The GenTree Dendroecological Collection, tree-ring and wood density data from seven tree species across Europe

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

GAM-7: an organic-inorganic hybrid layered aluminophosphate crystal formed by zeolite transformation

The interzeolite conversion (IZC) of metallo- and aluminophosphate zeolites was demonstrated using a non-cyclic secondary amine, diisopropylamine, as the organic structure-directing agent (OSDA). The IZC of AlPO4-5 with AFI topology could successfully produce an unknown highly crystalline phase, named GAM-7. In metallo-aluminophosphates (metal-AlPOs), SAPO-5 with a small amount of SiO2 was also interconverted to a single crystal GAM-7, designated as [Si]GAM-7. However, in other metal-AlPOs, the corresponding GAM-7 crystals were formed as either an intermediate or a mixture of plural crystals. Additionally, it was found that GAM-7 could also be obtained by hydrothermal synthesis (HTS), but not synthesized from a silico-aluminophosphate hydrogel, indicating that [Si]GAM-7 can be prepared by only the IZC technique. The obtained GAM-7 crystal was well-characterized to elucidate the framework structure, and this revealed that GAM-7 was a new organic-inorganic hybrid layered aluminophosphate having a terminal POH group in the corrugated layered sheets, and the OSDA molecule between the layers was robustly interacting with an acidic POH site by a hydrogen bond. Furthermore, we have performed the tracking of pH values during the transformation from AlPO4-5 into GAM-7 and have argued a reasonable consideration for the IZC process.

Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer

Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.

Filling the agronomic data gap through a minimum data collection approach

Context

Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist.

Objectives

Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas.

Methods

The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country.

Results

Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations.

Conclusions

The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems.

Significance

The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.

Gut microbiome-metabolome interactions predict host condition

BACKGROUND

The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment.

RESULTS

We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]).

CONCLUSION

These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

A simulation study evaluating how population survival and genetic diversity in a newly established population can be affected by propagule size, extinction rates, and initial heterozygosity

The introduction and establishment of invasive species in regions outside their native range, is one of the major threats for the conservation of ecosystems, affecting native organisms and the habitat where they live in, causing substantial biological and monetary losses worldwide. Due to the impact of invasive species, it is important to understand what makes some species more invasive than others. Here, by simulating populations using a forward-in-time approach combining ecological and single polymorphic nucleotides (SNPs) we evaluated the relation between propagule size (number of individuals = 2, 10, 100, and 1,000), extinction rate (with values 2%, 5%, 10%, and 20%), and initial heterozygosity (0.1, 0.3, and 0.5) on the population survival and maintenance of the heterozygosity of a simulated invasive crab species over 30 generations assuming a single introduction. Our results revealed that simulated invasive populations with initial propagule sizes of 2-1,000 individuals experiencing a high extinction rate (10-20% per generation) were able to maintain over 50% of their initial heterozygosity during the first generations and that under scenarios with lower extinction rates invasive populations with initial propagule sizes of 10-1,000 individuals can survive up to 30 generations and maintain 60-100% of their initial heterozygosity. Our results can help other researchers better understand, how species with small propagule sizes and low heterozygosities can become successful invaders.

When Do Traits Tell More Than Species about a Metacommunity? A Synthesis across Ecosystems and Scales

AbstractLinking species traits with the variation in species assemblages across habitats has often proved useful for developing a more mechanistic understanding of species distributions in metacommunities. However, summarizing the rich tapestry of a species in all of its nuance with a few key ecological traits can also lead to an abstraction that provides less predictability than when using taxonomy alone. As a further complication, taxonomic and functional diversities can be inequitably compared, either by integrating taxonomic-level information into the calculation of how functional aspects of communities vary or by detecting spurious trait-environment relationships. To remedy this, we here synthesize analyses of 80 datasets on different taxa, ecosystems, and spatial scales that include information on abundance or presence/absence of species across sites with variable environmental conditions and the species' traits. By developing analyses that treat functional and taxonomic diversity equitably, we ask when functional diversity helps to explain metacommunity structure. We found that patterns of functional diversity explained metacommunity structure and response to environmental variation in only 25% of the datasets using a multitrait approach but up to 59% using a single-trait approach. Nevertheless, an average of only 19% (interquartile range = 0%-29%) of the traits showed a significant signal across environmental gradients. Species-level traits, as typically collected and analyzed through functional diversity patterns, often do not bring predictive advantages over what the taxonomic information already holds. While our assessment of a limited advantage of using traits to explain variation in species assemblages was largely true across ecosystems, traits played a more useful role in explaining variation when many traits were used and when trait constructs were more related to species' status, life history, and mobility. We propose future research directions to make trait-based approaches and data more helpful for inference in metacommunity ecology.

A roadmap to define and select aquatic biological traits at different scales of analysis

Trait-based approaches are a powerful tool, as they not only improve understanding of ecological complexity and functioning but also allow comparison across different ecosystems and biogeographical regions. They may be used to unveil ecosystem processes and assess community structures, but their great potential becomes limited when dealing with scattered trait data and historically unstandardised trait nomenclature. The lack of standardisation allows authors to use the terminology of their preference, which inevitably leads to ambiguous misunderstandings and limits comparison between different studies. There have been some attempts to organise the trait vocabulary, but even these are mostly created from the perspective of a single ecosystem, which limits their applicability. In this work, we conducted a systematic literature review that identified and compiled 1127 traits across 37 datasets of fishes, invertebrates and zooplankton from freshwater, marine and transitional ecosystems. This dataset was then used to build on the Marine Species Traits Wiki and to propose a new, unified approach to a trait vocabulary based directly on readily available trait data. We propose a single standardised designation for all the different traits identified and provide a list of all the different synonyms commonly used for these traits. A roadmap to help the trait selection process is also provided, offering a guide through four main steps and important questions for choosing an adequate set of traits at the beginning of any study, which constitutes one of the main challenges in functional ecology research. Overall, this proposal will provide a solid baseline for tackling gaps in trait nomenclature and ensuring a clearer future for functional ecology studies.

Real time anatomical landmarks and abnormalities detection in gastrointestinal tract

Gastrointestinal (GI) endoscopy is an active research field due to the lethal cancer diseases in the GI tract. Cancer treatments result better if diagnosed early and it increases the survival chances. There is a high miss rate in the detection of the abnormalities in the GI tract during endoscopy or colonoscopy due to the lack of attentiveness, tiring procedures, or the lack of required training. The procedure of the detection can be automated to the reduction of the risks by identifying and flagging the suspicious frames. A suspicious frame may have some of the abnormality or the information about anatomical landmark in the frame. The frame then can be analysed for the anatomical landmarks and the abnormalities for the detection of disease. In this research, a real-time endoscopic abnormalities detection system is presented that detects the abnormalities and the landmarks. The proposed system is based on a combination of handcrafted and deep features. Deep features are extracted from lightweight MobileNet convolutional neural network (CNN) architecture. There are some of the classes with a small inter-class difference and a higher intra-class differences, for such classes the same detection threshold is unable to distinguish. The threshold of such classes is learned from the training data using genetic algorithm. The system is evaluated on various benchmark datasets and resulted in an accuracy of 0.99 with the F1-score of 0.91 and Matthews correlation coefficient (MCC) of 0.91 on Kvasir datasets and F1-score of 0.93 on the dataset of DowPK. The system detects abnormalities in real-time with the detection speed of 41 frames per second.

Cloning, sequencing, and characterizing of soil antibiotic active-producing Streptomyces species-specific DNA markers

Association of the antibiotic activity of the soil Streptomyces isolates to their genetic profiles analyzed through RAPD-PCR fingerprints prompted us here in this study to use the most common bands as specific markers to identify homologous proteins within these isolates by cloning, sequencing, and characterizing these markers. Six out of twelve DNA bands ranged between 600 and 1350 bp previously obtained by RAPD-PCR analysis were purified out of the RAPD gels, and then cloned into pGEM-T Easy vector system. Success of the cloning process was confirmed by digesting purified plasmids with EcoRI. The clones namely No. 54, 55, 20, 56, 57, and 58 were sequenced using the DNA BigDye Terminator Sequencing System utilizing the M13 primer. Results indicated that the size of the inserted sequences is 599, 566, 522, 870, 857, and 254 bp, in clones No. 54. 55, 20, 56, 57, and 58, respectively. Homologous proteins of the six cloned sequences generated by DNA blast software indicated that the highest score of protein homology was scored for clone No. 54 with 87 % homology to putative secreted pectate lyase [Streptomyces coelicolor A3(2)]. The other clones showed less homology with 77 % homology for the clones No. 55 and 56, 73 % homology for the clone No. 20, and 55 % homology for the clones No. 57 and 58. The association of homologous proteins to the reported RAPD pattern is confirmed here for the first time, and the resulting DNA cloned fragments deserve further molecular analysis.

Δ2 machine learning for reaction property prediction

The emergence of Δ-learning models, whereby machine learning (ML) is used to predict a correction to a low-level energy calculation, provides a versatile route to accelerate high-level energy evaluations at a given geometry. However, Δ-learning models are inapplicable to reaction properties like heats of reaction and activation energies that require both a high-level geometry and energy evaluation. Here, a Δ2-learning model is introduced that can predict high-level activation energies based on low-level critical-point geometries. The Δ2 model uses an atom-wise featurization typical of contemporary ML interatomic potentials (MLIPs) and is trained on a dataset of ∼167 000 reactions, using the GFN2-xTB energy and critical-point geometry as a low-level input and the B3LYP-D3/TZVP energy calculated at the B3LYP-D3/TZVP critical point as a high-level target. The excellent performance of the Δ2 model on unseen reactions demonstrates the surprising ease with which the model implicitly learns the geometric deviations between the low-level and high-level geometries that condition the activation energy prediction. The transferability of the Δ2 model is validated on several external testing sets where it shows near chemical accuracy, illustrating the benefits of combining ML models with readily available physical-based information from semi-empirical quantum chemistry calculations. Fine-tuning of the Δ2 model on a small number of Gaussian-4 calculations produced a 35% accuracy improvement over DFT activation energy predictions while retaining xTB-level cost. The Δ2 model approach proves to be an efficient strategy for accelerating chemical reaction characterization with minimal sacrifice in prediction accuracy.

Forest inventory tree core archive reveals changes in boreal wood traits over seven decades

Boreal forests play an important role in the global carbon (C) cycle, and there is great interest in understanding how they respond to environmental change, including nitrogen (N) and water limitation, which could impact future forest growth and C storage. Utilizing tree cores archived by the Swedish National Forest Inventory, we measured stemwood traits, including stable N and C isotope composition which provides valuable information related to N availability and water stress, respectively, as well as N and C content, and C/N ratio over 1950-2017 in two central Swedish counties covering an area of ca. 55,000 sq. km (n = 1038). We tested the hypothesis that wood traits are changing over time, and that temporal patterns would differ depending on alternative dendrochronological reconstruction methods, i.e. the commonly applied "single tree method" (STM) or a conceptually stronger "multiple tree method" (MTM). Averaged across all MTMs, our data showed that all five wood traits for Picea abies and Pinus sylvestris changed over time. Wood δ15N strongly declined, indicating progressive nitrogen limitation. The decline in δ13C tracked the known atmospheric δ13CO2 signal, suggesting no change in water stress occurred. Additionally, wood N significantly increased, while C and C/N ratios declined over time. Furthermore, wood trait patterns sometimes differed between dendrochronological methods. The most notable difference was for δ15N, where the slope was much shallower for the STM compared to MTMs for both species, indicating that mobility of contemporary N is problematic when using the STM, resulting in substantially less sensitivity to detect historical signals. Our study indicates strong temporal changes in boreal wood traits and also indicates that the field of dendroecology should adopt new methods and archiving practices for studying highly mobile element cycles, such as nitrogen, which are critical for understanding environmental change in high latitude ecosystems.

Unzipped chromosome-level genomes reveal allopolyploid nematode origin pattern as unreduced gamete hybridization

The formation and consequences of polyploidization in animals with clonal reproduction remain largely unknown. Clade I root-knot nematodes (RKNs), characterized by parthenogenesis and allopolyploidy, show a widespread geographical distribution and extensive agricultural destruction. Here, we generated 4 unzipped polyploid RKN genomes and identified a putative novel alternative telomeric element. Then we reconstructed 4 chromosome-level assemblies and resolved their genome structures as AAB for triploid and AABB for tetraploid. The phylogeny of subgenomes revealed polyploid RKN origin patterns as hybridization between haploid and unreduced gametes. We also observed extensive chromosomal fusions and homologous gene expression decrease after polyploidization, which might offset the disadvantages of clonal reproduction and increase fitness in polyploid RKNs. Our results reveal a rare pathway of polyploidization in parthenogenic polyploid animals and provide a large number of high-precision genetic resources that could be used for RKN prevention and control.

Dying together: A convergence analysis of fatalities during COVID-19

Governments implemented countermeasures to mitigate the spread of the COVID-19 virus. This had a severe effect on the economy. We examine convergence patterns in the evolution of COVID-19 deaths across countries. We aim to investigate whether countries that implemented different measures managed to limit the number of COVID-19 deaths. We extend the most recent macro-growth convergence methodology to examine convergence of COVID-19 deaths. We combine a long memory stationarity framework with the maximal clique algorithm. This provides a rich and flexible club formation strategy that goes beyond the stationary/non stationary approach adopted in the previous literature. Our results suggest that strict measures (even belated) or an aggressive vaccination scheme can confine the spread of the disease while maintaining the strictness of the measures steady can lead to a burst of the virus. Finally, we observe that fiscal measures did not have an effect on the containment of the virus.

Global tree growth resilience to cold extremes following the Tambora volcanic eruption

Although the global climate is warming, external forcing driven by explosive volcanic eruptions may still cause abrupt cooling. The 1809 and 1815 Tambora eruptions caused lasting cold extremes worldwide, providing a unique lens that allows us to investigate the magnitude of global forest resilience to and recovery from volcanic cooling. Here, we show that growth resilience inferred from tree-ring data was severely impacted by cooling in high latitudes and elevations: the average tree growth decreased substantially (up to 31.8%), especially in larch forests, and regional-scale probabilities of severe growth reduction (below -2σ) increased up to 1390%. The influence of the eruptions extended longer (beyond the year 1824) in mid- than in high-latitudes, presumably due to the combined impacts of cold and drought stress. As Tambora-size eruptions statistically occur every 200-400 years, assessing their influences on ecosystems can help humankind mitigate adverse impacts on natural resources through improved management, especially in high latitude and elevation regions.

Two-dimensional microporous GAM-6 formed by the interzeolite conversion of CoAPO-5

The interzeolite conversion of CoAPO-5 with piperidine assistance yielded a new microporous zeotype, GAM-6. Characterization and structural elucidation revealed that GAM-6 has a 3-ring local structure and two-dimensional 10-ring pore channels, and shows reasonable microporosity and solid acidity.

Stability of human gut microbiome: Comparison of ecological modelling and observational approaches

The gut microbiome plays a pivotal role in the human body, and perturbations in its composition have been linked to various disorders. Stability is an essential property of a healthy human gut microbiome, which allows it to maintain its functional richness under the external influences. This property has been explored through two distinct methodologies - mathematical modelling based on ecological principles and statistical analysis drawn from observations in interventional studies. Here we conducted a meta-analysis aimed to compare the two approaches utilising the data from 9 interventional and time series studies encompassing 3512 gut microbiome profiles obtained via 16S rRNA gene sequencing. By employing the previously published compositional Lotka-Volterra method, we modelled the dynamics of the microbial community and evaluated ecological stability measures. These measures were compared to those based on observed microbiome changes. There was a substantial correlation between the outcomes of the two approaches. Particularly, local stability assessed within the ecological paradigm was positively correlated with observational stability measures accounting for the compositional nature of microbiome data. Additionally, we were able to reproduce the previously reported inverse relationship between the community's robustness to microorganism loss and local stability, attributed to the distinct impacts of coefficient characterising the network decomposition on these two stability assessments. Our findings demonstrate harmonisation between the ecological and observational approaches to microbiome analysis, advancing the understanding of healthy gut microbiome concept. This paves the way to develop efficient microbiome-targeting interventions for disease prevention and treatment.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

The impact of COVID-19 vaccination on human mobility: The London case

The COVID-19 pandemic has become a global public health crisis, causing significant morbidity and mortality worldwide. As an early response, different lockdowns were imposed in the UK (and the world) to limit the spread of the disease. Although effective, these measures profoundly impacted mobility patterns across cities, significantly reducing the number of people commuting to work or travelling for leisure. As different governments introduced massive vaccination programs to tackle the pandemic, cities have significantly but slowly increased human mobility, enabling the resumption of travel, work, and social activities. Nevertheless, how much can this return to normal mobility patterns be attributed to vaccines? In this study, we answer this question using a statistical approach, analysing two different open urban mobility datasets to quantify the effect vaccination rollouts have had on increased human activities.

A double-pointed wooden throwing stick from Schöningen, Germany: Results and new insights from a multianalytical study

The site of Schöningen (Germany), dated to ca. 300,000 years ago, yielded the earliest large-scale record of humanly-made wooden tools. These include wooden spears and shorter double-pointed sticks, discovered in association with herbivores that were hunted and butchered along a lakeshore. Wooden tools have not been systematically analysed to the same standard as other Palaeolithic technologies, such as lithic or bone tools. Our multianalytical study includes micro-CT scanning, 3-dimensional microscopy, and Fourier transform infrared spectroscopy, supporting a systematic technological and taphonomic analysis, thus setting a new standard for wooden tool analysis. In illustrating the biography of one of Schöningen's double-pointed sticks, we demonstrate new human behaviours for this time period, including sophisticated woodworking techniques. The hominins selected a spruce branch which they then debarked and shaped into an aerodynamic and ergonomic tool. They likely seasoned the wood to avoid cracking and warping. After a long period of use, it was probably lost while hunting, and was then rapidly buried in mud. Taphonomic alterations include damage from trampling, fungal attack, root damage and compression. Through our detailed analysis we show that Middle Pleistocene humans had a rich awareness of raw material properties, and possessed sophisticated woodworking skills. Alongside new detailed morphometrics of the object, an ethnographic review supports a primary function as a throwing stick for hunting, indicating potential hunting strategies and social contexts including for communal hunts involving children. The Schöningen throwing sticks may have been used to strategically disadvantage larger ungulates, potentially from distances of up to 30 metres. They also demonstrate that the hominins were technologically capable of capturing smaller fast prey and avian fauna, a behaviour evidenced at contemporaneous Middle Pleistocene archaeological sites.

Learning inequality during Covid-19: Evidence from secondary schools in Colombia

During 2020, the Covid-19 pandemic unleashed a socioeconomic crisis in most countries, as well as strict restrictions on mobility and social distancing were implemented. The pandemic brought a severe socioeconomic shock -decreasing economic activity- and forced policy responses that affected the education sector -notably school closures-. There is scarce evidence on the implications of the pandemic and its socioeconomic consequences on learning inequality, particularly in the Latin-American context. The aim of this paper is to measure the change in learning inequality during the years of the pandemic (2020-2021) in Colombia. To measure learning inequality, we use the results of a standardize exam taken by all upper secondary education graduates in the country. To capture inequality, we use secondary-level students' characteristics, their households, and schools' characteristics. Our econometric results suggest an increase in learning inequality between 48% and 372% depending on the dimension studied, except for gender where learning inequality decreased. Moreover, through dynamic specifications, we determine that for all the analyzed dimensions the 2020-2021 period represents a change in the trend of learning inequality as in the periods prior to the pandemic inequality gaps decreased or, at least, remained constant. We conclude with concrete and immediate policy recommendations to target the learning processes of vulnerable students and bridge learning gaps.

Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition

The recognition of inner speech, which could give a 'voice' to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.

Two novel endophytic Tolypocladium species identified from native pines in south Florida

This study investigated the incidence and diversity of Tolypocladium within trunks of south Florida slash pines (Pinus densa). Thirty-five isolates were recovered from trunk tissue including living phloem, cambium, and sapwood. Two novel species of Tolypocladium (T. subtropicale and T. trecense) are described here based on morphological and molecular analysis of concatenated LSU, ITS, tef-1, tub, and RPB1 sequences. Our findings expand our understanding of the distribution, diversity, and ecology of this genus and confirm that it is widely spread as an endophyte across ecosystems and hosts. Strains collected in this survey will be used in future bioassays to determine their potential ecological roles as mycoparasites or entomopathogens. Citation: Soares JM, Karlsen-Ayala E, Salvador-Montoya CA, Gazis R (2023). Two novel endophytic Tolypocladium species identified from native pines in south Florida. Fungal Systematics and Evolution 11: 51-61. doi: 10.3114/fuse.2023.11.04.

In silico secretome analyses of the polyphagous root-knot nematode Meloidogyne javanica: a resource for studying M. javanica secreted proteins

BACKGROUND

Plant-parasitic nematodes (PPNs) that cause most damage include root-knot nematodes (RKNs) which are a major impediment to crop production. Root-knot nematodes, like other parasites, secrete proteins which are required for parasite proliferation and survival within the host during the infection process.

RESULTS

Here, we used various computational tools to predict and identify classically and non-classically secreted proteins encoded in the Meloidogyne javanica genome. Furthermore, functional annotation analysis was performed using various integrated bioinformatic tools to determine the biological significance of the predicted secretome. In total, 7,458 proteins were identified as secreted ones. A large percentage of this secretome is comprised of small proteins of ≤ 300 aa sequence length. Functional analyses showed that M. javanica secretome comprises cell wall degrading enzymes for facilitating nematode invasion, and migration by disintegrating the complex plant cell wall components. In addition, peptidases and peptidase inhibitors are an important category of M. javanica secretome involved in compatible host-nematode interactions.

CONCLUSION

This study identifies the putative secretome encoded in the M. javanica genome. Future experimental validation analyses can greatly benefit from this global analysis of M. javanica secretome. Equally, our analyses will advance knowledge of the interaction between plants and nematodes.

MOSAIC - A Unified Trait Database to Complement Structured Population Models

Despite exponential growth in ecological data availability, broader interoperability amongst datasets is needed to unlock the potential of open access. Our understanding of the interface of demography and functional traits is well-positioned to benefit from such interoperability. Here, we introduce MOSAIC, an open-access trait database that unlocks the demographic potential stored in the COMADRE, COMPADRE, and PADRINO open-access databases. MOSAIC data were digitised and curated through a combination of existing datasets and new trait records sourced from primary literature. In its first release, MOSAIC (v. 1.0.0) includes 14 trait fields for 300 animal and plant species: biomass, height, growth determination, regeneration, sexual dimorphism, mating system, hermaphrodism, sequential hermaphrodism, dispersal capacity, type of dispersal, mode of dispersal, dispersal classes, volancy, and aquatic habitat dependency. MOSAIC includes species-level phylogenies for 1,359 species and population-specific climate data. We identify how database integration can improve our understanding of traits well-quantified in existing repositories and those that are poorly quantified (e.g., growth determination, modularity). MOSAIC highlights emerging challenges associated with standardising databases and demographic measures.

Identification, Diversity, and Distribution of Meloidogyne spp. in Vegetable Fields of South Georgia, U.S.A

Root-knot nematode (RKN; Meloidogyne spp.) is the most prevalent plant-parasitic nematode in vegetable fields of Georgia, with an incidence of 67.3%. Because aggressive RKN species are reported in the southeastern United States, molecular-based identification of RKN species was conducted on soil samples taken from a nematode surveillance study in 2018 from 292 RKN-infested vegetable fields in southern Georgia. The RKN-infested soil was potted with tomato cultivar Rutgers, and individual nematode females were isolated from galled roots and subjected to species-specific PCR and mitochondrial haplotype-based RKN species identification. The incidence (%), mean, and maximum relative abundance (second-stage juveniles per 100 cm3 of soil) of the five RKN species identified consisted of M. incognita (91.9, 486, 14,144), M. arenaria (36.0, 707, 14,144), M. floridensis (2.2, 909, 5,264), M. javanica (5.5, 352, 1,488), and M. haplanaria (0.7, 8, 14). A large proportion of fields (29%) had mixed populations of M. incognita and M. arenaria, which may reflect the region's long history of cotton and peanut cultivation. For unknown reasons, mixed populations of M. incognita and M. arenaria were associated with higher population densities. M. incognita is the most important RKN species in vegetable fields, followed by M. arenaria; therefore, pure or mixed populations of these species should be addressed in nematode management programs. Although at a lower incidence, the newly detected species, M. floridensis and M. haplanaria, have the potential to become a major threat since they reproduce on vegetables with Mi-resistant genes.

Post-processed data and graphical tools for a CONUS-wide eddy flux evapotranspiration dataset

Large sample datasets of in situ evapotranspiration (ET) measurements with well documented data provenance and quality assurance are critical for water management and many fields of earth science research. We present a post-processed ET oriented dataset at daily and monthly timesteps, from 161 stations, including 148 eddy covariance flux towers, that were chosen based on their data quality from nearly 350 stations across the contiguous United States. In addition to ET, the data includes energy and heat fluxes, meteorological measurements, and reference ET downloaded from gridMET for each flux station. Data processing techniques were conducted in a reproducible manner using open-source software. Most data initially came from the public AmeriFlux network, however, several different networks (e.g., the USDA-Agricultural Research Service) and university partners provided data that was not yet public. Initial half-hourly energy balance data were gap-filled and aggregated to daily frequency, and turbulent fluxes were corrected for energy balance closure error using the FLUXNET2015/ONEFlux energy balance ratio approach. Metadata, diagnostics of energy balance, and interactive graphs of time series data are included for each station. Although the dataset was developed primarily to benchmark satellite-based remote sensing ET models of the OpenET initiative, there are many other potential uses, such as validation for a range of regional hydrologic and atmospheric models.

Global 30 meters spatiotemporal 3D urban expansion dataset from 1990 to 2010

Understanding the spatiotemporal dynamics of global 3D urban expansion over time is becoming increasingly crucial for achieving long-term development goals. In this study, we generated a global dataset of annual urban 3D expansion (1990-2010) using World Settlement Footprint 2015 data, GAIA data, and ALOS AW3D30 data with a three-step technical framework: (1) extracting the global constructed land to generate the research area, (2) neighborhood analysis to calculate the original normalized DSM and slope height of each pixel in the study area, and (3) slope correction for areas with a slope greater than 10° to improve the accuracy of estimated building heights. The cross-validation results indicate that our dataset is reliable in the United States(R² = 0.821), Europe(R² = 0.863), China(R² = 0.796), and across the world(R² = 0.811). As we know, this is the first 30-meter 3D urban expansion dataset across the globe, which can give unique information to understand and address the implications of urbanization on food security, biodiversity, climate change, and public well-being and health.

Potential cultivation areas of Euterpe edulis (Martius) for rainforest recovery, repopulation and açai production in Santa Catarina, Brazil

Euterpe edulis is an endangered palm species that provides the most important non-timber forest product exploited in its natural habitat, the Brazilian Atlantic Forest hotspot^1,4. From 1991 to 2017, pasturelands, agriculture, and monoculture of tree plantations were responsible for 97% of Atlantic Forest deforested areas in Brazil and Santa Catarina was one of the Brazilian states with the greatest loss of forest area¹⁴. In the last decade, E. edulis fruits reached their highest commercial value, producing the southeastern equivalent of Amazonian ''açai'' (Euterpe oleracea)^5,7,8. As a shade-tolerant species, E. edulis adapts very well to agroforestry systems^8,10. To evaluate potential areas for cultivation of E. edulis through agroforestry systems, we developed and applied a spatial model for mapping suitable areas. To accomplish this, we analyzed multisource biophysical data and E. edulis distribution data from the Forest Inventory of Santa Catarina. We identified two areas with potential occurrence of the species, one in the domains of coastal Dense Ombrophilous Forest where the species is more common and another in the domains of inland Deciduous Seasonal Forest where its occurrence was suspected, but not proven, until 2021. Today, Deciduous Seasonal Forest is the most fragmented and impacted by agriculture. Our model, together with confirmed areas of occurrence, indicates that deciduous seasonal forest region should be prioritized for production and recovery of E. edulis through agroforestry systems.

Expectations, reference points, and compliance with COVID-19 social distancing measures

We study the behavioral impact of announcements about the duration of a policy and their relationship with people's expectations in the context of the COVID-19 lockdowns. We surveyed representative samples of Italian residents at three moments of the first wave of the pandemic to test how intentions to comply with social-isolation measures depend on the duration of their possible extension. Individuals were more likely to reduce, and less likely to increase, their compliance effort if the hypothetical extension was longer than they expected, whereas positive surprises had a lesser impact. The behavioral response to the (mis)match between expected versus hypothesized extensions is consistent with expectations acting as reference points and can help explain the increase in observed physical proximity in Italy following lockdown extension announcements. Our findings suggest that public authorities should consider citizens' expectations when announcing policy changes.

A dataset on corporate sustainability disclosure

Enterprises, as key emitters, play a vital role in promoting sustainable development. Corporate sustainability disclosure provides a key channel for stakeholders to gain insights into a company's sustainability progress. However, few studies have been conducted to measure sustainability disclosure at the firm level. In this study, we apply the machine learning techniques to listed companies' management discussion and analysis (MD&A) documents and construct a dataset on corporate sustainability disclosure, including the Corporate Sustainability Disclosure Index (CSDI), CSDI_Economic Dimension (CSDI_ECO), CSDI_Environmental Dimension (CSDI_ENV), and CSDI_Social Dimension (CSDI_SOCI). The dataset will be updated annually. To the best of our knowledge, this is the first sustainability disclosure dataset constructed at the firm level. Our dataset reflects corporate managements' sustainability attitudes and promotes the implementation of corporate sustainability strategies and subsequent sustainable economic and social outcomes.

The long-run effects of fetal PM2.5 exposure on mental health: evidence from China

This paper investigates the long-run effects of PM_2.5 exposure in utero on the mental health of adolescents. Using nationally representative survey data from China, we instrument the PM_2.5 exposure with wind speed to tackle the possible endogeneity problem. Our results show that mothers' PM_2.5 exposure during their pregnancy negatively affects the mental health of their children aged between 10 and 15 years. A 1 µg/m³ increase in PM_2.5 exposure in utero increases the probability of having a severe mental illness for adolescents by 0.6%. Our evidence supports the "fetal origins" hypothesis. We also find that fetal PM_2.5 exposure leads adolescents to be more likely to be absent from school and quarrel with their parents, implying that fetal PM_2.5 exposure may affect individuals' behavior when they grow up.

A two-step downscaling method for high-scale super-resolution of daily temperature - a case study of Wei River Basin, China

Climate data with high spatial and temporal resolution were of great significance for regional environmental management, such as for early response to possible predicted local climate changes and extreme weather. However, the current downscaling targets for CMIP6 climate simulations were mostly medium-resolution (MR) reanalysis data, which were still coarse for local analysis. A two-step downscaling method was proposed for 100 × resolution enhancements of general circulation model (GCM) daily temperature data in this study. First, the historical GCM outputs were 10 × downscaled to a set of dynamically predictable MR data using a deep convolutional neural network (CNN), which included both encode-decode structure and long-short skip connections. Then, using high-resolution (HR) topographic data and MR climate data as auxiliary data, the GCM data were super-resolved to a series of images with spatial resolution of 1 km. A one-step downscaling analysis combined only with HR topographic data was performed as comparison. Seven evaluation metrics were selected to evaluate the prediction accuracy, and the results showed that the overall performance of two-step downscaling method was better than one-step downscaling method. Higher Nash-Sutcliffe efficiency (NSE) and lower mean absolute relative error (MARE) indicated that the two-step method performed better prediction of peak and low values. It was further confirmed by accuracy evaluation on the 10% max and 10% min values of the testing dataset. The introduction of dynamically predictable MR data could provide effective detailed information during the downscaling process and improve the prediction accuracies. Finally, the projected data of four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) during 2015-2050 were downscaled to the study area. The complex temporal and spatial variations indicated that there were great differences in temperature changes in a basin, and differentiated management measures should be proposed in advance.

Neural network potentials for chemistry: concepts, applications and prospects

Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale.

Genome structure-based Juglandaceae phylogenies contradict alignment-based phylogenies and substitution rates vary with DNA repair genes

In lineages of allopolyploid origin, sets of homoeologous chromosomes may coexist that differ in gene content and syntenic structure. Presence or absence of genes and microsynteny along chromosomal blocks can serve to differentiate subgenomes and to infer phylogenies. We here apply genome-structural data to infer relationships in an ancient allopolyploid lineage, the walnut family (Juglandaceae), by using seven chromosome-level genomes, two of them newly assembled. Microsynteny and gene-content analyses yield identical topologies that place Platycarya with Engelhardia as did a 1980s morphological-cladistic study. DNA-alignment-based topologies here and in numerous earlier studies instead group Platycarya with Carya and Juglans, perhaps misled by past hybridization. All available data support a hybrid origin of Juglandaceae from extinct or unsampled progenitors nested within, or sister to, Myricaceae. Rhoiptelea chiliantha, sister to all other Juglandaceae, contains proportionally more DNA repair genes and appears to evolve at a rate 2.6- to 3.5-times slower than the remaining species.

Large volcanic eruptions elucidate physiological controls of tree growth and photosynthesis

Forest productivity projections remain highly uncertain, notably because underpinning physiological controls are delicate to disentangle. Transient perturbation of global climate by large volcanic eruptions provides a unique opportunity to retrospectively isolate underlying processes. Here, we use a multi-proxy dataset of tree-ring records distributed over the Northern Hemisphere to investigate the effect of eruptions on tree growth and photosynthesis and evaluate CMIP6 models. Tree-ring isotope records denoted a widespread 2-4 years increase of photosynthesis following eruptions, likely as a result of diffuse light fertilization. We found evidence that enhanced photosynthesis transiently drove ring width, but the latter further exhibited a decadal anomaly that evidenced independent growth and photosynthesis responses. CMIP6 simulations reproduced overall tree growth decline but did not capture observed photosynthesis anomaly, its decoupling from tree growth or the climate sensitivities of either processes, highlighting key disconnects that deserve further attention to improve forest productivity projections under climate change.

Mechanisms of tropical cyclone response under climate change in the community earth system model

Climate change induces a myriad of effects which influences the global tropical cyclone (TC) genesis frequency. Here we explore how North Atlantic and Western Pacific TCs are affected under climate change using a present-day and a future (1% pCO2 scenario) ensemble of high resolution simulations. We find that the number of TCs decreases (- 45 % ) in the North Atlantic but increases (+ 15 % ) in the Western Pacific. Part of these opposing variations are linked to differences in the ocean's meridional overturning circulation, which gives rise to a different sea surface temperature response and air-sea fluxes between the two basins. The results show the important role of oceanic climate change on TC response.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00382-023-06680-3.

Semantic wikis as flexible database interfaces for biomedical applications

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm .

Development of a universal height response model for transfer of Norway spruce (Picea abies L. Karst) in Fennoscandia

Norway spruce is a major industrial tree species in Fennoscandia and future productivity of the species must be secured by matching the variation in adaptation of the species with suitable sites for optimized performance. An appropriate transfer model for forest reproductive material (FRM) is crucial for regeneration of productive forests in the changing climatic conditions that are predicted to occur in Fennoscandia. We have developed a transfer model for prediction of height of Norway spruce in Norway, Sweden, and Finland, using data acquired from 438 progeny and provenance trials with 1919 genetic entries of local and transferred origins. Transfer of genetic material at a given site was expressed in terms of the difference in daylength (photoperiod) between the site and its origin. This variable best reflected the nonlinear response to transfer that has been commonly reported in previous studies. Apart from the transfer variable, the height prediction model included the age of material when height measurements were acquired, annual temperature sum over 5 °C, precipitation during the vegetation period, and interaction terms between test site and transfer variables. The results show that long northward transfers (4-5° latitude) seem to be optimal for relatively mild sites in southern parts of the countries where growing season is longer, and shorter northward transfers (2-4° latitude) for harsher northern sites with shorter growing seasons. The transfer model also predicts that southward transfers of Norway spruce would result in height growth reductions. The developed model provides foundations for development of common or national recommendations for genetically improving Norway spruce material in Fennoscandia.

Correlation between SARS-CoV-2 RNA concentration in wastewater and COVID-19 cases in community: A systematic review and meta-analysis

Wastewater-based epidemiology (WBE) has been considered as a promising approach for population-wide surveillance of coronavirus disease 2019 (COVID-19). Many studies have successfully quantified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentration in wastewater (CRNA). However, the correlation between the CRNA and the COVID-19 clinically confirmed cases in the corresponding wastewater catchments varies and the impacts of environmental and other factors remain unclear. A systematic review and meta-analysis were conducted to identify the correlation between CRNA and various types of clinically confirmed case numbers, including prevalence and incidence rates. The impacts of environmental factors, WBE sampling design, and epidemiological conditions on the correlation were assessed for the same datasets. The systematic review identified 133 correlation coefficients, ranging from -0.38 to 0.99. The correlation between CRNA and new cases (either daily new, weekly new, or future cases) was stronger than that of active cases and cumulative cases. These correlation coefficients were potentially affected by environmental and epidemiological conditions and WBE sampling design. Larger variations of air temperature and clinical testing coverage, and the increase of catchment size showed strong negative impacts on the correlation between CRNA and COVID-19 case numbers. Interestingly, the sampling technique had negligible impact although increasing the sampling frequency improved the correlation. These findings highlight the importance of viral shedding dynamics, in-sewer decay, WBE sampling design and clinical testing on the accurate back-estimation of COVID-19 case numbers through the WBE approach.

MYRiAD: a multi-array room acoustic database

In the development of acoustic signal processing algorithms, their evaluation in various acoustic environments is of utmost importance. In order to advance evaluation in realistic and reproducible scenarios, several high-quality acoustic databases have been developed over the years. In this paper, we present another complementary database of acoustic recordings, referred to as the Multi-arraY Room Acoustic Database (MYRiAD). The MYRiAD database is unique in its diversity of microphone configurations suiting a wide range of enhancement and reproduction applications (such as assistive hearing, teleconferencing, or sound zoning), the acoustics of the two recording spaces, and the variety of contained signals including 1214 room impulse responses (RIRs), reproduced speech, music, and stationary noise, as well as recordings of live cocktail parties held in both rooms. The microphone configurations comprise a dummy head (DH) with in-ear omnidirectional microphones, two behind-the-ear (BTE) pieces equipped with 2 omnidirectional microphones each, 5 external omnidirectional microphones (XMs), and two concentric circular microphone arrays (CMAs) consisting of 12 omnidirectional microphones in total. The two recording spaces, namely the SONORA Audio Laboratory (SAL) and the Alamire Interactive Laboratory (AIL), have reverberation times of 2.1 s and 0.5 s, respectively. Audio signals were reproduced using 10 movable loudspeakers in the SAL and a built-in array of 24 loudspeakers in the AIL. MATLAB and Python scripts are included for accessing the signals as well as microphone and loudspeaker coordinates. The database is publicly available (https://zenodo.org/record/7389996).

Economic costs of invasive rodents worldwide: the tip of the iceberg

Background

Rodents are among the most notorious invasive alien species worldwide. These invaders have substantially impacted native ecosystems, food production and storage, local infrastructures, human health and well-being. However, the lack of standardized and understandable estimation of their impacts is a serious barrier to raising societal awareness, and hampers effective management interventions at relevant scales.

Methods

Here, we assessed the economic costs of invasive alien rodents globally in order to help overcome these obstacles. For this purpose, we combined and analysed economic cost data from the InvaCost database-the most up-to-date and comprehensive synthesis of reported invasion costs-and specific complementary searches within and beyond the published literature.

Results

Our conservative analysis showed that reported costs of rodent invasions reached a conservative total of US$ 3.6 billion between 1930 and 2022 (annually US$ 87.5 million between 1980 and 2022), and were significantly increasing through time. The highest cost reported was for muskrat Ondatra zibethicus (US$ 377.5 million), then unspecified Rattus spp. (US$ 327.8 million), followed by Rattus norvegicus specifically (US$ 156.6 million) and Castor canadensis (US$ 150.4 million). Of the total costs, 87% were damage-related, principally impacting agriculture and predominantly reported in Asia (60%), Europe (19%) and North America (9%). Our study evidenced obvious cost underreporting with only 99 documents gathered globally, clear taxonomic gaps, reliability issues for cost assessment, and skewed breakdowns of costs among regions, sectors and contexts. As a consequence, these reported costs represent only a very small fraction of the expected true cost of rodent invasions (e.g., using a less conservative analytic approach would have led to a global amount more than 80-times higher than estimated here).

Conclusions

These findings strongly suggest that available information represents a substantial underestimation of the global costs incurred. We offer recommendations for improving estimates of costs to fill these knowledge gaps including: systematic distinction between native and invasive rodents' impacts; monetizing indirect impacts on human health; and greater integrative and concerted research effort between scientists and stakeholders. Finally, we discuss why and how this approach will stimulate and provide support for proactive and sustainable management strategies in the context of alien rodent invasions, for which biosecurity measures should be amplified globally.

Evaluating the difference in walk patterns among normal-weight and overweight/obese individuals in real-world surfaces using statistical analysis and deep learning methods with inertial measurement unit data

Unusual walk patterns may increase individuals' risks of falling. Anthropometric features of the human body, such as the body mass index (BMI), influences the walk patterns of individuals. In addition to the BMI, uneven walking surfaces may cause variations in the usual walk patterns of an individual that will potentially increase the individual's risk of falling. The objective of this study was to statistically evaluate the variations in the walk patterns of individuals belonging to two BMI groups across a wide range of walking surfaces and to investigate whether a deep learning method could classify the BMI-specific walk patterns with similar variations. Data collected by wearable inertial measurement unit (IMU) sensors attached to individuals with two different BMI were collected while walking on real-world surfaces. In addition to traditional statistical analysis tools, an advanced deep learning-based neural network was used to evaluate and classify the BMI-specific walk patterns. The walk patterns of overweight/obese individuals showed a greater correlation with the corresponding walking surfaces than the normal-weight population. The results were supported by the deep learning method, which was able to classify the walk patterns of overweight/obese (94.8 ± 4.5%) individuals more accurately than those of normal-weight (59.4 ± 23.7%) individuals. The results suggest that application of the deep learning method is more suitable for recognizing the walk patterns of overweight/obese population than those of normal-weight individuals. The findings from the study will potentially inform healthcare applications, including artificial intelligence-based fall assessment systems for minimizing the risk of fall-related incidents among overweight and obese individuals.

How do emerging long-read sequencing technologies function in transforming the plant pathology research landscape?

Long-read sequencing technologies are revolutionizing the sequencing and analysis of plant and pathogen genomes and transcriptomes, as well as contributing to emerging areas of interest in plant-pathogen interactions, disease management techniques, and the introduction of new plant varieties or cultivars. Long-read sequencing (LRS) technologies are progressively being implemented to study plants and pathogens of agricultural importance, which have substantial economic effects. The variability and complexity of the genome and transcriptome affect plant growth, development and pathogen responses. Overcoming the limitations of second-generation sequencing, LRS technology has significantly increased the length of a single contiguous read from a few hundred to millions of base pairs. Because of the longer read lengths, new analysis methods and tools have been developed for plant and pathogen genomics and transcriptomics. LRS technologies enable faster, more efficient, and high-throughput ultralong reads, allowing direct sequencing of genomes that would be impossible or difficult to investigate using short-read sequencing approaches. These benefits include genome assembly in repetitive areas, creating more comprehensive and exact genome determinations, assembling full-length transcripts, and detecting DNA and RNA alterations. Furthermore, these technologies allow for the identification of transcriptome diversity, significant structural variation analysis, and direct epigenetic mark detection in plant and pathogen genomic regions. LRS in plant pathology is found efficient for identifying and characterization of effectors in plants as well as known and unknown plant pathogens. In this review, we investigate how these technologies are transforming the landscape of determination and characterization of plant and pathogen genomes and transcriptomes efficiently and accurately. Moreover, we highlight potential areas of interest offered by LRS technologies for future study into plant-pathogen interactions, disease control strategies, and the development of new plant varieties or cultivars.

A data-science approach to predict the heat capacity of nanoporous materials

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal-organic frameworks and covalent-organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity.

Non-uniform aspects of the SARS-CoV-2 intraspecies evolution reopen question of its origin

Several hypotheses have been presented on the origin of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) from its identification as the agent causing the current coronavirus disease 19 (COVID-19) pandemic. So far, no solid evidence has been found to support any hypothesis on the origin of this virus, and the issue continue to resurface over and over again. Here we have unfolded a pattern of distribution of several mutations in the SARS-CoV-2 proteins in 24 geo-locations across different continents. The results showed an evenly uneven distribution of the unique protein variants, distinct mutations, unique frequency of common conserved residues, and mutational residues across these 24 geo-locations. Furthermore, ample mutations were identified in the evolutionarily conserved invariant regions in the SARS-CoV-2 proteins across almost all geo-locations studied. This pattern of mutations potentially breaches the law of evolutionary conserved functional units of the beta-coronavirus genus. These mutations may lead to several novel SARS-CoV-2 variants with a high degree of transmissibility and virulence. A thorough investigation on the origin and characteristics of SARS-CoV-2 needs to be conducted in the interest of science and for the preparation of meeting the challenges of potential future pandemics.

The role of SARS-CoV-2 accessory proteins in immune evasion

Many questions on the SARS-CoV-2 pathogenesis remain to answer. The SARS-CoV-2 genome encodes some accessory proteins that are essential for infection. Notably, accessory proteins of SARS-CoV-2 play significant roles in affecting immune escape and viral pathogenesis. Therefore SARS-CoV-2 accessory proteins could be considered putative drug targets. IFN-I and IFN-III responses are the primary mechanisms of innate antiviral immunity in infection clearance. Previous research has shown that SARS-CoV-2 suppresses IFN-β by infecting host cells via ORF3a, ORF3b, ORF6, ORF7a, ORF7b, ORF8, and ORF9b. Furthermore, ORF3a, ORF7a, and ORF7b have a role in blocking IFNα signaling, and ORF8 represses IFNβ signaling. The ORF3a, ORF7a, and ORF7b disrupt the STAT1/2 phosphorylation. ORF3a, ORF6, ORF7a, and ORF7b could prevent the ISRE promoter activity. The main SARS-CoV-2 accessory proteins involved in immune evasion are discussed here for comprehensive learning on viral entry, replication, and transmission in vaccines and antiviral development.

Questioning the spatial association between the initial spread of COVID-19 and transit usage in Italy

Within the much broader framework of global interest, the dilemma concerning the real impact of mode of transport on the spread of COVID-19 has been a priority for transport stakeholders and policy-makers. How dangerous is it to move around a certain territory? Does the danger depend on the mode of transport? By considering a novel and detailed dataset at the level of local labour markets, we analysed the spatial association between the pre-pandemic propensity to use public transport and excess mortality in Italy attributable to the initial spread of COVID-19. We found that places characterised by larger commuting flows exhibit higher excess mortality during the first wave of the pandemic, but observed no significant spatial association between excess mortality and transit usage. Our results were obtained by considering a wide range of heterogeneity in the estimation of quantile regressions across a variety of specifications. Although we do not provide a definitive answer concerning the risk associated with transit use, our analysis suggests that mobility, not modal choice, should be considered a main driver of the initial contagion.