Binning Of Data Research Articles

The redshift dependence of the inferred H0 in a local void solution to the Hubble tension

Abstract Galaxy number counts suggest that we are located within the Gpc-scale KBC void. The Hubble tension might arise due to gravitationally driven outflow from this void, as explored in detail by Haslbauer et al. We explore how the impact of the void on redshift decays at large distances. We define H0(z) as the present expansion rate H0 that would be inferred from observations in a narrow redshift range centred on z. We find H0(z) in three different ways, all of which give similar results. We then compare these results with the observations of Jia et al., who were careful to minimise the impact of correlations between H0 measurements from data in different redshift bins. We find reasonable agreement with their results for the Gaussian and Exponential void underdensity profiles, although the agreement is less good in the Maxwell-Boltzmann case. The latter profile causes severe disagreement with the observed bulk flow curve at z < 0.1 (Mazurenko et al.), so the tension with higher redshift data further highlights that the deepest part of the KBC void is probably near its centre. The observations show a decline of H0(z) towards the background Planck value in qualitative agreement with the considered models, even if we use a larger void. The good overall agreement with the recent results of Jia et al. suggests that the local supervoid evident from the galaxy luminosity density out to a Gpc might also solve the Hubble tension while retaining a low background H0 consistent with Planck data, assuming enhanced structure formation on >100 Mpc scales.

The first all-sky survey of star-forming galaxies with eROSITA. Scaling relations and a population of X-ray luminous starbursts

In this work, we present the results from a study of X-ray normal galaxies, that is, galaxies not harbouring active galactic nuclei (AGN), using data from the first complete all-sky scan of the X-ray survey (eRASS1) obtained with eROSITA on board the Spectrum-Roentgen-Gamma observatory. eRASS1 provides the first unbiased X-ray census of local normal galaxies, thus allowing us to study the X-ray emission (0.2-8.0 ) from X-ray binaries (XRBs) and the hot interstellar medium in the full range of stellar population parameters present in the local Universe. By combining the updated version of the Heraklion Extragalactic Catalogue (HECATE v2.0) value-added catalogue of nearby galaxies (Distance ; lesssim \,200$Mpc) with the X-ray data obtained from eRASS1, we studied the integrated X-ray emission from normal galaxies as a function of their star-formation rate ( ), stellar mass ( ), metallicity, and stellar population age. After applying stringent optical and mid-infrared activity classification criteria, we constructed a sample of 18790 bona fide star-forming galaxies (HEC-eR1 galaxy sample) with measurements of their integrated X-ray luminosity (using each galaxy's D$_ $) over the full range of stellar population parameters present in the local Universe. By stacking the X-ray data in bins, we studied the correlation between the average X-ray luminosity and the average stellar population parameters. We also present updated and -metallicity scaling relations based on a completely blind galaxy sample and accounting for the scatter dependence on the The average X-ray spectrum of star-forming galaxies is well described by a power law ($ $) and a thermal plasma component ($kT = 0.70^ $). We find that the integrated X-ray luminosity of the individual HEC-eR1 star-forming galaxies is significantly elevated (reaching $10^ $) with respect to what is expected from the current standard scaling relations. The observed scatter is also significantly larger. This excess persists even when we measured the average luminosity of galaxies in and metallicity bins, and it is stronger (up to $ sim 2$ dex) towards lower s. Our analysis shows that the excess is not the result of the contribution by hot gas, low-mass XRBs, background AGN, low-luminosity AGN (including tidal disruption events), or stochastic sampling of the XRB X-ray luminosity function. We find that while the excess is generally correlated with lower metallicity galaxies, its primary driver is the age of the stellar populations. Our analysis reveals a sub-population of very X-ray luminous starburst galaxies with higher specific SFRs (sSFRs), lower metallicities, and younger stellar populations. This population drives upwards the X-ray scaling relations for star-forming galaxies and has important implications for understanding the population of XRBs contributing in the most X-ray luminous galaxies in the local and high-redshift Universe. These results demonstrate the power of large blind surveys such eRASS1, which can provide a more complete picture of the X-ray emitting galaxy population and their diversity, revealing rare populations of objects and recovering unbiased underlying correlations.

DiffMAP-GP: Continuous 2D Diffusion Maps from Particle Trajectories without Data Binning using Gaussian Processes.

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we develop a Bayesian framework (DiffMAP-GP) by placing Gaussian Process (GP) priors on the family of candidate maps. For sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.

Estimating received level in behavioral response studies through the use of ancillary data.

Marine mammals are known to respond to various human noises, including and in certain cases, strongly, to military active sonar. Responses include small and short-term changes in diving behavior, horizontal avoidance of an ensonified area, and mass strandings. Considerable research has been conducted using short-term biologging tags to understand these responses. Yet researchers and managers want a better understanding of responses to sound over longer periods of time in a variety of contexts. The Atlantic Behavioral Response Study examines responses across multiple spatial and temporal scales using vessel-based focal follows, short-term biologging tags, and medium-term satellite transmitting tags. Since the latter do not record sound, we must intersect positions with a sound propagation model to estimate received sound pressure levels. We use all available information from (1) the observed x,y positions from the tag(s) and from focal follow vessels; (2) the discrete depth bin data (z) from the tag; (3) ocean bathymetry; and (4) outputs from sound propagation models. All these disparate streams of data contain varying levels of error in x, y, or z. We account for as much uncertainty as possible and include here a refined approach to better estimate the range of sound levels received by animals.

Inherently interpretable machine learning for credit scoring: Optimal classification tree with hyperplane splits

An accurate and interpretable credit scoring model plays a crucial role in helping financial institutions reduce losses by promptly detecting, containing, and preventing defaulters. However, existing models often face a trade-off between interpretability and predictive accuracy. Traditional models like Logistic Regression (LR) offer high interpretability but may have limited predictive performance, while more complex models may improve accuracy at the expense of interpretability. In this paper, we tackle the credit scoring problem with imbalanced data by proposing two new classification models based on the optimal classification tree with hyperplane splits (OCT-H). OCT-H provides transparency and easy interpretation with ‘if-then’ decision tree rules. The first model, the cost-sensitive optimal classification tree with hyperplane splits (CSOCT-H). The second model, the optimal classification tree with hyperplane splits based on maximizing F1-Score (OCT-H-F1), aims to directly maximize the F1-score. To enhance model scalability, we introduce a data sample reduction method using data binning and feature selection. We then propose two solution methods: a heuristic approach and a method utilizing warm-start techniques to accelerate the solving process. We evaluated the proposed models on four public datasets. The results show that OCT-H significantly outperforms traditional interpretable models, such as Decision Trees (DT) and Logistic Regression (LR), in both predictive performance and interpretability. On certain datasets, OCT-H performs as well as or better than advanced ensemble tree models, effectively narrowing the gap between interpretable models and black-box models.

Methods to optimize optical sensing of biotic plant stress – combined effects of hyperspectral imaging at night and spatial binning

In spatio-temporal plant monitoring, optical sensing (including hyperspectral imaging), is being deployed to, non-invasively, detect and diagnose plant responses to abiotic and biotic stressors. Early and accurate detection and diagnosis of stressors are key objectives. Level of radiometric repeatability of optical sensing data and ability to accurately detect and diagnose biotic stress are inversely correlated. Accordingly, it may be argued that one of the most significant frontiers and challenges regarding widespread adoption of optical sensing in plant research and crop production hinges on methods to maximize radiometric repeatability. In this study, we acquired hyperspectral optical sensing data at noon and midnight from soybean (Glycine max) and coleus wizard velvet red (Solenostemon scutellarioides) plants with/without experimentally infestation of two-spotted spider mites (Tetranychus urticae). We addressed three questions related to optimization of radiometric repeatability: (1) are reflectance-based plant responses affected by time of optical sensing? (2) if so, are plant responses to two-spotted spider mite infestations (biotic stressor) more pronounced at midnight versus at noon? (3) Is detection of biotic stress enhanced by spatial binning (smoothing) of hyperspectral imaging data? Results from this study provide insight into calculations of radiometric repeatability. Results strongly support claims that acquisition of optical sensing data to detect and characterize stress responses by plants to detect biotic stressors should be performed at night. Moreover, the combination of midnight imaging and spatial binning increased classification accuracies with 29% and 31% for soybean and coleus, respectively. Practical implications of these findings are discussed. Study results are relevant to virtually all applications of optical sensing to detect and diagnose abiotic and biotic stress responses by plants in both controlled environments and in outdoor crop production systems.

Model independent estimation of expansion rate from low and high redshift SNIa

Understanding the expansion rate history is a primary goal in cosmology. There are various ways to measure the expansion rate at different redshifts and measuring it through the luminosity distance of SNIa is widely used in cosmology. In this work, we split the latest dataset of SNIa (the Pantheon+ sample) into several redshift bins and compute the expansion rate through a non-parametric technique. To do this, we use the Gaussian process to obtain the expansion rate considering data in different redshift bins. For the sake of comparison, we also consider the [Formula: see text]CDM model and find the expansion history through an MCMC analysis. Our results indicate that the degeneracy between free parameters prevents a good constraint using the low redshifts data but this is not the case in the GP scenario. Moreover, we study how high redshift SNIa can affect the current expansion rate and observe that data at [Formula: see text] has almost no impact on the current expansion rate in both approaches.

Waste management 2.0 leveraging internet of things for an efficient and eco-friendly smart city solution.

Waste management poses a major challenge for cities worldwide, with significant environmental, economic, and social impacts. This paper proposes a novel waste management system leveraging recent advances in the Internet of Things (IoT), algorithms, and cloud analytics to enable more efficient, sustainable, and eco-friendly waste collection and processing in smart cities. An ultrasonic sensor prototype is tailored for reliable fill-level monitoring. A LoRaWAN and cellular network architecture provides city-wide connectivity. A cloud platform handles sensor data storage, processing, and analytics. Dynamic route optimization algorithms minimize time, distance, and fuel use based on real-time bin data. Extensive pilot studies in 10 different locations across Lahore, Pakistan, validated the system, processing over 200 million data points. The results showed a 32% improvement in route efficiency, a 29% decrease in fuel consumption and emissions, a 33% increase in waste processing throughput, and 18% vehicle maintenance savings versus conventional practices. This demonstrates quantifiable benefits across operational, economic, and sustainability dimensions. The proposed IoT-enabled waste management system represents a significant advancement towards sustainable and ecologically responsible waste practices in smart cities worldwide. This research provides a replicable model for holistic smart city solutions integrating sensing, algorithms, and analytics to transition civic operations towards data-driven, efficient paradigms. It represents a significant advancement in sustainable waste practices for smart cities worldwide. Further work could apply emerging technologies like automation and artificial intelligence to create waste management 3.0.

A Histogram Publishing Method under Differential Privacy That Involves Balancing Small-Bin Availability First

Differential privacy, a cornerstone of privacy-preserving techniques, plays an indispensable role in ensuring the secure handling and sharing of sensitive data analysis across domains such as in census, healthcare, and social networks. Histograms, serving as a visually compelling tool for presenting analytical outcomes, are widely employed in these sectors. Currently, numerous algorithms for publishing histograms under differential privacy have been developed, striving to balance privacy protection with the provision of useful data. Nonetheless, the pivotal challenge concerning the effective enhancement of precision for small bins (those intervals that are narrowly defined or contain a relatively small number of data points) within histograms has yet to receive adequate attention and in-depth investigation from experts. In standard DP histogram publishing, adding noise without regard for bin size can result in small data bins being disproportionately influenced by noise, potentially severely impairing the overall accuracy of the histogram. In response to this challenge, this paper introduces the SReB_GCA sanitization algorithm designed to enhance the accuracy of small bins in DP histograms. The SReB_GCA approach involves sorting the bins from smallest to largest and applying a greedy grouping strategy, with a predefined lower bound on the mean relative error required for a bin to be included in a group. Our theoretical analysis reveals that sorting bins in ascending order prior to grouping effectively prioritizes the accuracy of smaller bins. SReB_GCA ensures strict ϵ-DP compliance and strikes a careful balance between reconstruction error and noise error, thereby not only initially improving the accuracy of small bins but also approximately optimizing the mean relative error of the entire histogram. To validate the efficiency of our proposed SReB_GCA method, we conducted extensive experiments using four diverse datasets, including two real-life datasets and two synthetic ones. The experimental results, quantified by the Kullback–Leibler Divergence (KLD), show that the SReB_GCA algorithm achieves substantial performance enhancement compared to the baseline method (DP_BASE) and several other established approaches for differential privacy histogram publication.

Quantification of antimicrobial use on Irish dairy farms: A comparison of three recording methods

Antimicrobial use (AMU) data are essential for monitoring usage over time, facilitating reduction strategies to combat the threat of antimicrobial resistance (AMR) to both human and animal health. The objective of this study was to measure and describe AMU over a 12-mo period in Irish dairy herds and compare 3 different recording methods to a reference method. A sample of 33 Irish dairy herds were randomly selected from 6 private veterinary practices across Ireland. The herds were followed for a 12-mo period and their AMU was monitored using 3 recording methods: 1. Veterinary prescription data (VET), 2. The inventory of medicine bins on the farms (BIN), and 3. Farmer treatment records from herd recording software (APP). Each recording method was compared with a previously developed reference method for AMU. The reference method used was based on pre- and poststudy medicine stock on the farms combined with veterinary prescription data. Antimicrobial use was analyzed using both mass- and dosed-based metrics, including mass (mg) of antimicrobial active ingredient per population correction unit (mg/PCU), defined daily doses for animals (DDDVET) and defined course doses for animals (DCDVET). Median AMU was 16.24, 10.47, 8.87 and 15.55 mg/PCU by mass, and 2.43, 1.55, 1.19 and 2.26 DDDVET by dose for VET, BIN, APP, and reference method data, respectively. Reliability of the agreement between each pair of methods was quantified using the concordance correlation coefficient (CCC). When compared with the reference method, VET data had excellent reliability [95% confidence interval (CI) of CCC: 0.992-0.998]. The BIN data had good to excellent reliability [95% CI of CCC: 0.776-0.936]. The APP data had poor reliability when compared with the reference method [95% CI of CCC: -0.167-0.156]. Our results highlight that a small number of herds were contributing most to overall use and farmers showed varying levels of consistency in recording AMU. Veterinary data were the most reliable approach for assessing AMU when compared with a reference method of AMU. This is an important finding for the future monitoring of AMU at a national level.

Alternative statistical interpretation for the apparent plateaus in the duration distributions of gamma-ray bursts

Context. The existence of a plateau in the short-duration tail of the observed distribution of cosmological long-soft gamma ray bursts (LGRBs) is posited to be the first direct evidence of collapsars. A similar plateau in the short-duration tail of the observed duration distribution of short-hard gamma ray bursts (SGRBs) has been suggested as evidence of compact binary mergers. Aims. We present an equally plausible alternative interpretation for this evidence, based on a purely statistical approach. Methods. Specifically, we show that the observed plateau in the short-duration tail of the duration distribution of LGRBs can naturally occur in the statistical distributions of strictly positive physical quantities, exacerbated by the effects of mixing with the duration distribution of SGRBs, observational selection effects, and data aggregation (e.g., binning) methodologies. The observed plateau in the short-duration tail of the observed distributions of SGRBs may likewise result from a combination of sample incompleteness and inhomogeneous binning of data. We further confirm the impact of these factors on the observation of a plateau in the duration distributions of GRBs through extensive numerical Monte Carlo simulations. Results. This analysis corroborates and strengthens a purely statistical and sample-incompleteness interpretation of the observed plateau in the duration distribution of LGRBs and SGRBs, without invoking the physics of collapsars or jet-propagation through the stellar envelope.

Multivariate data binning and examples generation to build a Diabetic Retinopathy classifier based on temporal clinical and analytical risk factors

In this paper, we explore the possibility of exploiting retrospective clinical data from Electronic Health Records (EHR) for classification tasks in chronic patients. The different intervals, short length and high class imbalance make it unfeasible to use traditional time series techniques. The first contribution of the paper is a preprocessing method to construct a multivariate time series dataset using EHR data, which infers missing data and regularizes the data frequency. The second contribution addresses class imbalance by using domain knowledge and existing short EHR series. We synthetically extrapolate patients’ data by using similar long time series and a fuzzy-based approach. The paper addresses the problem of detection of Diabetic Retinopathy (DR). Expert domain knowledge from ophthalmologists has been used in the proposed techniques to guide the processing of time series. The novelty in that case study consists in not using eye-fundus image analysis. Instead, the proposed methods are based solely on EHR data. Several multivariate multiclass time series classifiers are used to detect the four levels of DR severity from the pre-processed data sequences. Experiments prove the quality of the sequence preprocessing techniques proposed for EHR data. Results indicate that the TapNet classifier is the best one for DR grading. Despite being tested for DR detection, the proposed data preparation methods are applicable to other diseases with similar characteristics.

Ultrahigh-Resolution K-Edge Imaging of Coronary Arteries With Prototype Deep-Silicon Photon-Counting CT: Initial Results in Phantoms.

Background Photon-counting CT (PCCT) represents a recent advancement in CT, offering improved spatial resolution and spectral separability. By using multiple adjustable energy bins, PCCT enables K-edge imaging, allowing mixed contrast agent distinction. Deep-silicon is a new type of photon-counting detector with different characteristics compared with cadmium photon-counting detectors. Purpose To evaluate the performance of a prototype deep-Si PCCT scanner and compare it with that of a state-of-the-art dual-energy energy-integrating detector (EID) scanner in imaging coronary artery plaques enhanced with iodine and K-edge contrast agents. Materials and Methods A series of 10 three-dimensional-printed inserts (diameter, 3.5 mm) was prepared, and materials mimicking soft and calcified plaques were added to simulate stenosed coronary arteries. Inserts filled with an iodine- or gadolinium-based contrast agent (GBCA) were scanned. Virtual monoenergetic images (VMIs) and iodine maps were generated using two- and eight-energy bin data from EID CT and PCCT, respectively. Gadolinium maps were calculated for PCCT. The CT numbers of VMIs and iodine maps were compared. Spatial resolution and blooming artifacts were compared on the 70-keV VMIs in plaque-free and calcified coronary arteries. Results No evidence of a significant difference in the CT number of 70-keV images was found except in inserts containing GBCAs. In the absence of a GBCA, excellent (r > 0.99) agreement for iodine was found. PCCT could quantify the GBCA within 0.2 mg Gd/mL ± 0.8 accuracy of the ground truth, whereas EID CT failed to detect the GBCA. Lumen measurements were more accurate for PCCT than for EID CT, with mean errors of 167 versus 442 µm (P < .001) compared with the 3.5-mm ground truth. Conclusion Deep-Si PCCT demonstrated good accuracy in iodine quantification and could accurately decompose mixtures of two contrast agents. Its improved spatial resolution resulted in sharper images with blooming artifacts reduced by 50% compared with a state-of-the-art dual-energy EID CT scanner. © RSNA, 2024.

A data-driven distributionally robust expansion planning model for ADNs with multi-microgrids considering energy trading strategy based on game theory

With the increasing integration of distributed energy resources, demand response, microgrids, and automation technologies in modern distribution networks, a market framework for local energy trading among microgrids and the distribution utility is a reality. However, the expansion planning problem for active distribution networks (ADNs) with multiple microgrids is complex due to uncertainties associated with distributed generation and competitive scenarios. This paper proposes a novel data-driven distributionally robust model that considers energy trading and uncertainties in generation and load to solve the expansion planning problem for ADNs with multiple microgrids. The uncertainties are represented using data bins and their probabilities, and the energy trading strategy is modeled based on game theory. The proposed model enables the distribution system operator (DSO) to find the best expansion planning from their perspective, while each microgrid seeks its own benefits. We exemplify the proposed model using a modified version of the IEEE 123-bus test system. Results demonstrate the effects of surplus generation levels on the energy price strategy and the influence of competitive environments on the expansion planning decision. The proposed model provides a new approach to address the complexities associated with the expansion planning problem for ADNs with multiple microgrids and could have significant implications for the design and operation of future power systems.

The Present-day Mass Function of Star Clusters in the Solar Neighborhood

This work analyzes the present-day mass function (PDMF) of 93 star clusters utilizing Gaia Data Release 3 data, with membership determined by the StarGo machine-learning algorithm. The impact of unresolved binary systems on mass estimation is rigorously assessed, adopting three mass ratio profiles for correction. The PDMF is characterized by the power-law index, α, derived through a robust maximum likelihood method that avoids biases associated with data binning. The value of α for stars between the completeness limited mass of Gaia (with a mean 0.3 M ⊙ for our cluster samples) and 2 M ⊙ exhibits stability for clusters younger than 200 Myr, decreasing for older clusters, particularly when considering stars within the half-mass radius. The PDMF of these star clusters is consistent with a dynamically evolved Kroupa initial mass function via the loss of low-mass stars. Cluster morphology shows a correlation with α, as α values exhibit a decreasing trend from filamentary to tidal-tail clusters, mirroring the sequence of increasing cluster age. The dependence of α on the total cluster mass is weak, with a subtle increase for higher-mass clusters, especially outside the half-mass radius. We do not observe a correlation between α and the mean metallicity of the clusters. Younger clusters have lower metallicity compared to their older counterparts, which indicates that the older clusters might have migrated to the solar neighborhood from the inner disk. A comparison with numerical models incorporating a black hole population suggests the need for observations of distant, older, massive open clusters to determine whether or not they contain black holes.

Dynamical analysis of a hyperbolic solution in scale-covariant theory

In this paper, we study an isotropic flat FLRW-model in scale-covariant theory of gravity [Formula: see text] 1 which is explained in terms of ordinary and covariant differentiation of scalar field [Formula: see text]. As we know, the deceleration parameter (DP) is time-dependent, so we consider the DP q as the function of t. Using this methodology, we find all the important cosmological factors in terms of a hyperbolic function of the cosmic time t. In turn, we create the model having the behavior of the late-time universe, which is ever accelerated expanding and faces a Big Freeze at the end. The model shows the quintessence dark energy model from early to late times. We compute the constrained values of Hubble parameter [Formula: see text] and the model parameter [Formula: see text] using joint analysis of the OHD data of 77-points and Pantheon bin data. The model exhibits point-type singularity as it begins with a point of zero volume, infinite energy density and temperature. Furthermore, we obtain the present value of DP [Formula: see text]. Also, we examine the ultimate behavior of our model by the proper analysis of energy conditions (ECs), cosmographical parameters and Statefinder diagnostic. Finally, the proposed model behaves like a quintessence dark energy model.

The Respective Effects of Vapor Pressure Deficit and Soil Moisture on Ecosystem Productivity in Southwest China

This study aims to examine the individual and combined effects of soil moisture (SM) and vapor pressure deficit (VPD) on ecosystem productivity in Southwest China. Utilizing the community land model (CLM) to simulate the regional soil moisture and vapor pressure deficit, we analyzed their impacts on ecosystem productivity through a data binning approach and employed sun-induced chlorophyll fluorescence yield (SIFyield) as a productivity indicator. Our findings highlight a significant coupling effect between SM and VPD, which diminishes with finer temporal data resolution. The data binning analysis indicates that VPD has a predominant influence on SIFyield across 70% of the study area, whereas SM is more influential in the remaining 30%. Notably, the correlation between SIFyield and SM, modulated by VPD, is stronger in forest and shrubland ecosystems, whereas in grasslands, the influence pattern is reversed, with VPD having a more significant impact. The study concludes that in Southwest China, ecosystem productivity is more significantly affected by VPD than by SM.

Genome-resolved metagenomics identifies novel active microbes in biogeochemical cycling within methanol-enriched soil.

Metagenome assembled genomes (MAGs), generated from sequenced 13C-labelled DNA from 13C-methanol enriched soils, were binned using an ensemble approach. This method produced a significantly larger number of higher-quality MAGs compared to direct binning approaches. These MAGs represent both the primary methanol utilizers and the secondary utilizers labelled via cross-feeding and predation on the labelled methylotrophs, including numerous uncultivated taxa. Analysis of these MAGs enabled the identification of multiple metabolic pathways within these active taxa that have climatic relevance relating to nitrogen, sulfur and trace gas metabolism. This includes denitrification, dissimilatory nitrate reduction to ammonium, ammonia oxidation and metabolism of organic sulfur species. The binning of viral sequence data also yielded extensive viral MAGs, identifying active viral replication by both lytic and lysogenic phages within the methanol-enriched soils. These MAGs represent a valuable resource for characterizing biogeochemical cycling within terrestrial environments.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

A tool for analyzing complex epitope binning data of monoclonal antibodies

A tool for analyzing complex epitope binning data of monoclonal antibodies