Independent Sites Research Articles

Linkage-specific ubiquitin binding interfaces modulate the activity of the chlamydial deubiquitinase Cdu1 towards poly-ubiquitin substrates.

The chlamydial deubiquitinase Cdu1 of the obligate intracellular human pathogenic bacterium Chlamydia trachomatis plays important roles in the maintenance of chlamydial infection. Despite the structural similarities shared with its homologue Cdu2, both DUBs display remarkable differences in their enzymatic activity towards poly-UB chain substrates. Whereas Cdu1 is highly active towards K48- and K63- poly-UB chains, Cdu2 activity is restricted mostly to mono-UB substrates. Here, we shed light on the molecular mechanisms of the differential activity and the substrate specificity of Cdu1 to better understand the cellular processes it is involved in, including infection-related events. We found that the strikingly elevated activity of Cdu1 relative to its paralogue Cdu2 can be attributed to an N-terminally extended α-helix, which has not been observed in Cdu2. Moreover, by employing isothermal titration calorimetry and nuclear magnetic resonance spectroscopy, we demonstrate the differential recognition of K48- and K63-linked poly-UB substrates by Cdu1. Whereas K63-linked poly-UB substrates appear to be recognized by Cdu1 with only two independent ubiquitin interaction sites, up to four different binding interfaces are present for K48-linked ubiquitin chains. Combined, our data suggest that Cdu1 possesses a poly-UB chain directed activity that may enable its function as a multipurpose DUB with a broad substrate specificity.

Evaluation of a near-patient SARS-CoV-2 novel rapid diagnostic platform.

The goal of this study is to test a novel device and methodology based on the "Pebble" platform and real-time quantitative colorimetric loop-mediated isothermal amplification (qcLAMP) during SARS-CoV-2 detection using crude samples and extracted RNA. The new method employs an inexpensive lightweight device aimed toward rapid point-of-care testing. An extensive evaluation was performed consisting of 1,693 clinical samples across five independent clinical testing centers. Positive colorimetric results were observed within 20 minutes of testing. At a 20-minute time-to-positive cut-off, the specificity is 98.5% with a diagnostic accuracy of 91.9%, compared to qPCR assays. Our findings indicate that the SARS-CoV-2 qcLAMP diagnostic assay in conjunction with the Pebble device is ideal for point-of-care/near-patient testing.IMPORTANCEHere, we describe our analyses and validation of a novel real-time quantitative colorimetric loop-mediated isothermal amplification (qcLAMP) device, available under the name "Pebble" and associated SARS-CoV-2 diagnostic qcLAMP assay for clinical diagnostic use. The analyses were performed in five independent testing sites across Europe using clinical samples from the associated clinical sites and support the use of "pebble" and associated kit in the diagnostic environment.

Opposed Aromatic Surfaces Behave as Independent Binding Sites for Carbohydrate Stacking: Analysis of Sandwich-like CH/π/CH Complexes

Opposed Aromatic Surfaces Behave as Independent Binding Sites for Carbohydrate Stacking: Analysis of Sandwich-like CH/π/CH Complexes

Inference of Phylogenetic Networks from Sequence Data using Composite Likelihood.

While phylogenies have been essential in understanding how species evolve, they do not adequately describe some evolutionary processes. For instance, hybridization, a common phenomenon where interbreeding between two species leads to formation of a new species, must be depicted by a phylogenetic network, a structure that modifies a phylogenetic tree by allowing two branches to merge into one, resulting in reticulation. However, existing methods for estimating networks become computationally expensive as the dataset size and/or topological complexity increase. The lack of methods for scalable inference hampers phylogenetic networks from being widely used in practice, despite accumulating evidence that hybridization occurs frequently in nature. Here, we propose a novel method, PhyNEST (Phylogenetic Network Estimation using SiTe patterns), that estimates binary, level-1 phylogenetic networks with a fixed, user-specified number of reticulations directly from sequence data. By using the composite likelihood as the basis for inference, PhyNEST is able to use the full genomic data in a computationally tractable manner, eliminating the need to summarize the data as a set of gene trees prior to network estimation. To search network space, PhyNEST implements both hill climbing and simulated annealing algorithms. PhyNEST assumes that the data are composed of coalescent independent sites that evolve according to the Jukes-Cantor substitution model and that the network has a constant effective population size. Simulation studies demonstrate that PhyNEST is often more accurate than two existing composite likelihood summary methods (SNaQ and PhyloNet) and that it is robust to at least one form of model misspecification (assuming a less complex nucleotide substitution model than the true generating model). We applied PhyNEST to reconstruct the evolutionary relationships among Heliconius butterflies and Papionini primates, characterized by hybrid speciation and widespread introgression, respectively. PhyNEST is implemented in an open-source Julia package and is publicly available at https://github.com/sungsik-kong/PhyNEST.jl.

Comparison of Objectivity, Transparency, Contextualization, and Trust Between Fact-Checking Sites and News Organizations

ABSTRACT This study compares U.S. adults’ perception about the performance of news organizations and independent fact-checking sites. Drawing upon a survey of 1580 respondents, this study finds that rating of the objectivity and trust in news organizations is somewhat higher than that of fact-checking sites. Fact-checking sites, however, are perceived to give more contextual information about current topics than news organizations. In terms of transparency, no significant difference is found. The analysis also finds that the positive perceptions about objectivity, transparency, contextualization, and trust significantly predict fact-checking site use. The results suggest that audiences perceive fact-checking sites to play a journalistic role to some extent, which may blur the boundary of journalism.

The structural biology and dynamics of malate dehydrogenases.

Malate dehydrogenase (MDH) enzymes catalyze the reversible oxidoreduction of malate to oxaloacetate using NAD(P) as a cofactor. This reaction is vital for metabolism and the exchange of reducing equivalents between cellular compartments. There are more than 100 structures of MDH in the Protein Data Bank, representing species from archaea, bacteria, and eukaryotes. This conserved family of enzymes shares a common nucleotide-binding domain, substrate-binding domain, and subunits associate to form a dimeric or a tetrameric enzyme. Despite the variety of crystallization conditions and ligands in the experimental structures, the conformation and configuration of MDH are similar. The quaternary structure and active site dynamics account for most conformational differences in the experimental MDH structures. Oligomerization appears essential for activity despite each subunit having a structurally independent active site. There are two dynamic regions within the active site that influence substrate binding and possibly catalysis, with one of these regions adjoining the subunit interface. In this review, we introduce the reader to the general structural framework of MDH highlighting the conservation of certain features and pointing out unique differences that regulate MDH enzyme activity.

A New Coupled Biogeochemical Modeling Approach Provides Accurate Predictions of Methane and Carbon Dioxide Fluxes Across Diverse Tidal Wetlands

AbstractTidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO2) and methane (CH4) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO2 and CH4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO2 and CH4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (Reco) modules explained, on average, 73% of the variation in CO2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH4 module also explained the majority of variance in CH4 emissions in validation sites (R2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on Reco, (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH4 emissions, and (d) CH4 responses to non‐periodic changes in salinity.

Accuracy assessment of recent global ocean tide models in coastal waters of the European North West Shelf

The accuracy of global ocean tide models is assessed in coastal waters of the European North West Shelf to ascertain where higher resolution local (forecast) models are most needed for geophysical and navigational applications, and which global models are most suitable for providing boundary conditions for regional and local tide models. Five recent global ocean tide models (FES2014b, EOT20, TPXO9-atlas-v5, GOT4.10c, and DTU16) are considered, with the models first compared by interpolating them onto common grids and computing the mean absolute deviation at each grid point. Coastline tide gauge and offshore bottom pressure sensor data were collated from several sources to give a total of 279 observation sites for evaluating model accuracy, including observational values from 137 locations that have not previously been released and have therefore not been assimilated into any of the global models tested. The residual errors between each model’s predicted phasor and the corresponding observed phasor were calculated at each observation location, and quantified using the root mean square (RMS) and median absolute residual (MAR) for the eight tidal constituents M2, S2, N2, O1, K1, K2, P1, and Q1. To avoid RMS values being biased by observation point density, a Voronoi-weighted RMS based on the water area of the Voronoi polygon about each observation location was also developed and used. Four zones were defined based on ocean depth to gauge model performance, and model inaccuracy is again demonstrated in near-shore regions. Seven further zones were defined based on geographical areas, which reveals inhomogeneity among the global models. The smallest overall root sum square (RSS) RMS error across all eight constituents arises with FES2014b, although TPXO9-atlas-v5 has the best performance when using the MAR and Voronoi-weighted RMS metrics. Using only the 137 observation sites that have not been assimilated by any model and therefore provide an independent accuracy assessment, FES2014b exhibits the smallest errors at the coastline, with an RSS RMS of 24.46 cm. All models exhibit larger errors with the 137 independent observation sites than with all 279 observation sites, with an average overall increase in RSS RMS error of 12%, and an increase of 30% for coastline tide gauges, highlighting the need for local model development in these areas.

Ternary gallide Zr7Pd7–xGa3+x (0 ≤ x ≤ 1.8): Synthesis, crystal and electronic structures

The new ternary compound Zr7Pd7-xGa3+x (0 ≤ x ≤ 1.8) was synthesized by arc melting the elements under argon and subsequent annealing the ingots at 870 K for 720 h. The polycrystalline samples were characterized by powder X-ray diffraction (XRD) and the crystal structure of the compound was determined from single crystal X-ray diffraction data. Zr7Pd7Ga3 crystallizes with a ternary version of the Zr7Ni10 type of structure exhibiting statistical mixtures of palladium and gallium atoms on three crystallographically independent nickel sites (oS68, Cmce, a = 12.997(3), b = 9.623(2), c = 9.630(2) Ǻ, Z = 4, R1 = 0.033, wR2 = 0.067 for 719 unique reflections with Io> 2σ(Io) and 48 refined parameters). The crystal structure of Zr7Pd7Ga3 is represented by a 3D Pd–Ga framework built of sinusoidal layers of Pd and Ga stacking along the b axis sandwiching the Zr atoms. The homogeneity range of the compound Zr7Pd7–xGa3+x has been refined from powder XRD and EDX data: 0 ≤ x ≤ 1.8; variation of the lattice parameters is the following: a = 13.0174(8)–12.904(3), b = 9.6306(8)–9.559(8), c = 9.6348(8)–9.700(5) Å. Electronic structure calculations have been performed for an idealized model Zr7Pd10 as well as a model compound Zr7Pd6.5Ga3.5 revealing that the Ga-free Zr7Pd10 is significantly destabilized by strong Pd–Pd anti-bonding interactions. Substitution of Pd by Ga reduces those, stabilizing Zr7(Pd,Ga)10 which features strong heteroatomic bonding between Zr and the Pd/Ga substructure, putting it into the class of polar intermetallics.

An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India

On the global scale, agricultural crop yields have decreased or plateaued over the last several decades. This suggests that the current focus on selecting crop varieties based on a plant’s light-use efficiency (photosynthetic and nitrogen-use-efficiency metrics) may not be sensitive to the site’s edaphic parameters, which limit growth. This study introduces a new framework to determine if crops can achieve higher yield potentials by assessing how plants adapt to the edaphic properties that impact growth, especially when contending with climate change. The new approach calculates an Ecosystem Fit index using a ratio of remotely sensed (or observed) total net primary productivity to the theoretical maximum productivity of the site. Then, it uses that index as a benchmark to judge quantitatively whether any new crop species or variety is improving potential biomass or economic yields at that specific site. It can also determine the best soil types for those crop varieties and monitor their potential adaptability relative to climate change over time. This study used a database of 356 spatially independent reference sites to develop this framework using a landcover classification of crops across 21 ecoregions and five biomes in India. It includes total net primary productivity data, theoretical maximum productivity potential, and soil and climatic data. This comparison showed that the light-use efficiency model, as intended, was not sensitive to variations in soil characteristics, temperature, or precipitation. Our framework showed significant differences in growth by soil type and precipitation and three significant productivity thresholds by soil type. The results of this study demonstrate that total crop productivity and Ecosystem Fit create a useful index for local land managers to assess growth and yield potentials across diverse edaphic landscapes and for decision-making with changing climates.

Allometric equations for biomass and carbon pool estimation in short rotation Pinus radiata stands of the Western Cape, South Africa

AbstractThe quantification of tree growth and carbon storage over time is an important task for sustainable forest management and carbon sequestration projects. For the South African short-rotation Pinus radiata (D. Don) forests, this knowledge is lacking. We developed allometric equations and compared the estimated weights to previously published biomass studies and we used Dirichlet Regression (DR) modelling to ensure additivity of the component proportions. The biomass components and their contribution to carbon storage depend strongly on forest structure and mean tree size but also on-site conditions and tree architecture. Our first two hypotheses were that the (1) best model for stemwood (SW), bark and total mass will include the combined variable DBH2H and (2) that the DR will yield statistically similar estimates for all components when compared to the best models. Our third hypothesis was that allometric equations developed for sites with high resource availability (e.g. wet, fertile sites) will yield biased estimates when extrapolated to sites with lower levels of resource availability (drier and/or infertile sites). The results indicated that DBH2H was the best variable to describe SW, bark and total mass and the DR yield similar estimates for all component proportions when compared to the best models. There were strong similarities in the SW and total mass of independent test sites in comparison to the SW and total mass of this study but greater variability in the bark, needle and branch mass. This can be associated to site and seasonal differences as well as variability in tree architecture brought about by different silvicultural operations on individual sites. Previously developed equations by other authors for sites with high resource availability overpredicted the SW and total mass of the models developed in this study. Our set of additive component equations performed well even when applied to sites of similar productivity over a climate gradient. The presented new equations bridge the gap in knowledge where allometric equations for short rotation Radiata pine stands are lacking.

High site fidelity and reduced survival of a mycophagous mammal after prescribed fire

AbstractFire offers both opportunities and risks for wildlife. Its impact will depend on the fire’s scale, how it alters key resources and how animals move. Understanding how wildlife respond to fire is crucial as climate change is predicted to increase wildfire risk and will likely result in more frequent prescribed fire to reduce wildfire risk. Invasive predators and inappropriate fire regimes in south-eastern Australia threaten the long-nosed potoroo (Potorous tridactylus), a vulnerable marsupial often residing in areas frequently exposed to fire. The cumulative impacts of fire and predation may increase the threat to P. tridactylus after fire, as predators can be more effective in the immediate post-fire environment and P. tridactylus is often dependent on thick ground cover. We present a before-after control-impact experiment describing the influence of prescribed fire on P. tridactylus. We fitted GPS collars to 52 individuals at nine independent sites to test if exposure to prescribed burning reduced their survival or altered their movement behavior. Prescribed fire reduced P. tridactylus survival, yet range size and diffusion (movement) rate remained largely unaffected. With limited fire exposure, P. tridactylus tended to continue using burnt areas whereas activity became restricted to unburnt areas when larger proportions of their home range burnt. Site fidelity was very high - individuals rarely moved their home ranges after fire, regardless of fire exposure. Our results suggest recently burnt areas may be particularly dangerous for P. tridactylus: areas that can be attractive yet confer lower fitness outcomes. P. tridactylus may benefit from smaller fire scars, retention of structurally complex vegetation, and integrating invasive predator control with prescribed burning.

O31 VALIDATION ASSESSMENT OF A CUFF-BASED CENTRAL BLOOD PRESSURE DEVICE ACCORDING TO ARTERY SOCIETY RECOMMENDATIONS

Background and objective. Uscom BP+ is a cuff-based blood pressure (BP) device designed to non-invasively estimate central BP as distinct from conventional brachial BP. This study aimed to assess the accuracy of the Uscom BP+ device compared to invasively measured BP. Methods. Automated non-invasive cuff central BP (using the Uscom BP+ device) and invasive central aortic BP (using fluid-filled catheters) were recorded simultaneously in 191 participants (65% male, aged 66±11 years) receiving coronary angiography at three independent research sites (Hobart, Australia, Perugia, Italy and Wroclaw, Poland). Validation assessment procedures were undertaken according to the Artery Society recommendations and with a minimal acceptable error of ≤5±≤8 mmHg as pass criteria. Results. Using the device default calibration technique (brachial cuff systolic [SBP] and diastolic BP [DBP]), cuff central SBP underestimated invasive central SBP [mean(SD) difference: -10.2(11.2) mmHg] and cuff central DBP overestimated invasive central DBP [mean(SD) difference: 9.7(8.6) mmHg]. When calibrating by cuff mean arterial pressure and DBP in a sub-group (n=140), SBP accuracy was improved, but variability remained high [mean(SD) difference: -6.4(13.8) mmHg, p=0.002 vs default calibration], whereas DBP accuracy and variability remained similar [mean(SD) difference: 9.0(8.4) mmHg, p=0.23 vs default calibration]. Conclusions. The Uscom BP+ cuff device does not pass the Artery Society accuracy criteria compared with invasively measured central BP.

Lazerckerite, Ag3.75Pb4.5(Sb7.75Bi4)S24, from Kutná Hora, Czech Republic: a new Sb–Bi member of the andorite branch of the lillianite homologous series

Abstract. Lazerckerite, ideally Ag3.75Pb4.5(Sb7.75Bi4)S24, is a new mineral species found in medieval mine dumps of the historic Ag–Pb–Zn Kutná Hora ore district, Czech Republic. The mineral is associated with other Sb–Bi lillianite homologues (terrywallaceite, gustavite, holubite) and Ag,Bi-bearing galena, most frequently as grain aggregates and replacement rims of earlier Ag–Pb–Bi minerals, growing together in aggregates of up to 0.6×0.3 mm. Lazerckerite is opaque, is steel grey in colour, and has a metallic lustre; the calculated density is 5.920 g cm−3. In reflected light lazerckerite is greyish white, and bireflectance and pleochroism are weak with grey tints. Anisotropy is weak to moderate with grey to bluish-grey rotation tints. Internal reflections are not observed. Electron microprobe analyses yielded the empirical formula, based on 44 apfu, (Ag3.61Cu0.04)Σ3.65(Pb4.55Fe0.01Cd0.01)Σ4.57(Sb7.87Bi3.75)Σ11.62(S24.15Se0.01)Σ24.16. Its unit cell parameters are a=13.2083(9), b=19.4595(8), c=8.4048(13), β=90.032(7)°, V=2160.3(4) Å3, space group P21/c, and Z=2. The structure of lazerckerite contains two Pb sites (Pb1 and Pb2) in bicapped trigonal prismatic coordination, 8 independent octahedral sites, and 13 distinct sulfur positions. Four of the octahedral sites are mixed (Sb,Bi) and (Bi,Sb) sites, one is a mixed (Ag,Bi) site, and one is a mixed (Sb,Pb) site. The new mineral belongs to the andorite branch of the lillianite homologous series with N=4 and is a new addition to the group of Sb–Bi mixed members of the series. Lazerckerite is defined as a lillianite homologue with the three following requirements: N=4, L (Ag++ (Bi3+, Sb3+) ↔ 2 Pb2+ substitution) ≈ 90 %–95 %, and approximately one-third of atom percentage of antimony is replaced by bismuth (Bi/(Bi+Sb) ≈ 0.30–0.38). The new mineral has been approved by the Commission on New Minerals, Nomenclature and Classification (CNMNC) of the International Mineralogical Association (IMA 2022-113) and named after Lazarus Ercker, the supreme Royal Bergmeister of the Kingdom of Bohemia and the Master of Prague Mint.

Alterations in Gray Matter Structural Networks in Amnestic Mild Cognitive Impairment: A Source-Based Morphometry Study.

Amnestic mild cognitive impairment (aMCI), considered as the prodromal stage of Alzheimer's disease, is characterized by isolated memory impairment and cerebral gray matter volume (GMV) alterations. Previous structural MRI studies in aMCI have been mainly based on univariate statistics using voxel-based morphometry. We investigated structural network differences between aMCI patients and cognitively normal older adults by using source-based morphometry, a multivariate approach that considers the relationship between voxels of various parts of the brain. Ninety-one aMCI patients and 80 cognitively normal controls underwent structural MRI and neuropsychological assessment. Spatially independent components (ICs) that covaried between participants were estimated and a multivariate analysis of covariance was performed with ICs as dependent variables, diagnosis as independent variable, and age, sex, education level, and site as covariates. aMCI patients exhibited reduced GMV in the precentral, temporo-cerebellar, frontal, and temporal network, and increased GMV in the left superior parietal network compared to controls (pFWER < 0.05, Holm-Bonferroni correction). Moreover, we found that diagnosis, more specifically aMCI, moderated the positive relationship between occipital network and Mini-Mental State Examination scores (pFWER < 0.05, Holm-Bonferroni correction). Our results showed GMV alterations in temporo-fronto-parieto-cerebellar networks in aMCI, extending previous results obtained with univariate approaches.

Shedding Light on Viral Shedding: Novel Insights into Nuclear Assembly, Cytoplasmic Transformation and Extracellular Vesicle Release of the BK Virus.

Despite the high prevalence of BK polyomavirus (BKPyV) and the associated risk for BKPyV-associated nephropathy (BKPyVAN) in kidney transplant (KTX) recipients, many details on viral processes such as replication, maturation, assembly and virion release from host cells have not been fully elucidated. VP1 is a polyomavirus-specific protein that is expressed in the late phase of its replicative cycle with important functions in virion assembly and infectious particle release. This study investigated the localization and time-dependent changes in the distribution of VP1-positive viral particles and their association within the spectrum of differing cell morphologies that are observed in the urine of KTX patients upon active BKPyV infection. We found highly differing recognition patterns of two anti-VP1 antibodies with respect to intracellular and extracellular VP1 localization, pointing towards independent binding sites that were seemingly associated with differing stages of virion maturation. Cells originating from single clones were stably cultured out of the urine sediment of KTX recipients with suspected BKPyVAN. The cell morphology, polyploidy, virus replication and protein production were investigated by confocal microscopy using both a monoclonal (mAb 4942) and a polyclonal rabbit anti-VP1-specific antibody (RantiVP1 Ab). Immunoblotting was performed to investigate changes in the VP1 protein. Both antibodies visualized VP1 and the mAb 4942 recognized VP1 in cytoplasmic vesicles exhibiting idiomorphic sizes when released from the cells. In contrast, the polyclonal antibody detected VP1 within the nucleus and in cytoplasm in colocalization with the endoplasmic reticulum marker CNX. At the nuclear rim, VP1 was recognized by both antibodies. Immunoblotting revealed two smaller versions of VP1 in urinary decoy cell extracts, potentially from different translation start sites as evaluated by in silico analysis. Oxford Nanopore sequencing showed integration of BKPyV DNA in chromosomes 3, 4 and 7 in one of the five tested primary cell lines which produced high viral copies throughout four passages before transcending into senescence. The different staining with two VP1-specific antibodies emphasizes the modification of VP1 during the process of virus maturation and cellular exit. The integration of BKPyV into the human genome leads to high virus production; however, this alone does not transform the cell line into a permanently cycling and indefinitely replicating one.

On the Search for Data-Driven and Reproducible Schizophrenia Subtypes Using Resting State fMRI Data From Multiple Sites.

For decades, fMRI data have been used to search for biomarkers for patients with schizophrenia. Still, firm conclusions are yet to be made, which is often attributed to the high internal heterogeneity of the disorder. A promising way to disentangle the heterogeneity is to search for subgroups of patients with more homogeneous biological profiles. We applied an unsupervised multiple co-clustering (MCC) method to identify subtypes using functional connectivity data from a multisite resting-state data set. We merged data from two publicly available databases and split the data into a discovery data set (143 patients and 143 healthy controls (HC)) and an external test data set (63 patients and 63HC) from independent sites. On the discovery data, we investigated the stability of the clustering toward data splits and initializations. Subsequently we searched for cluster solutions, also called "views," with a significant diagnosis association and evaluated these based on their subject and feature cluster separability, and correlation to clinical manifestations as measured with the positive and negative syndrome scale (PANSS). Finally, we validated our findings by testing the diagnosis association on the external test data. A major finding of our study was that the stability of the clustering was highly dependent on variations in the data set, and even across initializations, we found only a moderate subject clustering stability. Nevertheless, we still discovered one view with a significant diagnosis association. This view reproducibly showed an overrepresentation of schizophrenia patients in three subject clusters, and one feature cluster showed a continuous trend, ranging from positive to negative connectivity values, when sorted according to the proportions of patients with schizophrenia. When investigating all patients, none of the feature clusters in the view were associated with severity of positive, negative, and generalized symptoms, indicating that the cluster solutions reflect other disease related mechanisms.

Machine learning and fluorosensing for estimation of maize nitrogen status at early growth-stages

Potential of mobile fluorescence sensor measurements have been in focus for quantifying plant nitrogen (N) variability early in the crop growing season. Real time estimation of such N status indicators at field scale would enable precision management of N fertilizers. In standard practice, linear regression analysis involves the use of several fluorescence channels and indices as predictive variables for estimating plant nitrogen content. Considering the multi-collinearity between these predictor variables, the conventional regression analysis (multiple linear regression) often fails to deliver a good range of prediction accuracies. Hence, machine learning regression techniques are utilized in this study to estimate N status indicators i.e., %N, above ground biomass, and N uptake at V6 and V9 growth stages of maize across three site-years in 2012 and 2013 crop growing seasons. The Multiplex®3 (FORCE-A) portable active fluorescence system was used to capture fluorescence information. Derived indices including four N balance indices (NBI_R, NBI_B, NBI_B, and NBI1), two chlorophyll indices (CHL and CHL1), and one flavonoid index (FLAV) were used as predictors. The independent site data were first utilized in a Support Vector Regression (SVR) model to assess the training and test accuracies in estimation of N status indicators considering a comparative analysis between V6 and V9 growth stages. The current research also involved assessing how well the machine learning-trained model could be applied to a different dataset and validated its performance in a cross-site experimental setting. Subsequently, cross-site comparisons of nitrogen status estimates were conducted to recommend the selection of machine learning strategies. These strategies include (1) Partial Least Square Regression, (2) Support Vector Regression, (3) Gaussian Process Regression, (4) Random Forest Regression, and (5) Artificial Neural Network. The comparative investigation demonstrated promising accuracy in estimating plant nitrogen content, above-ground biomass, and nitrogen uptake at the V6 stages of maize, with correlation coefficients in the moderate range (r = 0.72 ± 0.03) and Root Mean Square Error. Superior prediction accuracies were obtained at V9 growth stages than at V6. Among the various machine learning models assessed, Support Vector Regression consistently outperformed the others across the three site-year datasets, delivering error estimates well within acceptable ranges and demonstrating the lowest RMSE values for maize nitrogen indicators.

Improving high uncertainty of evapotranspiration products under extreme climatic conditions based on deep learning and ERA5 reanalysis data

Accurate quantification of Terrestrial Evapotranspiration (ET) is crucial for understanding global water resource management amid climate change. However, against the backdrop of frequent global extreme climate events, existing ET products don’t accurately reflect ground data under extreme climate conditions (high temperature and high vapor pressure deficit (VPD)) and no studies have addressed this uncertainty. This study, based on data from 150 flux towers (FLUXNET ET), utilizes four daily-scale ET product datasets (REA, GLEAMv3.6b, REA, ERA5) and ERA5 reanalysis meteorological data as inputs for Convolutional Neural Network (CNN), to explore ways to improve the accuracy of ET products under extreme climatic conditions, while three Machine Learning (ML) models were selected as comparisons. An additional 35 nearby sites provide independent validation for model applicability. The results show that the estimation accuracy of ET products under extreme climatic conditions has an average decrease of 37.5 % in the correlation coefficient (R2) and an average increase of 40 % in the root-mean-square-error (RMSE), whereas the CNN significantly improves this high uncertainty and outperforms the other ML models. The accuracy was significantly improved with the addition of ET products (R2 = 0.924, RMSE=0.323 mm/d) compared to having only meteorological data as input (R2 = 0.888, RMSE=0.369 mm/d). Under extreme climatic conditions, the R2 and RMSE of CNN are 0.786 (171.6 % average improvement) and 0.448 (65.4 % average reduction) compared to ET products, with PBias ranging from −9% to 9 % and the most significant improvement in the America (region), EBF (land cover type), and Cwa (climate type). Furthermore, using ET products as inputs significantly enhances the CNN’s estimation accuracy, yielding R2 and RMSE improvements between 10 % and 30 %. At the 35 independent validation sites, the CNN still performs well, with R2 and RMSE around 0.720 and 0.510 mm/d under extreme climatic conditions, and the accuracy within 500 km is even higher. This study enhances model precision, providing a valuable tool for optimizing water management and tackling climate change. These advancements help formulate precise irrigation strategies and water policies, particularly in extreme weather-prone regions, supporting ecological stability and mitigating water scarcity.

No general support for functional diversity enhancing resilience across terrestrial plant communities

AbstractAimUnderstanding the mechanisms promoting resilience in plant communities is crucial in times of increasing disturbance and global environmental change. Here, we present the first meta‐analysis evaluating the relationship between functional diversity and resilience of plant communities. Specifically, we tested whether the resilience of plant communities is positively correlated with interspecific trait variation (following the niche complementarity hypothesis) and the dominance of acquisitive and small‐size species (following the mass ratio hypothesis), and for the context‐dependent effects of ecological and methodological differences across studies.LocationGlobal.Time Period2004–2021.Major Taxa StudiedVascular plants.MethodsWe compiled a dataset of 69 independent sites from 26 studies that have quantified resilience. For each site, we calculated functional diversity indices based on the floristic composition and functional traits of the plant community (obtained from the TRY database) which we correlated with resilience of biomass and floristic composition. After transforming correlation coefficients to Fisher's Z‐scores, we conducted a hierarchical meta‐analysis, using a multilevel random‐effects model that accounted for the non‐independence of multiple effect sizes and the effects of ecological and methodological moderators.ResultsIn general, we found no positive functional diversity–resilience relationships of grand mean effect sizes. In contrast to our expectations, we encountered a negative relationship between resilience and trait variety, especially in woody ecosystems, whereas there was a positive relationship between resilience and the dominance of acquisitive species in herbaceous ecosystems. Finally, the functional diversity–resilience relationships were strongly affected by both ecological (biome and disturbance properties) and methodological (temporal scale, study design and resilience metric) characteristics.Main ConclusionsWe rejected our hypothesis of a general positive functional diversity–resilience relationship. In addition to strong context dependency, we propose that idiosyncratic effects of single resident species present in the communities before the disturbances and biological legacies could play major roles in the resilience of terrestrial plant communities.