Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobility Management in Industrial IoT Environments

The Internet Engineering Task Force (IETF) has defined the 6TiSCH architecture to enable the Industrial Inter-net of Things (IIoT). Unfortunately, 6TiSCH does not provide mechanisms to manage node mobility, while many industrial applications involve mobile devices (e.g., mobile robots or wearable devices carried by workers). In this paper, we consider the Synchronized Single-hop Multiple Gateway framework to manage mobility in 6TiSCH networks. For this framework, we address the problem of positioning Border Routers in a deployment area, which is similar to the "Art Gallery" problem, proposing an efficient deployment policy for Border Routers based on geometrical rules. Moreover, we define a flexible Scheduling Function that can be easily adapted to meet the requirements of various IIoT applications. We analyze the considered Scheduling Function in different scenarios with varying traffic patterns, and define an algorithm for sizing the system in such a way to guarantee the application requirements. Finally, we investigate the impact of mobility on the performance of the system. Our results show that the proposed solutions allow to manage node mobility very effectively, and without significant impact on the performance.
Topics on high dimensional statistical inference and ANOVA for longitudinal data

We propose simultaneous tests for coefficients in high dimensional linear regression models with factorial designs. The proposed tests are designed for the “large p, small n” situations where the conventional F-test is no longer applicable. We derive the asymptotic distribution of the proposed test statistic under the high dimensional null hypothesis and various scenarios of the alternatives, which allow power evaluations. We also evaluate the power of the F-test under very mild dimensionality. The proposed tests are employed to analyze a micro-array data on Yorkshire Gilts to find significant gene ontology terms which are significantly associated with the thyroid hormone after accounting for the designs of the experiment.
Trailing Edge Blowing on a Two-Dimensional Six-Percent Thick Elliptical Circulation Control Airfoil Up to Transonic Conditions

A wind tunnel test was conducted in the NASA Langley Transonic Dynamics Tunnel (TDT) on a six percent thick slightly cambered elliptical circulation control airfoil with both upper and lower surface blowing capability. Parametric evaluations of jet slot heights and Coanda surface shapes were conducted at momentum coefficients (Cm) from 0.0 to 0.12. Test data were acquired at Mach numbers of 0.3, 0.5, 0.7, 0.8, and 0.84 at Reynolds numbers per foot of 2.43 x 105 to 1.05 x 106. For a transonic condition, (Mach = 0.8 at alpha = 3 degrees), it was generally found the smaller slot and larger Coanda surface combination was overall more effective than other slot/Coanda surface combinations. Lower surface blowing was not as effective as the upper surface blowing over the same range of momentum coefficients. No appreciable Coanda surface, slot height, or slot blowing position preference was indicated transonically with the dual slot blowing.
Performance Study of YOLOv5 and Faster R-CNN for Autonomous Navigation around Non-Cooperative Targets

Autonomous navigation and path-planning around non-cooperative space objects is an enabling technology for on-orbit servicing and space debris removal systems. The navigation task includes the determination of target object motion, the identification of target object features suitable for grasping, and the identification of collision hazards and other keep-out zones. Given this knowledge, chaser spacecraft can be guided towards capture locations without damaging the target object or without unduly the operations of a servicing target by covering up solar arrays or communication antennas. One way to autonomously achieve target identification, characterization and feature recognition is by use of artificial intelligence algorithms. This paper discusses how the combination of cameras and machine learning algorithms can achieve the relative navigation task. The performance of two deep learning-based object detection algorithms, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLOv5), is tested using experimental data obtained in formation flight simulations in the ORION Lab at Florida Institute of Technology. The simulation scenarios vary the yaw motion of the target object, the chaser approach trajectory, and the lighting conditions in order to test the algorithms in a wide range of realistic and performance limiting situations. The data analyzed include the mean average precision metrics in order to compare the performance of the object detectors. This paper discusses implementing the feature recognition algorithms and integrating them into the spacecraft Guidance Navigation and Control system.
A spatialised agent-based model of NOTCH signalling pathway in Endothelial Cells predicts emergent heterogeneity due to continual dynamic phenotypic adjustments

Vascular Endothelial Cells (EC) plasticity is key to homeostasis and its disruption is a hallmark of diseases such as cancer, atherosclerosis, and diabetes. The EC lineage has evolved to address in parallel sensor and actuator functions. This ability is reflected in remarkable phenotypical heterogeneity of EC across different tissues, within the same tissue, and within the same vascular bed as demonstrated by single cell image analysis and transcriptomics studies. However, how the molecular signalling dynamics in EC could generate and maintain such heterogeneity in different contexts is still largely unexplored. Recently we reported that confluent EC have spatially heterogeneous NOTCH signalling pathway (NSP) levels in vitro as confirmed from analysis of available OMICS databases. Here, we show that spatial heterogeneity of NSP levels is a feature of aortic murine endothelia in vivo and recapitulated by human EC in culture despite absence of signalling from mural cells. We study lateral induction and inhibition, cis-interactions and signalling, and target genes autoregulation in NSP. Using mathematical models and experimental observations we report that NSP dynamics can generate stable, periodic, and asynchronous oscillations of the NSP target HES1. Importantly, we observe that cell contact dependent NSP signal oscillations is the most likely parsimonious mechanistic hypothesis justifying observed spatial heterogeneity in endothelia. We propose that NSP is sufficient to enable individual EC in monolayers to acquire different phenotypes dynamically explaining robustness of quiescent endothelia in performing parallel functions.
SUPREME: A cancer subtype prediction methodology integrating multiomics data using Graph Convolutional Neural Network

To pave the road towards precision medicine in cancer, patients with highly similar biology ought to be grouped into the same cancer subtypes. Utilizing high-dimensional multiomics datasets, several integrative computational approaches have been developed to uncover cancer subtypes. Recently, Graph Neural Networks (GNNs) was discovered to learn node embeddings while utilizing node features and node associations at the same time on graph-structured data. Although there are some commonly used architectures such as Graph Convolutional Network (GCN) for cancer subtype prediction, the existing prediction tools have some limitations in leveraging those architectures with multiomics integration on multiple networks. Addressing them, we developed SUPREME (a subtype prediction methodology) by comprehensively analyzing multiomics data and associations between patients with graph convolutions on multiple patient similarity networks. Unlike the existing tools, SUPREME generates patient embeddings from patient similarity networks, on which it utilizes all the multiomics features. In addition, SUPREME integrates all the possible combinations of embeddings with the raw multiomics features to capture the complementary signals. Extensive evaluation of all combinations makes SUPREME interpretable in terms of utilized networks and features. On three different datasets from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and both combined, our method significantly outperformed other integrative cancer (sub)type prediction tools and baseline methods, with overall consistent results. SUPREME-inferred subtypes had significant survival differences, mostly having more significance than ground truth (PAM50) labels, and outperformed nine cancer subtype differentiating tools and baseline methods. These results suggest that with proper utilization of multiple datatypes and patient associations, SUPREME could demystify the undiscovered characteristics in cancer subtypes that cause significant survival differences and could improve the ground truth label, which depends mainly on a single datatype. Source code for our tool is publicly available at https://github.com/bozdaglab/SUPREME.
Size And Locomotor Ecology Have Differing Effects on the External and Internal Morphologies of Squirrel (Rodentia: Sciuridae) Limb Bones

Mammals exhibit a diverse range of limb morphologies that are associated with different locomotor ecologies and structural mechanics. Much remains to be investigated, however, about the combined effects of locomotor modes and scaling on the external shape and structural properties of limb bones. Here, we used squirrels (Sciuridae) as a model clade to examine the effects of locomotor mode and scaling on the external shape and structure of the two major limb bones, the humerus and femur. We quantified humeral and femoral morphologies using 3D geometric morphometrics and bone structure analyses on a sample of 76 squirrel species across their four major ecotypes. We then used phylogenetic generalized linear models to test how locomotor ecology, size, and their interaction influenced morphological traits. We found that size and locomotor mode exhibit different relationships with the external shape and structure of the limb bones, and that these relationships differ between the humerus and femur. External shapes of the humerus and, to a lesser extent, the femur are best explained by locomotor ecology rather than by size, whereas structures of both bones are best explained by interactions between locomotor ecology and scaling. Interestingly, the statistical relationships between limb morphologies and ecotype were lost when accounting for phylogenetic relationships among species under Brownian motion. That assuming Brownian motion confounded these relationships is not surprising considering squirrel ecotypes are phylogenetically clustered; our results suggest that humeral and femoral variation partitioned early between clades and their ecomorphologies were maintained to the present. Overall, our results show how mechanical constraints, locomotor ecology, and evolutionary history may enact different pressures on the shape and structure of limb bones in mammals.
A cryptic transactivation domain of EZH2 binds AR and AR’s splice variant, promoting oncogene activation and tumorous transformation

Enhancer of Zeste Homolog 2 (EZH2) and androgen receptor (AR) are crucial chromatin regulators involved in the development and/or progression of prostate tumor, including advanced castration-resistant prostate cancer (CRPC). To sustain prostate tumorigenicity, EZH2 establishes noncanonical biochemical interaction with AR for mediating oncogene activation, in addition to its canonical role as a transcriptional repressor and enzymatic subunit of Polycomb Repressive Complex 2 (PRC2). However, the molecular basis underlying non-canonical activities of EZH2 in prostate cancer remains elusive and therapeutic strategies for targeting EZH2:AR-mediated oncogene activation activities are also lacking. Here, we report that a cryptic transactivation domain of EZH2 (EZH2TAD) binds both AR and AR spliced variant 7 (AR-V7, an AR variant enriched in CRPC), mediating assembly and/or recruitment of transactivation-related machineries at genomic sites that lack PRC2 binding. Such noncanonical targets of EZH2:AR/AR-V7:(co)activators are enriched for the clinically-relevant oncogenes. We also show that EZH2TAD is required for the chromatin recruitment of EZH2, for EZH2-mediated oncogene activation, and for CRPC growth in vitro and in vivo. To completely block EZH2’s multifaceted oncogenic activities in prostate cancer, we employed MS177, a recently developed proteolysis targeting chimera (PROTAC) of EZH2. Strikingly, MS177 achieved on-target depletion of both EZH2’s canonical (EZH2:PRC2) and noncanonical (EZH2TAD:AR/AR-V7:coactivators) complexes in prostate tumor, eliciting much more potent antitumor effects than the catalytic inhibitors of EZH2. Overall, this study reports previously unappreciated requirements of EZH2TAD for mediating EZH2’s noncanonical (co)activator recruitment and gene-activation functions in prostate tumor and suggests EZH2-targeting PROTACs as potentially attractive therapeutics for the treatment of aggressive prostate tumors that rely on the circuits wired by EZH2 and AR.
Statistical characterization of tactile scenes in three-dimensional environments reveals filter properties of somatosensory cortical neurons

Natural scenes statistics have been studied extensively using collections of natural images and sound recordings. These studies have yielded important insights about how the brain might exploit regularities and redundancies in visual and auditory stimuli. In contrast, natural scenes for somatosensation have remained largely unexplored. Here we use three-dimensional scans of natural and human-made objects to quantify natural scene statistics at the scale of the human fingertip. Using measurements of distance, slope, and curvature from the object surfaces, we show that the first order statistics follow similar trends as have been observed for images of natural and human-made environments. In addition, independent component analysis of curvature measurements reveals Gabor-like basis vectors similar to those found in natural images. A simple neural model using these filters showed responses that accurately capture the statistics of responses in primate primary somatosensory cortex.
Drift and diffusion in phase space

The problem of stability of action variables (i.e. of the adiabatic invariants) in perturbations of completely integrable (real analytic) hamiltonian systems with more than two degrees of freedom is considered. Extending the analysis of [A], we work out a general quantitative theory, from the point of view of dimensional analysis, for a priori unstable systems (i.e. systems for which the unperturbed integrable part possesses separatrices), proving, in general, the existence of the so-called Arnold's diffusion and establishing upper bounds on the time needed for the perturbed action variables to drift by an amount of O(1). The above theory can be extended so as to cover cases of a priori stable systems (i.e. systems for which separatrices are generated near the resonances by the perturbation). As an example we consider the «d'Alembert precession problem in Celestial Mechanics» (a planet modelled by a rigid rotational ellipsoid with small «flatness» η, resolving on a given Keplerian orbit of eccentricity e=η c , c>1, around a fixed star and subject only to Newtonian gravitational forces) proving in such a case the existence of Arnold's drift and diffusion; this means that there exist initial data for which for any η¬=0 small enough, the planet changes, in due (η-dependent) time, the inclination of the precession cone by an amount of O(1). The homo/heteroclinic angles (introduced in general and discussed in detail together with homoclinic splittings and scatterings) in the d'Alembert problem are not exponentially small with η (in site of first order predictions based upon Melnikov type integrals)
LambdaPP: Fast and accessible protein‐specific phenotype predictions

The availability of accurate and fast Artificial Intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first internet server making AI protein predictions available in 1992. Given a protein sequence as input, LambdaPP provides easily accessible visualizations of protein 3D structure, along with predictions at the protein level (GeneOntology, subcellular location), and the residue level (binding to metal ions, small molecules, and nucleotides; conservation; intrinsic disorder; secondary structure; alpha-helical and beta-barrel transmembrane segments; signal-peptides; variant effect) in seconds. The structure prediction provided by LambdaPP - leveraging ColabFold and computed in minutes - is based on MMseqs2 multiple sequence alignments. All other feature prediction methods are based on the pLM ProtT5. Queried by a protein sequence, LambdaPP computes protein and residue predictions almost instantly for various phenotypes, including 3D structure and aspects of protein function. Accessibility Statement LambdaPP is freely available for everyone to use under embed.predictprotein. org, the interactive results for the case study can be found under https://embed.predictprotein. org/o/Q9NZC2. The frontend of LambdaPP can be found on GitHub (github.com/sacdallago/embed.predictprotein.org), and can be freely used and distributed under the academic free use license (AFL-2). For high-throughput applications, all methods can be executed locally via the bio-embeddings (bioembeddings.com) python package, or docker image at ghcr.io/bioembeddings/bio_embeddings, which also includes the backend of LambdaPP. Impact Statement We introduce LambdaPP, a webserver integrating fast and accurate sequence-only protein feature predictions based on embeddings from protein Language Models (pLMs) available in seconds along with high-quality protein structure predictions. The intuitive interface invites experts and novices to benefit from the latest machine learning tools. LambdaPP’s unique combination of predicted features may help in formulating hypotheses for experiments and as input to bioinformatics pipelines.
Multiple pathways for SARS-CoV-2 resistance to nirmatrelvir

Nirmatrelvir, an oral antiviral targeting the 3CL protease of SARS-CoV-2, has been demonstrated to be clinically useful in reducing hospitalization or death due to COVID-191,2. However, as SARS-CoV-2 has evolved to become resistant to other therapeutic modalities3–9, there is a concern that the same could occur for nirmatrelvir. Here, we have examined this possibility by in vitro passaging of SARS-CoV-2 in increasing concentrations of nirmatrelvir using two independent approaches, including one on a large scale in 480 wells. Indeed, highly resistant viruses emerged from both, and their sequences revealed a multitude of 3CL protease mutations. In the experiment done at a larger scale with many replicates, 53 independent viral lineages were selected with mutations observed at 23 different residues of the enzyme. Yet, several common mutational pathways to nirmatrelvir resistance were preferred, with a majority of the viruses descending from T21I, P252L, or T304I as precursor mutations. Construction and analysis of 13 recombinant SARS-CoV-2 clones, each containing a unique mutation or a combination of mutations showed that the above precursor mutations only mediated low-level resistance, whereas greater resistance required accumulation of additional mutations. E166V mutation conferred the strongest resistance (~100-fold), but this mutation resulted in a loss of viral replicative fitness that was restored by compensatory changes such as L50F and T21I. Structural explanations are discussed for some of the mutations that are proximal to the drug-binding site, as well as cross-resistance or lack thereof to ensitrelvir, another clinically important 3CL protease inhibitor. Our findings indicate that SARS-CoV-2 resistance to nirmatrelvir does readily arise via multiple pathways in vitro, and the specific mutations observed herein form a strong foundation from which to study the mechanism of resistance in detail and to inform the design of next generation protease inhibitors.
Circulating tumor DNA profiling approach based on in silico background elimination guides patient classification of multiple cancers

Circulating tumor DNA (ctDNA) analysis is increasingly providing a promising minimally invasive alternative to tissue biopsies in precision oncology. However, current methods of ctDNA mutation profiling have limited resolution because of the high background noise and false-positive rate caused by benign variants in plasma cell-free DNA (cfDNA), majorly generated during clonal hematopoiesis. Although personalized parallel white blood cell (WBC) genome sequencing suppresses the noise of clonal hematopoiesis variances observed in ctDNA based liquid biopsy, the system cost and complexity restrict its extensive application in clinical settings. To address this challenge, we describe a Matched WBC Genome sequencing Independent CtDNA profiling (MaGIC) approach, which enables the sensitive detection of recurrent tumor mutant information harbored by ctDNA from a bulk cfDNA background based on hybrid capture cfDNA deep sequencing, in silico background elimination, and a reliable readout measurement by the calculation the number of key mutated exons. Leveraging somatic mutation data from 10163 patients across 24 cancer types obtained from The Cancer Genome Atlas, we confirm that the MaGIC approaches are of ideal performance in prediction of prognosis by tissue biopsy samples of patients across multiple cancers. Meanwhile, MaGIC approaches enable the classification of prostate cancer patients from heathy cohorts by ctDNA sequencing data. We further profiled the ctDNAs of 80 plasma samples from 40 patients with nasopharyngeal carcinoma before and during chemotherapy by MaGIC approaches. The MaGICv2 can predict the chemosensitivity with high accuracy by simply using one liquid biopsy sample of each patient before a stereotypical treatment course. We anticipate that this new approach has the potential utility of ctDNA detection in multiple clinical cancer contexts, thus facilitating precise cancer therapy. Teaser A liquid biopsy analysis method for multifunctional patient classification such as diagnosis and chemosensitivity prediction.
Inositol Pyrophosphate-Controlled Kinetochore Architecture and Mitotic Entry in S. pombe

Inositol pyrophosphates (IPPs) comprise a specific class of signaling molecules that regulate central biological processes in eukaryotes. The conserved Vip1/PPIP5K family controls intracellular IP8 levels, the highest phosphorylated form of IPPs present in yeasts, as it has both inositol kinase and pyrophosphatase activities. Previous studies have shown that the fission yeast S. pombe Vip1/PPIP5K family member Asp1 impacts chromosome transmission fidelity via modulation of spindle function. We now demonstrate that an IP8 analogue is targeted by endogenous Asp1 and that cellular IP8 is subject to cell cycle control. Mitotic entry requires Asp1 kinase function and IP8 levels are increased at the G2/M transition. In addition, the kinetochore, the conductor of chromosome segregation assembled on chromosomes is modulated by IP8. Members of the yeast CCAN kinetochore-subcomplex such as Mal2/CENP-O localize to the kinetochore depending on the intracellular IP8-level: higher than wild-type IP8 levels reduces Mal2 kinetochore targeting, while a reduction in IP8 has the opposite effect. As our perturbations of the inositol polyphosphate and IPP pathways demonstrate that kinetochore architecture depends solely on IP8 and not on other IPPs, we conclude that chromosome transmission fidelity is controlled by IP8 via an interplay between entry into mitosis, kinetochore architecture and spindle dynamics.