bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unsupervised integration of single-cell multi-omics datasets with disparities in cell-type representation
Authors: Demetci Pinar , Santorella Rebecca , Sandstede Björn , Singh Ritambhara 
Published: November 2021

AbstractIntegrated analysis of multi-omics data allows the study of how different molecular views in the genome interact to regulate cellular processes; however, with a few exceptions, applying multiple sequencing assays on the same single cell is not possible. While recent unsupervised algorithms align single-cell multi-omic datasets, these methods have been primarily benchmarked on co-assay experiments rather than the more common single-cell experiments taken from separately sampled cell populations. Therefore, most existing methods perform subpar alignments on such datasets. Here, we improve our previous work Single Cell alignment using Optimal Transport (SCOT) by using unbalanced optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. We show that our proposed method, SCOTv2, consistently yields quality alignments on five real-world single-cell datasets with varying cell-type proportions and is computationally tractable. Additionally, we extend SCOTv2 to integrate multiple (M ≥ 2) single-cell measurements and present a self-tuning heuristic process to select hyperparameters in the absence of any orthogonal correspondence information.Available at:http://rsinghlab.github.io/SCOT.
From shallow to deep: exploiting feature-based classifiers for domain adaptation in semantic segmentation
Authors: Matskevych Alex , Wolny Adrian , Pape Constantin , Kreshuk Anna 
Published: November 2021

ABSTRACTThe remarkable performance of Convolutional Neural Networks on image segmentation tasks comes at the cost of a large amount of pixelwise annotated images that have to be segmented for training. In contrast, feature-based learning methods, such as the Random Forest, require little training data, but never reach the segmentation accuracy of CNNs. This work bridges the two approaches in a transfer learning setting. We show that a CNN can be trained to correct the errors of the Random Forest in the source domain and then be applied to correct such errors in the target domain without retraining, as the domain shift between the Random Forest predictions is much smaller than between the raw data. By leveraging a few brushstrokes as annotations in the target domain, the method can deliver segmentations that are sufficiently accurate to act as pseudo-labels for target-domain CNN training. We demonstrate the performance of the method on several datasets with the challenging tasks of mitochondria, membrane and nuclear segmentation. It yields excellent performance compared to microscopy domain adaptation baselines, especially when a significant domain shift is involved.
The Role of Extended Range of Interactions in the Dynamics of Interacting Molecular Motors
Authors: Spaulding Cade , Teimouri Hamid , Narasimhan S.L. , Kolomeisky Anatoly B. 
Published: November 2021

AbstractMotor proteins, also known as biological molecular motors, play important roles in various intracellular processes. Experimental investigations suggest that molecular motors interact with each other during the cellular transport, but the nature of such interactions remains not well understood. Stimulated by these observations, we present a theoretical study aimed to understand the effect of the range of interactions on dynamics of interacting molecular motors. For this purpose, we develop a new version of the totally asymmetric simple exclusion processes in which nearest-neighbor as well as the next nearest-neighbor interactions are taken into account in a thermodynamically consistent way. A theoretical framework based on a cluster mean-field approximation, which partially takes correlations into account, is developed to evaluate the stationary properties of the system. It is found that fundamental current-density relations in the system strongly depend on the strength and the sign of interactions, as well as on the range of interactions. For repulsive interactions stronger than some critical value, increasing the range of interactions leads to a change from unimodal to trimodal dependence in the flux-density fundamental diagram. Theoretical calculations are tested with extensive Monte Carlo computer simulations. Although in most ranges of parameters excellent agreement between theoretical predictions and computer simulations is observed, there are situations when the cluster mean-field approach fails to describe properly the dynamics in the system. Theoretical arguments to explain these observations are presented. Our theoretical analysis clarifies the microscopic picture of how the range of interactions influences the dynamics of interacting molecular motors.
Recurrent exon-deleting activating mutations in AHR act as drivers of urinary tract cancer
Authors: Vlaar Judith M. , Borgman Anouska , Kalkhoven Eric , Westland Denise , Besselink Nicolle , Shale Charles , Faltas Bishoy M. , Priestley Peter , Kuijk Ewart , Cuppen Edwin 
Published: November 2021

AbstractBladder cancer has a high recurrence rate and low survival of advanced stage patients. Few genetic drivers of bladder cancer have thus far been identified. We performed in-depth structural variant analysis on whole-genome sequencing data of 206 metastasized urinary tract cancers. In ~10% of the patients, we identified recurrent in-frame deletions of exons 8 and 9 in the aryl hydrocarbon receptor gene (AHRΔe8-9), which codes for a ligand-activated transcription factor. Pan-cancer analyses show that AHRΔe8-9 is highly specific to urinary tract cancer and mutually exclusive with other bladder cancer drivers. In AHRΔe8-9 the ligand-binding domain is disrupted and we show that this results in ligand-independent AHR-pathway activation. In bladder organoids, AHRΔe8-9 induces a transformed phenotype that is characterized by upregulation of AHR target genes, downregulation of differentiation markers and upregulation of genes associated with stemness and urothelial cancer. Furthermore, AHRΔe8-9 expression results in anchorage independent growth of bladder organoids, indicating tumorigenic potential. DNA-binding deficient AHRΔe8-9 fails to induce transformation, suggesting a role for AHR target genes in the acquisition of the oncogenic phenotype. In conclusion, we show that AHRΔe8-9 is a novel driver of urinary tract cancer and that the AHR pathway could be an interesting therapeutic target.
Immune escape mutations selected by neutralizing antibodies in natural HIV-1 infection can alter coreceptor usage repertoire of the transmitted/founder virus
Authors: Marichannegowda Manukumar Honnayakanahalli , Song Hongshuo 
Published: November 2021

AbstractThe ability of HIV-1 to evade neutralizing antibodies (NAbs) in vivo is well demonstrated, but the impact of NAb escape mutations on HIV-1 phenotype other than immune escape itself has rarely been studied. Here, we show that immune escape mutations selected by V3-glycan specific NAbs in vivo can alter the coreceptor usage repertoire of the transmitted/founder (T/F) HIV-1. In a participant developed V3-glycan NAb response, naturally selected mutations at the V3 N301 and N332 glycan sites abrogated CCR8 usage while conferred APJ usage on the cognate T/F strain. Mutations at the N301 glycan also impaired CCR3 usage and partially compromised the efficiency in using CCR5, which could be fully restored by a single escape mutation at the N332 glycan site. Our study demonstrates the link between NAb escape and coreceptor usage alteration in natural HIV-1 infection and indicates that NAb response could drive virus entry tropism evolution in vivo.
Predicting efficiency of writing short sequences into the genome using prime editing
Authors: Koeppel Jonas , Peets Elin Madli , Weller Juliane , Pallaseni Ananth , Liberante Fabio , Parts Leopold 
Published: November 2021

SUMMARYShort sequences can be precisely written into a selected genomic target using prime editing. This ability facilitates protein tagging, correction of pathogenic deletions, and many other exciting applications. However, it remains unclear what types of sequences prime editors can easily insert, and how to choose optimal reagents for a desired outcome. To characterize features that influence insertion efficiency, we designed a library of 2,666 sequences up to 69 nt in length and measured the frequency of their insertion into four genomic sites in three human cell lines, using different prime editor systems. We discover that insertion sequence length, nucleotide composition and secondary structure all affect insertion rates, and that mismatch repair proficiency is a strong determinant for the shortest insertions. Combining the sequence and repair features into a machine learning model, we can predict insertion frequency for new sequences with R = 0.69. The tools we provide allow users to choose optimal constructs for DNA insertion using prime editing.
Resonant Learning in Scale-free Networks
Authors: Goldman Samuel , Aldana Maximino , Cluzel Philippe 
Published: November 2021

ABSTRACTOver the last decades, analyses of the connectivity of large biological and artificial networks have identified a common scale-free topology, where few of the network elements, called hubs, control many other network elements. In monitoring the dynamics of networks’ hubs, recent experiments have revealed that they can show behaviors oscillating between ‘on’ and ‘off’ states of activation. Prompted by these observations, we ask whether the existence of oscillatory hubs states could contribute to the emergence of specific network dynamical behaviors. Here, we use Boolean threshold networks with scale-free architecture as representative models to demonstrate how periodic activation of the network hub can provide a network-level advantage in learning specific new dynamical behaviors. First, we find that hub oscillations with distinct periods can induce robust and distinct attractors whose lengths depend upon the hub oscillation period. Second, we determine that a given network can exhibit series of different attractors when we sequentially change the period of hub pulses. Using rounds of evolution and selection, these different attractors could independently learn distinct target functions. We term this network-based learning strategy resonant learning, as the emergence of new learned dynamical behaviors depends on the choice of the period of the hub oscillations. Finally, we find that resonant learning leads to convergence towards target behaviors over an order of magnitude faster than standard learning procedures. While it is already known that modular network architecture contributes to learning separate tasks, our results reveal an alternative design principle based on forced oscillations of the network hub.SIGNIFICANCELarge networks of interconnected components such as genes or machines can coordinate complex behavioral dyamics. One outstanding question has been to identify the design principles that allow networks to learn new behaviors. Here, in simulating learning cycles, we randomly modify the interactions between components and select networks that exhibit a desired behavior. Surprisingly, we find that networks can learn new behaviors faster when the state of the most connected network component is forced to oscillate during learning. Remarkably, using distinct periods of oscillations allows a given network to learn distinct behaviors. While it is known that modular network architecture contributes to learning separate tasks, our results reveal an alternative design principle for which modules are not needed.
Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model
Authors: Zubair Asif , Chapple Richard H. , Natarajan Sivaraman , Wright William C. , Pan Min , Lee Hyeong-Min , Tillman Heather , Easton John , Geeleher Paul 
Published: November 2021

ABSTRACTSpatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.
Alterations in the Antioxidant Metabolites in Cotton Leaf Curl Disease Infected and Healthy Cotton Leaves
Authors: Rehman Atiq Ur , Zia Muhammad Anjum , Mubin Muhammad , Shahid Muhammad 
Published: November 2021

AbstractThe upland cotton (Gossypium hirsutum) belongs to the family Malvaceae and cultivated since ancient times in warmer parts of the world. Pakistan is the 4th largest cotton producing country and prominent cotton yarn exporter of the world. Cotton is the major source of natural fiber and significantly contributes to the economy of Pakistan. There are many factors (biotic and abiotic) affecting the cotton yield in Pakistan. The cotton leaf curl disease (CLCuD) is one of the biotic factors and endemic in the most cotton growing areas of the country. The antioxidant enzymes and antioxidant biochemical metabolites play important role during stress. The present study was planned to compare the antioxidant enzymes and metabolites from healthy and CLCuD infected cotton leaves. Antioxidant enzymes activities including catalase, peroxidase and peroxidase were measured through different antioxidant assays and different antioxidant metabolites were also determined. During stress condition antioxidant metabolites serve as signal for the activation of antioxidant enzymes. Present study revealed that antioxidant enzymes were significantly higher in the infected cotton leaves as compared to the healthy cotton leaves. While the amount of antioxidant metabolites like total phenolic contents (TPC) and total flavonoid contents (TFC) were higher in healthy cotton leaves as compared to the infected leaves.
The rate and impact of substance misuse in psychiatric intensive care units (PICUs) in the UK
Authors: Moyes Hattie Catherine Ann , MacNaboe Lana , Townsend Kate 
Published: November 2021

Purpose
This paper aims to understand the current scale of substance misuse in psychiatric intensive care units (PICUs), identify how substance misuse affects members of staff, patients and the running of wards and explore with staff what resources would be most useful to more effectively manage substance misuse and dual diagnosis on PICUs.

Design/methodology/approach
The paper used a mixed-methods approach, using a quantitative survey to determine the extent of substance use in PICUs and a co-design workshop to understand the impact of substance misuse on PICU wards, staff and patients.

Findings
The estimated rate of substance misuse in PICUs over a 12-month period is 67%, with cannabis the most frequently used substance. Despite the range of problems experienced on PICUs because of substance misuse, the availability of training and resources for staff was mixed.

Research limitations/implications
The findings may not be fully generalisable as research participants were members of a national quality improvement programme, and therefore, may not be representative of all PICUs. Data was collected from clinicians only; if patients were included, they might have provided another perspective on substance misuse on PICUs.

Practical implications
This paper emphasises the importance of substance misuse training for PICU staff to adequately respond to patients who misuse substances, improve the ward environment, staff well-being and patient outcomes.

Originality/value
This paper provides an updated estimation of rates of substance misuse in PICUs over a 12-month period and make suggestions for a training programme that can better support staff to address substance misuse on PICUs.
Chemical potential variation effect of new design of graphene antenna for satellite applications
Authors: Rabah Mohammed Amin , Mohammed Bekhti 
Published: November 2021

Purpose
The present work aims to analyze the performance of a newly designed graphene-based patch antenna by varying the chemical potential in graphene sheet, using the CST Microwave Studio ® software. This study mainly seeks to discuss and assess the advantage of using graphene, instead of copper, as the radiating patch. It should be noted that graphene is a new material that possesses unique properties. Its parameters are optimized for the purpose of introducing it in satellite technology.

Design/methodology/approach
The use of graphene as a radiating patch of space technology applications, where a polygonal graphene patch antenna element is designed by the CST Microwave Studio ® software with Taconic RF-41 substrate to resonate in the satellite bands.

Findings
Analysis of a graphene patch sheet by a variation in the chemical potential to ensure operation in a space environment.

Originality/value
The increase in the chemical potential for a graphene patch antenna has shown a prominent increase in the values of the gain. A new contribution, by the combination of the antenna performance improvement techniques and the use of graphene as a radiating patch of space technology applications.
Big data and analytics in hospitality and tourism: a systematic literature review
Authors: Mariani Marcello , Baggio Rodolfo 
Published: November 2021

PurposeThe purpose of this work is to survey the body of research revolving around big data (BD) and analytics in hospitality and tourism, by detecting macro topical areas, research streams and gaps and to develop an agenda for future research.Design/methodology/approachThis research is based on a systematic literature review of academic papers indexed in the Scopus and Web of Science databases published up to 31 December 2020. The outputs were analyzed using bibliometric techniques, network analysis and topic modeling.FindingsThe number of scientific outputs in research with hospitality and tourism settings has been expanding over the period 2015–2020, with a substantial stability of the areas examined. The vast majority are published in academic journals where the main reference area is neither hospitality nor tourism. The body of research is rather fragmented and studies on relevant aspects, such as BD analytics capabilities, are virtually missing. Most of the outputs are empirical. Moreover, many of the articles collected relatively small quantities of records and, regardless of the time period considered, only a handful of articles mix a number of different techniques.Originality/valueThis work sheds new light on the emergence of a body of research at the intersection of hospitality and tourism management and data science. It enriches and complements extant literature reviews on BD and analytics, combining these two interconnected topics.
Mapping direct flood impacts from a 2020 extreme flood event in Central Vietnam using spatial analysis techniques
Authors: Luu Chinh , Bui Quynh Duy , von Meding Jason 
Published: November 2021

Purpose
In October 2020, Vietnam was repeatedly hit by large storms, including Linfa, Nangka, Saudel and Molave, causing heavy rains and whirlwinds in the Central provinces of Vietnam. The heavy rain led to severe flooding in many localities. The water levels on major rivers broke records of historical flood events in 1950, 1979, 1999, 2007, 2010 and 2016. In response, this paper aims to quantify the impacts of 2020 flooding to support flood risk management activities and the relief agencies that can use the analysis.

Design/methodology/approach
This study demonstrates an approach to quickly map flood impacts on population, schools, health-care facilities, agriculture, transportation and business facilities and assess flood risks using available data and spatial analysis techniques.

Findings
The results show that all districts of Quang Binh were affected by the event, in which 1,014 residential areas, 70 schools, 13 health-care facilities, 32,558 ha of agriculture lands, 402 km road length, 29 km railway, 35 bridges on roads and 239 business facilities were exposed within flooded areas.

Research limitations/implications
This study is limited to direct or tangible impacts, including flooded residential areas, schools, health-care facilities, agriculture land categories, road networks and business facilities. Indirect or intangible impacts such as health, flood pollution and business disruption should be considered in further studies.

Practical implications
These detailed impact maps can support decision-makers and local authorities in implementing recovery activities, allocating relief and devoting human resources and developing flood risk management action plans and land-use planning in the future.

Social implications
This study investigates the context of flood impacts on population, schools, health-care facilities, agriculture, transportation and business facilities. Based on this research, decision-makers can better understand how to support affected communities and target the most at risk people with interventions.

Originality/value
This paper presents a framework to quantify the impacts of the 2020 extreme flood event using available data and spatial analysis techniques in support of flood risk management activities.