Complete Map Research Articles

Bounds on injective dimension and exceptional complete intersection maps

We prove that if f : R → S is a local homomorphism of noetherian local rings, and M is a non-zero finitely generated or artinian S-module whose injective dimension over R is bounded by the difference of the embedding dimensions of R and S, then M is an injective S-module and f is an exceptional complete intersection map.

Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador

Among the most intense geological disasters, landslides frequently occur throughout the world. These phenomena have been studied using space geodetic techniques, including Global Navigation Satellite Systems (GNSS) and Multi-Temporal Interferometric Synthetic Aperture Radars (MT-InSAR). Nevertheless, complete mapping and analysis of landslides’ surface deformation in most areas can be complicated due to a large diversity in kinematics, such as periods of quiescence and acceleration in the toe and crown. One of these landslides is the Cuenca landslides in Ecuador, where the geological investigation revealed that the toe of the landslides was located in urban areas, with more noticeable deformation effects. In contrast, its crown was located mainly in a rural and green land area. In this study, we show the potential of a synergistic use of COSMO-SkyMed (CSK) and Sentinel-1A (S1A) synthetic aperture radar (SAR) data for comprehensively monitoring the Cuenca landslides. To this aim, we have used Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) as two different Deep Learning Algorithms (DLAs) to integrate results in the temporal and spatial domain, respectively. A cross-comparison of the results was made with the nine GPS-derived deformations and the visual effects (i.e. crack width and pattern) on the field. This validation against GPS observation reveals that the RMSEs of the final MT-InSAR-derived velocity after applying the synergic double band SAR dataset decrease at more than 73% of nine GPS stations. Highlights Synergic MT-InSAR approach for studying landslide deformation in diverse kinematic areas. Utilized DLAs (LSTM and CNNs) for effective temporal and spatial interpolation of InSAR results. Findings emphasize the potential of multi-sensor SAR and DLAs for landslide monitoring regarding improving the RMSE at nine stations with an average of 73%.

Multiline observations of hydrogen, helium, and carbon radio-recombination lines toward Orion A: A detailed dynamical study and direct determination of physical conditions

We present a study of hydrogen, helium, and carbon millimeter-wave radio-recombination lines (RRLs) toward 10 representative positions throughout the Orion Nebula complex, using the Yebes 40 m telescope in the Q band (31.3 GHz to 50.6 GHz) at an angular resolution of about 45″ (~0.09 pc). The observed positions include the Orion Nebula (M42) with the Orion Molecular Core 1, M43, and the Orion Molecular Core 3 bordering on NGC 1973, 1975, and 1977. While hydrogen and helium RRLs arise in the ionized gas surrounding the massive stars in the Orion Nebula complex, carbon RRLs stem from the neutral gas of the adjacent photo-dissociation regions (PDRs). The high velocity resolution (0.3 km s−1) enables us to discern the detailed dynamics of the RRL emitting neutral and ionized gas. We compare the carbon RRLs with SOFIA/upGREAT observations of the [C II] 158 µm line and IRAM 30 m observations of the 13CO (J = 2−1) line (the complete map is presented here for the first time). We observe small differences in peak velocities between the different tracers, which cannot always be attributed to geometry but potentially to shear motions. Using the far-infrared [C II] and [13C II] intensities with the carbon RRL intensities, we can infer physical conditions (electron temperature Te and electron density ne, converted to hydrogen nuclei density nH by dividing by the carbon gas-phase abundance 𝒜c ≃ 1.4 × 10−4) in the PDR gas using nonlocal thermal equilibrium excitation models. For positions in OMC1, we infer ne ≃ 20–40 cm−3 and Te ≃ 210–240 K. On the border between OMC1 and M43, we observe two gas components with ne ≃ 2 cm−3 and ne ≃ 8 cm−3, and Te ≃ 100 K and Te ≃ 150 K. In M43, we infer ne ≃ 2–3 cm−3 and Te ≃ 140 K. The Extended Orion Nebula southeast of OMC1 is characterized by ne ≃ 2 cm−3 and Te ≃ 180 K, while OMC3 has ne ≃ 1 cm−3 and Te ≃ 130 K. Our observations are sensitive enough to detect faint lines toward two positions in OMC1, in the BN/KL PDR and the PDR close to the Trapezium stars, that may be attributed to RRLs of C+ or O+. In general, the RRL line widths of both the ionized and neutral gas, as well as the [C II] and 13CO line widths, are broader than thermal, indicating significant turbulence in the interstellar medium, which transitions from super-Alfvénic and subsonic in the ionized gas to sub-Alfvénic and supersonic in the molecular gas. At the scales probed by our observations, the turbulent pressure dominates the pressure balance in the neutral and molecular gas, while in the ionized gas the turbulent pressure is much smaller than the thermal pressure.

The Chilean exposome-based system for ecosystems (CHiESS): a framework for national data integration and analytics platform.

The double burden of diseases and scarce resources in developing countries highlight the need to change the conceptualization of health problems and translational research. Contrary to the traditional paradigm focused on genetics, the exposome paradigm proposed in 2005 that complements the genome is an innovative theory. It involves a holistic approach to understanding the complexity of the interactions between the human being’s environment throughout their life and health. This paper outlines a scalable framework for exposome research, integrating diverse data sources for comprehensive public health surveillance and policy support. The Chilean exposome-based system for ecosystems (CHiESS) project proposes a conceptual model based on the ecological and One Health approaches, and the development of a technological dynamic platform for exposome research, which leverages available administrative data routinely collected by national agencies, in clinical records, and by biobanks. CHiESS considers a multilevel exposure for exposome operationalization, including the ecosystem, community, population, and individual levels. CHiESS will include four consecutive stages for development into an informatic platform: (1) environmental data integration and harmonization system, (2) clinical and omics data integration, (3) advanced analytical algorithm development, and (4) visualization interface development and targeted population-based cohort recruitment. The CHiESS platform aims to integrate and harmonize available secondary administrative data and provide a complete geospatial mapping of the external exposome. Additionally, it aims to analyze complex interactions between environmental stressors of the ecosystem and molecular processes of the human being and their effect on human health. Moreover, by identifying exposome-based hotspots, CHiESS allows the targeted and efficient recruitment of population-based cohorts for translational research and impact evaluation. Utilizing advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and blockchain, this framework enhances data security, real-time monitoring, and predictive analytics. The CHiESS model is adaptable for international use, promoting global health collaboration and supporting sustainable development goals.

Remote Sensing for Mapping Natura 2000 Habitats in the Brière Marshes: Setting Up a Long-Term Monitoring Strategy to Understand Changes

On a global scale, wetlands are suffering from a steady decline in surface area and environmental quality. Protecting them is essential and requires a careful spatialisation of their natural habitats. Traditionally, in our study area, species discrimination for floristic mapping has been achieved through on-site field inventories, but this approach is very time-consuming in these difficult-to-access environments. Usually, the resulting maps are also not spatially exhaustive and are not frequently updated. In this paper, we propose to establish a complete map of the study area using remote sensors and set up a long-term and regular observatory of environmental changes to monitor the evolution of a major French wetland. This methodology combines three dataset acquisition technologies, airborne hyperspectral and WorldView-3 multispectral images, supplemented by LiDAR images, which we compared to evaluate the difference in performances. To do so, we applied the Random Forest supervised classification methods using ground reference areas and compared the out-of-bag score (OOB score) as well as the matrix of confusion resulting from each dataset. Thirteen habitats were discriminated at level 4 of the European Nature Information System (EUNIS) typology, at a spatial resolution of around 1.2 m. We first show that a multispectral image with 19 variables produces results which are almost as good as those produced by a hyperspectral image with 58 variables. The experiment with different features also demonstrates that the use of four bands derived from LiDAR datasets can improve the quality of the classification. Invasive alien species Ludwigia grandiflora and Crassula helmsii were also detected without error which is very interesting when applied to these endangered environments. Therefore, since WV-3 images provide very good results and are easier to acquire than airborne hyperspectral data, we propose to use them going forward for the regular observation of the Brière marshes habitat we initiated.

EMCFN: Edge-based Multi-scale Cross Fusion Network for video frame interpolation

Video frame interpolation (VFI) is used to synthesize one or more intermediate frames between two frames in a video sequence to improve the temporal resolution of the video. However, many methods still face challenges when dealing with complex scenes involving high-speed motion, occlusions, and other factors. To address these challenges, we propose an Edge-based Multi-scale Cross Fusion Network (EMCFN) for VFI. We integrate a feature enhancement module (FEM) based on edge information into the U-Net architecture, resulting in richer and more complete feature maps, while also enhancing the preservation of image structure and details. This contributes to generating more accurate and realistic interpolated frames. At the same time, we use a multi-scale cross fusion frame synthesis model (MCFM) composed of three GridNet branches to generate high-quality interpolation frames. We have conducted a series of experiments and the results show that our model exhibits satisfactory performance on different datasets compared with the state-of-the-art methods.

Endoscopic ultrasound-guided needle-based confocal laser endomicroscopy for pancreatic cystic lesions: current status and future prospects.

Pancreatic cystic lesions (PCLs) have increased in prevalence due to the increased usage and advancements in cross-sectional abdominal imaging. Current diagnostic techniques cannot distinguish between PCLs requiring surgery, close surveillance, or expectant management. This has increased the morbidity and healthcare costs from inappropriately aggressive and conservative management strategies. Endoscopic ultrasound (EUS) needle-based confocal laser endomicroscopy (nCLE) allows for microscopic examination and delineation of the surface epithelium of PCLs. Landmark studies have identified characteristics distinguishing various types of PCLs, confirmed the high diagnostic yield of EUS-nCLE (especially for PCLs with an equivocal diagnosis), and shown that EUS-nCLE helps to change management and reduce healthcare costs. Refining procedure technique and reducing procedure length have improved the safety of EUS-nCLE. The utilization of artificial intelligence and its combination with other EUS-based advanced diagnostic techniques would further improve the results of EUS-based PCL diagnosis. A structured training program and device improvements to allow more complete mapping of the pancreas cyst epithelium will be crucial for the widespread adoption of this promising technology.

Parameterized complexity of candidate nomination for elections based on positional scoring rules

Consider elections where the set of candidates is partitioned into parties, and each party must nominate exactly one candidate. The Possible President problem asks whether some candidate of a given party can become the unique winner of the election for some nominations from other parties. We perform a multivariate computational complexity analysis of Possible President for several classes of elections based on positional scoring rules. We consider the following parameters: the size of the largest party, the number of parties, the number of voters and the number of voter types. We provide a complete computational map of Possible President in the sense that for each choice of the four possible parameters as (i) constant, (ii) parameter, or (iii) unbounded, we classify the computational complexity of the resulting problem as either polynomial-time solvable or NP-complete, and for parameterized versions as either fixed-parameter tractable or W[1]-hard with respect to the parameters considered.

Information System Model and Key Technologies of High-Definition Maps in Autonomous Driving Scenarios.

Background: High-definition maps can provide necessary prior data for autonomous driving, as well as the corresponding beyond-line-of-sight perception, verification and positioning, dynamic planning, and decision control. It is a necessary element to achieve L4/L5 unmanned driving at the current stage. However, currently, high-definition maps still have problems such as a large amount of data, a lot of data redundancy, and weak data correlation, which make autonomous driving fall into difficulties such as high data query difficulty and low timeliness. In order to optimize the data quality of high-definition maps, enhance the degree of data correlation, and ensure that they better assist vehicles in safe driving and efficient passage in the autonomous driving scenario, it is necessary to clarify the information system thinking of high-definition maps, propose a complete and accurate model, determine the content and functions of each level of the model, and continuously improve the information system model. Objective: The study aimed to put forward a complete and accurate high-definition map information system model and elaborate in detail the content and functions of each component in the data logic structure of the system model. Methods: Through research methods such as the modeling method and literature research method, we studied the high-definition map information system model in the autonomous driving scenario and explored the key technologies therein. Results: We put forward a four-layer integrated high-definition map information system model, elaborated in detail the content and functions of each component (map, road, vehicle, and user) in the data logic structure of the model, and also elaborated on the mechanism of the combined information of each level of the model to provide services in perception, positioning, decision making, and control for autonomous driving vehicles. This article also discussed two key technologies that can support autonomous driving vehicles to complete path planning, navigation decision making, and vehicle control in different autonomous driving scenarios. Conclusions: The four-layer integrated high-definition map information model proposed by this research institute has certain application feasibility and can provide references for the standardized production of high-definition maps, the unification of information interaction relationships, and the standardization of map data associations.

Study on Multiscale Virtual Environment Construction and Spatial Navigation Based on Hierarchical Structure

Multiscale virtual environments (MSVEs) allow the integration of elements and environments at different scale levels into a unified space, which facilitates researchers’ perception, understanding, and experimental research of complex geospatial spaces. Although there have been several methods for achieving multiscale effects in virtual environments (VEs), they cannot assist users in constructing more complete spatial cognitive maps and presenting multiscale information efficiently. This study proposes a hierarchical-structure-based MSVE construction method, which can effectively integrate multiscale information and ensure that the richness of details of information is gradually enhanced with the progression of the hierarchical structure. In addition, a spatial navigation study is conducted, considering the relationship between users’ perspective changes and spatial cognition, and the effects of users’ perspective changes on their spatial cognition in an MSVE are explored. A multiscale virtual wetland environment covering four levels is constructed to conduct a case study of a virtual environment of a wetland of Poyang Lake. The research results show that the proposed method is feasible. Moreover, the spatial navigation based on the change in the hierarchical perspective is in line with the spatial cognitive habits of users, which can satisfy the cognitive needs of users from the macro-region to specific wetland landscapes.

The location of pain in cluster headache: Data from the International Cluster Headache Questionnaire.

To identify the most common locations of cluster headache pain from an international, non-clinic-based survey of participants with cluster headache, and to compare these locations to other cluster headache features as well as to somatotopic maps of peripheral, brainstem, thalamic, and cortical areas. Official criteria for cluster headache state pain in the orbital, supraorbital, and/or temporal areas, yet studies have noted pain extending beyond these locations, and the occipital nerve appears relevant, given the effectiveness of suboccipital corticosteroid injections and occipital nerve stimulation. Furthermore, cranial autonomic features vary between patients, and it is not clear if the trigeminovascular reflex is dermatome specific (e.g., do patients with maxillary or V2 division pain have more rhinorrhea?). Finally, functional imaging studies show early activation of the posterior hypothalamus in a cluster headache attack. However, the first somatosensory area to be sensitized is unclear; the first area can be hypothesized based on the complete map of pain locations. The International Cluster Headache Questionnaire was an internet-based cross-sectional survey that included a clickable pain map of the face. These data were compared to several other datasets: (1) a meta-analysis of 22 previous publications of pain location in cluster headache (consisting of 6074 patients); (2) four cephalic dermatome maps; (3) participants' survey responses for demographics, autonomic features, and effective medications; and (4) previously published somatotopic maps of the brainstem, thalamus, primary somatosensory cortex, and higher order somatosensory cortex. One thousand five hundred eighty-nine participants completed the pain map portion of the survey, and the primary locations of pain across all respondents was the orbital, periorbital, and temporal areas with a secondary location in the lower occiput; these primary and secondary locations were consistent with our meta-analysis of 22 previous publications. Of the four cephalic dermatomes (V1, V2, V3, and a combination of C2-3), our study found that most respondents had pain in two or more dermatomes (range 85.7% to 88.7%, or 1361-1410 of 1589 respondents, across the four dermatome maps). Dermatomes did not correlate with their respective autonomic features or with medication effectiveness. The first area to be sensitized in the canonical somatosensory pathway is either a subcortical (brainstem or thalamus) or higher order somatosensory area (parietal ventral or secondary somatosensory cortices) because the primary somatosensory cortex (area 3b) and somatosensory area 1 have discontinuous face and occipital regions. The primary pain locations in cluster headache are the orbital, supraorbital, and temporal areas, consistent with the official International Classification of Headache Disorders criteria. However, activation of the occiput in many participants suggests a role for the occipital nerve, and the pain locations suggest that somatosensory sensitization does not start in the primary somatosensory cortex.

A novel methodology for map-based model fitting: A case study with a Dual Source Heat Pump experimental dataset

This paper presents a new methodology to adjust map-based models to experimental data and reports the main results of a comprehensive experimental campaign of a Dual Source Heat Pump (DSHP) prototype. The prototype tested incorporates variable speed components (compressor, circulation pumps, and fan). The novelty of this prototype lies in its ability to select two possible heat sources: air or ground. Thus, it can operate as a geothermal or aerothermal heat pump, as well as a chiller, thanks to the additional capacity to reverse the cycle. Thanks to this hybrid approach, several advantages can be obtained compared to conventional equipment, such as higher efficiency, the requirement of smaller borehole heat exchangers, or the absence of defrost cycles. In a prior study, polynomial models were developed to accurately characterize the DSHP’s performance (i.e., condenser and evaporator capacities and electrical energy consumption). These models were obtained considering the external variables to the unit as independent variables to facilitate their applicability using variables commonly measured in real installations. Due to the complexity of heat pump performance, which in current equipment can be influenced by up to 5 or 6 independent variables, the search for suitable polynomial models required the availability of a complete working map including more than 3000 working points. Thus, this previous work developed these models based only on simulation results. In this sense, this paper concludes the development of these models by focusing on two critical issues concerning empirical model development. The first aspect involves determining the minimum number and location of testing points needed to define the experimental sample for the model adjustment. The reported experimental data were obtained by analyzing the most suitable experimental design methodology to create the experimental matrices in each operating mode of the DSHP. The second aspect focuses on the final adjustment of models using experimental data. A novel fitting approach for empirical models is introduced in the last part of this study. The developed methodology enables the integration of simulation and experimental results for the final fitting of empirical models through a two-step adjustment. The first step involves analyzing and defining polynomial functionals from the complete working maps generated by simulation. Subsequently, in a second step, the polynomial models are refitted to a suitable experimental sample using the methodology presented in this work. The latter allows for the increase of the accuracy of the models and the minimization of experimental costs. This novel approach ensures a robust characterization of systems with many independent variables using a minimum amount of experimental data. Significant benefits can be obtained from its application, such as the reduction of experimental cost and an increase in the model’s accuracy through an effective combination of both experimental and simulated information. Furthermore, it can be considered of general applicability to other engineering problems where the characterization of physical systems influenced by a high number of independent variables is required.

Development of Village Industry Through an Integrated Information System in Nagari Simarasok, Baso Agam District

The problems of Simarasok UMKM were identified: (1). The products have not been maximized due to marketing weaknesses, capital, and the purchasing power of woven/songket products tends to decrease, (2). Embroidery UMKM are part-time livelihoods for unmarried women and housewives, (3). There is no group of innovative skilled workers, (4). Marketing strategies are more on personal relationships, close proximity and less online sales, (5). Product quality/quantity can be improved, but constrained by external internal factors. The research used qualitative techniques and descriptive analysis; informants were collected by purposive sampling. Data findings, there are specialties of UMKM: 1). The embroidery industry has developed and is carried out by almost half of the female population, 2). Sugarcane and tobacco processing industry, conducted by men. There is a difference between the type of industry and the gender of the actors, which is not interchangeable. 3). Based on scale, the industries are small. Interestingly, UMKM development is carried out through an integrated information system. Although located in rural areas, Nagari Simarasok has utilized spatial technology as an integrated information system in mapping the condition of its UMKM. In this Nagari, a complete map with specifications such as maps of industrial asset ownership, maps of agricultural land potential, maps of economic potential and maps of natural resource potential can be accessed. With the completeness of the map, access to buyers, consumers and or other parties can more easily access it. This Nagari breakthrough has not even existed in urban villages in the city center.

A pan-cancer analysis of EphA family gene expression and its association with prognosis, tumor microenvironment, and therapeutic targets.

Erythropoietin-producing human hepatocellular (Eph) receptors stand out as the most expansive group of receptor tyrosine kinases (RTKs). Accumulating evidence suggests that within this expansive family, the EphA subset is implicated in driving cancer cell progression, proliferation, invasion, and metastasis, making it a promising target for anticancer treatment. Nonetheless, the extent of EphA family involvement across diverse cancers, along with its intricate interplay with immunity and the tumor microenvironment (TME), remains to be fully illuminated. The relationships between EphA gene expression and patient survival, immunological subtypes, and TME characteristics were investigated based on The Cancer Genome Atlas (TCGA) database. The analyses employed various R packages. A significant difference in expression was identified for most EphA genes when comparing cancer tissues and non-cancer tissues. These genes independently functioned as prognostic factors spanning multiple cancer types. Moreover, a significant correlation surfaced between EphA gene expression and immune subtypes, except for EphA5, EphA6, and EphA8. EphA3 independently influenced the prognosis of papillary renal cell carcinoma (KIRP). This particular gene exhibited links with immune infiltration subtypes and clinicopathologic parameters, holding promise as a valuable biomarker for predicting prognosis and responsiveness to immunotherapy in patients with KIRP. By meticulously scrutinizing the panorama of EphA genes in a spectrum of cancers, this study supplemented a complete map of the effect of EphA family in Pan-cancer and suggested that EphA family may be a potential target for cancer therapy.

Discovering thematic change and evolution of political connections research

This research aims to enlighten current and future research trends on political connections by providing complete bibliometric mapping through network studies on political connections. Utilizing Biblioshiny, the Bibliometrics R package, multi-perspective research publications on political connections from 2003 to the third quarter of 2023 were discovered and analyzed. This bibliometric analysis provides valuable insights regarding current and future publications on political connections. The most prolific contributors, the most used keywords, the most productive countries and sources, the most cited publications, and the most productive sources of information are network analysis data about co-occurrence networks. The discourse revolves around issue mapping data on political connection studies. The search method used to find related literature is the Scopus database. This research contributes valuable information to help other researchers map ongoing and future investigations into political connections. This bibliometric analysis of 294 documents is the first regarding political connections known to researchers. This evaluation emphasizes annual publication trends, authors, publications, countries, organizations, and most productive sources, determining potential future research objectives. The research on political connections was published in early 2003. No papers were released on this subject for 3 years after 2003 (2003–2005), which shows that the growth of this academic field slowed down for ten years. There were significant changes in 2011, and many more linked papers have been released. Authors from the USA and China significantly contributed to the initial scientific journal papers on their political connections. Hong Kong, China, and Australia are recognized as leading nations in scholarly contributions to studying political connections. Chinese scholars spearheaded the collaborative publication effort focused on political connections, with the USA and Australian experts following suit. Based on indexed terms, political connections, sustainable development, and financial performance are trending keywords. These results show possible directions for further research into political connections.

Storage estimation in morphology modeling of the human whole brain at the nanoscale

The human brain is an enormous scientific challenge. Knowledge of the complete map of neuronal connections (connectome) is essential for understanding how neuronal circuits encode information and the brain works in health and disease. Nanoscale connectomes are created for a few small animals but not yet for the human. The key challenges in the development of a whole human brain model at the nanoscale are data acquisition and computing including big data and high performance computing. This work focuses on big data and volumetric and geometric modeling of brain morphology at the micro- and nanoscales. It presents the volumetric and four geometric neuronal models and estimates the storage required for them. It introduces four geometric neuronal models: straight wireframe, enhanced wireframe, straight polygonal, and enhanced polygonal. The volumetric model requires approximately from 4.2 to 33.6 petabytes (PB) at the microscale up to 5,600,000 exabytes (EB) at the nanoscale. The straight wireframe model requires 18 PB at the microscale and 24 PB at the nanoscale. The enhanced parabolic wireframe model needs 36 PB at the microscale and 48 PB at the nanoscale, whereas the enhanced cubic model requires 54 PB at the microscale and 72 PB at the nanoscale. The straight polygonal model requires 24 PB at the microscale and 32 PB at the nanoscale. The enhanced parabolic polygonal model needs 48 PB at the microscale and 64 PB at the nanoscale, while the enhanced cubic model needs 72 PB at the microscale and 96 PB at the nanoscale. The straight wireframe model of 18 PB is sufficient to enable computing of the human synaptome and subsequently the connectome. The only operational supercomputer able to provide such storage is the world’s first exascale supercomputer Frontier. The sizes of the volumetric and geometric models are comparable at the microscale, however, their difference is dramatic at the nanoscale; for the 10 nm resolution the geometric models are smaller approximately from 58 to 233 thousand times, and for the 1 nm resolution from 58 to 233 million times. This novel work is an extended version of a conference paper [15] and it represents a step forward toward the development of the human whole brain model at the nanoscale.

Multimodal framework to resolve variants of uncertain significance in TSC2.

Efforts to resolve the functional impact of variants of uncertain significance (VUS) have lagged behind the identification of new VUS; as such, there is a critical need for scalable VUS resolution technologies. Computational variant effect predictors (VEPs), once trained, can predict pathogenicity for all missense variants in a gene, set of genes, or the exome. Existing tools have employed information on known pathogenic and benign variants throughout the genome to predict pathogenicity of VUS. We hypothesize that taking a gene-specific approach will improve pathogenicity prediction over globally-trained VEPs. We tested this hypothesis using the gene TSC2, whose loss of function results in tuberous sclerosis, a multisystem mTORopathy affecting about 1 in 6,000 individuals born in the United States. TSC2 has been identified as a high-priority target for VUS resolution, with (1) well-characterized molecular and patient phenotypes associated with loss-of-function variants, and (2) more than 2,700 VUS already documented in ClinVar. We developed Tuberous sclerosis classifier to Resolve variants of Uncertain Significance in T SC2 (TRUST), a machine learning model to predict pathogenicity of TSC2 missense VUS. To test whether these predictions are accurate, we further introduce curated loci prime editing (cliPE) as an accessible strategy for performing scalable multiplexed assays of variant effect (MAVEs). Using cliPE, we tested the effects of more than 200 TSC2 variants, including 106 VUS. It is highly likely this functional data alone would be sufficient to reclassify 92 VUS with most being reclassified as likely benign. We found that TRUST's classifications were correlated with the functional data, providing additional validation for the in silico predictions. We provide our pathogenicity predictions and MAVE data to aid with VUS resolution. In the near future, we plan to host these data on a public website and deposit into relevant databases such as MAVEdb as a community resource. Ultimately, this study provides a framework to complete variant effect maps of TSC1 and TSC2 and adapt this approach to other mTORopathy genes.

Greece’s Economic Odyssey: Persistent Challenges and Pathways Forward

Two years after the COVID-19 pandemic, the Greek economy seems to have overcome the turmoil of the pandemic crisis as well as that of the following energy crisis. Nevertheless, it would be wrong to assume that the Greek economy has returned to a sound state, since this was not really the case even before the pandemic. Furthermore, the anemic growth rates of the pre-pandemic period were followed by an equally weak average growth rate (including the impact of the pandemic), as some of the significant fundamental weaknesses of the Greek economy, which had accumulated over time and constituted the real origin of the Greek crisis, have not been properly addressed yet. This paper attempts a complete mapping of the current state of the Greek economy, offering an insight into the external and internal determinants affecting it.

Learning Sea Surface Height Interpolation From Multi‐Variate Simulated Satellite Observations

AbstractSatellite‐based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space‐borne altimetry provides valuable Sea Surface Height (SSH) measurements, used to estimate surface geostrophic currents. Due to the sensor technology employed, important gaps occur in SSH observations. Complete SSH maps are produced using linear Optimal Interpolations (OI) such as the widely used Data Unification and Altimeter Combination System (duacs). On the other hand, Sea Surface Temperature (SST) products have much higher data coverage and SST is physically linked to geostrophic currents through advection. We propose a new multi‐variate Observing System Simulation Experiment (OSSE) emulating 20 years of SSH and SST satellite observations. We train an Attention‐Based Encoder‐Decoder deep learning network (abed) on this data, comparing two settings: one with access to ground truth during training and one without. On our OSSE, we compare abed reconstructions when trained using either supervised or unsupervised loss functions, with or without SST information. We evaluate the SSH interpolations in terms of eddy detection. We also introduce a new way to transfer the learning from simulation to observations: supervised pre‐training on our OSSE followed by unsupervised fine‐tuning on satellite data. Based on real SSH observations from the Ocean Data Challenge 2021, we find that this learning strategy, combined with the use of SST, decreases the root mean squared error by 24% compared to OI.

Data-driven design of high pressure hydride superconductors using DFT and deep learning

The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H3S and LaH10) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature ( Tc ) of over 900 hydride materials under a pressure range of (0–500) GPa, where we found 122 dynamically stable structures with a Tc above MgB2 (39 K). To accelerate screening, we trained a graph neural network (GNN) model to predict Tc and demonstrated that a universal machine learned force-field can be used to relax hydride structures under arbitrary pressures, with significantly reduced cost. By combining DFT and GNNs, we can establish a more complete map of hydrides under pressure.