scholarly journals Sonoporation: Underlying Mechanisms and Applications in Cellular Regulation

2021 ◽  
Author(s):  
Yue Li ◽  
Zhiyi Chen ◽  
Shuping Ge
Keyword(s):  
Gene Expression ◽  
Cell Membrane ◽  
State Of The Art ◽  
Cellular Functions ◽  
Cellular Regulation ◽  
Current State ◽  
Cellular Apoptosis ◽  
Ultrasonic Parameters ◽  
Or Gene ◽  
Underlying Mechanisms

Ultrasound combined with microbubble-mediated sonoporation has been applied to enhance drug or gene intracellular delivery. Sonoporation leads to the formation of openings in the cell membrane, triggered by ultrasound-mediated oscillations and destruction of microbubbles. Multiple mechanisms are involved in the occurrence of sonoporation, including ultrasonic parameters, microbubbles size, and the distance of microbubbles to cells. Recent advances are beginning to extend applications through the assistance of contrast agents, which allow ultrasound to connect directly to cellular functions such as gene expression, cellular apoptosis, differentiation, and even epigenetic reprogramming. In this review, we summarize the current state of the art concerning microbubble–cell interactions and sonoporation effects leading to cellular functions.

ON THE HARDNESS OF THE BORDER LENGTH MINIMIZATION PROBLEM ON A RECTANGULAR ARRAY

2010 ◽  
Vol 21 (06) ◽  
pp. 1089-1100 ◽  
Author(s):  
VAMSI KUNDETI ◽  
SANGUTHEVAR RAJASEKARAN
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
State Of The Art ◽  
Combinatorial Problem ◽  
Np Hard ◽  
Microarray Technology ◽  
Rectangular Grid ◽  
Rectangular Array ◽  
Current State ◽  
Invaluable Tool

DNA microarray technology has proven to be an invaluable tool for molecular biologists. Microarrays are used extensively in SNP detection, genomic hybridization, alternative splicing and gene expression profiling. However the manufacturers of the microarrays are often stuck with the problem of minimizing the effects of unwanted illumination (border length minimization (BLM)) which is a hard combinatorial problem. In this paper we prove that the BLM problem on a rectangular grid is NP-hard – this however does not mean the BLM problem on a square grid is NP-hard. We also give the first integer linear programming (ILP) formulation to solve BLM problem optimally. Experimental results indicate that our ILP method produces superior results (both in runtime and cost) compared to the current state of the art algorithms to solve the BLM problem optimally.

scholarly journals In vitro Models of the Small Intestine for Studying Intestinal Diseases

2022 ◽  
Vol 12 ◽  
Author(s):  
Sang-Myung Jung ◽  
Seonghun Kim
Keyword(s):  
Small Intestine ◽  
State Of The Art ◽  
Ex Vivo ◽  
3D Culture ◽  
In Vitro Models ◽  
Cellular Functions ◽  
Current State ◽  
Culture Models ◽  
Composition Structure

The small intestine is a digestive organ that has a complex and dynamic ecosystem, which is vulnerable to the risk of pathogen infections and disorders or imbalances. Many studies have focused attention on intestinal mechanisms, such as host–microbiome interactions and pathways, which are associated with its healthy and diseased conditions. This review highlights the intestine models currently used for simulating such normal and diseased states. We introduce the typical models used to simulate the intestine along with its cell composition, structure, cellular functions, and external environment and review the current state of the art for in vitro cell-based models of the small intestine system to replace animal models, including ex vivo, 2D culture, organoid, lab-on-a-chip, and 3D culture models. These models are described in terms of their structure, composition, and co-culture availability with microbiomes. Furthermore, we discuss the potential application for the aforementioned techniques to these in vitro models. The review concludes with a summary of intestine models from the viewpoint of current techniques as well as their main features, highlighting potential future developments and applications.

scholarly journals Extracellular mRNA transported to the nucleus exerts translation-independent function

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Takeshi Tomita ◽  
Masayoshi Kato ◽  
Taishi Mishima ◽  
Yuta Matsunaga ◽  
Hideki Sanjo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Membrane ◽  
Cell Nucleus ◽  
Untranslated Region ◽  
Rna Binding ◽  
Nk Cell ◽  
Interferon Γ ◽  
Independent Function ◽  
Cellular Functions ◽  
Migration Activity

AbstractRNA in extracellular vesicles (EVs) are uptaken by cells, where they regulate fundamental cellular functions. EV-derived mRNA in recipient cells can be translated. However, it is still elusive whether “naked nonvesicular extracellular mRNA” (nex-mRNA) that are not packed in EVs can be uptaken by cells and, if so, whether they have any functions in recipient cells. Here, we show the entrance of nex-mRNA in the nucleus, where they exert a translation-independent function. Human nex-interleukin-1β (IL1β)-mRNA outside cells proved to be captured by RNA-binding zinc finger CCCH domain containing protein 12D (ZC3H12D)-expressing human natural killer (NK) cells. ZC3H12D recruited to the cell membrane binds to the 3′-untranslated region of nex-IL1β-mRNA and transports it to the nucleus. The nex-IL1β-mRNA in the NK cell nucleus upregulates antiapoptotic gene expression, migration activity, and interferon-γ production, leading to the killing of cancer cells and antimetastasis in mice. These results implicate the diverse actions of mRNA.

scholarly journals Interpretable Machine Learning for Genomics

2021 ◽  
Author(s):  
David Watson
Keyword(s):  
Machine Learning ◽  
Cell Function ◽  
State Of The Art ◽  
Future Research ◽  
Computational Statistics ◽  
Close Collaboration ◽  
Current State ◽  
Interpretable Machine Learning ◽  
Underlying Mechanisms ◽  
Generation Sequencing

Abstract High-throughput technologies such as next generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state of the art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.

scholarly journals Mathematical Issues in the Inference of Causal Interactions among Multichannel Neural Signals

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Young-Jin Jung ◽  
Kyung Hwan Kim ◽  
Chang-Hwan Im
Keyword(s):  
State Of The Art ◽  
Brain Activity ◽  
Applied Mathematics ◽  
Neural Signals ◽  
Brain Areas ◽  
Mathematical Background ◽  
Current State ◽  
Causality Inference ◽  
Computational Sciences ◽  
Underlying Mechanisms

Within the last few decades, attempts have been made to characterize the underlying mechanisms of brain activity by analyzing neural signals recorded, directly or indirectly, from the human brain. Accordingly, inference of functional connectivity among neural signals has become an indispensable research tool in modern neuroscience studies aiming to explore how different brain areas are interacting with each other. Indeed, remarkable advances in computational sciences and applied mathematics even allow the estimation of causal interactions among multichannel neural signals. Here, we introduce the brief mathematical background of the use of causality inference in neuroscience and discuss the relevant mathematical issues, with the ultimate goal of providing applied mathematicians with the current state-of-the-art knowledge on this promising multidisciplinary topic.

scholarly journals Prognostic model for multiple myeloma progression integrating gene expression and clinical features

GigaScience ◽  
2019 ◽  
Vol 8 (12) ◽  
Author(s):  
Chen Sun ◽  
Hongyang Li ◽  
Ryan E Mills ◽  
Yuanfang Guan
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Clinical Features ◽  
Prognostic Model ◽  
State Of The Art ◽  
Cox Model ◽  
Functional Enrichment ◽  
Machine Learning Techniques ◽  
Right Censored Data ◽  
Current State

Abstract Background Multiple myeloma (MM) is a hematological cancer caused by abnormal accumulation of monoclonal plasma cells in bone marrow. With the increase in treatment options, risk-adapted therapy is becoming more and more important. Survival analysis is commonly applied to study progression or other events of interest and stratify the risk of patients. Results In this study, we present the current state-of-the-art model for MM prognosis and the molecular biomarker set for stratification: the winning algorithm in the 2017 Multiple Myeloma DREAM Challenge, Sub-Challenge 3. Specifically, we built a non-parametric complete hazard ranking model to map the right-censored data into a linear space, where commonplace machine learning techniques, such as Gaussian process regression and random forests, can play their roles. Our model integrated both the gene expression profile and clinical features to predict the progression of MM. Compared with conventional models, such as Cox model and random survival forests, our model achieved higher accuracy in 3 within-cohort predictions. In addition, it showed robust predictive power in cross-cohort validations. Key molecular signatures related to MM progression were identified from our model, which may function as the core determinants of MM progression and provide important guidance for future research and clinical practice. Functional enrichment analysis and mammalian gene-gene interaction network revealed crucial biological processes and pathways involved in MM progression. The model is dockerized and publicly available at https://www.synapse.org/#!Synapse:syn11459638. Both data and reproducible code are included in the docker. Conclusions We present the current state-of-the-art prognostic model for MM integrating gene expression and clinical features validated in an independent test set.

scholarly journals Gene Expression Induced by Cerebral Ischemia: An Apoptotic Perspective

1997 ◽  
Vol 17 (8) ◽  
pp. 815-832 ◽  
Author(s):  
John P. MacManus ◽  
Matthew D. Linnik
Keyword(s):  
Gene Expression ◽  
Cerebral Ischemia ◽  
State Of The Art ◽  
Converting Enzyme ◽  
Current State ◽  
Execution Phase ◽  
New Information ◽  
Extensive Collection ◽  
Areas Of Interest ◽  
Experimental Cerebral Ischemia

The flow of new information on gene expression related to apoptosis has been relentless in the last several years. This has also been the case with respect to gene expression after cerebral ischemia. Many of genes associated with an apoptotic mode of cell death have now been studied in the context of experimental cerebral ischemia from the immediate early genes through modulating genes such as bcl-2 to genes in the final execution phase such as interleukin-1β converting enzyme (ICE)-related proteases. It was impossible to adequately cite all primary reports on these subjects. However, many excellent reviews have appeared in the last year, which together, cover all these areas of interest. In this review, we have elected to cite only reports published since January 1996 and use an extensive collection of reviews (indicated in italics) to guide the reader to the earlier literature. Our intent is to provide the reader with a timely and useful analysis of the current state of the art. It is hoped that this approach does not cause offense with our colleagues whose contributions before 1996 laid the foundation for much of this work.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

Schizophrenia Research: Current State of the Art

1976 ◽  
Vol 21 (7) ◽  
pp. 497-498
Author(s):  
STANLEY GRAND
Keyword(s):  
State Of The Art ◽  
Current State

Umbral Calculus

10.37236/24 ◽  
2002 ◽  
Vol 1000 ◽  
Author(s):  
A. Di Bucchianico ◽  
D. Loeb
Keyword(s):  
19Th Century ◽  
Finite Differences ◽  
State Of The Art ◽  
Umbral Calculus ◽  
Current State ◽  
Logical Foundation ◽  
Complete Bibliography ◽  
The 19Th Century ◽  
Mathematical Literature

We survey the mathematical literature on umbral calculus (otherwise known as the calculus of finite differences) from its roots in the 19th century (and earlier) as a set of “magic rules” for lowering and raising indices, through its rebirth in the 1970’s as Rota’s school set it on a firm logical foundation using operator methods, to the current state of the art with numerous generalizations and applications. The survey itself is complemented by a fairly complete bibliography (over 500 references) which we expect to update regularly.

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