Extrinsic Features Research Articles

Demonstration of extrinsic chirality in self-assembled asymmetric plasmonic metasurfaces and nanohole arrays

Chirality, the lack of mirror symmetry, can be mimicked in nanophotonics and plasmonics by breaking the symmetry in light-nanostructure interaction. Here we report on versatile use of nanosphere lithography for the fabrication of low-cost metasurfaces, which exhibit broadband handedness- and angle-dependent extinction in the near-infrared range, thus offering extrinsic chiro-optical behavior. We measure wavelength and angle dependence of the extinction for four samples. Two samples are made of polystyrene nanospheres asymmetrically covered by silver and gold in one case and silver only in the other case, with a nanohole array at the bottom. The other two samples are nanohole arrays, obtained after the nanosphere removal from the first two samples. Rich extrinsic chiral features are governed by different chiro-optical mechanisms in the three-dimensional plasmonic semi-shells and planar nanohole arrays. We also measure Stokes parameters in the same wavelength and incidence angle range and show that the transmitted fields follow the extrinsic chirality features of the extinction dissymmetry. We further study the influences of the nanostructured shapes and in-plane orientations on the intrinsic vs extrinsic chirality. The nanoholes are modelled as oval shapes in metal, showing good agreement with the experiments. We thus confirm that nanosphere lithography can provide different geometries for chiral light manipulation at the nanoscale, with the possibility to extend functionalities with optimized oval shapes and combination of constituent metals.

Molybdenum Sulfide Nanoflowers as Electrodes for Efficient and Scalable Lithium-Ion Capacitors.

Hybrid supercapacitors (HSCs) bridge the unique advantages of batteries and capacitors and are considered promising energy storage devices for hybrid vehicles and other electronic gadgets. Lithium-ion capacitors (LICs) have attained particular interest due to their higher energy and power density than traditional supercapacitor devices. The limited voltage window and the deterioration of anode materials upsurged the demand for efficient and stable electrode materials. Two-dimensional (2D) molybdenum sulfide (MoS2) is a promising candidate for developing efficient and durable LICs due to its wide lithiation potential and unique layer structure, enhancing charge storage efficiency. Modifying the extrinsic features, such as the dimensions and shape at the nanoscale, serves as a potential path to overcome the sluggish kinetics observed in the LICs. Herein, the MoS2 nanoflowers have been synthesized through a hydrothermal route. The developed LIC exhibited a specific capacitance of 202.4 F g-1 at 0.25 A g-1 and capacitance retention of >90 % over 5,000 cycles. Using an ether electrolyte improved the voltage window (2.0 V) and enhanced the stability performance. The ex-situ material characterization after the stability test reveals that the storage mechanism in MoS2-LICs is not diffusion-controlled. Instead, the fast surface redox reactions, especially intercalation/deintercalation of ions, are more prominent for charge storage.

Drivers of genomic differentiation landscapes in populations of disparate ecological and geographical settings within mainland Apis cerana.

Elucidating the evolutionary processes that drive population divergence can enhance our understanding of the early stages of speciation and inform conservation management decisions. The honeybee Apis cerana displays extensive population divergence, providing an informative natural system for exploring these processes. The mainland lineage A. cerana includes several peripheral subspecies with disparate ecological and geographical settings radiated from a central ancestor. Under this evolutionary framework, we can explore the patterns of genome differentiation and the evolutionary models that explain them. We can also elucidate the contribution of non-genomic spatiotemporal mechanisms (extrinsic features) and genomic mechanisms (intrinsic features) that influence these genomic differentiation landscapes. Based on 293 whole genomes, a small part of the genome is highly differentiated between central-peripheral subspecies pairs, while low and partial parallelism partly reflects idiosyncratic responses to environmental differences. Combined elements of recurrent selection and speciation-with-gene-flow models generate the heterogeneous genome landscapes. These elements weight differently between central-island and other central-peripheral subspecies pairs, influenced by glacial cycles superimposed on different geomorphologies. Although local recombination rates exert a significant influence on patterns of genomic differentiation, it is unlikely that low-recombination rates regions were generated by structural variation. In conclusion, complex factors including geographical isolation, divergent ecological selection and non-uniform genome features have acted concertedly in the evolution of reproductive barriers that could reduce gene flow in part of the genome and facilitate the persistence of distinct populations within mainland lineage of A. cerana.

Dual-Graph Attention Convolution Network for 3-D Point Cloud Classification.

Three-dimensional point cloud classification is fundamental but still challenging in 3-D vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and high-level intrinsic features together. These two levels of features are critical to improving classification accuracy. To this end, we propose a dual-graph attention convolution network (DGACN). The idea of DGACN is to use two types of graph attention convolution operations with a feedback graph feature fusion mechanism. Specifically, we exploit graph geometric attention convolution to capture low-level extrinsic features in 3-D space. Furthermore, we apply graph embedding attention convolution to learn multiscale low-level extrinsic and high-level intrinsic fused graph features together. Moreover, the points belonging to different parts in real-world 3-D point cloud objects are distinguished, which results in more robust performance for 3-D point cloud classification tasks than other competitive methods, in practice. Our extensive experimental results show that the proposed network achieves state-of-the-art performance on both the synthetic ModelNet40 and real-world ScanObjectNN datasets.

Abstract 5100: Multimodal real world data reveals immunogenomic drivers of acquired and primary resistance to immune checkpoint blockade

Abstract Background: Why some patients fail or have short lived response to immune checkpoint blockade (ICB) immunotherapy remains largely unknown. While baseline molecular assessments have provided clues to prognostic factors, insights into resistance drivers remains elusive. This is partially due to the difficulty in getting access to post progression samples from patients that were either primary resistant or developed acquired resistance after an initial response to ICB. Thus, the tumor-intrinsic and -extrinsic features that are selected for during progression and potentially drive primary and acquired resistance to immunotherapy remain underexplored. Methods: To compare clinical features and immunogenomic drivers of acquired and primary resistance to ICB across major cancers, we analysed and annotated de-identified patient records in the Tempus real-world database. We built an immuno-oncology cohort consisting of >2500 multimodal (DNA, RNA and clinical outcome data) pre-treatment baseline with >1500 post-treatment tumour biopsy samples from mainly NSCLC, TNBC, HNC and Bladder cancer patients. We used bulk RNA-seq data to estimate activation of the hallmark oncogenic pathways and immune cell composition and used panel DNA-seq data (>500 genes) to quantify mutation selection at the gene and pathway levels using dndscv. Results: Compared to acquired, primary resistant patients tended to have a higher observation of liver lesions at progression. Post-ICB, acquired resistant NSCLC and HNC patients showed a significantly inflamed tumour microenvironment (TME) characterised by higher estimation of infiltration of T cells and myeloid cells and higher activation of interferon gamma (IFNg) signalling as compared to primary resistant patients. In addition, in post-ICB acquired resistance in NSCLC we observed selection for mutations in genes involved in known immunomodulatory pathways, including loss-of-function mutations in B2M. Consistently, acquired resistance patients showed stronger selection for mutations in Hedgehog and Notch pathways as compared to primary resistance patients across NSCLC, HNC and TNBC post-ICB. Conclusions: Acquired and primary ICB resistant patients have distinct clinical and molecular features at progression. Their tumours’ TME is fundamentally different with acquired resistance TMEs being infiltrated with immune cells albeit escaped post progression. In addition, ICB selects mutations that potentially activate pathways such as Notch and Hedgehog. This multi-modal Real-World Data with post therapy biopsies has given insights for patient selection strategies and provides rational into combination treatment options for acquired resistant patients. Citation Format: Mohamed Reda Keddar, Sebastian Carrasco Pro, Kathleen Burke, Ana Camelo Stewart, Mark Cobbold, Ross Stewart, Sajan Khosla, Ben Sidders, Scott Hammond, Douglas C. Palmer, Jonathan Dry, Martin L. Miller. Multimodal real world data reveals immunogenomic drivers of acquired and primary resistance to immune checkpoint blockade [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5100.

Abstract 2593: Spatial multi-omics profiling of tumor cell phenotypes in primary cutaneous melanoma at single-cell resolution

Abstract Primary cutaneous melanomas develop in stages from melanoma precursor cells that may progress to melanoma in situ (MIS) and eventually to invasive disease. Molecular changes, such as tumor cell state switches from melanocytic to undifferentiated and neural-crest-like phenotypes, have been associated with the development of melanoma metastases and therapeutic resistance. However, it is unknown whether cell states or gene expression changes enriched in advanced tumors exist at the premalignant stage and if they can elicit tumor-infiltrating lymphocytes (TILs). To address this gap, we analyzed a cohort of clinical samples from 43 patients with primary cutaneous melanoma using whole-slide cyclic multiplex immunofluorescence imaging (CyCIF), morphology-guided micro-regional RNA sequencing, and high-resolution 3D imaging. Each specimen contained multiple histologically-distinct regions representing a progression axis from normal to precursor to melanoma in situ to invasion. Subsequently, we examined the molecular profiles of both atypical melanocytes and tumor cells, and the gene expression and tumor microenvironmental (TME) changes that occurred during these progression stages. We identified major intra- and inter-specimen heterogeneity in the tumor intrinsic and extrinsic features across progression stages. Multiple tumor samples contained regions where publicly available gene signatures linked to both melanocytic and undifferentiated cell phenotypes were enriched. CyCIF analysis confirmed that various tumor cell phenotypes can coexist in close proximity to each other in early-stage untreated melanomas, and that the majority of undifferentiated tumor cells were located within regions of dermal invasion. Moreover, the cellular neighborhoods around undifferentiated tumor cells had a higher proportion of CD8+ T cells and cells of myeloid lineage compared to the neighborhoods around melanocytic tumor cell phenotypes. When assessing the localization and abundance of various tumor cell phenotypes, the levels of spatial and regional heterogeneity increased proportionally along normal to premalignant to malignant progression. Furthermore, the regional entropy of tumor cell phenotypes decreased along tumor depth in the majority (63%) of the samples, and even the melanoma in situ regions showed varying levels of spatial heterogeneity. Our in-depth multimodal molecular analyses highlight the presence of spatial heterogeneity among tumor cell phenotypes as well as the organization of early-stage primary melanomas, and are suggestive of tumor cell adaptation to the local environment. Citation Format: Tuulia Vallius, Edward Novikov, Ajit Nirmal Johnson, Shi Yingxiao, Roxanne Pelletier, Shishir Pant, Zoltan Maliga, Alyce Chen, Clarence Yapp, Sabrina Chan, Christine Lian, George F. Murphy, Sandro Santagata, Peter Sorger. Spatial multi-omics profiling of tumor cell phenotypes in primary cutaneous melanoma at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2593.

Consumer Insights to Eco-Design a Hot Sauce: Understanding Household Use for Product Optimization through Focus Groups and a Home-Use-Test Study.

Promoting sustainable choices requires making food with proper environmental performance readily available to consumers, but these products must be appealing to ensure market success. The aim of the present study was to investigate the acceptability and perception of an eco-designed product using a home-use-test approach to identify its desired extrinsic features and to better understand how consumers would use the product in a real consumption context. First, three focus groups were conducted to identify the key perceived aspects of the product. A consumer survey was designed with the information gathered from the focus groups, and a home-use-test was then performed (n = 207). Results showed high acceptance of the product, as well as its potential corrections, e.g., to thicken the texture of the sauce. A total of 64% of consumers expressed their willingness to switch from a well-known brand to the new developed product, and this hypothetical shift could lead up to a 58% reduction in greenhouse gas emissions associated with the product use. Understanding consumer expectations at every design stage helps the development of market-viable and sustainable products, and the present research proposes an interesting methodology that can be effectively applied during the final stages of eco-designed food development.

Real estate price estimation through a fuzzy partition-driven genetic algorithm

Evaluating the actual price of a residential property is a critical issue in the real estate market. Real estate market practitioners gauge a property's price by considering features such as property type and residential area. Subsequently, they evaluate the property's intrinsic features, such as condition, sun exposure, scenic views, and ancillary amenities. Finally, extrinsic features such as the proximity of services and infrastructure are assessed. This paper proposes a new genetic approach for selecting residential properties that meet the purchase offer and the intrinsic and extrinsic characteristics desired by the client. Since the real estate market's changes can influence extrinsic features, the method introduces price fluctuations of properties. Extrinsic features are modelled as fuzzy partitions: each fuzzy set describes a qualitative aspect of the corresponding feature that, expressed in a linguistic term, has a human-like interpretation. Then, a deviation value (fluctuation) from the average price of the property is considered for each fuzzy set in the partition. All the property features, extrinsic and intrinsic, are encoded in the chromosome genes of the genetic algorithm. The fitness function calculates the distance between the unit price of the property and the purchase offer. Some case studies were conducted in various Italian municipalities, using the average price per square meter of residential properties the Osservatorio del Mercato Immobiliare (OMI) assigned. Depending on customer requirements and preferences, different OMI zones were selected using additional characteristics such as type, location, conservation, and proximity to various urban services. The results demonstrated the effectiveness of the proposed approach for all the case studies, showing how the optimal solution represents a good compromise between customer preferences and market offerings.

MGDHGS[formula omitted] Gene-bridged metabolite-disease relationships prediction via GraphSAGE and self-attention mechanism

Metabolites represent the underlying information of biological systems. Revealing the links between metabolites and diseases can facilitate the development of targeted drugs. Traditional biological experiments can be used to validate the relationships of metabolite-disease, but these methods are time-consuming and labor-intensive. In contrast, the prevailing computational methods have improved efficiency but primarily rely on the metabolite-disease interactions, overlooking the impact of other biological components. To remedy the problem, we present a novel computational framework (MGDHGS) based on metabolite-gene-disease heterogeneous network to forecast potential associations. Specifically, we initially integrate data from multiple sources to construct metabolite-gene-disease heterogeneous network that includes known associations and computationally-derived similarities. Then, the GraphSAGE is harnessed to learn the low dimensional neighborhood representation in the heterogeneous network and self-attention mechanism is applied to effectively capture the connectivity patterns, which contributions to combine with nodes intrinsic and extrinsic features. Finally, the ultimate relationships probability scores are predicted by linear regression based on the these characteristics. The five-fold cross-validation showcases impressive AUC (0.9734) and PR (0.9718) for MGDHGS compared with five state-of-the-art methods, and the case studies validate that the metabolite-disease associations predicted by MGDHGS can be substantiated through pertinent biological experiments. The findings of this study show great potential contribution in the development of targeted drugs as well as offering solid support for our understanding of the complex interactions between metabolites, genes and diseases.

Evaluation of ferroelectric profile of poly(vinylidene fluoride‐co‐trifluoroethylene) (P(VDF‐TrFE)) thin films by positive‐up–negative‐down and fatigue measurement

Organic polymers are of interest due to their numerous promising potential applications in the area of energy harvesting and sensing. In this context, piezoelectric and ferroelectric polymers are subject to intense research; however, it is challenging to understand polarization switching in such systems. The focus of the present study is to thoroughly evaluate the finer details of polarization reversal in poly(vinylidene fluoride‐co‐trifluoroethylene) (P(VDF‐TrFE)) thin films. The intrinsic features arising from the true values of (switching) polarization and extrinsic features associated with the presence of non‐switching contributions, i.e. leakage current and linear dielectric component, have been investigated using the positive‐up–negative‐down technique. These contributions are quantified and discussed. Furthermore, fatigue endurance has been examined by repeated switching cycles. Increase in polarization increases is associated with the improved crystallinity and enhancement of the β‐phase. However, a higher degree of crystallinity, for extended numbers of repeated cycles of the bipolar electric field, resulted in degradation of the (switching) polarization. Fatigue significantly affects the leakage and ferroelectric components while the paraelectric contribution remains almost constant. The underlying mechanism of such trends are discussed in terms of hysteresis losses. © 2023 Society of Industrial Chemistry.

Supervision and early warning of abnormal data in Internet of Things based on unsupervised attention learning

With the continuous development of artificial intelligence and big data, more and more communication protocols have appeared in the network connection. Because the network protocol content specification should not need to obtain the content of the common protocol by any means, it faces different attacks, which increases the complexity of network security detection to an unlimited extent. The absolute security analysis of the content of the relevant data protocol is not too limited. Based on the content of the unknown binary number protocol in the Internet of Things, this paper does the reverse research. To address the inefficiency, learning is focused on the inverse aspect of the content processing logic of the protocol for unsupervised execution attention. This paper proposes a method based on the name of the Dirichlet process mixture model normal field and the unknown protocol of the Internet of Things. It also establishes the behavioral criteria that process the logical inference least squares method to infer the content of the target protocol. Based on the original external characteristics of abnormal data in the Internet of Things, the neural network model of attention mechanism and the model checking of variational self-encoder are chosen. An innovative multi-dimensional spatial neural network model with a centralized control mechanism is taken to extract the spatial relevant information from the original external features, which are regarded as the intermediate external features. The intermediate extrinsic features are input to a variational self-encoder for computation and reconstruction. The experimental results verify that the time required by the proposed model is 45% less than that of the traditional structure. The flow processing improves by 36.7% compared with the optimization of the parameter control function. The specific method only relies on the original external characteristics of the Internet of Things traffic and can achieve better data anomaly detection and classification under the condition of not significantly improving the detection time. The abnormal data detection scheme of the Internet of Things designed in this paper not only fills the detection gap of the current mainstream Internet of Things system which lacks an autonomous supervision mechanism, but also effectively blocks external network intrusion and further improves the management authority verification system. It plays an indispensable role in the long-term stable operation of the Internet of Things platform.

Spin States and Spin Order in Perovskite Oxide Oxygen Evolution Electrocatalysts

The ‘power-to-hydrogen’ strategy aims at splitting water into O2 and H2 via the oxygen and hydrogen evolution reactions. The complex four-step oxygen evolution reaction (OER) limits the overall efficiency of hydrogen production. An important reason of the low efficiency is that the production of ground-state (triplet) O2 is a spin-forbidden reaction: in fact, the reactants, OH- or H2O, are diamagnetic, but the final product, O2, is a paramagnetic molecule. Recently, this was well-recognized theoretically1 and the use of spin selective catalysts was described as a possible way to promote the OER.2 . However, it remains complex to understand and exploit intrinsic and extrinsic magnetic features to enhance catalytic performance. Here, we investigate the role of magnetic moments in individual active sites in the catalyst surface layer and the role of spin order in ferromagnetic vs. paramagnetic catalysts, focussing on perovskite oxides.First, we investigated the role of Ni magnetic moment in the the (001), (110) and (111) facet of LaNiO3 electrocatalysts, which we studied using electrochemical measurements, X-ray photoelectron spectroscopy (XPS), X-ray absorption spectroscopy (XAS), and density functional theory (DFT+U) calculations.3 The results show a facet-dependent activity, where the (111) overpotential is ~60 mV lower as compared to the other facets. Closer investigation of the (001) and (111) facets reveals a surface transformation to a oxyhydroxide-like NiOO with edge-sharing octahedra,4 and we observed that the transformed surface is thicker for (111) than for (001).3 The detailed DFT+U analysis reveals important distinctions that give rise to the increased activity: the transformed LaNiO3 (111) surface exhibits a better match to the underlying perovskite layer. Moreover, protonation induces reduced Ni3+ with a finite magnetic moment. A moderate Jahn-Teller distortion enables a favorable binding of reaction intermediates. In contrast, the structural mismatch to the underlying LaNiO3(001)-substrate leads to a strong distortion of the transformed layer for this orientation and a weak binding of *O and ultimately to a different potential determining step (PDS), *OH→*O, compared to *O→*OOH for the transformed LaNiO3(111) surface.Second, we experimentally demonstrate the effect of intrinsic magnetic order on the OER on catalytic performance. Thin films of La0.67Sr0.33MnO3 grown by pulsed laser deposition with appropriate magnetic and electronic properties were chosen as well-defined model systems. Using the ferromagnetic to paramagnetic transition at the Curie temperature in these ferromagnetic perovskite oxides, the magnetic order of the catalysts were switched in situ during the OER by changing the temperature. For ferromagnetic films, the decrease in current density with decreasing temperature, induced by the reduction of thermal energy, was suppressed for temperatures below the Curie temperature, indicating that the presence of ferromagnetic ordering below Curie temperature enhances OER activity. This claim is further supported by an enhancement of OER activity for the same ferromagnetic film upon alignment of magnetic domains with an external magnetic field. All in all, our results reveal that the spin state, intrinsic spin order, and extrinsic magnetic fields are decisive for the OER activity. Biz, C., Fianchini, M. & Gracia, J. Strongly Correlated Electrons in Catalysis: Focus on Quantum Exchange. ACS Catal 11, 14249–14261 (2021).Sun, Y. et al. Spin‐Related Electron Transfer and Orbital Interactions in Oxygen Electrocatalysis. Advanced Materials 32, 2003297 (2020).Füngerlings, A. et al. Crystal-facet-dependent surface transformation dictates the oxygen evolution reaction activity in lanthanum nickelate. in preparation (2023).Baeumer, C. et al. Tuning electrochemically driven surface transformation in atomically flat LaNiO3 thin films for enhanced water electrolysis. Nat Mater 20, 674–682 (2021).

Correlating the Gut Microbiota and Circulating Hormones with Acne Lesion Counts and Skin Biophysical Features.

Acne vulgaris is a common inflammatory condition that is multi-factorial and impacted by both intrinsic and extrinsic features. Several previous studies have assessed for correlations between factors such as circulating hormones, stress, or the microbiome. However, there have not been any correlations specifically against lesion counts or differentiating correlations between inflammatory and non-inflammatory lesion counts. Here, we correlate several factors against acne lesions. Twenty men and women with mild to moderate acne were recruited, and their hormonal levels and their gut microbiome were collected and correlated against their inflammatory and non-inflammatory lesions of acne. Facial non-inflammatory lesions were weakly correlated to sebum excretion rate and weakly inversely correlated to forehead and cheek hydration. We examined stress through the use of a normalized peak-to-trough ratio (higher numbers indicated less stress), which correlated with skin hydration and inversely correlated with sebum excretion rate. Sebum excretion rate was weakly correlated to testosterone levels, and facial hydration correlated with estradiol levels. Correlations with the gut microbiome showed differential correlations with inflammatory and non-inflammatory lesions, with Clostridium sp AF 23-8 correlating to inflammatory lesion counts, while Actinomyces naeslundii str Howell 279 correlated to non-inflammatory lesions. Overall, measures of stress and circulating hormones correlate to skin biophysical properties and acne lesion counts. Also, different gut bacteria correlate with either inflammatory or non-inflammatory lesion counts. We hope that our findings stimulate further work on the gut-mind-stress-skin axis within acne.

A comparative study of the characteristics of hate speech propagators and their behaviours over Twitter social media platform

The internet and social media have facilitated diverse communication genres, enabling widespread and rapid opinions-sharing. However, hate speech imposes a contemporary challenge on individuals and communities, given the user anonymity, freedom, and inadequate regulation. Therefore, it is imperative to identify the perpetrators responsible for spreading hate content and examine their behaviour to prevent and mitigate the negative impact. This study aimed to compare the characteristics of hate speech propagators and their behaviour with non-hate users on Twitter for the first time in Sri Lanka. The intrinsic and extrinsic profile features were extensively analyzed, employing Sinhala and English text analysis techniques. A corpus of 102882 posts from 530 hate and non-hate Twitter user profiles was selected for the study. This study investigates the unique characteristics of hate speech propagators and non-hate users by examining their profile self-presentation, conducting social network analysis, and analyzing sentiment and emotion through linguistic analysis. Hate users often refrained from expression, with infrequent account verification and geotagging. They tend to have a higher follower and following counts and more favourites, group memberships, and statuses than non-hate users. However, general Twitter user engagement with hate users was significantly low, with fewer likes, retweets, and replies. The limited involvement of normal users with hate content indicates that audiences can be effectively utilized to combat hate speech. The sentiment analysis between languages showed polarisation of negative tweets towards Sinhala, with the synergistic effect of English language users using positive sentiment to spread hate content. The novel findings shed light on the characteristics of hate users, facilitating their early detection and moderation of hate speech and aiding in developing algorithms to rank and categorize hate users using artificial intelligence. Moreover, it can be used for policy reforms, awareness programmes, and building social cohesion while combating hate speech.

AWEDD: a descriptor simultaneously encoding multiscale extrinsic and intrinsic shape features

AWEDD: a descriptor simultaneously encoding multiscale extrinsic and intrinsic shape features

Quantifying the cohesive strength of rock materials by roughness analysis using a domain based multifractal framework

Cohesive strength or intrinsic tensile strength as well as cohesive length are two important unknowns for cohesive modelling of fracture and failure analysis of quasi-brittle materials including rocks. There is no direct method for measuring these parameters and their quantification is always challenging and controversial. In this study, a novel multifractal framework is employed to quantify the cohesive length of four different rock types including sandstone, marble, fine-grained granite and coarse-grained granite by analysing the roughness of their fracture surfaces in a wide range of length scales. On the one hand, microstructural heterogeneities of rock material at small enough length scales will cause multifractality of the roughness of its fractured surface. On the other hand, this intrinsic heterogeneity together with extrinsic features including loading and environmental conditions as well as geometrical features including shape and size of a quasi-brittle specimen or structure are forming a fracture process zone (FPZ) in front of any stress concentrators before crack propagation. Therefore, it is proposed that locating the transition from multifractality to mono-fractality of a rough rock fractured surface using the employed statistical mechanics method leads to quantifying the effective length of FPZ of a sharp crack or the cohesive length. This length is quantified for the studied rocks ranging from 0.4 to 1.1 mm. Moreover, by employing the theory of critical distances, the cohesive strength σc of these rocks is also quantified ranging from 15 to 36 MPa.

DIPG-08. THE LANDSCAPE OF TUMOR CELL STATES AND SPATIAL ORGANIZATION IN H3-K27M MUTANT DIFFUSE MIDLINE GLIOMA ACROSS AGE AND LOCATION

Abstract Histone 3 lysine27-to-methionine mutations (H3-K27M) frequently occur in childhood diffuse midline gliomas (DMGs) of the pons, thalamus, and spinal cord, presumed to be driven by the specific spatiotemporal context of these midline locations during postnatal development. While most common in the pons and at mid-childhood ages, the same oncohistone mutation is recurrently detected in adult DMGs and throughout different midline regions. The potential heterogeneity of tumors at different ages and in different anatomical locations of the midline are vastly understudied. Through dissecting the transcriptomic, epigenomic and spatial architectures of a comprehensive cohort of patient H3-K27M DMGs - spanning the age range from 2-68 years and locations from spinal cord to thalamus - at single cell resolution, we delineate how age- and location-dependent contexts shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. We identify that oligodendrocyte precursor (OPC)-like cells constitute the stem-like compartment in H3-K27M DMGs across all clinico-anatomical groups, however, depending on location, display varying levels of maturity resembling less differentiated pre-OPCs or more mature OPCs further differentiated along the oligodendroglial lineage. We further demonstrate increased mesenchymal cell states in adult tumors, which we link to age-related differences in glioma-associated immune cell compartments, in particular an increase of macrophages in adult compared to pediatric tumors. Furthermore, we resolve the spatial organization of H3-K27M DMG cell types and states in intact patient tissues, identifying a local niche of the oligodendroglial lineage. Our study provides a powerful resource for rational modeling and therapeutic frameworks taking into account determinants of age and location in this lethal glioma group.

Social Relationship Analysis Using State-of-the-art Embeddings

Detection of human relationships from their interactions on social media is a challenging problem with a wide range of applications in different areas, like targeted marketing, cyber-crime, fraud, defense, planning, and human resource, to name a few. All previous work in this area has only dealt with the most basic types of relationships. The proposed approach goes beyond the previous work to efficiently handle the hierarchy of social relationships. This article introduces a novel technique named Quantifiable Social Relationship (QSR) analysis for quantifying social relationships to analyze relationships between agents from their textual conversations. QSR uses cross-disciplinary techniques from computational linguistics and cognitive psychology to identify relationships. QSR utilizes sentiment and behavioral styles displayed in the conversations for mapping them onto level II relationship categories. Then, for identifying the level III relationship categories, QSR uses level II relationships, sentiments, interactions, and word embeddings as key features. QSR employs natural language processing techniques for feature engineering and state-of-the-art embeddings generated by word2vec, global vectors (glove), and bidirectional encoder representations from transformers (bert). QSR combines the intrinsic conversational features with word embeddings for classifying relationships. QSR achieves an accuracy of up to 89% for classifying relationship subtypes. The evaluation shows that QSR can accurately identify the hierarchical relationships between agents by extracting intrinsic and extrinsic features from textual conversations between agents.

Cancer stem cells: an insight into the development of metastatic tumors and therapy resistance.

The term "cancer stem cells" (CSCs) refers to cancer cells that exhibit traits parallel to normal stem cells, namely the potential to give rise to every type of cell identified in a tumor microenvironment. It has been found that CSCs usually develops from other neoplastic cells or non-cancerous somatic cells by acquiring stemness and malignant characteristics through particular genetic modifications. A trivial number of CSCs, identified in solid and liquid cancer, can give rise to an entire tumor population with aggressive anticancer drug resistance, metastasis, and invasiveness. Besides, cancer stem cells manipulate their intrinsic and extrinsic features, regulate the metabolic pattern of the cell, adjust efflux-influx efficiency, modulate different signaling pathways, block apoptotic signals, and cause genetic and epigenetic alterations to retain their pluripotency and ability of self-renewal. Notably, to keep the cancer stem cells' ability to become malignant cells, mesenchymal stem cells, tumor-associated fibroblasts, immune cells, etc., interact with one another. Furthermore, CSCs are characterized by the expression of particular molecular markers that carry significant diagnostic and prognostic significance. Because of this, scientific research on CSCs is becoming increasingly imperative, intending to understand the traits and behavior of cancer stem cells and create more potent anticancer therapeutics to fight cancer at the CSC level. In this review, we aimed to elucidate the critical role of CSCs in the onset and spread of cancer and the characteristics of CSCs that promote severe resistance to targeted therapy.

Melt Instability Identification Using Unsupervised Machine Learning Algorithms

AbstractIn industrial extrusion processes, increasing shear rates can lead to higher production rates. However, at high shear rates, extruded polymers and polymer compounds often exhibit melt instabilities ranging from stick‐slip to sharkskin to gross melt fracture. These instabilities result in challenges to meet the specifications on the extrudate shape. Starting with an existing published data set on melt instabilities in polymer extrusion, we assess the suitability of clustering, unsupervised machine learning algorithms combined with feature selection, to extract and identify hidden and important features from this data set, and their possible relationship with melt instabilities. The data set consists of both intrinsic features of the polymer as well as extrinsic features controlled and measured during an extrusion experiment. Using a range of commonly available clustering algorithms, it is demonstrated that the features related to only the intrinsic properties of the data set can be reliably divided into two clusters, and that in turn, these two clusters may be associated with either the stick‐slip or sharkskin instability. Furthermore, using a feature ranking on both the intrinsic and extrinsic features of the data set, it is shown that the intrinsic properties of molecular weight and polydispersity are the strongest indicators of clustering.