bluuebunny/crossref_metadata_2025_split huggingface dataset:
CITEseq analysis of non-small-cell lung cancer lesions reveals an axis of immune cell activation associated with tumor antigen load andTP53mutations
Authors: Leader Andrew M. , Grout John A. , Chang Christie , Maier Barbara , Tabachnikova Alexandra , Walker Laura , Lansky Alona , LeBerichel Jessica , Malissen Naussica , Davila Melanie , Martin Jerome , Magri Giuliana , Tuballes Kevin , Zhao Zhen , Petralia Francesca , Samstein Robert , D’Amore Natalie Roy , Thurston Gavin , Kamphorst Alice , Wolf Andrea , Flores Raja , Wang Pei , Beasley Mary Beth , Salmon Helene , Rahman Adeeb H. , Marron Thomas U. , Kenigsberg Ephraim , Merad Miriam 
Published: July 2020

SUMMARYImmunotherapy is becoming a mainstay in the treatment of NSCLC. While tumor mutational burden (TMB) has been shown to correlate with response to immunotherapy, little is known about the relation of the baseline immune response with the tumor genotype. Here, we profiled 35 early stage NSCLC lesions using multiscale single cell sequencing. Unsupervised clustering identified in a subset of patients a key cellular module consisting ofPDCD1+ CXCL13+ activated T cells, IgG+ plasma cells, andSPP1+ macrophages, referred to as the lung cancer activation module (LCAMhi). Transcriptional data from two NSCLC cohorts confirmed a subset of patients with LCAMhienrichment, which was independent of overall immune cell content. The LCAMhimodule strongly correlated with TMB, expression of cancer testis antigens, and withTP53mutations in smokers and non-smokers. These data establish LCAM as a key mode of immune cell activation associated with high tumor antigen load and driver mutations.
Bibliome Variant Database: Automated Identification and Annotation of Genetic Variants in Primary Literature
Authors: Baker Samuel W. , Ganguly Arupa 
Published: July 2020

ABSTRACTThe Bibliome Variant Database (BVdb) is a freely available reference database containing over 1 million human genetic variants mapped to the human genome that have been mined from primary literature. The BVdb is designed to facilitate variant interpretation in clinical and research contexts by reducing or eliminating the time required to search for literature describing a given variant. Users can search the database using gene symbols, HGVS variant nomenclature, genomic positions, or rsIDs. Each variant page lists references in the database that describe the variant, as well as the exact gene symbol and variant text description identified in each reference.AVAILABILITY AND IMPLEMENTATIONThe BVdb is freely available athttp://bibliome.ai
A novel high-content phenotypic screen to identify inhibitors of mitochondrial DNA maintenance in trypanosomes
Authors: Miskinyte Migla , Dawson John C. , Makda Ashraff , Doughty-Shenton Dahlia , Carragher Neil O. , Schnaufer Achim 
Published: July 2020

AbstractKinetoplastid parasites cause diverse neglected diseases in humans and livestock, with an urgent need for new treatments. Survival of kinetoplastids depends on their uniquely structured mitochondrial genome (kDNA), the eponymous kinetoplast. Here we report development of a high-content screen for pharmacologically induced kDNA loss, based on specific staining of parasites and automated image analysis. As proof-of-concept we screened a diverse set of ∼14,000 small molecules and exemplify a validated hit as a novel kDNA-targeting compound.
Artificial intelligence based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s disease
Authors: Tsuji Shingo , Hase Takeshi , Yachie Ayako , Nishino Taiko , Ghosh Samik , Kikuchi Masataka , Shimokawa Kazuro , Aburatani Hiroyuki , Kitano Hiroaki , Tanaka Hiroshi 
Published: July 2020

AbstractBackgroundIdentification of novel therapeutic targets is a key for successful drug development. However, the cost to experimentally identify therapeutic targets is huge and only 400 genes are targets for FDA-approved drugs. Therefore, it is inevitable to develop powerful computational tools to identify potential novel therapeutic targets. Because proteins make their functions together with their interacting partners, a protein-protein interaction network (PIN) in human could be a useful resource to build computational tools to investigate potential targets for therapeutic drugs. Network embedding methods, especially deep-learning based methods would be useful tools to extract an informative low-dimensional latent space that contains enough information required to fully represent original high-dimensional non-linear data of PINs.ResultsIn this study, we developed a deep learning based computational framework that extracts low-dimensional latent space embedded in high-dimensional data of the human PIN and uses the features in the latent space (latent features) to infer potential novel targets for therapeutic drugs. We examined the relationships between the latent features and the representative network metrics and found that the network metrics can explain a large number of the latent features, while several latent features do not correlate with all the network metrics. The results indicate that the features are likely to capture information that the representative network metrics can not capture, while the latent features also can capture information obtained from the network metrics. Our computational framework uses the latent features together with state-of-the-art machine learning techniques to infer potential drug target genes. We applied our computational framework to prioritized novel putative target genes for Alzheimer’s disease and successfully identified key genes for potential novel therapeutic targets (e.g., DLG4, EGFR, RAC1, SYK, PTK2B, SOCS1). Furthermore, based on these putative targets, we inferred repositionable candidate-compounds for the disease (e.g., Tamoxifen, Bosutinib, and Dasatinib)DiscussionsOur computational framework could be powerful computational tools to efficiently prioritize new therapeutic targets and drug repositioning. It is pertinent to note here that our computational platform is easily applicable to investigate novel potential targets and repositionable compounds for any diseases, especially for rare diseases.
Digital Cell Sorter (DCS): a cell type identification, anomaly detection, and Hopfield landscapes toolkit for single-cell transcriptomics
Authors: Domanskyi Sergii , Hakansson Alex , Bertus Thomas , Paternostro Giovanni , Piermarocchi Carlo 
Published: July 2020

MotivationAnalysis of singe cell RNA sequencing (scRNA-seq) typically consists of different steps including quality control, batch correction, clustering, cell identification and characterization, and visualization. The amount of scRNA-seq data is growing extremely fast, and novel algorithmic approaches improving these steps are key to extract more biological information. Here, we introduce: (i) two methods for automatic cell type identification (i.e. without expert curator) based on a voting algorithm and a Hopfield classifier, (ii) a method for cell anomaly quantification based on isolation forest, and (iii) a tool for the visualization of cell phenotypic landscapes based on Hopfield energy-like functions. These new approaches are integrated in a software platform that includes many other state-of-the-art methodologies and provides a self-contained toolkit for scRNA-seq analysis.ResultsWe present a suite of software elements for the analysis of scRNA-seq data. This Python-based open source software, Digital Cell Sorter (DCS), consists in an extensive toolkit of methods for scRNA-seq analysis. We illustrate the capability of the software using data from large datasets of peripheral blood mononuclear cells (PBMC), as well as plasma cells of bone marrow samples from healthy donors and multiple myeloma patients. We test the novel algorithms by evaluating their ability to deconvolve cell mixtures and detect small numbers of anomalous cells in PBMC data.AvailabilityThe DCS toolkit is available for download and installation through the Python Package Index (PyPI). The software can be deployed using the Python import function following installation. Source code is also available for download on Zenodo: doi.org/10.5281/zenodo.2533377Contactdomansk6@msu.edu
“He Iwi tahi tatou”: Aotearoa and the legacy of state-sponsored national narrative
Authors: Kinnear Susan Lilico 
Published: July 2020

PurposeThe purpose of this paper is to discuss the internal historical forces that shaped national identity in New Zealand and how state-sponsored ideographs and cultural narratives, played out in nation branding, government–public relations activity, film and the literature, contributed to the rise of present days’ racism and hostility towards non-Pakeha constructions of New Zealand’s self-imagining.Design/methodology/approachThe paper takes a cultural materialist approach, coupled with postcolonial perspectives, to build an empirical framework to analyse specific historical texts and artefacts that were supported and promoted by the New Zealand Government at the point of decolonisation. Traditional constructions of cultural nationalism, communicated through state-sponsored advertising, public information films and national literature, are challenged and re-evaluated in the context of race, gender and socio-economic status.FindingsA total of three major groupings or themes were identified: crew, core and counterdiscourse cultures that each projected a different construction of New Zealand’s national identity. These interwoven themes produced a wider interpretation of identity than traditional cultural nationalist constructions allowed, still contributing to exclusionary formations of identity that alienated non-Pakeha New Zealanders and encouraged racism and intolerance.Research limitations/implicationsThe research study is empirical in nature and belongs to a larger project looking at a range of Pakeha constructions of identity. The article itself does not therefore fully consider Maori constructions of New Zealand’s identity.Originality/valueThe focus on combining cultural materialism, postcolonial approaches to analysis and counterdiscourse in order to analyse historical national narrative provides a unique perspective on the forces that contribute to racism and intolerance in New Zealand’s society. The framework developed can be used to evaluate the historical government communications activity and to better understand how nation branding leads to the exclusion of minority communities.
Two efficient computational technique for fractional nonlinear Hirota–Satsuma coupled KdV equations
Authors: Prakash Amit , Verma Vijay 
Published: July 2020

Purpose
The purpose of this paper is to apply an efficient hybrid computational numerical technique, namely, q-homotopy analysis Sumudu transform method (q-HASTM) and residual power series method (RPSM) for finding the analytical solution of the non-linear time-fractional Hirota–Satsuma coupled KdV (HS-cKdV) equations.

Design/methodology/approach
The proposed technique q-HASTM is the graceful amalgamations of q-homotopy analysis method with Sumudu transform via Caputo fractional derivative, whereas RPSM depend on generalized formula of Taylors series along with residual error function.

Findings
To illustrate and validate the efficiency of the proposed technique, the authors analyzed the projected non-linear coupled equations in terms of fractional order. Moreover, the physical behavior of the attained solution has been captured in terms of plots and by examining the L2 and L∞ error norm for diverse value of fractional order.

Originality/value
The authors implemented two technique, q-HASTM and RPSM to obtain the solution of non-linear time-fractional HS-cKdV equations. The obtained results and comparison between q-HASTM and RPSM, shows that the proposed methods provide the solution of non-linear models in form of a convergent series, without using any restrictive assumption. Also, the proposed algorithm is easy to implement and highly efficient to analyze the behavior of non-linear coupled fractional differential equation arisen in various area of science and engineering.
Design information release under alternative design-construction modes
Authors: Kalach Mayssa , Abdul-Malak Mohamed-Asem , Srour Issam 
Published: July 2020

PurposeThe purpose of this paper is to analyse the impact of schedule compression under alternative project delivery methods (APDMs) on the design information release (DIR). The objectives are to understand the dynamics and the respective implications of the DIR under alternative design-construction (DC) modes (e.g. fast-track).Design/methodology/approachThe methodology of work includes: (1) identifying the relevant factors that may impact the release of design deliverables under APDMs, (2) representing the selected APDMs with corresponding DC sequencing modes and (3) conceptualizing the possible alternative dynamics of the released deliverables.FindingsThe findings reveal that in contrast to the traditional one-time packaging of design deliverables, multiple DIR – with less certainty on their scope, timing, frequency and coordination quality – are released under alternative DC modes. This uncertainty mainly emanates from the deduced impact of the identified factors (e.g. DC overlapping intensity and degree of pressure by the builder) on the design deliverables dynamics.Practical implicationsThis study can be of benefit to Architecture/Engineering (A/E) professionals as well as to project owners in better planning for their roles and responsibilities under each of the identified modes, as it helps raising their awareness on new issues brought about by APDMs. For instance, the presented analysis indirectly informs designers, design review professionals and project owners about a potentially increased liability exposure emanating from the reduced certainty on the DIR's coordination quality. Moreover, it informs design managers about the need for a design team's re-formation and/or re-structuring (i.e. of the involved staff) in order to accommodate for the hypothesized DIR dynamics.Originality/valueThis work offers a novel study that theorizes the impact of time-reduction-related factors, under alternative DC modes, on the release of design deliverables.
Carbon Roots International (CRI)
Authors: Altay Nezih , Pal Raktim 
Published: July 2020

Learning outcomes
The learning outcomes are as follows: successful students will demonstrate an understanding of challenges in producing and delivering a product in emerging economies; they will be able to analyze the tradeoffs in operational decisions of a social enterprise; and students will apply supply chain principles to solve social and environmental challenges.

Case overview/synopsis
Carbon Roots International is a social enterprise in Haiti producing and selling charcoal from sugar cane waste. Their operational challenge is designing a supply chain, which enables them to accomplish their social goals while building a profitable enterprise.

Complexity academic level
This case can be used in graduate operations management and supply chain management courses. The company in the case is a social enterprise.

Supplementary materials
Teaching Notes are available for educators only.

Subject code
CSS 9: Operations and Logistics
Mentoring development at student international business case competitions
Authors: Damnjanovic Vesna , Proud William , Milosavljevic Milos 
Published: July 2020

PurposeManagement education has received an immense amount of attention from both scholars and practitioners in the last few decades. Most research efforts have aimed at improving classroom education processes, programs and outcomes. Only a limited amount of research has focused on extracurricular activities such as mentoring top talented students for international business case competitions. This paper aims to examine the knowledge flow between mentors and strategic management undergraduates while they prepare for international business case competitions. This flow provides the opportunity for the development of phronetic skills, knowledge and leadership potential of undergraduates.Design/methodology/approachTo understand the flow, 55 mentors worldwide were examined to provide evidence of their mentoring practices, perceived development of undergraduates and reverse learning of the business schools' top talents. For the analysis, the study used partial least squares–structural equation modeling.FindingsThe results of the study indicate that extracurricular mentoring of business case competitors strongly affects their development and creates recursive effects on the development of mentoring activities.Research limitations/implicationsGiven the parsimony of mentors for premier global case study competitions, the findings of this study cannot easily be generalized to mentoring as such.Originality/valueDespite the importance of the topics, to the best of the authors’ knowledge, a study of this kind has never been conducted before. For the construct of the authors’ model, the study was inspired by the multistage mentoring process theory.
Camouflage detection with texture statistical characterization in autonomous systems
Authors: Chennamsetty Pulla Rao , Avula Guruvareddy , Chunduri buchhi Ramarao 
Published: July 2020

PurposeThe purpose of the research work is to detect camouflaged objects in autonomous systems of military applications and civilian applications such as detecting insects in paddy fields, identifying duplicate products in different texture environments.Design/methodology/approachCamouflaged objects detection is performed by smoothing texture with nonlinear models and characterizing with statistical methods to detect the objects.FindingsThere are few challenges in existing camouflaged objects detection due to the complexities involved in the detection process. This work proposes a constructive approach with texture statistical characterization for camouflage detection. The proposed technique is found to be better than existing methods while assessing its performance using precision and recall.Research limitations/implicationsEven though there is lot of research work carried, there are few challenges for autonomous systems in camouflage detection due to the complexities involved in the detection process such as texture modeling and dynamic background problems and environment conditions for autonomous system.Practical implicationsCamouflage detection finds potential applications in security systems, surveillance, military and autonomous systems. The proposed work is implemented in different environments for camouflage detection.Social implicationsSocial problems such as image acquisition environment, time of day, desert, forest and grass fields of paddy.Originality/valueThe proposed method detects camouflaged objects in autonomous systems where it is applied for images of different kinds. It is found to be effective on images recorded in battlefield and challenging environments.
Does the origin matter? The effects of cross-border mergers and acquisitions in France
Authors: Carril-Caccia Federico 
Published: July 2020

PurposeThe present article analyses the effects of cross-border mergers and acquisitions (CBM&As) on targets' total factor productivity (TFP), employment, wages and intangible-asset investment. The author investigates whether the impact of CBM&As differs depending on the origin of the investing multinational (MNE). The author distinguishes between CBM&As from European countries, other developed countries and emerging countries.Design/methodology/approachThe author makes use of a unique firm-level data set of foreign direct investment in the French manufacturing sector. The authors applies propensity score matching and difference in differences to estimate the effect of CBM&As.FindingsThe results show that the consequences of CBM&As differ strongly depending on the origin. CBM&As from European MNEs have a positive impact on TFP, wages and intangible-asset investment, and those from emerging countries seem to increase wages and intangible-asset investments. In contrast, CBM&As that originate from MNEs from other developed countries do not have a significant effect.Originality/valueThis article contributes to the growing literature on the effects of foreign direct investment that highlights the relevance of accounting for the MNEs' origin. In particular, it is the first to address the impact of emerging-country MNEs' CBM&As in Europe.
Social economic impact of COVID-19 outbreak in India
Authors: M. Sandeep Kumar , V. Maheshwari , J. Prabhu , M. Prasanna , P. Jayalakshmi , P. Suganya , M.B. Benjula Anbu Malar , Jothikumar R. 
Published: July 2020

Purpose
The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the internet of things (IoT) market. To overcome all the above issues, IoT devices and sensors can be used to track and monitor the movement of the people, so that necessary actions can be taken to prevent the spread of coronavirus disease (COVID-19). Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition.

Design/methodology/approach
To respond to the global COVID-19 outbreak, the social-economic implications of COVID-19 on specific dimensions of the global economy are analyzed in this study. The situations of COVID-19 will certainly have an adverse effect over and above health care on factors of the IoT market. To overcome these issues IoT devices and sensors can be used to track and monitor the movement of the people so that necessary actions can be taken to prevent the spread of COVID-19. Mobile devices can be used for contact tracing of the affected person by analyzing the geomap of the travel history. This will prevent the spread and reset the economy to the normal condition. A few reviews, approaches, and guidelines are provided in this article along these lines. Moreover, insights about the effects of the pandemic on various sectors such as agriculture, medical industry, finance, information technology, manufacturing and many others are provided. These insights may support strategic decision making and policy framing activities for the top level management in private and government sectors.

Findings
With insecurities of a new recession and economic crisis, key moments such as these call for strong and powerful governance in health, business, government, and large society. Instant support measures have to be initiated and adapted for those who can drop through the cracks. Mid- and long-term strategies are required to stabilize and motivate the economy during this recession.

Originality/value
A comprehensive social-economic development strategy that consists of sector by sector schemes and infrastructure that supports business to ensure the success of those with reliable and sustainable business models is necessary. From the literature analysis and real world observations it is concluded that the IoT, sensors, wearable devices and computational technologies plays major role in preserving the economy of the country by preventing the spread of COVID-19.