bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effective Matrix Designs for COVID-19 Group Testing
Authors: Brust David , Brust Johannes J. 
Published: August 2022

AbstractBackgroundGrouping samples with low prevalence of positives into pools and testing these pools can achieve considerable savings in testing resources compared with individual testing in the context of COVID-19. We review published pooling matrices, which encode the assignment of samples into pools and describe decoding algorithms, which decode individual samples from pools. Based on the findings we propose new one-round pooling designs with high compression that can efficiently be decoded by combinatorial algorithms. This expands the admissible parameter space for the construction of pooling matrices compared to current methods.ResultsBy arranging samples in a grid and using polynomials to construct pools, we develop direct formulas for an Algorithm (Polynomial Pools (PP)) to generate assignments of samples into tests. Designs from PP guarantee to correctly decode all samples with up to a specified number of positive samples. PP includes recent combinatorial methods for COVID-19, and enables new constructions that can result in more effective designs.ConclusionFor low prevalences of COVID-19, group tests can save resources when compared to individual testing. Constructions from the recent literature on combinatorial methods have gaps with respect to the possibilities of designs. We develop a method (PP), which includes previous constructions and enables new designs that can be advantageous in various situations.
A Robust Backscatter Modulation Scheme for Uninterrupted Ultrasonic Powering and Back-Communication of Deep Implants
Authors: Holzapfel Lukas , Giagka Vasiliki 
Published: August 2022

AbstractTraditionally, implants are powered by batteries, which have to be recharged by an inductive power link. In the recent years, ultrasonic power links are being investigated, promising more available power for deeply implanted miniaturized devices. These implants often need to transfer back information. For ultrasonically powered implants, this is usually achieved with On-Off Keying based on backscatter modulation, or active driving of a secondary transducer. In this paper, we propose to superimpose subcarriers, effectively leveraging Frequency-Shift Keying, which increases the robustness of the link against interference and fading. It also allows for simultaneous powering and communication, and inherently provides the possibility of frequency domain multiplexing for implant networks. The modulation scheme can be implemented in miniaturized application specific integrated circuits, field programmable gate arrays, and microcontrollers. We have validated this modulation scheme in a water tank during continuous ultrasound and movement. We achieved symbol rates of up to 104 kBd, and were able to transfer data through 20 cm of water, with additional misalignment and during movements. This approach could provide a robust uplink for miniaturized implants that are located deep inside the body and need continuous ultrasonic powering.
Digital polarimetric second harmonic generation microscopy of partially oriented fiber structures
Authors: Alizadeh Mehdi , Krouglov Serguei , Barzda Virginijus 
Published: August 2022

AbstractSecond harmonic generation (SHG) in biological tissue originates predominantly from noncentrosymmetric fibrillar structures partially oriented within the focal volume (voxel) of a multiphoton excitation microscope. The study is aimed to elucidate fibrillar organization factors influencing SHG intensity, as well as achiral, R, and chiral, C, nonlinear susceptibility tensor component ratios. SHG response is calculated for various configurations of fibrils in a voxel using digital nonlinear microscope. The R and C ratios are calculated using linear incident and outgoing polarization states that simulate polarization-in polarization-out (PIPO) polarimetric measurements. The investigation shows strong SHG intensity dependence on parallel/antiparallel fiber organization. The R and C ratio is strongly influenced by the fiber chirality, tilting of the fibers out of image plane and crossing of the fibers. The study facilitates interpretation of polarimetric SHG microscopy images in terms of ultrastructural organization of fibers in the imaged structures.Statement of SignificanceSecond harmonic generation microscopy is widely used for imaging non-centrosymmetric biological structures such as collagen. The ultrastructure of collagen can be determined with polarimetric SHG microscopy. The coherent nonlinear response of biological structures depends on the 3D orientations and positions of the collagen fibers in the focal volume of the microscope. Here, we show how different fiber organizations and 3D orientations in the focal volume can affect the polarimetric SHG responses. The results are important for understanding and interpreting images obtained with polarimetric SHG microscopy.
Enhanced Recombination Among SARS-CoV-2 Omicron Variants Contributes to Viral Immune Escape
Authors: Shiraz Rishad , Tripathi Shashank 
Published: August 2022

ABSTRACTSARS-CoV-2 virus evolution occurs as a result of antigenic drift and shift. Although antigenic drift has been extensively studied, antigenic shift, which for SARS-CoV-2 occurs through genetic recombination, has been examined scarcely. To gain a better understanding of the emergence and prevalence of recombinant SARS-CoV-2 lineages through time and space, we analyzed SARS-CoV-2 genome sequences from public databases. Our study revealed an extraordinary increase in the emergence of SARS-CoV-2 recombinant lineages during the Omicron wave, particularly in Northern America and Europe. This phenomenon was independent of sequencing density or genetic diversity of circulating SARS-CoV-2 strains. In SARS-CoV-2 genomes, recombination breakpoints were found to be more concentrated in the 3’ UTR followed by ORF1a. Additionally, we noted enrichment of certain amino acids in the spike protein of recombinant lineages, which have been reported to confer immune escape from neutralizing antibodies, increase ACE2 receptor binding, and enhance viral transmission in some cases. Overall, we report an important and timely observation of accelerated recombination in the currently circulating Omicron variants and explore their potential contribution to viral fitness, particularly immune escape.
Why Many Molecular Simulation Research Findings Might Be False: An Analysis of Inter-Simulations Differences Based on Simulation Time and Number of Replicas
Authors: Knapp Bernhard , Deane Charlotte M. 
Published: August 2022

AbstractMolecular simulations are a common technique to investigate the dynamics of proteins, DNA and RNA. A typical application is the simulation of a wild-type structure and a mutant structure where the mutant has a significantly higher (or lower) potency to trigger a signalling cascade. The study would then analyse the observed differences between the wild-type and mutant simulation and link these to their differences. However differences in the simulations cannot always be reproduced by other research groups even if the same parameters as in the original simulations are used. This is caused by the rugged energy landscape of many biological structures which means that minor differences in hardware or software can cause simulation to take different paths. This would not be a problem if the simulation time would be infinitely long but in real life the simulation time is always finite.In this study we use large scale molecular simulations of four different systems (a 10-mer peptide wild-type and mutant as well as a T-cell receptor, peptide and MHC complex as wild-type and mutant) with 100 replicas each totalling 620 000 ns to quantify the magnitude of (non-) reproducibility when comparing inter-simulation differences (e.g. wild-type vs mutant).Using a bootstrapping approach we found that simulation times of at least 2 to 3 times the experimental folding time using a minimum of 3 replicas are necessary for reproducible results. However, for most complexes of interest such long simulation times are far out of reach which means that it is only possible to sample the local phase space neighbourhood of the x-ray structure. To sample this neighbourhood reliably around 10 to 20 replicas are needed.Graphical abstract
Modeling neural activity in neurodegenerative diseases through a neural field model with variable density of neurons
Authors: Reyes Ronaldo García , Martinez Montes Eduardo 
Published: August 2022

AbstractIn recent years, a vertiginous advance has occurred within the Neural Field Theory with the development of the so-called Next Generation Neural Field models. Unlike the phenomenological models, these models manage to describe neuronal activity, macroscopically, from the thermodynamic limit of microscopic laws under the assumption of a homogeneous density of neurons. The study of neural activity during neurodegenerative processes associated to Alzheimer’s, Parkinson’s or Glioblastomas, should include a variable density of neurons. In this work, we propose an update of the Next Generation Neural Field model, extracted from the thermodynamic limit of the quadratic integration-and-fire model with realistic synaptic coupling and a variable density of neurons at the microscopic level. The thermodynamic limit of the system will allow us to study the patterns of synchronized neural activity that appear as the result of different spatial distribution of neurodegeneration. In particular, we demonstrate that during neurodegenerative processes, the relationship established between the thermodynamic states of the Neural Field and the Kuramoto order parameter (Measure of Neural Synchronization) differs from the classic results of the Next Generation Neural Field literature. Instead, the variation in neuron density directly modifies the Kuramoto order parameter. This might help us explain the diverse patterns of activity that can be found in different neurodegenerative processes and that could become experimental biomarkers of such pathologies.
In Vitro Analysis of Bacterial Microcompartments and Shell Protein Superstructures by Confocal Microscopy
Authors: Trettel Daniel S. , Winkler Wade C. 
Published: August 2022

AbstractThe shell proteins that comprise bacterial microcompartments (BMCs) can self-assemble into an array of superstructures such as nanotubes, flat sheets, and icosahedrons. Physical characterization of BMCs and these superstructures typically relies on electron microscopy, which decouples samples from their solution context. We hypothesize that an investigation of fluorescently tagged BMCs and shell protein superstructures in vitro with high resolution confocal microscopy will lead to new insights into the solution behavior of these entities. We find that confocal imaging is able to capture nanotubes and sheets previously reported by TEM. Using a combination of fluorescent tags, we present qualitative evidence that these structures intermix with one another in a hetero- and homotypic fashion. Complete BMCs are also able to accomplish intermixing as evidenced by colocalization data. Finally, a simple colocalization experiment suggests that fluorescently modified encapsulation peptides (EPs) may prefer certain shell protein binding partners. Together, these data demonstrate that high resolution confocal microscopy is a powerful tool for investigating microcompartment-related structures in vitro, particularly for colocalization analyses. These results also support the notion that BMCs may intermix protein components, presumably from the outer shell.ImportanceMicrocompartments are large, organelle-like structures that help bacteria catabolize targeted metabolites, while also protecting the cytosol against highly reactive metabolic intermediates. Their protein shell self-assembles into a polyhedral structure of approximately 100-200 nm in diameter. Inside the shell are thousands of copies of cargo enzymes, which are responsible for a specific metabolic pathway. While different approaches have revealed high-resolution structures of individual microcompartment proteins, it is less clear how these factors self-assemble to form the full native structure. In this study, we show that laser scanning confocal microscopy can be used to study microcompartment proteins. We find that this approach allows researchers to investigate the interactions and potential exchange of shell protein subunits in solution. From this, we conclude that confocal microscopy offers advantages for studying the in vitro structures of other microcompartments, as well as carboxysomes and other bacterial organelles.
Bringing language to dynamic brain states: the default network dominates neural responses to evolving movie stories
Authors: Yang Enning , Milisav Filip , Kopal Jakub , Holmes Avram J. , Mitsis Georgios D. , Misic Bratislav , Finn Emily S. , Bzdok Danilo 
Published: August 2022

AbstractNaturalistic neuroscience opened the door to new insights into neural circuits that serve real-world dynamic perception. Such studies have often neglected the rich texture of the movie narrative itself, but semantic content can be used to contextualize the induced neural responses. Here, we translated natural language processing tools from machine learning to characterize brain states estimated from hidden Markov models. Our analytical strategy allowed pitting shallow unimodal against the deep associative brain network layers in explaining how semantic content of the movie links to observed neural activity. Pooling information across >53,000 brain image time points watching Forrest Gump, we could show that distinct dynamic brain states capture unique semantic facets along the unfolding movie narrative. The spatiotemporal dynamics of brain states explicitly captured subject-level responses throughout the brain network hierarchy. Across all analyses, the default network was most intimately linked to semantic information integration, and this neural system switched online for longest durations during movie watching. Further, we identified and described two mechanisms of how the default network liaises dynamically with microanatomically defined subregion partners: the amygdala and the hippocampus. Our study thus unlocks the potential of natural language processing to explore neural processes in everyday life situations that engage key aspects of conscious awareness.
Human anti-ACE2 monoclonal antibodies as pan-sarbecovirus prophylactic agents
Authors: Zhang Fengwen , Jenkins Jesse , de Carvalho Renan V.H. , Nakandakari-Higa Sandra , Chen Teresia , Abernathy Morgan E. , Nyakatura Elisabeth , Andrew David , Lebedeva Irina , Lorenz Ivo C , Hoffmann H.-Heinrich , Rice Charles M. , Victora Gabriel D. , Barnes Christopher O. , Haziioannou Theodora , Bieniasz Paul D. 
Published: August 2022

AbstractHuman monoclonal antibodies from convalescent individuals that target the SARS-CoV-2 spike protein have been deployed as therapeutics against SARS-CoV-2. However, nearly all of these antibodies have been rendered obsolete by SARS-CoV-2 variants that evolved to resist similar, naturally occurring antibodies. Here, we describe the development of human monoclonal antibodies that bind the ACE2 receptor rather than the viral spike protein. These antibodies block infection by all ACE2 binding sarbecoviruses, including emergent SARS-CoV-2 variants. Structural and biochemical analyses revealed that the antibodies target an ACE2 epitope that engages SARS-CoV-2 spike. Importantly, the antibodies do not inhibit ACE2 enzymatic activity, nor do they induce ACE depletion from cell surfaces. The antibodies exhibit favorable pharmacology and protect human ACE2 knock-in mice against SARS-CoV-2 infection. Such antibodies should be useful prophylactic and treatment agents against any current and future SARS-CoV-2 variants, as well as ACE2-binding sarbecoviruses that might emerge as future pandemic threats.
Immediate and Early Engagement of Same-Day Antiretroviral Therapy Initiation among newly diagnosed people living with HIV in Urban Zambia: A Retrospective Cohort Study
Authors: Mukumbwa-Mwenechanya Mpande , Chilyabanyama Obvious Nchimunya , Kalunkumya Estella , Kunda Violet , Sikombe Kombatende , Pry Jake M , Nyirenda Godwin , Lumpa Mwansa , Sharma Anjali , Bosomprah Samuel , Bolton-Moore Carolyn 
Published: August 2022

AbstractIntroductionAs Zambia moves towards attaining human immunodeficiency virus (HIV) epidemic control, it is clear significant efforts are required to facilitate achievement of UNAIDS treatment targets by 2030. To accelerate progress towards global target of 95% of people living with HIV (PLHIV) knowing their status, country is promoting community based HIV testing and same day antiretroviral therapy (ART) initiation. However, there are uncertainties around acceptability of this strategy and how it affects immediate and early engagement in program settings.MethodsWe included all newly diagnosed PLHIV aged 18 years or older and provided same day ART initiation between October 2018 and January 2019 in Lusaka District. Immediate engagement was estimated as proportion of newly diagnosed PLHIV who visited the health facility at least once within 14 days after same-day ART initiation, whereas early engagement as proportion of newly diagnosed PLHIV active 6 months after same day ART initiation. Pearson’s chi-squared test was used to assess association of outcomes with key background characteristics.ResultsOf 12,777 newly diagnosed PLHIV who initiated same day ART 7,943 (62%) were tested and initiated in the community. Overall, 6,257 (49%) engaged within 14 days (median 15, IQR:13 37). Older individuals (36-49 years) were more likely to be engaged at 14 days (aRR 1.29: 95%CI 1.06-1.18; p<0.001) and retained at 6 months (aRR:1.27;95%CI 1.21-1.34P<0.001) whilst risk of attrition at 6 months was highest in younger ages (18-24 years) (aRR 0.79;95 %CI 0.76-0.82; p<0.001).ConclusionTo adequately address the HIV epidemic targeted engagement approaches are required particularly in the younger ages.
Non-linear feature analysis of public emotion evolution for online teaching during the COVID-19 pandemic
Authors: Wang Xu , Sun Shan , Feng Xin , Chen Xuan 
Published: August 2022

PurposeNowadays, the breakout of the COVID-19 pandemic has caused an important change in teaching models. The emotional experience of this change has an important impact on online teaching. This paper aims to explore its time evolution characteristics and provide reference for the development of online teaching in the post epidemic era.Design/methodology/approachThe article firstly crawls the online teaching-related comment text data on Zhihu platform and performs emotional calculation to obtain a one-dimensional time series of daily average emotional values. Then, by using non-linear time-series analysis, this paper reconstructs the daily average emotion value time series in high-dimensional phase space, calculates the maximum Lyapunov exponent and correlation dimension and finally, explores the feature patterns through recurrence plot and recurrence quantification analysis.FindingsIt was found that the sequence has typical non-linear chaotic characteristics; its correlation dimension indicates that it contains obvious fractal characteristics; the public emotional evolution shows a cyclical rise and fall. By text mining and temporal evolution analysis, this paper explores the evolution law over chronically of the daily average emotion value time series, provides feasible strategies to improve students' online learning experience and quality and continuously optimizes this new teaching model in the era of pandemic.Originality/valueBased on social knowledge sharing platform of Q&A, this paper models and analyzes users interaction data under online teaching-related topics. This paper explores the evolution law over a long time period of the daily average emotion value time series using text mining and temporal evolution analysis. It then offers workable solutions to enhance the quality and experience of students' online learning, and it continuously improves this new teaching model in the age of pandemics.
The evolution of electric technology in the context of China’s low-carbon transformation: a patent analysis
Authors: Hu Ruifeng , Xu Weiqiao , Yang Yalin 
Published: August 2022

Purpose
Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis.

Design/methodology/approach
Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology.

Findings
Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology.

Research limitations/implications
The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed.

Originality/value
This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.
Research on the experiment and prediction method of clothing energy consumption based on TS-SVR
Authors: Sui Xiuwu , Liu Qijun , Zhang Fangteng 
Published: August 2022

PurposeAt present, the research on energy consumption of human clothing mainly focuses on behavior observation method, questionnaire survey method, heart rate monitoring method and electronic motion sensor, etc. In order to solve the problem of energy consumption caused by clothing with different characteristics, an identification method of energy consumption for different types of clothing was proposed.Design/methodology/approachThe model robot was designed to reproduce the motion state by simulating the human body in the working mode, and the protective energy consumption test platform was built. In order to explore the influence of different characteristics of clothing on the energy consumption of equipment system, orthogonal experiments were carried out on the model robot experimental platform, and a mathematical model for predicting the energy consumption of clothing based on Tabu search algorithm to optimize support vector machine regression (TS-SVR) optimized by tabu algorithm was proposed.FindingsCompared with three regression prediction algorithms, the accuracy of the model was quantified by the determination coefficient and root mean square error according to the predicted value of the model and the actual value of the experiment. The results showed that the model based on TS-SVM can predict the energy consumption of human body more accurately.Originality/valueBased on TS-SVR model, it can well predict the relationship between clothing with different characteristics and physical energy consumption, and can accurately evaluate the clothing grade of different characteristics.