When a phenotype is not the genotype: Implications of phenotype misclassification and pedigree errors in genomics-assisted breeding of sweetpotato [Ipomoea batatas(L.) Lam.]

AbstractExperimental error, especially through genotype misclassification and pedigree errors, negatively affects breeding decisions by creating ‘noise’ that compounds the genetic signals for selection. Unlike genotype-by-environment interactions, for which different methods have been proposed to address, the effect of ‘noise’ due to pedigree errors and misclassification has not received much attention in most crops. We used two case studies in sweetpotato, based on data from the International Potato Center’s breeding program to estimate the level of phenotype misclassification and pedigree error and to demonstrate the consequences of such errors when combining phenotypes with the respective genotypes. In the first case study, 27.7% phenotype misclassification was observed when moving genotypes from a diversity panel throughin-vitro, screenhouse and field trialing. Additionally, 22.7% pedigree error was observed from misclassification between and within families. The second case study involving multi-environment testing of a full-sib population and quantitative trait loci (QTL) mapping showed reduced genetic correlations among pairs of environments in mega-environments with higher phenotype misclassification errors when compared to the mega-environments with lower phenotype misclassification errors. Additionally, no QTL could be identified in the low genetic correlation mega-environments. Simulation analysis indicated that phenotype misclassification was more detrimental to QTL detection when compared to missingness in data. The current information is important to inform current and future breeding activities involving genomic-assisted breeding decisions in sweetpotato, and to facilitate putting in place improved workflows that minimize phenotype misclassification and pedigree errors.

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Nanotherapeutic approaches to overcome distinct drug resistance barriers in models of breast cancer

Nanophotonics ◽

10.1515/nanoph-2021-0142 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Tanmoy Saha ◽

Jayanta Mondal ◽

Sachin Khiste ◽

Hrvoje Lusic ◽

Zhang-Wei Hu ◽

...

Keyword(s):

Drug Resistance ◽

Targeted Delivery ◽

Kinase Inhibitor ◽

Resistance Mechanisms ◽

Optical Antenna ◽

Surface Lipid ◽

First Case

Abstract Targeted delivery of drugs to tumor cells, which circumvent resistance mechanisms and induce cell killing, is a lingering challenge that requires innovative solutions. Here, we provide two bioengineered strategies in which nanotechnology is blended with cancer medicine to preferentially target distinct mechanisms of drug resistance. In the first ‘case study’, we demonstrate the use of lipid–drug conjugates that target molecular signaling pathways, which result from taxane-induced drug tolerance via cell surface lipid raft accumulations. Through a small molecule drug screen, we identify a kinase inhibitor that optimally destroys drug tolerant cancer cells and conjugate it to a rationally-chosen lipid scaffold, which enhances anticancer efficacy in vitro and in vivo. In the second ‘case study’, we address resistance mechanisms that can occur through exocytosis of nanomedicines. Using adenocarcinoma HeLa and MCF-7 cells, we describe the use of gold nanorod and nanoporous vehicles integrated with an optical antenna for on-demand, photoactivation at ∼650 nm enabling release of payloads into cells including cytotoxic anthracyclines. Together, these provide two approaches, which exploit engineering strategies capable of circumventing distinct resistance barriers and induce killing by multimodal, including nanophotonic mechanisms.

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ДИНИТРОЗИЛЬНЫЕ КОМПЛЕКСЫ ЖЕЛЕЗА C ТИОЛСОДЕРЖАЩИМИ ЛИГАНДАМИ ПРЕДСТАВЛЕНЫ В ЖИВЫХ ОРГАНИЗМАХ В ОСНОВНОМ ИХ БИЯДЕРНОЙ ФОРМОЙ

Биофизика ◽

10.31857/s0006302920060149 ◽

2020 ◽

Vol 65 (6) ◽

pp. 1142-1153

Author(s):

В.Д. Микоян ◽

◽

Е.Н. Бургова ◽

Р.Р. Бородулин ◽

А.Ф. Ванин ◽

...

Keyword(s):

Electron Paramagnetic Resonance ◽

Sodium Nitrite ◽

Iron Complexes ◽

Paramagnetic Resonance ◽

First Case ◽

Dinitrosyl Iron ◽

Electron Paramagnetic ◽

Dinitrosyl Complexes

The number of mononitrosyl iron complexes with diethyldithiocarbamate, formed in the liver of mice in vivo and in vitro after intraperitoneal injection of binuclear dinitrosyl iron complexes with N-acetyl-L-cysteine or glutathione, S-nitrosoglutathione, sodium nitrite or the vasodilating drug Isoket® was assessed by electron paramagnetic resonance (EPR). The number of the said complexes, in contrast to the complexes, formed after nitrite or Isoket administration, the level of which sharply increased after treatment of liver preparations with a strong reducing agent - dithionite, did not change in the presence of dithionite. It was concluded that, in the first case, EPR-detectable mononitrosyl iron complexes with diethyldithiocarbamate in the absence and presence of dithionite appeared as a result of the reaction of NO formed from nitrite with Fe2+-dieth- yldithiocarbamate and Fe3+-diethyldithiocarbamate complexes, respectively. In the second case, mononitrosyl iron complexes with diethyldithiocarbamate appeared as a result of the transition of iron-mononitosyl fragments from ready-made iron-dinitrosyl groups of binuclear dinitrosyl complexes, which is three to four times higher than the content of the mononuclear form of these complexes in the tissue...

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Faculty Opinions recommendation of Defining fractional inhibitory concentration index cutoffs for additive interactions based on self-drug additive combinations, Monte Carlo simulation analysis, and in vitro-in vivo correlation data for antifungal drug combinations against Aspergillus fumigatus.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1282045.749142 ◽

2009 ◽

Author(s):

Annette Fothergill

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Concentration Index ◽

Inhibitory Concentration ◽

Simulation Analysis ◽

Fractional Inhibitory Concentration ◽

Fractional Inhibitory Concentration Index ◽

Correlation Data

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Scales and Units

10.1093/oso/9780190657543.003.0011 ◽

2018 ◽

Author(s):

Kathryn M. de Luna

Keyword(s):

Language Change ◽

Historical Linguistics ◽

Bantu Languages ◽

The North ◽

First Case ◽

Linguistic Evidence ◽

River Region ◽

History Of ◽

Alternative Approaches

This chapter uses two case studies to explore how historians study language movement and change through comparative historical linguistics. The first case study stands as a short chapter in the larger history of the expansion of Bantu languages across eastern, central, and southern Africa. It focuses on the expansion of proto-Kafue, ca. 950–1250, from a linguistic homeland in the middle Kafue River region to lands beyond the Lukanga swamps to the north and the Zambezi River to the south. This expansion was made possible by a dramatic reconfiguration of ties of kinship. The second case study explores linguistic evidence for ridicule along the Lozi-Botatwe frontier in the mid- to late 19th century. Significantly, the units and scales of language movement and change in precolonial periods rendered visible through comparative historical linguistics bring to our attention alternative approaches to language change and movement in contemporary Africa.

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Applications of random graphs

10.1093/oso/9780198709893.003.0011 ◽

2017 ◽

Author(s):

A.C.C. Coolen ◽

A. Annibale ◽

E.S. Roberts

Keyword(s):

Social Networks ◽

Random Graphs ◽

Interaction Network ◽

Power Grids ◽

Protein Protein Interaction ◽

Topological Features ◽

First Case ◽

The Stability ◽

Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

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Combining species specific in vitro & in silico models to predict in vivo food effect in a preclinical stage – case study of Venetoclax

European Journal of Pharmaceutical Sciences ◽

10.1016/j.ejps.2021.105840 ◽

2021 ◽

pp. 105840

Author(s):

Laura J. Henze ◽

Niklas J. Koehl ◽

Joseph P. O'Shea ◽

René Holm ◽

Maria Vertzoni ◽

...

Keyword(s):

In Silico ◽

Food Effect ◽

Preclinical Stage ◽

Species Specific ◽

In Silico Models

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Investigating the potential of redundant manipulators in narrow channels

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220964512 ◽

2020 ◽

pp. 095440622096451

Author(s):

Ashish Singla ◽

Jyotindra Narayan ◽

Himanshu Arora

Keyword(s):

Case Studies ◽

Fuzzy Inference ◽

Redundant Manipulators ◽

Redundancy Resolution ◽

Inference System ◽

Narrow Channels ◽

First Case ◽

Task Priority ◽

Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

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Lymphocutaneous spread of Mycobacterium elephantis in an immunocompetent individual: A case report

SAGE Open Medical Case Reports ◽

10.1177/2050313x211034913 ◽

2021 ◽

Vol 9 ◽

pp. 2050313X2110349

Author(s):

Brett D Edwards ◽

Ranjani Somayaji ◽

Dina Fisher ◽

Justin C Chia

Keyword(s):

Case Report ◽

Chronic Lung Disease ◽

Contaminated Soil ◽

Lung Abscess ◽

Immunocompetent Individual ◽

First Case ◽

Antimycobacterial Agents ◽

Vitro Data ◽

In Vitro Data

Mycobacterium elephantis was first described when isolated from an elephant that succumbed to lung abscess. However, despite this namesake, it is not associated with animals and has been described most often as a probable colonizer rather than pathogen in humans with chronic lung disease. In this report, we describe the first case of lymphocutaneous infection from M. elephantis, likely as a result of cutaneous inoculation with contaminated soil. This offers further evidence to its capabilities as a pathogen. We provide a review of the limited prior reports of M. elephantis and outline the available in vitro data on efficacy of various antimycobacterial agents.

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Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

BMC Medical Imaging ◽

10.1186/s12880-021-00551-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Markus J. Ankenbrand ◽

Liliia Shainberg ◽

Michael Hock ◽

David Lohr ◽

Laura M. Schreiber

Keyword(s):

Neural Networks ◽

Sensitivity Analysis ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Cardiac Mri ◽

Magnetic Resonance Images ◽

Model Interpretation ◽

First Case ◽

Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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