Miracle-dz/UniFAR-FTUs huggingface dataset:
Topographical Landmarks for Ground-Level Terrain Relative Navigation on Mars

Many of the tasks planned for future generation Mars rovers rely heavily on having accurate knowledge of the rover's location in a Martian body-fixed coordinate system. Current solutions for localization require regular human intervention in order to detect and rectify drift, and thus stand to benefit from systems that can run in real-time on the rover itself. We study the feasibility and performance of an automated approach to localization in which the rover makes bearing-only measurements to geographic features in its surroundings (hills, boulders, peaked ridge-lines, etc.). When the location of these landmarks can be cross-referenced with a map of Mars, the resulting solution will be globally registered and will help correct any drift during visual-odometry-aided drives. This paper studies two related problems: First, how can we locate geographic features that the rover can feasibly see, when provided an elevation map of the surrounding terrain? We provide a software tool that can extract features from elevation maps for comparison to imagery. However, once these landmarks are identified, it is not obvious if a given path for the rover will contain sufficient features to navigate autonomously. Accuracy will depend on the quantity, range, and relative geometry of landmarks that are available. Thus, the second contribution is to provide a GIS plugin that analyzes the terrain informativeness of large operating areas as well as more specific paths. We present an analysis of Jezero Crater in which Perseverance's Navcam is used as the hypothetical sensor. In certain favorable regions, worst-case localization accuracy in the 10 meter range is achieved from a single set of measurements (comparable to GPS on Earth). The map overlays generated by this analysis have the potential to aid in long-term mission planning by highlighting broad areas of high or low informativeness. These tools are computationally efficient and will be made open source to allow Mars mission planners, formulation studies, and rover drivers to plan for any future image-based self-localization capability.
Fast Screening of High-Risk Lines Under False Data Injection Attacks

False data injection attacks can compromise power system operational security by causing severe branch overloads. A set of transmission lines can be more vulnerable to the attack. These lines can be quickly identified by a high-risk line fast screening (HLFS) approach proposed in this paper. The HLFS has a high computational efficiency which facilitates its applications in large-scale systems. In the meantime, for an in-depth security assessment, a critical-line identification tool is also presented to identify a subset of critical lines with simultaneous tripping risk under the attack. The effectiveness and efficiency of the proposed approach are verified by benchmarking its performance against a basic linear-programming checking method in the case study which includes simulations on multiple large-scale MATPOWER cases.
A New Method of Determining Eddy Transport Coefficients of Turbulence

By treating eddy viscosity and eddy diffusivity as optimal control parameters, a new method is developed to determine the eddy viscosity, eddy diffusivity and turbulent Prandtl number from first principles without appealing to adjustable empirical constants. The resultant turbulent Prandtl number is compatible with experimental data. This method enables us to estimate the error of Boussinesq's eddy viscosity (and diffusivity) model and compare various methods of determining eddy transport coefficients.
Elucidation of the Relationship Between a Song's Spotify Descriptive Metrics and its Popularity on Various Platforms

The music industry and personal music consumption have evolved dramatically with the advent of streaming plat-forms. In this evolving landscape, there is considerable interest in understanding what factors contribute to a song's popularity. Extrinsic (i.e. non-acoustic) features of a given song, such as the record label, and/or intrinsic (i.e. acoustic) features such as its energy may contribute to popularity on a given digital platform. In this work, we, for the first time, sought to systematically study how a song's Spotify acoustic descriptive features correlated with popularity metrics on various Internet platforms. Since each platform defines “popularity” according to platform-specific metrics, a large-scale correlation-based analysis was generated. The digital platforms considered in this article are Google Trends, WhoSampled, TikTok, Twitter, YouTube, and the Billboard Top-100. Platform-specific scrapers were created and all data was aggregated with the Spotify Echo Nest dataset of descriptive acoustic metrics. While the majority of correlations were unre-markable considering both Spearman and Pearson coefficients, a number of corroborating and contradictory findings resulted, with notable implications for acoustic features on various digital platforms. Notably, the YouTube view count was found to be positively correlated to the Spotify song popularity (p = 0.822), year (p = 0.600), and energy (p = 0.455) and moderately negatively correlated to accousticness (p = −0.542) and instrumentalness (p = −0.345). All reproducing code and aggregated data from this work are open-source for use by the broader research community.
Spacecraft Time-Series Online Anomaly Detection Using Extreme Learning Machines

Detecting anomalies in spacecraft telemetry channels is a high priority, especially considering the harshness of the spacecraft operating environment. These anomalies often function as precursors for system failure. Currently, telemetry channel monitoring is done manually by domain experts, which is time-consuming and limited in scope. Given that each satellite system has thousands of channels to monitor, an automated approach to anomaly detection would be ideal. Machine learning models have been shown to be effective at detecting the normal behavior of the channels and flagging any abnormalities. However, a unique model needs to be trained for each channel, and high performing models have been shown to require an increased training time. We propose using an ensemble of online sequential extreme learning machines to quickly understand the behavior of a given channel and identify anomalies in near real-time. This greatly reduces the amount of training time and data required to obtain a model for each channel. We present the results of our approach to show that we can achieve performance comparable to state-of-the-art spacecraft anomaly detection methods with minimal training time and data.
Real-Time Techniques for Fault Detection on Railway Door Systems

This paper focuses on real-time techniques for fault detection in railway assets through large real-world datasets. It aims to investigate data mining methods to detect faulty behaviour in time series data. A fault detection on railway door systems is carried out using motor current and encoder signal. The door data highlighted start-stop characteristics, with discontinuities in the data. This paper presents a successful fault detection technique, which is a feature-based machine learning method that requires several steps for time-series data processing, such as signal segmentation and the extraction of features. Principal Component Analysis (PCA) is applied to reduce the dimensionality of the extracted feature set and generate condition indicators. Then, the k-means algorithm is employed to separate normal and abnormal behaviour. This is followed by an evaluation of the proposed method and discussion about current challenges and prognosis possibility.
Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA

Unlike globular proteins, mutational effects on the function of Intrinsically Disordered Proteins (IDPs) are not well-studied. Deep Mutational Scanning of a yeast surface displayed mutant library yields insights into sequence-function relationships in the CcdA IDP. The approach enables facile prediction of interface residues and local structural signatures of the bound conformation. In contrast to previous titration-based approaches which use a number of ligand concentrations, we show that use of a single rationally chosen ligand concentration can provide quantitative estimates of relative binding constants for large numbers of protein variants. This is because the extended interface of IDP ensures that energetic effects of point mutations are spread over a much smaller range than for globular proteins. Our data also provides insights into the much-debated role of helicity and disorder in partner binding of IDPs. Based on this exhaustive mutational sensitivity dataset, a model was developed to predict mutational effects on binding affinity of IDPs that form alpha-helical structures upon binding.
Differential axonal trafficking of Neuropeptide Y-, LAMP1-, and RAB7-tagged organelles in vivo

Different organelles traveling through neurons exhibit distinct properties in vitro, but this has not been investigated in the intact brain. We established simultaneous dual color 2- photon microscopy to visualize the trafficking of Neuropeptide Y-, LAMP1- and RAB7- tagged organelles in thalamocortical axons imaged in mouse cortex in vivo. This revealed that LAMP1- and RAB7-tagged organelles move significantly faster than NPY tagged organelles in both anterograde and retrograde direction. NPY travelled more selectively in anterograde direction than LAMP1 and RAB7. By using a synapse marker and a calcium sensor we further investigated the transport dynamics of NPY-tagged organelles. We found that these organelles slow down and pause at synapses. In contrast to previous in vitro studies, a significant increase of transport speed was observed after spontaneous activity and elevated calcium levels in vivo as well as electrically stimulated activity in acute brain slices. Together, we show a remarkable diversity in speeds and properties of three axonal organelle marker in vivo that differ from properties previously observed in vitro.
Rates but not acoustic features of ultrasonic vocalizations are related to non-vocal behaviors in mouse pups

Mouse pups produce ultrasonic vocalizations (USVs) in response to isolation from the nest (i.e., isolation USVs). Rates and acoustic features of isolation USVs change dramatically over the first two weeks of life, and there is also substantial variability in the rates and acoustic features of isolation USVs at a given postnatal age. The factors that contribute to within-age variability in isolation USVs remain largely unknown. Here, we explore the extent to which non-vocal behaviors of mouse pups relate to the within-age variability in rates and acoustic features of their USVs. We recorded non-vocal behaviors of isolated C57BL/6J mouse pups at four postnatal ages (postnatal days 5, 10, 15, and 20), measured rates of isolation USV production, and applied a combination of hand-picked acoustic feature measurements and an unsupervised machine learning-based vocal analysis method to examine USV acoustic features. When we considered different categories of non-vocal behavior, our analyses revealed that mice in all postnatal age groups produce higher rates of isolation USVs during active non-vocal behaviors than when lying still. Moreover, rates of isolation USVs are correlated with the intensity (i.e., magnitude) of non-vocal body and limb movements within a given trial. In contrast, USVs produced during different categories of non-vocal behaviors and during different intensities of non-vocal movement do not differ substantially in their acoustic features. Our findings suggest that levels of behavioral arousal contribute to within-age variability in rates, but not acoustic features, of mouse isolation USVs.
Atm loss does not radiosensitize a primary mouse model of Pten-deleted brainstem glioma

Diffuse midline gliomas arise in the brainstem and other midline brain structures and cause a large proportion of childhood brain tumor deaths. Radiation therapy is the most effective treatment option, but these tumors ultimately progress. Inhibition of the phosphoinositide-3-kinase (PI3K)-like kinase ataxia telangiectasia mutated (ATM), which orchestrates the cellular response to radiation-induced DNA damage, may enhance the efficacy of radiation therapy. Diffuse midline gliomas in the brainstem contain loss-of-function mutations in the tumor suppressor PTEN, or functionally similar alterations in the phosphoinositide-3-kinase (PI3K) pathway, at moderate frequency. Here, we sought to determine if Atm inactivation could radiosensitize a primary mouse model of brainstem glioma driven by Pten loss. Using Cre/loxP recombinase technology and the RCAS/TVA retroviral gene delivery system, we established a mouse model of brainstem glioma driven by Pten deletion. We find that Pten-null brainstem gliomas are relatively radiosensitive at baseline. In addition, we show that deletion of Atm in the tumor cells does not extend survival of mice bearing Pten-null brainstem gliomas after focal brain irradiation. These results characterize a novel primary mouse model of PTEN-mutated brainstem glioma and provide insights into the mechanism of radiosensitization by Atm deletion, which may guide the design of future clinical trials. Brief Summary We develop a mouse model of PTEN-mutated brainstem glioma and find that perturbation of the ATM does not enhance radiation efficacy in this model.
Convection and extracellular matrix binding control interstitial transport of extracellular vesicles

Extracellular vesicles (EVs) influence a host of normal and pathophysiological processes in vivo. Compared to soluble mediators, EVs are relatively large (~30-150 nm) and can traffic a wide range of proteins on their surface including extracellular matrix (ECM) binding proteins. We isolated EVs from the MCF10 series – a model human cell line of breast cancer progression – and demonstrated increasing presence of laminin-binding integrins α3β1 and α6β1 on the EVs as the malignant potential of the MCF10 cells increased. Transport of the EVs within a microfluidic device under controlled physiological interstitial flow (0.15-0.75 μm/s) demonstrated that convection was the dominant mechanism of transport. Binding of the EVs to the ECM enhanced the spatial concentration and gradient, which was partially mitigated by blocking integrins α3β1 and α6β1. Our studies demonstrate that convection and ECM binding are the dominant mechanisms controlling EV interstitial transport and should be leveraged in the design of nanotherapeutics.
Human endogenous retrovirus-R envelope is a host restriction factor against severe acute respiratory syndrome-coronavirus-2

Coronavirus induced disease-19 (COVID-19), caused by the SARS-CoV-2 remains a major global health challenge. Human endogenous retroviruses (HERVs) represent retroviral elements that got integrated into the ancestral human genome. HERVs are important in development and diseases, including cancer, inflammation and viral infections. Here, we analyzed the expression of several HERVs in SARS-CoV-2 infected cells and observed increased activity of HERV-E, HERV-V, HERV-FRD, HERV-MER34, HERV-W and HERV-KHML2. In contrast, HERV-R-envelope was downregulated in cell-based models and COVID-19 patient PBMCs. HERV-R overexpression inhibited SARS-CoV-2 replication, suggesting its antiviral action. Further studies demonstrated the role of extracellular signal-regulated kinase (ERK) in regulating HERV-R antiviral activity. Cross-talk between the ERK and p38 MAPK controls HERV-R envelope synthesis, which in turn modulates the replication of SARS-CoV-2. These findings establish the importance of HERV-R envelope as a host restriction factor against SARS-CoV-2 and illustrate the advantage of integration and evolutionary maintenance of retroviral-elements in the human genome.
Resource asynchrony and landscape homogenization as drivers of virulence evolution

In the last years, the emergence of zoonotic diseases and the frequency of disease outbreaks have increased substantially, fuelled by habitat encroachment and asynchrony of biological cycles due to global change. The virulence of these diseases is a key aspect for their success. In order to understand the complex processes of pathogen virulence evolution in the global change context, we adapted an established individual-based model of host-pathogen dynamics. Our model simulates a population of social hosts affected by an evolving pathogen in a dynamic landscape. Pathogen virulence evolution is explored by the inclusion of multiple strains in the model that differ in their transmission capability and lethality. Simultaneously, the host’s resource landscape is subjected to spatial and temporal dynamics, emulating effects of global change. We found an increase in pathogenic virulence and a shift in strain dominance with increasing landscape homogenisation. Our model further shows a trend to lower virulence pathogens being dominant in fragmented landscapes, although pulses of highly virulent strains are expected under resource asynchrony. While all landscape scenarios favour coexistence of low and high virulent strains, when host density increases, the high virulence strains capitalize on the high possibility for transmission and are likely to become dominant. Author Summary Disease outbreaks primarily caused by contact with animals are increasing in recent years, related to habitat destruction and altered biological cycles due to climate change. Pathogens associated with such outbreaks will be more successful the more effectively they can spread in a population. Thus, understanding the conditions over which those pathogens evolve will help us to limit the impact of disease outbreaks in the future. To this end, we used an individual based model that allowed us to study different scenarios. Our model had three main components: a host-pathogen system, a dynamic resource landscape with different degrees of fragmentation and temporal resource mismatches. We used dynamic landscapes with varying resource amounts over the years and consisting of multiple large or smaller habitat clusters. Our simulations showed that homogenous landscapes resulted in higher virulent pathogens and fragmented landscapes in lesser virulent pathogens. However, across all scenarios, high and low virulent pathogen strains were able to coexist.
Mechanistic dissection of antibody inhibition of influenza entry yields unexpected heterogeneity

Neutralizing antibodies against influenza have generally been classified according to their recognition sites, with antibodies against the head domain of hemagglutinin thought to inhibit attachment and antibodies against the stalk region thought to inhibit fusion. Here, we report the development of a microfluidic assay to measure neutralization of viral entry that can clearly differentiate between effects on attachment and fusion. Testing multiple broadly-neutralizing antibodies against the hemagglutinin stalk domain, we obtain a surprising result: some broadly-neutralizing antibodies inhibit fusion only, while others inhibit both fusion and viral attachment. Antibodies binding the globular head domain primarily inhibit attachment but can also reduce the fusogenic capability of viral particles that nonetheless bind receptor. These findings shed light on the unexpectedly heterogeneous mechanisms of antibody neutralization even within similar recognition sites. The assay we have developed also provides a tool to optimize vaccine design by permitting assessment of the elicited antibody response with greater mechanistic resolution.
Differential patterns of taxonomic and functional diversity for two groups of canopy arthropods across spatial scales

Aim Arthropod diversity is often linked to variation in resource use, dispersal ability, habitat connectivity, and climate factors that differ across spatial scales. The aim of this research was to examine how species richness, functional diversity, and community composition of two taxa differing in functional roles and dispersal ability are structured across spatial scales and to identify the importance of vegetation, climate, and landscape in explaining these patterns at different scales. Location 96 trees in 24 stands of 6 deciduous forest sites in 2 ecoregions of the eastern USA (North-Central Till Plain and Western Allegheny Plateau) Time period 2000 Major taxa studied Canopy dwelling ants (Hymenoptera: Formicidae) and spiders (Araneae) Methods Organisms were collected from tree canopies using insecticidal fogging. Ant and spider taxonomic and functional beta diversity were partitioned across four hierarchical spatial scales (individual tree, forest stand, site, and ecoregion). The contribution of climactic, landscape, and vegetation variables was determined using model selection. Results Ant and spider species richness, functional diversity, and community composition differed between taxa and across spatial scales. Alpha diversity (within trees) was lower than expected for both taxa and types of diversity, with host tree species supporting different species of ants and spiders. While beta components of species diversity among trees and forest stands was greater than expected for both taxa, spiders also showed significant levels of beta diversity among sites. Functional beta diversity was less scale-dependent than taxonomic beta diversity. Stand-level patterns of beta diversity were significantly predicted by variation in climate and landscape connectivity. Main conclusions Effects of climate and landscape fragmentation on the diversity and community structure of both taxa indicate that anthropogenic climate change and land use change will alter canopy arthropod communities. Results also suggest that patterns of diversity among fragmentation metrics is influenced by differences in dispersal ability.
Characterizing the genetic polymorphisms in 370 challenging medically relevant genes using long-read sequencing data from 41 human individuals among 19 global populations

Numerous challenging medically relevant genes (CMRGs) cannot be adequately investigated using next-generation sequencing, hindering the detection of functional variation among these genes. In this study, long-read sequencing data from 41 human individuals across 19 populations were analyzed using the current version of the human reference genome assembly (GRCh38) and a telomere-to-telomere assembly of the human genome (T2T-CHM13). After excluding 142 CMRGs containing windows with a depth of coverage (DoC) significantly deviating from the average DoC value of proteincoding regions in the GRCh38 (138) or T2T-CHM13 (47) assemblies, 179 and 263 CMRGs exhibited copy number variation (CNV) signal in GRCh38 and T2T-CHM13, respectively. In addition, 451 high-impact short variants were detected in 188 CMRGs. Further, some genetic alterations were individual- or continental-superpopulation-specific, suggesting a strong need to consider genetic background differences in future genetic testing and drug design studies. Finally, side-by-side comparisons of short variant calls in CMRGs using NGS and LRS data from 13 samples indicated that 15.79% to 33.96% of high-impact short variants in different individuals could only be detected using LRS data. The results described herein will be an important reference for future clinical and pharmacogenetic studies to further improve precision medicine.