Sample Data Research Articles

Evidence Against a Traumatic Brain Injury "July Effect": An Analysis of 3 160 452 Patients From the National Inpatient Sample.

The "July Effect" hypothesizes increased morbidity and mortality after the addition of inexperienced physicians at the beginning of an academic year. However, the impact of newer members on neurosurgical teams managing patients with traumatic brain injury (TBI) has yet to be examined. This study conducted a nationwide analysis to evaluate the existence of the "July Effect" in the setting of patients with TBI. The Healthcare Cost and Utilization Project Central Distributor's National Inpatient Sample data set was queried for patients with TBI using International Classification of Diseases (ICD)-9 and ICD-10 codes. Discharges were included for diagnoses of traumatic epidural, subdural, or subarachnoid hemorrhages. Only patients treated at teaching hospitals were included to ensure resident involvement in care. Patients were grouped into July admission and non-July admission cohorts. A subgroup of patients with neurotrauma undergoing any form of cranial surgery was created. Perioperative variables were recorded. Rates of different complications were assayed. Groups were compared using χ2 tests (qualitative variables) and t-tests or Mann-Whitney U-tests (quantitative variables). Logistic regression was used for binary variables. Gamma log-linked regression was used for continuous variables. The National Inpatient Sample database yielded a weighted average of 3 160 452 patients, of which 312 863 (9.9%) underwent surgical management. Patients admitted to the hospital in July had a 5% decreased likelihood of death (P = .027), and a 5.83% decreased likelihood of developing a complication (P < .001) compared with other months of the year. July admittance to a hospital showed no significant impact on mean length of stay (P = .392) or routine discharge (P = .147). Among patients with TBI who received surgical intervention, July admittance did not significantly affect the likelihood of death (P = .053), developing a complication (P = .477), routine discharge (P = .986), or mean length of stay (P = .385). The findings suggested that there is no "July Effect" on patients with TBI treated at teaching hospitals in the United States.

IMHB-Mediated Chameleonicity in Drug Design: A Focus on Structurally Related PROTACs.

Molecular chameleonicity may enable compounds to compensate for the unfavorable ADME properties typically associated with complex molecules, such as PROTACs. Here we present a few in silico strategies to implement chameleonicity considerations in drug design. Initially, we identified six structurally related CRBN-based PROTACs targeting BET proteins and experimentally verified whether chameleonicity is needed to obtain an acceptable physicochemical profile. Then, we utilized experimental data to validate our novel computational strategies based on tools crafted to encompass a spectrum of complexities and innovative features. After confirming that the formation of IMHBs is the primary driving factor behind chameleonicity, we initially utilized conformational sampling data to define cChameCS, an IMHB-mediated, simple, and rapid chameleonicity predictor index suitable for early drug discovery. Subsequently, we identified dynamic IMHB patterns relevant to chameleonicity through molecular dynamics simulations. Finally, we proposed a workflow for designing structurally related chameleonic PROTACs of potential application in the lead optimization process.

Spatio-temporal dynamics of phytoplankton community structure in the coastal waters of the Southern Beibu Gulf.

This study was conducted during October 2021 (autumn) and April 2022 (spring) to explore the phytoplankton community structure, their distribution characteristics, and the influence of environmental factors in the coastal waters of the Southern Beibu Gulf. The 15 sampling sites were grouped based on the difference in offshore distance to analyze the temporal and spatial differences in community structure and environmental driving in the investigated sea area of the coastal waters of the Southern Beibu Gulf. Permutational multivariate analysis of variance was conducted on the sample data in time and space, revealing that there is no significant difference in space (p > 0.05), but there is significant difference in time (p < 0.05). Notably, water pressure, pH, chemical oxygen demand, nitrite, and labile phosphate were higher in autumn, while total ammonia nitrogen, dissolved oxygen, and suspended solids were significantly higher in spring. Additionally, the study identified 87 phytoplankton species belonging to 6 phyla, dominating by Bacillariophyta, followed by Dinophyta and Cyanophyta. The phytoplankton density, Shannon Weiner's diversity index (H'), Pielou's evenness index (J), and Margalef's richness index (D) ranged from 84.88 to 4675.33 cells L-1, 0.56 to 2.58, 0.26 to 0.89, and 1.21 to 3.64, respectively. Permutational multivariate analysis of variance showed non-significant spatial differences in phytoplankton composition (p > 0.05) but seasonal differences (p < 0.05). Furthermore, canonical correspondence analysis (CCA) identified pH, dissolved oxygen, suspended solids, chemical oxygen demand, nitrite, and labile phosphate as key environmental factors influencing the phytoplankton community structure (p < 0.05). In this study, the dynamic changes of phytoplankton community structure and environmental factors in the southern coastal waters of Beibu Gulf were analyzed in detail from two aspects of time and space. The key environmental factors to protect the ecological environment in the southern coastal area of Beibu Gulf were found out. It provides a reference method and theoretical basis for the management and protection of Beibu Gulf and other tropical marine environment.

Portable X-ray fluorescence spectrometry: a cost-effective method for analysing trace metals in deposited dust.

For projects requiring extensive environmental sampling and rapid decision-making to identify trace metal contamination using dust wipes, the cost and time required for wet chemistry analysis can be prohibitive. Under such circumstances there is a need for a suitable screening method that is cost-effective, efficient, and portable. To address this need, this study investigated the utility of portable X-ray fluorescence (pXRF) for the analysis of trace metals in dust wipes. Here, 316 dust wipe samples from three different geographical settings co-located with mining and smelting operations were investigated for their trace metal loadings (μg m-2) of arsenic (As), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) using pXRF. Results collected using pXRF were compared against inductively coupled plasma mass spectrometry (ICP-MS) concentrations using matched dust wipes (n = 87) to assess reproducibility. A subset of dust wipes (n = 4) were subject to different pXRF analytical scenarios (ranging from 1 to 12 pXRF measurements) using a standardised test duration of 30 seconds to identify the most efficient number of tests for analytical precision. Conducting four pXRF tests on a single wipe (total exposure time of 120 seconds) returned comparable results to ICP-MS and was adopted for analysis of all samples. Results from dust wipes analysed with both ICP-MS and pXRF (n = 87) showed moderate to strong Spearman Rho correlations (rs = 0.489-0.956, p < 0.01) and linear regression coefficients of variation demonstrated good agreement between methods (R2 = 0.432-0.989, p < 0.05). Linear regression equations were used to correct pXRF data to the ICP-MS dust wipe data for samples analysed by both approaches, and applied to pXRF data that were not subject to ICP-MS analysis (n = 229). Application of the correction formula resulted in a substantial improvement of pXRF's accuracy and precision, confirming its effectiveness for assessing trace metals in dust wipes.

Jointly efficient encoding and decoding in neural populations.

The efficient coding approach proposes that neural systems represent as much sensory information as biological constraints allow. It aims at formalizing encoding as a constrained optimal process. A different approach, that aims at formalizing decoding, proposes that neural systems instantiate a generative model of the sensory world. Here, we put forth a normative framework that characterizes neural systems as jointly optimizing encoding and decoding. It takes the form of a variational autoencoder: sensory stimuli are encoded in the noisy activity of neurons to be interpreted by a flexible decoder; encoding must allow for an accurate stimulus reconstruction from neural activity. Jointly, neural activity is required to represent the statistics of latent features which are mapped by the decoder into distributions over sensory stimuli; decoding correspondingly optimizes the accuracy of the generative model. This framework yields in a family of encoding-decoding models, which result in equally accurate generative models, indexed by a measure of the stimulus-induced deviation of neural activity from the marginal distribution over neural activity. Each member of this family predicts a specific relation between properties of the sensory neurons-such as the arrangement of the tuning curve means (preferred stimuli) and widths (degrees of selectivity) in the population-as a function of the statistics of the sensory world. Our approach thus generalizes the efficient coding approach. Notably, here, the form of the constraint on the optimization derives from the requirement of an accurate generative model, while it is arbitrary in efficient coding models. Moreover, solutions do not require the knowledge of the stimulus distribution, but are learned on the basis of data samples; the constraint further acts as regularizer, allowing the model to generalize beyond the training data. Finally, we characterize the family of models we obtain through alternate measures of performance, such as the error in stimulus reconstruction. We find that a range of models admits comparable performance; in particular, a population of sensory neurons with broad tuning curves as observed experimentally yields both low reconstruction stimulus error and an accurate generative model that generalizes robustly to unseen data.

Формирование цифровой модели угольного месторождения в горно-геологической информационной системе МАЙНФРЭЙМ

Digitalization of enterprise management using Russian software is the main trend in the development of the country's mining sector. Sharp fluctuations in demand and prices in the mineral markets, as well as the development of technical tools for mechanization and automation of technological processes along with a systematic increase in their cost affect the financial performance of mining companies. This determines the need for a rapid adoption of technical, technological and organizational decisions, which, along with the requirements of the company's owners and regulators to obtain prompt, reliable and the most complete information about the production process explains the urgent need for the prompt introduction of digital control systems for mining operations. In the article the authors describe an algorithm to design a digital model of the mining and geological system, including a digital geological model of the deposit, digital models of mine workings and the surface topography, based on the experience of creating three-dimensional digital models of complex-structured coal deposits. The basic software suite for designing a digital management system for mining operations is the MINEFRAME mining and geological information system developed at the Mining Institute of the Kola Scientific Center of the Russian Academy of Sciences. MINEFRAME allows to perform a full range of tasks aimed at creation of a digital 3D model of the mining and geological system for a coal deposit with the possibility to account for the complex bedding in the modelling. High accuracy and correctness of the input geological sampling data is achieved by means of tools to check for errors in the tables of borehole collar coordinates, inclinometry and sampling. The wireframe modelling methods implemented in MINEFRAME provide the possibility to create wireframe models of seams with high detail in the fracture zones.

Exploring the link between creative tourist experiences and intentions among Gen Z: a sequential mediation modeling approach in creative tourism

Purpose This study aims to explore the influence of creative tourist experiences (CTE) on tourists’ recommendation intentions (RCI) toward creative tourist destinations. This study examines the roles of tourist engagement and satisfaction in this relationship, proposing a sequential mediation model based on the cognitive-affective-conative model. Design/methodology/approach The research surveyed travelers to Kinnaur, Himachal Pradesh, India, using a structured questionnaire. Through purposive sampling data were collected from 413 generation Z (Gen Z in short) tourists. The study used exploratory factor analysis, confirmatory factor analysis and sequential mediation analysis. The present research used Model 6 by Andrew Hayes in the SPSS process macro to test the serial mediation. Findings The findings emphasize the importance of active engagement in creative tourism and its potential to transform cultural heritage into enriching experiences, thereby influencing tourists’ recommendation behaviors. Results of sequential mediation analysis show that there is a significant and positive impact of CTE on tourist’s RCI. Further, results showed the sequential mediation effect of tourist engagement and satisfaction on the relationship between CTE and RCI. Practical implications The findings offer valuable insights for destination managers and policymakers in creative tourism. Enhancing tourist engagement and satisfaction through creative experiences can effectively increase RCI, contributing to the sustainable management of cultural resources and preventing the over-commercialization of cultural heritage. Originality/value This research contributes to the literature on creative tourism by empirically testing the sequential mediation model and highlighting the pivotal role of tourist engagement and satisfaction in transforming creative experiences into positive behavioral outcomes. This study provides a nuanced understanding of the factors influencing tourists’ RCI in creative tourism contexts.

Pengelolaan Permukiman Pesisir dan di Atas Air Berkelanjutan di Desa Teluk Sunting, Kabupaten Kepulauan Anambas Provinsi Kepulauan Riau

Coastal settlements are highly vulnerable because they are greatly affected by global climate change. Population growth will further increase the use of unorganized built space in the coastal area of Teluk Sunting Village which in the future could lead to conflict as a result of regulations and policies regarding coastal areas. This research was conducted in the coastal residential area of Teluk Sunting Village, Central Siantan District, Anambas Islands Regency. The research method consists of data collection techniques, sampling techniques, data requirements, data presentation, analysis methods and data analysis techniques. The results obtained are that the administrative area of Teluk Sunting Village contains cultivation and protected areas according to the RTRW Spatial Pattern Plan. The area is 5.29 Ha of mangrove ecosystem area and 34.83 Ha of protected forest area. Area of 19.22 Ha cultivation area. The environmental quality results obtained in RT 1 settlements are low quality settlements, RT 3 is medium quality settlements and RT 2 and RT 4 settlements are high quality settlements. The conclusions obtained from the four approaches taken to identify land use patterns, namely land use patterns, land use and use with land tenure, conformity with the RTRW and conformity with the zoning of Aquatic Tourism Parks, are in accordance with the designation. The involvement of stakeholders in the management of coastal and water areas in Teluk Sunting Village is very appropriate to support and ensure the sustainability of the area in the future.

Relation Extraction in Underexplored Biomedical Domains: A Diversity-optimised Sampling and Synthetic Data Generation Approach

Abstract The sparsity of labeled data is an obstacle to the development of Relation Extraction (RE) models and the completion of databases in various biomedical areas. While being of high interest in drug-discovery, the literature on natural products, reporting the identification of potential bioactive compounds from organisms, is a concrete example of such an overlooked topic. To mark the start of this new task, we created the first curated evaluation dataset and extracted literature items from the LOTUS database to build training sets. To this end, we developed a new sampler, inspired by diversity metrics in ecology, named Greedy Maximum Entropy sampler (https://github.com/idiap/gme-sampler). The strategic optimization of both balance and diversity of the selected items in the evaluation set is important given the resource-intensive nature of manual curation. After quantifying the noise in the training set, in the form of discrepancies between the text of input abstracts and the expected output labels, we explored different strategies accordingly. Framing the task as an end-to-end Relation Extraction, we evaluated the performance of standard fine-tuning (BioGPT, GPT-2, and Seq2rel) and few-shot learning with open Large Language Models (LLMs) (LLaMA 7B-65B). In addition to their evaluation in few-shot settings, we explore the potential of open LLMs as synthetic data generators and propose a new workflow for this purpose. All evaluated models exhibited substantial improvements when fine-tuned on synthetic abstracts rather than the original noisy data.We provide our best performing (F1-score = 59:0) BioGPT-Large model for end-to-end RE of natural products relationships along with all the training and evaluation datasets. See more details at https://github.com/idiap/abroad-re.

Search for the leptonic decays D*+→e+νe and D*+→μ+νμ

We present the first search for the leptonic decays D*+→e+νe and D*+→μ+νμ by analyzing a data sample of electron-positron collisions recorded with the BESIII detector at center-of-mass energies between 4.178 and 4.226 GeV, corresponding to an integrated luminosity of 6.32 fb−1. No significant signal is observed. The upper limits on the branching fractions for D*+→e+νe and D*+→μ+νμ are set to be 1.1×10−5 and 4.3×10−6 at 90% confidence level, respectively. Published by the American Physical Society 2024

Measurements of Normalized Differential Cross Sections of Inclusive η Production in e+e− Annihilation at Energy from 2.0000 to 3.6710 GeV

Using data samples collected with the BESIII detector operating at the BEPCII storage ring, the cross section of the inclusive process e+e−→η+X, normalized by the total cross section of e+e−→hadrons, is measured at eight center-of-mass energy points from 2.0000 to 3.6710 GeV. These are the first measurements with momentum dependence in this energy region. Our measurement shows a significant discrepancy compared to the existing fragmentation functions. To address this discrepancy, a new QCD analysis is performed at the next-to-next-to-leading order with hadron mass corrections and higher twist effects, which can explain both the established high-energy data and our measurements reasonably well. Published by the American Physical Society 2024

Research on Emotion Analysis Based on a Topic Emotion Fusion Model: The Case of the "Post-holiday Symptoms of Pharyngitis" Event on the Weibo Platform

[Objective] From early 2023 to 2024, multiple public health incidents related to "post-holiday pharyngitis" erupted in china, impacting public health and education among other areas. This study analyzes the data dissemination trends and the emotional development of these incidents. The insights are aimed at enhancing the government and disease control agencies' mechanisms for guiding and responding to such public sentiment incidents, thereby improving preparedness and response capabilities for potential future public health events. [Methods] The methodology employed involves using three instances of "post-holiday pharyngitis" events on the Weibo platform as data samples. The study applies the LDA model integrated with TD-IDF weighted Word2vec algorithm for thematic organization across different periods. The DUTIR sentiment dictionary is used to analyze public sentiment, and data visualization techniques are employed to present the thematic-emotional evolution within the incidents. [Results] The results suggest that the proposed thematic-emotional fusion model effectively extracts and analyzes the dissemination trends and emotional evolution of the event, providing actionable suggestions for government and public health agencies in managing such public sentiment incidents.

Quadratic Speed‐ups in Quantum Kernelized Binary Classification

AbstractClassification is at the core of data‐driven prediction and decision‐making, representing a fundamental task in supervised machine learning. Recently, several quantum machine learning algorithms that use quantum kernels as a measure of similarities between data have emerged to perform binary classification on datasets encoded as quantum states. The potential advantages of quantum kernels arise from the ability of quantum computers to construct kernels that are more effective than their classical counterparts in capturing patterns in data or computing kernels more efficiently. However, existing quantum kernel‐based classification algorithms do not harness the capability of having data samples in quantum superposition for additional enhancements. This work demonstrates how such capability can be leveraged in quantum kernelized binary classifiers (QKCs) through Quantum Amplitude Estimation (QAE) for quadratic speed‐up. Additionally, new quantum circuits are proposed for the QKCs in which the number of qubits is reduced by one, and the circuit depth is reduced linearly with respect to the number of sample data. The quadratic speed‐up over previous methods is verified through numerical simulations on the Iris dataset.

Search for long-lived heavy neutral leptons decaying in the CMS muon detectors in proton-proton collisions at s=13 TeV

A search for heavy neutral leptons (HNLs) decaying in the CMS muon system is presented. A data sample is used corresponding to an integrated luminosity of 138 fb−1 of proton-proton collisions at s=13 TeV, recorded at the CERN LHC in 2016–2018. Decay products of long-lived HNLs could interact with the shielding materials in the CMS muon system and create hadronic and electromagnetic showers detected in the muon chambers. This distinctive signature provides a unique handle to search for HNLs with masses below 4 GeV and proper decay lengths of the order of meters. The signature is sensitive to HNL couplings to all three generations of leptons. Candidate events are required to contain a prompt electron or muon originating from a vertex on the beam axis and a displaced shower in the muon chambers. No significant deviations from the standard model background expectation are observed. In the electron (muon) channel, the most stringent limits to date are set for HNLs in the mass range of 2.1–3.0 (1.9–3.3) GeV, reaching mixing matrix element squared values as low as 8.6(4.6)×10−6. © 2024 CERN, for the CMS Collaboration 2024 CERN

Evaluation of navigation capacity in the Northeast Arctic passage: evidence from multiple factors

ABSTRACT Arctic ice is melting away as a result of global warming, making possible the further commercial exploitations of the Arctic shipping routes, both to satisfy the shipping demand and to avoid the growing Somali piracy. In this research, the hierarchical regression is utilized to examine the impact of natural and economic factors on the Northeast Arctic Route (NER) through sample data on cargo volume from 2011 to 2021. From the results, the natural factors related to sea ice have a negative impact on the navigability of the NER, whereas economic factors and sea ice melting periods have a positive impact. Additionally, all variables are standardized to compare their respective significances on the cargo volume. Furthermore, Russian energy exports have a greater impact on shipping capacity than other variables, followed by the number of ship transits. Finally, the interrupted time-series analysis is introduced to find that the project of Yamal liquefied natural gas (LNG) has a positive effect on the NER, which could not offset the side effect of the COVID-19 epidemic.

Physicochemical Analysis of Groundwater Quality of Some Selected Villages of Tosham Region

To evaluate the groundwater quality of the Tosham area of the Bhiwani district in the Indian state of Haryana, a comprehensive study has been conducted. Eleven physicochemical parameters, including pH, electrical conductivity, total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, chloride, nitrate, and sulphate, were analysed on 600 groundwater samples that were collected over the course of different seasons from ten selected villages. Various physicochemical parameters, including pH, total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, chloride, nitrate, and sulphate, are found to be higher than the desirable limits suggested by the ICMR, according to analytical data of some groundwater samples from the study areas. Groundwater quality parameters show that some sampling stations groundwater samples are highly unfit for human consumption due to high levels of total alkalinity, total hardness, total dissolved solids, calcium, magnesium, fluoride, and chloride. As a result, appropriate recommendations for improving the groundwater quality have been made

Flow cytometric genome size estimation of Hungarian grass varieties

In Keszthely, the breeding of grass species have several decades long history. Among other things, the aim of the maintenance work of the registered varieties is to preserve the ploidy levels that have been reported in the variety descriptions. Flow cytometry is a fast, modern tool for examining plant DNA content. In our pilot study, we compared the genome size of the four grass species maintained at the MATE Georgikon Campus with the data of known samples of similar ploidy levels published for the given grass species in the international database. Our results showed a high degree of agreement with the theoretically expected values, supporting the applicability of flow cytometry in plant breeding and in variety maintenance.

SCRN: Single-cell Gene Regulatory Network Identification in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease, and it consumes considerable medical resources with increasing number of patients every year. Mounting evidence show that the regulatory disruptions altering the intrinsic activity of genes in brain cells contribute to AD pathogenesis. To gain insights into the underlying gene regulation in AD, we proposed a graph learning method, Single-Cell based Regulatory Network (SCRN), to identify the regulatory mechanisms based on single-cell data. SCRN implements the γ-decaying heuristic link prediction based on graph neural networks and can identify reliable gene regulatory networks using locally closed subgraphs. In this work, we first performed UMAP dimension reduction analysis on single-cell RNA sequencing (scRNA-seq) data of AD and normal samples. Then we used SCRN to construct the gene regulatory network based on three well-recognized AD genes (APOE, CX3CR1, and P2RY12). Enrichment analysis of the regulatory network revealed significant pathways including NGF signaling, ERBB2 signaling, and hemostasis. These findings demonstrate the feasibility of using SCRN to uncover potential biomarkers and therapeutic targets related to AD.

Homicide Research in Southeast Europe

The paper critically analyses the methodology and practical aspects of conducting criminological homicide research in Southeast Europe. The analysis is based on two major studies of(lethal) violence: (1) the Balkan Homicide Study – a prosecution and court casefile-based study conducted between 2016 and 2019 in Croatia, Hungary, Kosovo, North Macedonia,Romania, and Slovenia, on a total sample of 2,073 cases involving 2,416 offenders and 2,379victims; and (2) the Croatian Violence Monitor – a court casefile-based study conducted between 2021 and 2023 in four major Croatian cities (Zagreb, Split, Rijeka and Osijek). The discussion covers key aspects of conceptualising, designing and utilising unique data collection instruments, choice of sampling strategies, data representativeness, normative and statistical context data, as well as practical aspects of empirical fieldwork and data analysis. The aimis to transparently and critically analyse the main conceptual, methodological and practical(dis)advantages of both studies as well as (lethal) violence research more generally. The said discussion is rounded up by presenting preliminary findings on the phenomenology of lethal violence in Croatia based on data from the Croatian Violence Monitor. Thereby we aim not only to contribute to a culturally much more aware criminology of (conducting)homicide research, but also to provide for most recently available original empirical data about the phenomenology of lethal violence. Ultimately, the analysed challenges will also highlight specific regional opportunities for further empirical research in the Balkans with proposals on how these might best be explored in future international and European comparative research undertakings.

High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning

Characterizing the spatial distribution of particles smaller than 10 μm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM10 at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM10 concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM10, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM10 in Beijing, with coefficient of determination of 0.75. MAML improves the PM10 estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.