recent approaches exploiting the linkages between shortwave and thermal data were adapted and tested, with differences in the inputs, the physical-mathematical framework, or the treatment of the LST residuals, and two options for the original 1 km S3 LST data were considered. Despite the large range of temperatures registered (295–330 K), differences with observed values resulted in an average RMSE < 3.0 K and a negligible systematic deviation, showing good results even in small fields ∼1 ha. Results confirm the need for appropriate adjustment techniques of the LST residuals obtained to better capture the low temperature conditions. The systematic overestimations introduced by the use of the operational sea and land surface temperature radiometer L2 LST product, and the limitations associated with certain irrigation management are discussed. Results in this work offer a solution to the lack of high-resolution satellite TIR data, and provide new opportunities for LST applications in agricultural areas.

Eddy-covariance (EC) flux measurements in Indianapolis were used to quantify the impact of the COVID-19 lockdown on CO and CO2 emissions from a highway and a suburban neighborhood. CO2 fluxes were measured for 6 weeks pre-lockdown (January 22, 2020–March 3, 2020) and during lockdown (March 25, 2020– May 5, 2020) using EC instrumentation at 41 m AGL. Fossil fuel CO2 emissions (CO2ff) were estimated by calculating eddy diffusivity to obtain CO flux and then scaling by the CO:CO2ff emissions ratio (RCO). Flux measurements segregated by wind direction were compared to hourly emissions from the 2020 Hestia inventory model. The lockdown CO2ff average weekday emissions from the highway estimated by EC decreased by 51.5 ± 10.9% (11.2 ± 2.2 µmol m−2 s−1) compared to pre-lockdown, similar to Hestia’s estimate 56 ± 7% (12 ± 1 µmol m−2 s−1). The EC measurements detected a significant (2.2 ± 0.7 µmol m−2 s−1) but smaller magnitude decrease in CO2ff emissions from the suburban neighborhood. The daily cycles of CO2ff emissions were significantly correlated with Hestia estimates from the highway but not from the suburbs. This study demonstrates that EC flux towers and high-resolution inventory models in regions with mixed and spatially heterogeneous sources can quantify abrupt changes in sector- and source-specific CO2 fluxes.

Context: Amyloid fibrils are self-assembled fibrous protein aggregates that are associated with several presently incurable diseases such as Alzheimer's. disease that is characterized by the accumulation of amyloid fibrils in the brain, which leads to the formation of plaques and the death of brain cells. Disaggregation of amyloid fibrils is considered a promising approach to cure Alzheimer's disease. The mechanism of amyloid fibril formation is complex and not fully understood, making it difficult to develop drugs that can target the process. Diacetonamine and cystathionine are potential lead compounds to induce disaggregation of amyloid fibrils. Methods: In the current research, we have used long timescale molecular simulation studies and replica exchange molecular dynamics (REMD) for 1000 ns (1 μs) to examine the mechanisms by which natural metabolites can disaggregate amyloid-beta fibrils. Molecular docking was carried out using Glide and with prior protein minimization and ligand preparation. We focused on a screening a database of natural metabolites, as potential candidates for disaggregating amyloid fibrils. We used Desmond with OPLS 3e as a force field. MM-GBSA calculations were performed. Blood-brain barrier permeability, SASA, and radius of gyration parameters were calculated. [ABSTRACT FROM AUTHOR]

In order to understand the structure of asphaltene after its disaggregating by chemical processing, a comprehensive study for exploring the structural changes of asphaltene after acylation as a case of chemical processing was conducted. Functional groups, crystal parameters, hydrogen types, and micromorphology of acylated asphaltenes were analyzed by FT-IR, XPS, XRD, 1H NMR, SEM and IFM (inverted fluorescence microscope) methods. Additionally, fluorescence spectroscopy methods were performed to analyze the effect of acylation on the aggregation ability of asphaltene. Experimental results indicated that the C=O double bond was enhanced and the content of O–C=O was increased on the surface of acylated asphaltene. The number of stacking layers and the aggregate size of asphaltene decreased obviously after acylation. Fluorescence spectral analysis showed that the critical aggregation concentration of acylated asphaltenes increased compared to the raw asphaltenes. These results indicate that acylation reaction can disaggregate asphaltenes and hinder their re-aggregation in a solution. The disaggregation effect of asphaltene acylation can be attributed to the weakening of a hydrogen bonding and enhancement of a steric hindrance in the asphaltene molecule. This study provided the further understanding of the structural changes of asphaltene after the chemical treatment. [ABSTRACT FROM AUTHOR]

This paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decompose the aggregated energy load from households in events and algorithms based on expert knowledge to assign each of these events to four types of appliances: fridge, dishwasher, microwave, and washer/dryer. The methodology was developed to work with smart meters that have a granularity of 1 min and was evaluated using the Reference Energy Disaggregation Dataset. The results show that the algorithm can disaggregate the refrigerator with high accuracy and the usefulness of the proposed methodology to extract relevant features from other appliances, such as the power use and duration from the heating cycles of a dishwasher. [ABSTRACT FROM AUTHOR]

Recognizing the diversity of preferences for, and values ascribed to, ecosystems in decision‐making can help to realize more sustainable and equitable policies for transformative change.The goal of this paper was to assess how rankings of ecosystem products (i.e. their relative importance in people's lives) relate to people's individual characteristics, their social–ecological context and the values they ascribe to each ecosystem product.In our case study in southwestern Ethiopia, we considered 11 ecosystem products and four value types (direct use, exchange, relational, intrinsic). We used descriptive statistics, hierarchical clustering and chi‐square tests of independence to analyse the data.On average, maize and teff were ranked as most important, and direct use and relational value were the most important value types. Beneficiaries often ascribed multiple values to each ecosystem product, and direct use and relational values better explained overall importance rankings than exchange or intrinsic values.Five groups of beneficiaries, who each prioritized a different set of ecosystem products, differed in their occupation, and in their social–ecological context, in terms of the villages they lived in and the ecosystem products they produced. Beneficiaries in each of the five groups ascribed different value types to their prioritized ecosystem products, and these did not always align with the value types that were generally judged most important by the group.We recommend that sustainable landscape management should reflect the diversity of people's value ascription, including non‐exchange values. Read the free Plain Language Summary for this article on the Journal blog. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

We develop a measure of disclosure quality using disaggregation of financial statement items from the Form 10-K XBRL filing. Our measure (ITEMS) extends Chen, Miao, and Shevlin's (2015)DQ measure and is distinct from R. Hoitash and U. Hoitash's (2018)ARC measure. Our measure provides a simple measure of disaggregation by counting the balance sheet and income statement line items, it does not depend on the data aggregators' collection process and is readily available shortly after the Form 10-K is filed. We validate ITEMS by showing that firm fundamentals correlate to ITEMS in the predicted direction using OLS regression. We find that ITEMS explains consequences of disclosure quality: forecast error, forecast dispersion, bid-ask spread, and cost of equity capital. Further, ITEMS has explanatory power of disclosure quality consequences incremental to DQ and ARC, and it is distinct from ARC evident from different associations with disclosure quality consequences and reporting quality. Data Availability: Data are available from public sources identified in the text. JEL Classifications: M10; M40; M41. [ABSTRACT FROM AUTHOR]

The Eastern Anatolia Observatory (DAG), to accommodate a 4-m telescope, is under construction in the city of Erzurum at Karakaya Peak of the Palandöken Mountain Range. Owing to the vulnerability of the telescope and its optical components to earthquakes and the high demand for pointing accuracy for quality astronomical observations, the seismic hazard needs to be reliably determined before any design and construction work begins. A relevant rectangular zone, extending 250 km from the observatory in each direction, is selected as the study area. Then, the seismic hazard at the DAG site is estimated by using the probabilistic hazard analysis method. Using different magnitude scales, several earthquake catalogues are recompiled by converting them into a uniform moment magnitude (Mw) scale, and the seismic sources that could affect the study area are determined. The results are aggregated for epistemic uncertainties using the logic-tree method. Finally, peak ground acceleration (PGA) values are obtained for two recurrence periods corresponding to two different design levels (operating basis and design basis earthquakes) using two different ground motion attenuation models. The disaggregation of the seismic hazard permits association of the PGA estimates with various faults. Disaggregation also provides source information for synthetic ground motion generation. Effects of rupture directivity and topographical amplification are discussed. [ABSTRACT FROM AUTHOR]

Aims: To explore the association between known risk factors for diabetes and diabetes prevalence among disaggregated Asian American sub-ethnic groups and to highlight the importance of looking at data in disaggregated form. Methods: Ten years of data collected by the California Health Interview Survey were pooled and analyzed. Logistic regression models, controlling for age, gender, and marital status, were used for the analysis. Comparisons were made among an aggregated sample of Asian Americans (N = 21,267) and a disaggregated sample of Asian Americans (i.e., Chinese, Japanese, Koreans, Filipinos, South Asians, and Vietnamese). Results: Disaggregated data showed that Korean Americans who smoked had three times the odds of having diabetes compared to non-smoking Korean Americans [OR = 3.02 (95% CI 1.43, 6.39)]. High blood pressure puts both Japanese and Filipino Americans at twice the odds of having diabetes as those who do not have high blood pressure ([OR = 2.23 (95% CI 1.09, 4.54) and [OR = 2.82 (95% CI 1.82, 4.36)], respectively). Although close to 10% of Vietnamese Americans in the sample were current smokers, smoking status was not associated with diabetes. Conclusions: The association between diabetes and known risk factors differ across Asian American sub-ethnic groups. Findings highlight the importance of disaggregated data and discourage a one-size-fits-all approach to diabetes care strategies. [ABSTRACT FROM AUTHOR]

The aim of this article is to show the methodological principles that govern the interrelationship of the main ideas, concepts, and theories of modern biology. In recent years, several prominent biological researchers attempted to offer insights into the development of biological knowledge. However, a critical examination of these attempts indicates that a methodological analysis is a more fitting approach for achieving this goal. Indeed, the challenge of establishing inter-theoretical connections between biological theories and discoveries falls not within the purview of biology itself but rather within the methodology of biology. This paper explores the key research findings that determine the vertical order of biological knowledge, thereby deepening our cognitive understanding of this field. It also explores the theoretical advancements that expand the horizontal scope of this knowledge. The conceptual framework is illustrated through two diagrams Map I and Map II, which illustrate the methodological structure of biological ideas, concepts, and theories from the era of Linnaeus to the early 21st century. Map II further demonstrates how complex explanatory narratives can be constructed based on contemporary biological and evolutionary knowledge. [ABSTRACT FROM AUTHOR]

Heat stress can cause cell death by triggering the aggregation of essential proteins. In bacteria, aggregated proteins are rescued by the canonical Hsp70/AAA+ (ClpB) bi-chaperone disaggregase. Man-made, severe stress conditions applied during, e.g., food processing represent a novel threat for bacteria by exceeding the capacity of the Hsp70/ClpB system. Here, we report on the potent autonomous AAA+ disaggregase ClpL from Listeria monocytogenes that provides enhanced heat resistance to the food-borne pathogen enabling persistence in adverse environments. ClpL shows increased thermal stability and enhanced disaggregation power compared to Hsp70/ClpB, enabling it to withstand severe heat stress and to solubilize tight aggregates. ClpL binds to protein aggregates via aromatic residues present in its N-terminal domain (NTD) that adopts a partially folded and dynamic conformation. Target specificity is achieved by simultaneous interactions of multiple NTDs with the aggregate surface. ClpL shows remarkable structural plasticity by forming diverse higher assembly states through interacting ClpL rings. NTDs become largely sequestered upon ClpL ring interactions. Stabilizing ring assemblies by engineered disulfide bonds strongly reduces disaggregation activity, suggesting that they represent storage states.

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work is devoted to the calculation of the cooling tower. The explanatory note reflects the material, thermal, hydraulic calculations, guided by which it is possible to select the type of apparatus and its design dimensions. The temperature diagram of the process, the technological diagram of the process, the structural diagram of the apparatus, sketches of the main elements of the apparatus are also shown. The field of velocities of the gas phase, the movement of solid particles, the kinetics of disaggregation of lumps of material, the effect of the concentration of the inert packing and the swirl angle of the coolant on the kinetics of disaggregation of the coolant, was studied experimentally and the choice of the type of inert substance and its concentration was determined. With the help of these techniques how much technical water is saved is also given. In addition, the kinetics of material drying was studied taking into account its disaggregation in a swirling coolant flow.

This study aimed to examine the mechanisms of the photoplethysmography (PPG) signal formation using Monte Carlo simulations of light transport in biological tissues and experimental observations. Based on a three-layer skin model in backscattering geometry, we sequentially simulated volumetric blood changes and the aggregation/disaggregation of erythrocytes in the dermal layer and estimated their contribution to the registered PPG signal. The calculations were conducted for two wavelengths: 525 nm and 810 nm. For green light, absorption predominates over scattering in the formation of a PPG signal, whereas, for near-infrared light, scattering prevails over absorption. This theoretical result was verified using the Modified Beer–Lambert law and clinical in vivo PPG data of seven healthy subjects. Changes in the size of the scatterers during erythrocyte aggregation and disaggregation can significantly contribute to the PPG signal at near-infrared light. Thus, for the green waveband, the classical volumetric model can be considered dominant in the PPG signal formation. In contrast, for the near-infrared range, both volumetric and aggregation effects must be considered as being approximately equal.

The expected halt of traditional technology scaling is motivating increased heterogeneity in high-performance computing (HPC) systems with the emergence of numerous specialized accelerators. As heterogeneity increases, so does the risk of underutilizing expensive hardware resources if we preserve today's rigid node configuration and reservation strategies. This has sparked interest in resource disaggregation to enable finer-grain allocation of hardware resources to applications. However, there is currently no data-driven study of what range of disaggregation is appropriate in HPC. To that end, we perform a detailed analysis of key metrics sampled in NERSC's Cori, a production HPC system that executes a diverse open-science HPC workload. In addition, we profile a variety of deep-learning applications to represent an emerging workload. We show that for a rack (cabinet) configuration and applications similar to Cori, a central processing unit with intra-rack disaggregation has a 99.5% probability to find all resources it requires inside its rack. In addition, ideal intra-rack resource disaggregation in Cori could reduce memory and NIC resources by 5.36% to 69.01% and still satisfy the worst-case average rack utilization.

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic significantly disrupted international economic activities. This study examines its impact on the world economy, analyzing different regions and income groups separately. The empirical findings reveal that COVID-19 had a negative and substantial impact on global trade performance. Surprisingly, high-income countries experienced a positive impact on trade due to COVID-19, showing greater adaptability and, in some cases, emerging as exporters of medicines and COVID-19-related products. In contrast, middle-income and low-income countries did not observe any significant impact on trade performance from COVID-19. This finding implies that trade deterioration during the pandemic might be attributed to mediating factors rather than the direct influence of COVID-19 itself. Furthermore, when examining the effects by region, the results are mixed, reflecting the diverse characteristics of different regions. Consequently, it becomes evident that the COVID-19 pandemic has exacerbated existing inequalities, with the trade performance of affluent and advanced countries being the least negatively affected. In conclusion, the COVID-19 pandemic had far-reaching consequences on global trade, impacting various regions and income groups differently. The study highlights the need for targeted policies to address the disparities in trade performance and foster a more resilient and equitable global economy. [ABSTRACT FROM AUTHOR]

Flood risk assessment at the mesoscale requires data that are spatially and thematically detailed enough to provide reliable estimates at the catchment level. However, data availability and suitability are often contradictory: available data are rarely suitable at the required level of detail. To overcome this problem, numerous disaggregation methods have been proposed in recent decades, often based on somewhat generalised imperviousness characteristics derived from the available urban land use/land cover (LULC) nomenclature. To reduce generalisation, we propose a new disaggregation approach using a spatially distributed imperviousness density (IMD) layer at a very detailed spatial resolution of 10 m as ancillary data to improve the thematic detail of the urban classes of the available LULC datasets (Coastal Zones, Natura 2000) and the dasymetric mapping of the census data. The nomenclature of the urban classes and the impervious density thresholds were taken from the detailed Urban Atlas dataset. The disaggregation of the census data is then built on the resulting geometry of thematically improved residential classes. Assuming that IMD values indicate a built-up density, the proposed weighting scheme is IMD-dependent: it accounts for variability in the built-up density and, hence, variability in population. The approach was tested in three catchments in Croatia, each with a different degree of urbanisation. The resulting statistics (mean square error and percentage error) indicate that residential areas and population density depend on IMD. Using IMD as additional data therefore greatly improves the assessment of elements that are exposed to flooding and, consequently, the damage and flood risk assessment. [ABSTRACT FROM AUTHOR]

The use of compacted bentonites in radioactive waste repository barriers is a relevant application of geoenvironmental engineering. The on-site structural characteristics of the bentonites determine the performance and integrity of the barrier. The present work addresses the adaptation of the standardized sand equivalent shaking method for the controlled disaggregation of Wyoming bentonite specimens prepared at low, medium, and high compaction. The evolution of the macrostructural units' size distribution was determined by sieving at different shaking times. The stability of the compacted material increases with dry density. However, if enough energy is applied in the disaggregation process, the size distribution of the macrostructural units after disaggregation has the same characteristics as that of the uncompacted starting material, regardless of the applied degree of compaction. Since the disaggregation rate is a function of the aggregation level (compaction), it follows that compaction energy is not only spent on reducing porosity but also on generating more stable macrostructural units. These findings pave the way for future research with different materials and test conditions (compaction, moisture, etc.). In addition, the proposed shaking method is adaptable and could also be used in other sectors, such as agriculture, to determine the structural stability of natural soils. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Type 2 diabetes (T2D) is one of the leading causes of death among Asian Americans. Despite being a culturally diverse racial group with differences in history, language, religion, and values, Asian Americans are often viewed as a monolith. With the high prevalence rate of T2D, a careful examination of self-management interventions across Asian Americans is needed to develop effective and culturally sensitive interventions. OBJECTIVE: To describe existing literature by examining study characteristics, different intervention components, and outcome measures of various T2D interventions among Asian Americans. METHODS: Using Arksey and OMalleys framework to ground this review, six online databases were used to identify studies. RESULTS: A total of 18 publications were included. Thirteen studies were published after 2013, with 44% and 22% of these studies focused on Chinese Americans and Korean Americans. We found a lack of geographic diversity in the location of the studies. Majority of the participants were females. Most of the interventions were implemented in person. Licensed health care providers were the most common interventionists, with a number of studies using community health workers. Outcome measures focused on three key areas: physiological, psychosocial and behavioral, and program-related outcomes. Many of the studies measured changes in HbA1C, self-efficacy, distress, depression, and quality of life. Overall, we saw improvements in physiological measures in most of the studies. For example, majority of the studies showed a decline in the participants HbA1C. Most studies showed an increase or improvement in healthy behaviors. Studies that measured efficacy, knowledge, attitude, motivation, quality of life, or general health showed improvement from baseline. All the studies that measured distress or depression showed a reduction of symptoms postintervention. CONCLUSION: Overall, we found that culturally tailored interventions that focus on specific Asian American subpopulations saw an improvement in physiological, psychosocial, or behavioral measures. There were several gaps in the existing T2D self-management programs or interventions among Asian Americans studied in the United States. Based on our analysis, we recommend when designing or implementing self-management interventions among Asian Americans, considerations should be made for targeted recruitment for understudied Asian American subgroups, gender, and location.

ObjectiveTo improve the fidelity of the cellular transcriptome of disaggregated synovial tissue for applications such as single-cell RNA sequencing (scRNAseq) by modifying the disaggregation technique.MethodsOsteoarthritis (OA) and rheumatoid arthritis (RA) synovia were collected at arthroplasty. RNA was extracted from intact or disaggregated replicate pools of tissue fragments. Disaggregation was performed with either a proprietary protease, Liberase TL (Lib) as a reference method, Liberase TL with an RNA polymerase inhibitor flavopyridol (Flavo), or a cold digestion with subtilisin A (SubA). qPCR on selected markers and RNAseq were used to compare disaggregation methods using the original intact tissue as reference.ResultsDisaggregated cell yield and viability were similar for all three methods with some viability improved (SubA). Candidate gene analysis showed that Lib alone dramatically increased expression of several genes involved in inflammation and immunity compared with intact tissue and was unable to differentiate RA from OA. Both alternative methods reduced the disaggregation induced changes. Unbiased analysis using bulk RNAseq and the 3 protocols confirmed the candidate gene studies and showed that disaggregation-induced changes were largely prevented. The resultant data improved the ability to distinguish RA from OA synovial transcriptomes.ConclusionsDisaggregation of connective tissues such as synovia has complex and selective effects on the transcriptome. We found that disaggregation with an RNA polymerase inhibitor or using a cold enzyme tended to limit induction of some relevant transcripts during tissue processing. The resultant data in the disaggregated transcriptome better represented the in situ transcriptome. The specific method chosen can be tailored to the genes of interest and the hypotheses being tested in order to optimize the fidelity of technique for applications based on cell suspensions such as sorted populations or scRNAseq.

Ensuring the sustainable management of water and sanitation for all is the primary goal of Sustainable Development Goal 6 (SDG 6). SDG indicator 6.4.2 (level of water stress) is critical for monitoring the progress toward SDG 6. The assessment of the SDG indicator 6.4.2 is currently based on statistical data at the national scale, i.e., one value for one country, which cannot reflect spatial variability in water stress in a country. The lack of data at sub-national scales limits the assessment of water stress in sub-national regions. This study developed a method of disaggregating national statistical renewable water resources (TRWR) and freshwater withdrawals (TFWW) to estimate the SDG 6.4.2 water stress indicator at a sub-national scale by combining satellite remote sensing data and model simulated data. Remote sensing (RS)-based precipitation (P); the difference between precipitation and evapotranspiration (P-ET); and the difference between precipitation, evapotranspiration, terrestrial water storage change (P-ET-dS), and model-simulated naturized runoff and withdrawal water use were used as spatial and temporal surrogates to disaggregate the national-scale statistics of TRWR and TFWW to the grid scale. Gridded TRWR and TFWW can be used to calculate the water stress of any interest regions. Disaggregated TRWR, TFWW, and water stress estimation were validated at three different spatial scales, from major river basins and provinces to prefectures in China, by comparing the corresponding statistical data. The results show that the disaggregation for TRWR is generally better than for TFWW, and the overall accuracy for water stress estimation can reach up to 91%. The temporal evolution of disaggregated variables also showed good consistency with statistical time series data. The RS-based P-ET and P-ET-dS have great potential for disaggregating TRWR at different spatiotemporal scales, with no obvious differences with the results using the model simulation as a surrogate for the disaggregation of SDG indicator 6.4.2. The disaggregation accuracy can be further improved when the sub-regional statistical data of TRWR and TFWW are applied to the disaggregation approach.

Abstract Background The Hispanic population is heterogeneous with differences in health behaviors across subgroups by nativity and preferred language. We evaluated cervical cancer screening adherence among English- and Spanish-speaking Hispanic patients receiving care at a safety net health system. Methods Electronic health records were used to identify 46,094 women aged 30–65. Up to date (UTD) screening was defined based on date of last Pap test, human papillomavirus (HPV) test, or Pap/HPV co-test. Results Overall, 81.5% of 31,297 Hispanic women were UTD. English-speaking Hispanic women had a lower prevalence of being UTD when compared to Spanish-speaking Hispanic women (aPR: 0.94, 95% CI: 0.93 – 0.96). Further, those with indigent healthcare plans had a higher prevalence of being UTD when compared to those with private insurance (aPR: 1.10, 95% CI: 1.09 – 1.12), while all other health insurance plans were associated with lower UTD screening when compared to private insurance. Conclusions These findings suggest screening differences within the Hispanic population, highlighting the need for disaggregated research assessing heterogeneity within racial/ethnic groups, specifically among Hispanic populations.

Abstract The paper provides a disaggregated mixed-frequency framework for the estimation of GDP. The GDP is disaggregated into components that can be forecasted based on information available at higher sampling frequency, i.e., monthly, weekly, or daily. The model framework is applied for Greek GDP nowcasting. The results provide evidence that the more accurate nowcasting estimations require (i) the disaggregation of GDP, (ii) the use of a multilayer mixed-frequency framework, and (iii) the inclusion of financial information on a daily frequency. The simulation study provides evidence in favor of the disaggregation into components despite the inclusion of multiple sources of forecast errors.