The semi-empirical, kernel-driven, linear RossThick-LiSparseReciprocal (RTLSR) Bidirectional Reflectance Distribution Function (BRDF) model is used to generate the routine MODIS BRDF/Albedo product ...due to its global applicability and the underlying physics. A challenge of this model in regard to surface reflectance anisotropy effects comes from its underestimation of the directional reflectance signatures near the Sun illumination direction; also known as the hotspot effect. In this study, a method has been developed for improving the ability of the RTLSR model to simulate the magnitude and width of the hotspot effect. The method corrects the volumetric scattering component of the RTLSR model using an exponential approximation of a physical hotspot kernel, which recreates the hotspot magnitude and width using two free parameters (C1 and C2, respectively). The approach allows one to reconstruct, with reasonable accuracy, the hotspot effect by adjusting or using the prior values of these two hotspot variables. Our results demonstrate that: (1) significant improvements in capturing hotspot effect can be made to this method by using the inverted hotspot parameters; (2) the reciprocal nature allow this method to be more adaptive for simulating the hotspot height and width with high accuracy, especially in cases where hotspot signatures are available; and (3) while the new approach is consistent with the heritage RTLSR model inversion used to estimate intrinsic narrowband and broadband albedos, it presents some differences for vegetation clumping index (CI) retrievals. With the hotspot-related model parameters determined a priori, this method offers improved performance for various ecological remote sensing applications; including the estimation of canopy structure parameters.
•We have developed a new method to refine the hotspot obtained from the MODIS BRDF retrieval.•The method uses an exponential function to correct the Ross kernel.•The method was evaluated using multi-resolution BRDF data sets.•We examined the sensitivity of the hotspot parameters in characterizing hotspot effect.•We examined this method in retrieving intrinsic albedo and clumping index values.
•The eastern part of Supersegment is more depleted than the western part.•The Supersegment melting anomaly is not related to ridge-hotspot interaction.•Micro-hotspot, driven by plate tectonic process ...can cause the melting anomaly.•Micro-hotspot can represent regional mantle fertility, or extreme melt focusing.
Micro hotspots represent excess volcanism at the ultraslow spreading Southwest Indian Ridge (SWIR) unrelated to mantle hotspots, but to focused melt flow in the mantle, wide volcano spacing, and/or increased mantle fertility. Individual micro hotspot can reflect variations in regional mantle fertility, simultaneously affecting 100's of km of ridge, or extreme melt focusing to isolated segments in regions with unusually thick lithosphere. The Dragon Flag melting anomaly, erupting isotopically moderately depleted low-K tholeiite, is the best example of the former: an enormous ridge-centered volcano at 50.5°E. It is far from Crozet, and lies at the apex of a V-shaped trace consisting of ∼1 km anomalously elevated seafloor that extends ∼400 km to the northwest and southeast, indicating a sudden onset of excess volcanism at ∼11-8 Ma. This trend is opposite to that predicted by the hotspot framework, with inconsistent geochemistry. Narrowgate at 14.7°E is one of many micro hotspots that represent isolated large volcanic centers bounded by long amagmatic ridge sections. It also lies at the apex of an eastward V-shaped bathymetric trend, but erupts large volumes of alkali basalt.
Both micro hotspot varieties are an order of magnitude smaller than Wilson's classic hotspots, and are not fixed in the hotspot referenced frame. They can grow and remain stationary for extended periods, or migrate freely with respect to each other; driven by the evolving plate tectonic stress field. Dragon Flag, represents a plate reorganization during a prolong period of enhanced melt supply. Narrowgate, on the other hand represents excess volcanism due to extreme deep melt focusing of low degree melt from an unusually wide region in the mantle to an isolated volcanic segment where thick lithosphere caps melting at great depth. To constrain the origins of the micro hotspots six examples are evaluated in terms of their major and isotopic composition, prior plate history, depth and extent of melting and tectonic context. This includes new major, trace element, and heavy isotope data for the Dragon Flag Supersegment, and unpublished data for the Joseph Mayes and Narrowgate segments.
Regions harbouring high unique phylogenetic diversity (PD) are priority targets for conservation. Here, we analyse the global distribution of plant PD, which remains poorly understood despite plants ...being the foundation of most terrestrial habitats and key to human livelihoods. Capitalising on a recently completed, comprehensive global checklist of vascular plants, we identify hotspots of unique plant PD and test three hypotheses: (1) PD is more evenly distributed than species diversity; (2) areas of highest PD (often called 'hotspots') do not maximise cumulative PD; and (3) many biomes are needed to maximise cumulative PD. Our results support all three hypotheses: more than twice as many regions are required to cover 50% of global plant PD compared to 50% of species; regions that maximise cumulative PD substantially differ from the regions with outstanding individual PD; and while (sub-)tropical moist forest regions dominate across PD hotspots, other forest types and open biomes are also essential. Safeguarding PD in the Anthropocene (including the protection of some comparatively species-poor areas) is a global, increasingly recognised responsibility. Having highlighted countries with outstanding unique plant PD, further analyses are now required to fully understand the global distribution of plant PD and associated conservation imperatives across spatial scales.
•Using endemic-plant richness, we identified drivers and hotspots within hotspots.•Specifically, we identified nano- and micro-hotspots in two Mediterranean regions.•Richness was positively related ...to altitude and precipitation in all case studies.•Different levels of hotspots nested in hotspots were organized in a hierarchy.•This downscaling approach may help to focus conservation efforts on a given hotspot.
Detecting smaller hotspots within larger hotspots could be an essential tool to focus conservation efforts. In this study, we identified hotspots at two scales of analysis within the Mediterranean overall hotspot. Particularly, based on the distribution of endemic-vascular-plant richness (EVPR), we identified micro-hotspots, among the richest floristic territories of the Sardinian and Baetic regions, and nano-hotspots, among the richest 1-km2 grid cells of Sierra Nevada and Gennargentu massifs, located within these regions. In addition, we explored environmental drivers of EVPR, performing both simple- and multiple-regression models. Our results showed that even in areas previously defined as hotspots, the endemic-plant richness was not uniformly distributed, but rather depended largely on environmental conditions. Relationships between environmental drivers and EVPR have been poorly studied in the Mediterranean context, where we found patterns consistent among scales and regions. Specifically, EVPR was positively linked to altitude and precipitation, particularly in the driest period. Hence, the different levels of hotspots nested in hotspots were organized in a hierarchy. This downscaling approach may help to focus conservation efforts within a given hotspot, e.g. the identification of narrow hotspots could be useful to find gaps in the protected-area networks. Specifically, the identified nano-hotspots are certainly priority sites for plant conservation, since the whole of the nano-hotspots in each region represented less than 1% of the surface area but contained more than 19% of the regional EVPR. Moreover, an examination of both where hotspots are and under what environmental conditions they appear, would enable the detection of specific threats.
Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within ...protein sequences. They have gained significant attention for their promising applications across various areas, including the sequence-based prediction of secondary and tertiary protein structure, the discovery of new functional protein sequences/folds, and the assessment of mutational impact on protein fitness. However, their utility in learning to predict protein residue properties based on scant datasets, such as protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore the feasibility of using protein language-learned representations as features for machine learning to predict PPI-hotspots using a dataset containing 414 experimentally confirmed PPI-hotspots and 504 PPI-nonhot spots.
Our findings showcase the capacity of unsupervised learning with protein language models in capturing critical functional attributes of protein residues derived from the evolutionary information encoded within amino acid sequences. We show that methods relying on protein language models can compete with methods employing sequence and structure-based features to predict PPI-hotspots from the free protein structure. We observed an optimal number of features for model precision, suggesting a balance between information and overfitting.
This study underscores the potential of transformer-based protein language models to extract critical knowledge from sparse datasets, exemplified here by the challenging realm of predicting PPI-hotspots. These models offer a cost-effective and time-efficient alternative to traditional experimental methods for predicting certain residue properties. However, the challenge of explaining why specific features are important for determining certain residue properties remains.
The Mascarene Islands in the western Indian Ocean, encompassing La Réunion, Mauritius, and Rodrigues, are the recent (<10 Ma) surface expressions of the Réunion hotspot. Ocean island basalts (OIB) ...from these islands exhibit a remarkably homogeneous long-lived radiogenic isotopic composition, coinciding with the convergence field of many global OIB trends in the mantle array. Réunion plume-related OIB therefore provide one of Earth’s most pristine representations of this “focal zone” component, which may have a primordial heritage. Besides this signature, Mascarene lavas have been suggested to retain contributions from sources with distinct compositions, including: (1) Archaean-aged zircons assimilated from continental crust within the oceanic lithosphere by trachytic magmas from Mauritius; (2) more deeply recycled continental crust components preserved by elevated 87Sr/86Sr and 208Pb/206Pb in lavas from the Piton des Neiges volcano of Réunion; and (3) an isotopically depleted mantle component resulting from interaction with Central Indian Ridge material.
In this study we use Sr-Nd-Pb isotope systematics, along with major and trace element compositions of basaltic lavas from all three Mascarene Islands to investigate the relationship of their sources to well-characterized mantle endmembers. Among the Mascarene Islands, Rodrigues lavas are the most enriched in highly incompatible elements, likely reflecting shallower and lower degrees of partial melting than Réunion or Mauritius. Combined Sr-Nd-Pb isotope compositions indicate that lavas from the Older Series of Mauritius resemble those from Réunion, whereas lavas from the Younger and Intermediate Series, together with Rodrigues, are consistent with contributions from an isotopically depleted component. In addition, the Pb isotopic compositions of Rodrigues samples require an additional contribution from a component with a long-term enrichment in its Th/U ratio. Based on isotope mixing models, direct assimilation of continental crust embedded within the oceanic lithosphere is unlikely to account for the Pb isotopic variation of Rodrigues. A metasomatized mantle component, previously envisaged as a “fossil” Réunion plume near the Central Indian Ridge, is partially able to reproduce the trace element signature, but not the observed Sr-Nd-Pb isotopic compositions of Rodrigues. Instead, small proportions (<5%) of an EM1-like component provide a preferred endmember. The composition and origin of this component, which is exclusively reflected within Rodrigues lavas, may constitute a new geochemical feature of the Mascarene Islands and is consistent with geodynamical predictions that small-scale enriched mantle domains may be widespread in the source regions of hotspot volcanoes.
Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at ...a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.
•This study identifies deadly landslides in 128 countries and it encompasses their role, for a 20 years period.•It is considered representative for establishing a relationship between landslides and their meteorological triggers.•A hot spot analysis for the daily record of landslide locations was performed.•A percentile-based approach was used to evaluate the trend of extreme rainfall events for each occurrence.•For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally.•A significant global rise in the number of deadly landslides and hotspots across the studied period was observed.•This study confirmed more than half of the events were in areas exposed to the risk of extreme rainfall.
The New England‐Québec Igneous Province is considered to be a continental expression of Great Meteor Hotspot magmatism, though other geodynamic scenarios have been suggested. Existing geochronologic ...data lack the needed accuracy and precision to permit tests of potential causal mechanisms. We provide zircon U‐Pb ages for four igneous centers and a suite of plate reconstructions and show that the duration between magmatism in each branch of this province is ca. 3–6 Myr shorter and ca. 10 Myr older than predicted if the spatial‐temporal distribution of magmatism conformed to a well‐defined age progression. However, in addition to uncertainties in plate reconstructions, variable regional crustal thickness or lithospheric topography likely played a role in mediating the rates of melt transport to emplacement depth and we therefore cannot reject the hotspot hypothesis. Our results place the best‐available chronological constraints on continental magmatism associated with one of the oldest and longest‐lived hotspots.
Plain Language Summary
We provide zircon U‐Pb age constraints on a purported episode of continental magmatism associated with the Great Meteor Hotspot. Combined with a suite of plate reconstructions, our results show broad consistency with a hotspot model and provide key chronological constraints on one of the longest‐lived hotspot tracks and the tectono‐magmatic evolution of the Eastern North American Margin.
Key Points
Zircon U‐Pb geochronology tests hotspot model for origin of continental magmatism typically associated with the Great Meteor Hotspot
New ages indicate a relatively brief episode of magmatism at ca. 122.8–126.1 Ma
Modeled hotspot tracks spatially consistent with igneous centers but generally underestimate timing of magmatism by ca. 10 Myr
Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Unlike current ...state-of-the-art works, which unite pattern matching and machine-learning engines, we fully exploit the strengths of machine learning using novel techniques. By combing topological classification and critical feature extraction, our hotspot detection framework achieves very high accuracy. Furthermore, to speed-up the evaluation, we verify only possible layout clips instead of full-layout scanning. We utilize feedback learning and present redundant clip removal to reduce the false alarm. Experimental results show that the proposed framework is very accurate and demonstrates a rapid training convergence. Moreover, our framework outperforms the 2012 CAD contest at International Conference on ComputerAided Design (ICCAD) winner on accuracy and false alarm.
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•Genomics recombination is a crucial factor in generating biodiversity and a driving force for evolution.•An innovative automated machine learning approach improves feature extraction ...and boosts prediction accuracy.•Our proposed model performs better when comparing with published models.•We conducted interpretability analysis on the model, investigated the impact of features on the model, and established a recombination hotspot prediction tool.
Meiotic recombination plays a pivotal role in genetic evolution. Genetic variation induced by recombination is a crucial factor in generating biodiversity and a driving force for evolution. At present, the development of recombination hotspot prediction methods has encountered challenges related to insufficient feature extraction and limited generalization capabilities. This paper focused on the research of recombination hotspot prediction methods. We explored deep learning-based recombination hotspot prediction and scrutinized the shortcomings of prevalent models in addressing the challenge of recombination hotspot prediction. To addressing these deficiencies, an automated machine learning approach was utilized to construct recombination hotspot prediction model. The model combined sequence information with physicochemical properties by employing TF-IDF-Kmer and DNA composition components to acquire more effective feature data. Experimental results validate the effectiveness of the feature extraction method and automated machine learning technology used in this study. The final model was validated on three distinct datasets and yielded accuracy rates of 97.14%, 79.71%, and 98.73%, surpassing the current leading models by 2%, 2.56%, and 4%, respectively. In addition, we incorporated tools such as SHAP and AutoGluon to analyze the interpretability of black-box models, delved into the impact of individual features on the results, and investigated the reasons behind misclassification of samples. Finally, an application of recombination hotspot prediction was established to facilitate easy access to necessary information and tools for researchers. The research outcomes of this paper underscore the enormous potential of automated machine learning methods in gene sequence prediction.
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