bluuebunny/crossref_metadata_2025_split huggingface dataset:
Minimal machines: augmented reality for filament-construction of partially ordered systems in architecture
Authors: Bonavia Elaine , Farmer Jessica , Mballa-Ekobena Alexandre , Rosenthal Nikolai , Douny Laurence , Dierichs Karola 
Published: August 2023

AbstractWearable augmented reality-supported technology allows for tracking and informing the interrelation of craftspeople with the architectural structure they are working on. Especially when dealing with partially ordered rather than fully ordered material systems, this feedback is relevant since toolpaths cannot be established a priori but rather evolve during the architectural construction process itself. On the one hand, partially ordered material systems have the potential of adapting to conditions both internal and external to the structure. On the other hand, they can be considered as structures that are constantly evolving: instead of demolishing a building, it could be continuously repaired. While a large range of investigations involve robots equipped with sensory feedback to address this topic, only few studies have attempted to equip humans with a minimal amount of technology so as to harness human sensory intelligence, merely enhancing it with technology. This article introduces the current state of the field of augmented reality and partially ordered systems in architectural construction with a focus on filament-laying processes. Then, it presents a newly developed framework for augmented construction with designed filaments for partially ordered fabrics in architecture, encompassing both the wearable hardware and the custom-developed software. The principles of systems in human-made filament-based architecture are introduced and set in relation to similar role model systems in animal-made architecture. Then, three experiments of increasing complexity investigate the human-to-machine, the machine-to-human and the machine-to-human-to-machine communication. A final integrative demonstrator serves to investigate the framework for augmented reality in construction on a full architectural scale. As an outlook, areas of further research—such as the integration of artificial intelligence into the feedback loop—are discussed.
A grammatical evolution approach to the automatic inference of P systems
Authors: Nadizar Giorgia , Pietropolli Gloria 
Published: August 2023

AbstractP systems are a bio-inspired framework for defining parallel models of computation. Despite their relevance for both theoretical and application scenarios, the design and the identification of P systems remain tedious and demanding tasks, requiring considerable time and expertise. In this work, we try to address these problems by proposing an automated methodology based on grammatical evolution (GE)—an evolutionary computation technique—which does not require any domain knowledge. We consider a setting where observations of successive configurations of a P system are available, and we rely on GE for automatically inferring the P system, i.e., its ruleset. Such approach directly addresses the identification problem, but it can also be employed for automated design, requiring the designer to simply express the configurations of the P system rather than its full ruleset. We assess the practicability of the proposed method on six problems of various difficulties and evaluate its behavior in terms of inference capability and time consumption. Experimental results confirm our approach is a viable strategy for small problem sizes, where it achieves perfect inference in a few seconds without any human intervention. Moreover, we also obtain promising results for larger problem sizes in a human-aided context, paving the way for fully or partially automated design of P systems.
Effect of Slag Sand on Mechanical Strengths and Fatigue Performance of Paving Grade Geopolymer Concrete
Authors: Girish M. G. , Shetty Kiran K. , Nayak Gopinatha 
Published: August 2023

AbstractGeopolymer concrete with time is gaining recognition as a sustainable alternative to Portland cement concrete, offering lower carbon emissions and utilising industrial by-products. While prior research focussed on its mechanical properties for structural purposes, this study explores its potential for paving applications. A unique geopolymer concrete mixture suitable for paving, referred to as paving quality geopolymer concrete (PQGC), was developed by incorporating slag sand (SS) as a substitute for river sand (RS). The investigation primarily assessed fatigue life PQGC mixes. The findings demonstrate that the inclusion of SS in PQGC has no significant impact on its setting times or workability. However, PQGC formulated with 100% SS, w.r.t PQGC containing RS, exhibited enhanced mechanical strength of 7% in compression, 16.7% in flexure and 8.3% in split tensile tests at 28 days of open-air curing. Following a 14-day period of curing, PQGC achieved a compressive strength of 46 MPa, surpassing the minimum requirement of 40 MPa as per IRC:58 2015 for pavement quality concrete (PQC) by 15%. To evaluate fatigue performance, PQGC beam specimens were subjected to repeated loading at stress levels ranging from 0.9 to 0.6. The resulting fatigue data were employed to develop stress-fatigue life (SN) and Weibull two-parameter fatigue failure models. Comparative analysis with fatigue failure models such as Indian Road Congress (IRC) and Darter revealed that PQGC exhibits superior fatigue resistance and longer life expectancy compared to PQGC. In conclusion, this study confirms that PQGC incorporating SS is an efficient and eco-friendly choice for constructing rigid pavements with advantages such as curing without water, enabling early opening of roadways to traffic, and excellent resilience under cyclic loads.
Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes
Authors: Nevin Josh W. , Sillekens Eric , Sohanpal Ronit , Galdino Lidia , Nallaperuma Sam , Bayvel Polina , Savory Seb J. 
Published: August 2023

Abstract
We present a novel methodology for optimizing fiber optic network performance by determining the ideal values for attenuation, nonlinearity, and dispersion parameters in terms of achieved signal-to-noise ratio (SNR) gain from digital backpropagation (DBP). Our approach uses Gaussian process regression, a probabilistic machine learning technique, to create a computationally efficient model for mapping these parameters to the resulting SNR after applying DBP. We then use simplicial homology global optimization to find the parameter values that yield maximum SNR for the Gaussian process model within a set of a priori bounds. This approach optimizes the parameters in terms of the DBP gain at the receiver. We demonstrate the effectiveness of our method through simulation and experimental testing, achieving optimal estimates of the dispersion, nonlinearity, and attenuation parameters. Our approach also highlights the limitations of traditional one-at-a-time grid search methods and emphasizes the interpretability of the technique. This methodology has broad applications in engineering and can be used to optimize performance in various systems beyond optical networks.
Familial co-segregation and the emerging role of long-read sequencing to re-classify variants of uncertain significance in inherited retinal diseases
Authors: Gupta Pankhuri , Nakamichi Kenji , Bonnell Alyssa C. , Yanagihara Ryan , Radulovich Nick , Hisama Fuki M. , Chao Jennifer R. , Mustafi Debarshi 
Published: August 2023

AbstractPhasing genetic variants is essential in determining those that are potentially disease-causing. In autosomal recessive inherited retinal diseases (IRDs), reclassification of variants of uncertain significance (VUS) can provide a genetic diagnosis in indeterminate compound heterozygote cases. We report four cases in which familial co-segregation demonstrated a VUS resided in trans to a known pathogenic variant, which in concert with other supporting criteria, led to the reclassification of the VUS to likely pathogenic, thereby providing a genetic diagnosis in each case. We also demonstrate in a simplex patient without access to family members for co-segregation analysis that targeted long-read sequencing can provide haplotagged variant calling. This can elucidate if variants reside in trans and provide phase of genetic variants from the proband alone without parental testing. This emerging method can alleviate the bottleneck of haplotype analysis in cases where genetic testing of family members is unfeasible to provide a complete genetic diagnosis.
No evidence that Chinese playtime mandates reduced heavy gaming in one segment of the video games industry
Authors: Zendle David , Flick Catherine , Gordon-Petrovskaya Elena , Ballou Nick , Xiao Leon Y. , Drachen Anders 
Published: August 2023

AbstractGovernments around the world are considering regulatory measures to reduce young people’s time spent on digital devices, particularly video games. This raises the question of whether proposed regulatory measures would be effective. Since the early 2000s, the Chinese government has been enacting regulations to directly restrict young people’s playtime. In November 2019, it limited players aged under 18 to 1.5 hours of daily playtime and 3 hours on public holidays. Using telemetry data on over seven billion hours of playtime provided by a stakeholder from the video games industry, we found no credible evidence for overall reduction in the prevalence of heavy playtime following the implementation of regulations: individual accounts became 1.14 times more likely to play heavily in any given week (95% confidence interval 1.139–1.141). This falls below our preregistered smallest effect size of interest (2.0) and thus is not interpreted as a practically meaningful increase. Results remain robust across a variety of sensitivity analyses, including an analysis of more recent (2021) adjustments to playtime regulation. This casts doubt on the effectiveness of such state-controlled playtime mandates.
The influence of a single water molecule on the reaction of BrO + HO2
Authors: Zhang Peng , Ma Lu , Zhao Meilian , Sun Yuxi , Chen Wanping , Zhang Yunju 
Published: August 2023

AbstractThe influence of a single water molecule on the BrO + HO2 hydrogen extraction reaction has been explored by taking advantage of CCSD(T)/aug-cc-pVTZ//B3LYP/6-311 +  + G(d,p) method. The reaction in the absence of water have two distinct kinds of H-extraction channels to generate HOBr + O2 (1Δg) and HBr + O3, and the channel of generation of HOBr + O2 (1Δg) dominated the BrO + HO2 reaction. The rate coefficient of the most feasible channel for the BrO + HO2 reaction in the absence of water is estimated to be 1.44 × 10–11 cm3 molecule−1 s−1 at 298.15 K, which is consistent with the experiment. The introduction of water made the reaction more complex, but the products are unchanged. Four distinct channels, beginning with HO2…H2O with BrO, H2O…HO2 with BrO, BrO…H2O with HO2, H2O…BrO with HO2 are researched. The most feasible channels, stemming from H2O…HO2 with BrO, and BrO…H2O with HO2, are much slower than the reaction of BrO + HO2 without water, respectively. Thus, the existence of water molecule takes a negative catalytic role for BrO + HO2 reaction.
Capability accumulation patterns across economic, innovation, and knowledge-production activities
Authors: Patelli Aurelio , Napolitano Lorenzo , Cimini Giulio , Pugliese Emanuele , Gabrielli Andrea 
Published: August 2023

AbstractThe evolution of economic and innovation systems at the national scale is shaped by a complex dynamics related to the multi-layer network connecting countries to the activities in which they are proficient. Each layer represents a different domain, related to the production of knowledge and goods: scientific research, technology innovation, industrial production and trade. Nestedness, a footprint of a complex dynamics, emerges as a persistent feature across these multiple kinds of activities (i.e. network layers). We observe that, in the layers of innovation and trade, the competitiveness of countries correlates unambiguously with their diversification, while the science layer shows some peculiar features. The evolution of the scientific domain leads to an increasingly modular structure, in which the most developed countries become relatively less active in the less advanced scientific fields, where emerging countries acquire prominence. This observation is in line with a capability-based view of the evolution of economic systems, but with a slight twist. Indeed, while the accumulation of specific know-how and skills is a fundamental step towards development, resource constraints force countries to acquire competitiveness in the more complex research fields at the expense of more basic, albeit less visible (or more crowded) ones. This tendency towards a relatively specialized basket of capabilities leads to a trade-off between the need to diversify in order to evolve and the need to allocate resources efficiently. Collaborative patterns among developed countries reduce the necessity to be competitive in the less sophisticated research fields, freeing resources for the more complex ones.
Transcriptomic clock predicts vascular changes of prodromal diabetic retinopathy
Authors: Toh Huishi , Smolentsev Alexander , Sadjadi Ryan , Clegg Dennis , Yan Jingqi , Stewart Ron , Thomson James A. , Jiang Peng 
Published: August 2023

AbstractDiabetic retinopathy is a common complication of long-term diabetes and that could lead to vision loss. Unfortunately, early diabetic retinopathy remains poorly understood. There is no effective way to prevent or treat early diabetic retinopathy until patients develop later stages of diabetic retinopathy. Elevated acellular capillary density is considered a reliable quantitative trait present in the early development of retinopathy. Hence, in this study, we interrogated whole retinal vascular transcriptomic changes via a Nile rat model to better understand the early pathogenesis of diabetic retinopathy. We uncovered the complexity of associations between acellular capillary density and the joint factors of blood glucose, diet, and sex, which was modeled through a Bayesian network. Using segmented regressions, we have identified different gene expression patterns and enriched Gene Ontology (GO) terms associated with acellular capillary density increasing. We developed a random forest regression model based on expression patterns of 14 genes to predict the acellular capillary density. Since acellular capillary density is a reliable quantitative trait in early diabetic retinopathy, and thus our model can be used as a transcriptomic clock to measure the severity of the progression of early retinopathy. We also identified NVP-TAE684, geldanamycin, and NVP-AUY922 as the top three potential drugs which can potentially attenuate the early DR. Although we need more in vivo studies in the future to support our re-purposed drugs, we have provided a data-driven approach to drug discovery.
A diminutive new basilosaurid whale reveals the trajectory of the cetacean life histories during the Eocene
Authors: Antar Mohammed S. , Gohar Abdullah S. , El-Desouky Heba , Seiffert Erik R. , El-Sayed Sanaa , Claxton Alexander G. , Sallam Hesham M. 
Published: August 2023

AbstractSoon after whales originated from small terrestrial artiodactyl ancestors, basal stem forms (archaeocetes) came to inhabit more specialized aquatic ecologies and underwent a tremendous adaptive radiation that culminated in the adoption of a fully aquatic lifestyle. This adaptive strategy is first documented by the geographically widespread extinct family Basilosauridae. Here we report a new basilosaurid genus and species, Tutcetus rayanensis, from the middle Eocene of Fayum, Egypt. This new whale is not only the smallest known basilosaurid, but it is also one of the oldest records of this family from Africa. Tutcetus allows us to further test hypotheses regarding basilosaurids’ early success in the aquatic ecosystem, which lasted into the latest Eocene, and their ability to outcompete amphibious stem whales and opportunistically adapt to new niches after they completely severed their ties to the land. Tutcetus also significantly expands the size range of the basilosaurids and reveals new details about their life histories, phylogeny, and paleobiogeography.
Dynamic thresholding and tissue dissociation optimization for CITE-seq identifies differential surface protein abundance in metastatic melanoma
Authors: Lischetti Ulrike , Tastanova Aizhan , Singer Franziska , Grob Linda , Carrara Matteo , Cheng Phil F. , Martínez Gómez Julia M. , Sella Federica , Haunerdinger Veronika , Beisel Christian , Levesque Mitchell P. 
Published: August 2023

AbstractMulti-omics profiling by CITE-seq bridges the RNA-protein gap in single-cell analysis but has been largely applied to liquid biopsies. Applying CITE-seq to clinically relevant solid biopsies to characterize healthy tissue and the tumor microenvironment is an essential next step in single-cell translational studies. In this study, gating of cell populations based on their transcriptome signatures for use in cell type-specific ridge plots allowed identification of positive antibody signals and setting of manual thresholds. Next, we compare five skin dissociation protocols by taking into account dissociation efficiency, captured cell type heterogeneity and recovered surface proteome. To assess the effect of enzymatic digestion on transcriptome and epitope expression in immune cell populations, we analyze peripheral blood mononuclear cells (PBMCs) with and without dissociation. To further assess the RNA-protein gap, RNA-protein we perform codetection and correlation analyses on thresholded protein values. Finally, in a proof-of-concept study, using protein abundance analysis on selected surface markers in a cohort of healthy skin, primary, and metastatic melanoma we identify CD56 surface marker expression on metastatic melanoma cells, which was further confirmed by multiplex immunohistochemistry. This work provides practical guidelines for processing and analysis of clinically relevant solid tissue biopsies for biomarker discovery.
Finite key performance of satellite quantum key distribution under practical constraints
Authors: Sidhu Jasminder S. , Brougham Thomas , McArthur Duncan , Pousa Roberto G. , Oi Daniel K. L. 
Published: August 2023

AbstractGlobal-scale quantum communication networks will require efficient long-distance distribution of quantum signals. While optical fibre communications are range-limited due to exponential losses in the absence of quantum memories and repeaters, satellites enable intercontinental quantum communications. However, the design of satellite quantum key distribution (SatQKD) systems has unique challenges over terrestrial networks. The typical approach to modelling SatQKD has been to estimate performances with a fully optimised protocol parameter space and with few payload and platform resource limitations. Here, we analyse how practical constraints affect the performance of SatQKD for the Bennett-Brassard 1984 (BB84) weak coherent pulse decoy state protocol with finite key size effects. We consider engineering limitations and trade-offs in mission design including limited in-orbit tunability, quantum random number generation rates and storage, and source intensity uncertainty. We quantify practical SatQKD performance limits to determine the long-term key generation capacity and provide performance benchmarks to support the design of upcoming missions.
Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models
Authors: D’Sa Karishma , Evans James R. , Virdi Gurvir S. , Vecchi Giulia , Adam Alexander , Bertolli Ottavia , Fleming James , Chang Hojong , Leighton Craig , Horrocks Mathew H. , Athauda Dilan , Choi Minee L. , Gandhi Sonia 
Published: August 2023

AbstractParkinson’s disease is a common, incurable neurodegenerative disorder that is clinically heterogeneous: it is likely that different cellular mechanisms drive the pathology in different individuals. So far it has not been possible to define the cellular mechanism underlying the neurodegenerative disease in life. We generated a machine learning-based model that can simultaneously predict the presence of disease and its primary mechanistic subtype in human neurons. We used stem cell technology to derive control or patient-derived neurons, and generated different disease subtypes through chemical induction or the presence of mutation. Multidimensional fluorescent labelling of organelles was performed in healthy control neurons and in four different disease subtypes, and both the quantitative single-cell fluorescence features and the images were used to independently train a series of classifiers to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whereas image-based deep neural networks predict control and four distinct disease subtypes with an accuracy of 95%. The machine learning-trained classifiers achieve their accuracy across all subtypes, using the organellar features of the mitochondria with the additional contribution of the lysosomes, confirming the biological importance of these pathways in Parkinson’s. Altogether, we show that machine learning approaches applied to patient-derived cells are highly accurate at predicting disease subtypes, providing proof of concept that this approach may enable mechanistic stratification and precision medicine approaches in the future.
Higher food prices can reduce poverty and stimulate growth in food production
Authors: Headey Derek , Hirvonen Kalle 
Published: August 2023

AbstractFood prices spiked sharply in 2007–2008, in 2010–2011 and again in 2021–2022. However, the impacts of these spikes on poverty remain controversial; while food is a large expense for the poor, many poor people also earn income from producing or marketing food, and higher prices should incentivize greater food production. Short-run simulation models assume away production and wage adjustments, and probably underestimate food production by the poor. Here we analyse annual data on poverty rates, real food price changes and food production growth for 33 middle-income countries from 2000 to 2019 based on World Bank poverty measures. Panel regressions show that year-on-year increases in the real price of food predict reductions in the US$3.20-per-day poverty headcount, except in more urban or non-agrarian countries. A plausible explanation is that rising food prices stimulate short-run agricultural supply responses that induce increased demand for unskilled labour and increases in wages.