Data Sampling Strategies Research Articles

Improving Accessibility and Delivery of Healthcare Services to Underserved Children through Technology: A Comprehensive Approach of Literature Review

This study investigates the use of technology to improve healthcare accessibility and delivery for children in underprivileged communities through article review comprehensively. A literature review with qualitative method that involved four distinct steps. In the initial stage, the search was conducted using Google Scholar (GS) and Scopus website. The second stage was filtering, which entails selecting the journals that will be examined. The last stage was analysis the articles, this analysis uses a 4-step PRISMA process to identify 123 articles published from 2019 to 2024 in Scopus journal. The review was evaluated the efficacy of telemedicine, mobile health applications, and wearable devices in monitoring and addressing children's health, while considering genetic predispositions and underlying medical issues that influence growth patterns. The research emphasizes the importance of a comprehensive strategy in evaluating child growth and development, utilizing health data to tailor interventions. Online systems, such as mobile phones and PDAs, can support healthcare workers in underserved rural areas. The integration of technology is essential for addressing unique challenges in underserved areas and ensuring access to high-quality healthcare services for all children. Current monitoring methods include regular check-ups, growth charts, and developmental screenings, with advancements in technology providing new tools like mobile health apps and wearable devices. Involving various stakeholders in the evaluation process is crucial, with participatory research focusing on community empowerment, ownership, and capacity building. Online systems offer efficient and accessible ways to track health data but may come with privacy concerns and data breach risks. The study focuses on the effectiveness of online systems for monitoring child health indicators, discussing data collection methods, sampling strategies, and ethical considerations. Findings reveal the need to address areas such as limited social interaction and incorporate interactive activities and group projects into online curriculum. Understanding the impact of online learning on students' health and well-being is crucial for tailoring approaches to ensure comprehensive support. The research findings have significant implications for child health policy and practice, emphasizing the importance of social connection and mental well-being in online learning environments.

Leveraging local data sampling strategies to improve federated learning

Leveraging local data sampling strategies to improve federated learning

Machine Learning of Reactive Potentials.

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

Strengthening Financial Resilience: A Holistic Approach to Combatting Fraud

Financial fraud poses a persistent threat to individuals, businesses, and the broader economy, requiring a proactive and collaborative response. This paper navigates through the intricate landscape of financial fraud, addressing its various dimensions and offering a comprehensive strategy to fortify defenses. The overarching theme revolves around the critical nexus of innovation and entrepreneurship in the financial sector. Recognizing the significance of these elements in shaping the contemporary business environment, the paper elucidates the key challenges and opportunities they present. The literature review provides a thorough examination of existing studies on innovation and entrepreneurship, identifying gaps and areas for further research. Major theories, frameworks, and models relevant to the theme are explored, supplemented by insightful case studies showcasing successful endeavors in these domains. A meticulously crafted conceptual framework integrates essential concepts related to innovation and entrepreneurship. This framework not only clarifies key terms but also informs the subsequent analysis by establishing theoretical underpinnings. The research methodology section delineates the chosen approach, whether qualitative, quantitative, or a mix of both. It justifies the selected methods, outlines data collection techniques, sampling strategies, and acknowledges any inherent limitations. Moving into the heart of the paper, the analysis and findings section presents the results of research or analysis. Visuals such as charts or graphs augment the discussion, offering a visual representation of key findings. These findings are then dissected and connected back to the conceptual framework and the literature reviewed. In the subsequent discussion section, the results are interpreted within the broader context of innovation and entrepreneurship. Practical implications for businesses, policymakers, or researchers are dissected, addressing unexpected results and limitations encountered during the study. Possible avenues for future study are also discovered. The concluding section ties together the main points, revisits the initial objectives, and emphasizes the paper’s key contributions to the field. The conclusion doesn’t merely recapitulate; it offers practical insights and recommendations derived from the collective insights gained throughout the paper. In essence, this paper provides a roadmap for fortifying defenses against financial fraud, proposing recommendations for businesses, policymakers, and regulatory bodies. By embracing collaboration, technological innovation, and prioritizing awareness, the strategies outlined aim to contribute to a more secure financial landscape.

Towards automated assessment of participation and value creation in networked learning

This paper investigates the integration of artificial intelligence (AI) in Massive Open Online Courses (MOOCs) to improve student engagement and learning outcomes. MOOCs have transformed the landscape of online education, offering wide accessibility but at the price of high dropout rates and limited ability to measure student engagement. With MOOCs' expansive user base, it has been difficult until now to move beyond conventional metrics of course completion and quiz scores, which are insufficient for understanding the multifaceted nature of student engagement. The study addresses these challenges through a novel method of applying a large language model (LLM) to develop a more complex and meaningful understanding of student engagement and experiences in MOOCs.The research is grounded in the networked learning (NL) research tradition by using the Community of Inquiry framework to explore social, cognitive, and teaching presence in online educational environments and automate the assessment of student learning participation. The study focuses on social engagement and interaction, crucial for perceived value creation. Theoretical soundness, data targeting and quality, interpretation benchmarks, and robust data sampling and analysis strategies constitute the foundational pillars of the proposed predictive system. This framework guides the study's approach to address common MOOC issues, such as learner isolation and limited interaction, which are critical factors in students' perceived value and learning.The methodology involves a dual approach of human and LLM coding, testing the ability of LLMs to replicate human coding accuracy. The study was conducted in a non-massive open online course to test system effectiveness before applying it to larger MOOCs. Analysis indicates that an initial noncomplex LLM model can match human coding performance to 60% reliability, representing a significant step forward in automating qualitative analysis in online education, with potential for enhanced results using more complex LLM modelling. Key findings show that AI can effectively measure and enhance student engagement in MOOCs. The use of LLMs in coding qualitative data offers a reliable method for understanding student participation, which is vital for improving educational outcomes in online courses. The research highlights the potential of AI in transforming online education by providing deeper insights into student engagement and learning processes.The study also provides insights into the interplay of AI-measured engagement and learners' value perception, advancing the understanding of networked learning participation and its impact on educational outcomes. Implications extend to curriculum development, pedagogical strategies, and the broader field of online education. Future work includes expanding this methodology to more extensive MOOC environments and incorporating qualitative interviews to enrich the analysis.

Abstract 4926: Advancements in somatic variant calling from UG100 whole genome and whole exome sequencing data

Abstract Somatic variant calling involves the identification of genomic alterations that occur in somatic cells, requiring deep coverage to enable high sensitivity for low-frequency variants. Characterizing somatic variants across the entire genome therefore benefits from novel cost-efficient sequencing platforms, such as UG100. Here, we present optimization of variant calling tools for short and structural variants on WGS and WES data from UG100. For calling short variants, we optimized DeepVariant (DV) for somatic calling using data from matched tumor-normal sample pairs, improving both variant calling accuracy and pipeline running time (up to 10-fold). We defined the task of somatic variant calling as deciding if the pileup image containing reads from the tumor and normal samples represents a true somatic variant (vs a germline variant or artifact). The challenge of robust variant calling using deep learning models is exacerbated in somatic calling, where sequencing depth and coverage variability are typically high. Our optimized DV overcomes these challenges by several data sampling strategies. First, allele-frequency preserving down-sampling reduces randomness of read sub-sampling in high coverage regions. Second, alternative allele prioritization samples alt-allele supporting reads first allowing to call variants at very high coverage loci without sacrificing sensitivity and computational efficiency. Finally, a Panel-of-Normals based on targeted WES data provides an additional improvement of precision for this assay type. We used these strategies to train two models, one for tumor characterization using WGS (T/N coverage: 40x-150x/40x-100x), and one for deep WES (T/N coverage: >500x/>120x). We called variants on simulated tumors using the WGS model. For VAF>10% the model showed SNV recall >98% and indel recall >95% with false-positive rate of 0.2/Mb. For VAF range of 5-10%, indel recall was 67% and SNV recall was 86%. To demonstrate the utility of our somatic variant calling, we applied the models to call somatic variants from well characterized cancer cell lines: COLO829, HCC1395 and HCC1143. Results showed F1>90% for variants with VAF>10%. The WES model was used to reliably call variants at VAF>5% on simulated tumors with average SNV recall of 99% with precision >99% and indel recall >86% with precision >94%. To analyze structural and copy-number variations, we optimized the assembly engine of GRIDSS to enable fast calling of structural variations and demonstrate that Control-FREEC can be used to call copy number variants. SV calling on COLO829/COLO829BL achieved sensitivity >95%. In conclusion, our research highlights the utility of UG100 within the field of oncology, demonstrating its capacity for comprehensive and precise somatic variant detection, both on WGS and WES data. Citation Format: Doron Shem-Tov, Maya Levy, Gil Hornung, Ilya Soifer, Hila Benjamin, Ariel Jaimovich, Adam Blattler, William Brandler, Robert Sugar, Isaac Kinde, Omer Barad, Doron Lipson. Advancements in somatic variant calling from UG100 whole genome and whole exome sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4926.

Few-shot learning for defect detection in manufacturing

Quality control is being increasingly automatised in the context of Industry 4.0. Its automatisation reduces inspection times and ensures the same criteria are used to evaluate all products. One of the challenges when developing supervised machine learning models is the availability of labelled data. Few-shot learning promises to be able to learn from few samples and, therefore, reduce the labelling effort. In this work, we combine this approach with unsupervised methods that learn anomaly maps on unlabelled data, providing additional information to the model and enhancing the classification models' discriminative capability. Our results show that the few-shot learning models achieve competitive results compared to those trained in a classical supervised classification setting. Furthermore, we develop novel active learning data sampling strategies to label an initial support set. The results show that using sampling strategies to create and label the initial support set yields better results than selecting samples at random. We performed the experiments on four datasets considering real-world data provided by Philips Consumer Lifestyle BV and Iber-Oleff - Componentes Tecnicos Em Plástico, S.A.

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things.

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.

An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping

Data-driven flood susceptibility modeling is an efficient way to map the spatial distribution of flood likelihood. The quality of the flood susceptibility model relies on the learning technique and the data used for learning. The performance of learning techniques has been extensively examined. However, to date, the impact of data sampling strategies has received limited attention. Random sampling is widely favored because of its ease of use. It treats flood-related data as tabular and excludes their spatial dimensions. Flood occurrence is typically uneven over space. Therefore, non-flood sampling should not be completely random. To represent the impact of the spatial dimension, this study proposed a new sampling approach based on spatial dependence, called inverse-occurrence sampling. It selects more non-flood data in low-risk areas than in high-risk areas. The new sampling approach was compared with random and stratified sampling, using six machine learning techniques in two urban areas in Guangzhou, China, with distinct flood mechanisms, that is, Tianhe (flood density 1.5/km2, clustered distribution, average slope 9.02°, downtown district) and Panyu (flood density 0.15/km2, random distribution, average slope 4.55°, suburban district). Learning techniques include support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), convolutional neural networks (CNNs), CNN-SVM, and CNN-RF. The main findings of this study were as follows: (1) Sampling approaches had a greater impact on model performance than learning techniques in terms of area under the receiver operating characteristic curve (AUC). The AUC variations caused by learning techniques ranged from 0.04 to 0.09. Meanwhile, the AUC variations caused by sampling approaches were between 0.15 and 0.22, all larger than 0.1. (2) The new sampling approach outperformed that of the other two sampling approaches for high average AUC values and small AUC variations. The outperformance is robust in regard to multiple learning techniques and different flooding mechanisms. AUCs in the inverse group had a narrower range (0.14–0.18 in Tianhe and 0.35–0.39 in Panyu) than in the random group (0.24–0.28 in Tianhe and 0.43–0.53 in Panyu) and the stratified group (0.23–0.30 in Tianhe and 0.42–0.48 in Panyu). (3) The most accurate learning technique for AUC was CNN-RF, followed by SVM, CNN-SVM, RF, CNN, and ANN. (4) ANN- and CNN-based models tended to produce polarized patterns in flood susceptibility maps, contradicting the ascending order of flood density with increasing susceptibility levels. Flood density outliers tended to appear in the models derived using RF and CNN-RF. Finally, the newly proposed sampling approach is suggested to be applied to flood susceptibility mapping to reflect the impact of spatial dependence.

Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems

Agroforestry is a land-use system that combines arable and/or livestock management with tree cultivation, which has been shown to provide a wide range of socio-economic and ecological benefits. It is considered a promising strategy for enhancing resilience of agricultural systems that must remain productive despite increasing environmental and societal pressures. However, agroforestry systems pose a number of challenges for experimental research and scientific hypothesis testing because of their inherent spatiotemporal complexity. We reviewed current approaches to data analysis and sampling strategies of bio-physico-chemical indicators, including crop yield, in European temperate agroforestry systems to examine the existing statistical methods used in agroforestry experiments. We found multilevel models, which are commonly employed in ecology, to be underused and under-described in agroforestry system analysis. This Short Communication together with a companion R script are designed to act as an introduction to multilevel models and to promote their use in agroforestry research.

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.

Mapping Landslide Susceptibility Over Large Regions With Limited Data

AbstractLandslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub‐regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

Inconsistent discharge diagnoses for young cardiac arrest episodes: insights from a statewide registry.

Administrative coding of out-of-hospital cardiac arrest (OHCA) is heterogeneous, with the prevalence of noninformative diagnoses uncertain. To characterize the prevalence and type of non-informative diagnoses in a young cardiac arrest population. Hospital discharge diagnoses provided to a statewide OHCA registry were characterised as either 'informative' or 'noninformative.' Informative diagnoses stated an OHCA had occurred or defined OHCA as occurring due to coronary artery disease, cardiomyopathy, channelopathy, definite noncardiac cause, or no known cause. Noninformative diagnoses were blank, stated presenting cardiac rhythm only, provided irrelevant information or presented a complication of the OHCA as the main diagnosis. Characteristics of patients receiving informative versus noninformative diagnoses were compared. Of 1479 patients with OHCA aged 1 to 50 years, 290 patients were admitted to 15 hospitals. Ninety diagnoses (31.0%) were noninformative (arrest rhythm=50, blank=21, complication=10 and irrelevant=9). Two hundred diagnoses (69.0%) were informative (cardiac arrest=84, coronary artery disease=54, noncardiac diagnosis=48, cardiomyopathy=8, arrhythmia disorder=4 and unascertained=2). Only 10 diagnoses (3.5%) included both OHCA and an underlying cause. Patients receiving a noninformative diagnosis were more likely to have survived OHCA or been referred for forensic assessment (P= 0.011) and had longer median length of stay (9 vs 5 days, P= 0.0019). Almost one third of diagnoses for young patients discharged after an OHCA included neither OHCA nor any underlying cause. Underestimating the burden of OHCA impacts ongoing patient and at-risk family care, data sampling strategies, international statistics and research funding.

CROCO: CROSS-MODAL CONTRASTIVE LEARNING FOR LOCALIZATION OF EARTH OBSERVATION DATA

Abstract. It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map a digital elevation model (DEM) rasterized from aerial LiDAR point cloud on the aerial imagery. We proposed a contrastive learning-based method that trains on DEM and high-resolution optical imagery and experiment the framework on different data sampling strategies and hyperparameters. In the best scenario, the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained. The proposed method is promising for feature learning from RGB and DEM for localization and is potentially applicable to other data sources too. Source code is released at https://github.com/wtseng530/AVLocalization.

Comparison of Data Sampling Strategies for Approximate Processing of Queries to a Large Database

Interactive analytics requires database systems to promptly process aggregation queries. As the volume of data continues to grow at a high rate, this becomes more and more challenging. In the past, the database community has come up with two ideas to solve this problem: Approximate Query Processing (AQP) and Aggregate Precomputation (AggPre). In this article, we will focus on APQ. Many techniques were proposed for optimizing AQP performance. Most of them are based on the creation of the best stratified samples. But none of the existing approaches can answer the following questions: "Under what conditions will the sampling strategy be optimal? What is the maximum error in estimating an aggregate when using the strategy in case of data skew?" The article provides answers to the questions posed. The article discusses three strategies for selecting segments from a data array: 1) uniform, 2) based on the distribution of read records by segments, 3) based on the distribution of read records and aggregate values by segments. The article defines conditions for the optimality of each strategy. Proposed analytical methods solve the optimization problem and determine the conditions under which the value of the confidence interval of the estimated aggregate will be minimal. The paper also studied the effect of data skews on the accuracy of the estimation of aggregated values. The maximum value of the confidence interval is calculated at the border of the admissible region. Experiments on depersonalized banMng transactions demonstrated that the 3rd sampling strategy is much better than the first two strategies.

PLDA inspired Siamese networks for speaker verification

The deep learning methodologies in state-of-the-art speaker recognition systems are predominantly limited to the extraction of recording level embeddings. This is usually followed by generative modeling of the embeddings to output the verification score. In this paper, we explore a fully neural approach where the neural model outputs the verification score directly, given the acoustic feature inputs. This model, termed as Siamese neural network (SiamNN), combines the embedding extraction and back-end modeling into a single processing pipeline. The back-end modeling is achieved using a neural approach to PLDA modeling, called neural probabilistic linear discriminant analysis (NPLDA). In the NPLDA model, the verification score is computed as a discriminative similarity function. The development of the single neural SiamNN model allows the joint optimization of all the modules using a verification cost. Several speaker recognition experiments are performed using SITW, VOiCES, and NIST SRE datasets where the proposed SiamNN model is shown to significantly improve over the state-of-art x-vector PLDA baseline system (relative improvements of up to 35% in the primary cost metric). We also provide a detailed analysis of the influence of hyper-parameters, choice of loss functions, and data sampling strategies for training the model. In particular, we highlight that the proposed soft detection cost function based optimization improves over other loss functions considered.

A Literature Review of Studies that Have Compared the Use of Face-To-Face and Online Focus Groups

Online communication in our work and private lives has increased significantly since the COVID-19 pandemic. Qualitative research has evolved with this trend with many studies adopting online methods. It is therefore timely to assess the use and utility of online focus groups compared to face-to-face focus groups. Traditional Pearl Growing Methodology was used to identify eligible papers. Data were extracted on data collection methods, recruitment and sampling strategies, analytical approaches to comparing data sets, the depth of data produced, participant interactions and the required resources. A total of 26 papers were included in the review. Along with face-to-face focus groups ( n = 26) 16 studies conducted synchronous, eight asynchronous and two both online focus group methods. Most studies ( n = 22) used the same recruitment method for both face-to-face and online focus groups. A variety of approaches to compare data sets were used in studies. Of the studies reporting on depth of data ( n = 19), nine found that face-to-face groups produced the most in-depth data, four online groups and six equivalent data. Participant interaction was reported to be greater during face-to-face groups in 10 studies; three reported online groups produced greater interaction and six equivalent interaction. Detailed resource use comparisons were not presented in any of the studies. This review demonstrates that to date there is not a clear consensus as to whether face-to-face or online focus groups hold specific advantages in terms of the data produced and the resources required. Given these findings it may be appropriate for researchers to consider using online focus groups where time and resources are constrained, or where these are more practicable.

Towards Robust Calculation of Interannual CO2 Growth Signal from TCCON (Total Carbon Column Observing Network)

The CO2 growth rate is one of the key geophysical quantities reflecting the dynamics of climate change as atmospheric CO2 growth is the primary driver of global warming. As recent studies have shown that TCCON (Total Carbon Column Observing Network) measurement footprints embrace quasi-global coverage, we examined the sensitivity of TCCON to the global CO2 growth. To this end, we used the aggregated TCCON observations (2006-2019) to retrieve Annual Growth Rate of CO2 (AGR) at global scales. The global AGR estimates from TCCON (AGRTCCON) are robust and independent, from (a) the station-wise seasonality, from (b) the differences in time series across the TCCON stations, and from (c) the type of TCCON stations used in the calculation (“background” or “contaminated” by neighboring CO2 sources). The AGRTCCON potential error, due to the irregular data sampling is relatively low (2.4–17.9%). In 2006–2019, global AGRTCCON ranged from the minimum of 1.59 ± 2.27 ppm (2009) to the maximum of 3.27 ± 0.82 ppm (2016), whereas the uncertainties express sub-annual variability and the data gap effects. The global AGRTCCON magnitude is similar to the reference AGR from satellite data (AGRSAT = 1.57–2.94 ppm) and the surface-based estimates of Global Carbon Budget (AGRGCB = 1.57–2.85). The highest global CO2 growth rate (2015/2016), caused by the record El Niño, was nearly perfectly reproduced by the TCCON (AGRTCCON = 3.27 ± 0.82 ppm vs. AGRSAT = 3.23 ± 0.50 ppm). The overall agreement between global AGRTCCON with the AGR references was yet weakened (r = 0.37 for TCCON vs. SAT; r = 0.50 for TCCON vs. GCB) due to two years (2008, 2015). We identified the drivers of this disagreement; in 2008, when only few stations were available worldwide, the AGRTCCON uncertainties were excessively high (AGRTCCON = 2.64 ppm with 3.92 ppm or 148% uncertainty). Moreover, in 2008 and 2015, the ENSO-driven bias between global AGRTCCON and the AGR references were detected. TCCON-to-reference agreement is dramatically increased if the years with ENSO-related biases (2008, 2015) are forfeited (r = 0.67 for TCCON vs. SAT, r = 0.82 for TCCON vs. GCB). To conclude, this is the first study that showed promising ability of aggregated TCCON signal to capture global CO2 growth. As the TCCON coverage is expanding, and new versions of TCCON data are being published, multiple data sampling strategies, dynamically changing TCCON global measurement footprint, and the irregular sensitivity of AGRTCCON to strong ENSO events; all should be analyzed to transform the current efforts into a first operational algorithm for retrieving global CO2 growth from TCCON data.

Four-Dimensional Flow MRI of Abdominal Veins: A Systematic Review.

The aim of this systematic review is to provide an overview of the use of Four-Dimensional Magnetic Resonance Imaging of vector blood flow (4D Flow MRI) in the abdominal veins. This study was composed according to the PRISMA guidelines 2009. The literature search was conducted in MEDLINE, Cochrane Library, EMBASE, and Web of Science. Quality assessment of the included studies was performed using the QUADAS-2 tool. The initial search yielded 781 studies and 21 studies were included. All studies successfully applied 4D Flow MRI in abdominal veins. Four-Dimensional Flow MRI was capable of discerning between healthy subjects and patients with cirrhosis and/or portal hypertension. The visual quality and inter-observer agreement of 4D Flow MRI were rated as excellent and good to excellent, respectively, and the studies utilized several different MRI data sampling strategies. By applying spiral sampling with compressed sensing to 4D Flow MRI, the blood flow of several abdominal veins could be imaged simultaneously in 18–25 s, without a significant loss of visual quality. Four-Dimensional Flow MRI might be a useful alternative to Doppler sonography for the diagnosis of cirrhosis and portal hypertension. Further clinical studies need to establish consensus regarding MRI sampling strategies in patients and healthy subjects.

An updated classification of the jumping plant-lice (Hemiptera: Psylloidea) integrating molecular and morphological evidence

The classification of the superfamily Psylloidea is revised to incorporate findings from recent molecular studies, and to integrate a reassessment of monophyla primarily based on molecular data with morphological evidence and previous classifications. We incorporate a reinterpretation of relevant morphology in the light of the molecular findings and discuss conflicts with respect to different data sources and sampling strategies. Seven families are recognised of which four (Calophyidae, Carsidaridae, Mastigimatidae and Triozidae) are strongly supported, and three (Aphalaridae, Liviidae and Psyllidae) weakly or moderately supported. Although the revised classification is mostly similar to those recognised by recent authors, there are some notable differences, such as Diaphorina and Katacephala which are transferred from Liviidae to Psyllidae. Five new subfamilies and one new genus are described, and one secondary homonym is replaced by a new species name. A new or revised status is proposed for one family, four subfamilies, four tribes, seven subtribes and five genera. One tribe and eight genera / subgenera are synonymised, and 32 new and six revised species combinations are proposed. All recognised genera of Psylloidea (extant and fossil) are assigned to family level taxa, except for one which is considered a nomen dubium.