scholarly journals HBMITool: a user-friendly software for labeling Human Brain Microscopy Images

2019 ◽  
Author(s):  
Hungju Wang ◽  
Lea T Grinberg ◽  
Maryana Alegro
Keyword(s):  
Postmortem Brain ◽  
Cell Counting ◽  
Cell Detection ◽  
Human Postmortem Brain ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Lipofuscin Pigment ◽  
Manual Interaction ◽  
High Level ◽  
User Friendly

AbstractOne of the most popular tools for quantifying protein expression is Immunofluorescence (IF). Although IF is widely applied in drug discovery research and assessing disease mechanisms, it has great room for improvement on the task of analyzing human postmortem brain samples. IF analysis of postmortem human tissue relies mostly on manual interaction, which is often error-prone and leading to low inter and intra-observer reproducibility. The high level of autofluorescence caused by accumulation of lipofuscin pigment during aging impedes systematic analyses of human postmortem brain samples. A method for automating cell counting and classification in IF microscopy of human postmortem brains was proposed before, which speeds up the quantification task while improving reproducibility. To correct for misclassified cells by the algorithm, we created HBFMTool, a software package that ease the process of editing the result produced by cell detection/classification algorithm.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

scholarly journals Towards a contemporary player learning in development framework for sports practitioners

2021 ◽  
pp. 174795412110023
Author(s):  
Mark O Sullivan ◽  
Carl T Woods ◽  
James Vaughan ◽  
Keith Davids
Keyword(s):  
Linear Process ◽  
Task Environment ◽  
Coach Education ◽  
Development Framework ◽  
Novel Approach ◽  
Non Linear ◽  
And Performance ◽  
Learning Development ◽  
High Level ◽  
User Friendly

As it is appreciated that learning is a non-linear process – implying that coaching methodologies in sport should be accommodative – it is reasonable to suggest that player development pathways should also account for this non-linearity. A constraints-led approach (CLA), predicated on the theory of ecological dynamics, has been suggested as a viable framework for capturing the non-linearity of learning, development and performance in sport. The CLA articulates how skills emerge through the interaction of different constraints (task-environment-performer). However, despite its well-established theoretical roots, there are challenges to implementing it in practice. Accordingly, to help practitioners navigate such challenges, this paper proposes a user-friendly framework that demonstrates the benefits of a CLA. Specifically, to conceptualize the non-linear and individualized nature of learning, and how it can inform player development, we apply Adolph’s notion of learning IN development to explain the fundamental ideas of a CLA. We then exemplify a learning IN development framework, based on a CLA, brought to life in a high-level youth football organization. We contend that this framework can provide a novel approach for presenting the key ideas of a CLA and its powerful pedagogic concepts to practitioners at all levels, informing coach education programs, player development frameworks and learning environment designs in sport.

scholarly journals iPSC technology-based regenerative medicine for kidney diseases

2021 ◽  
Author(s):  
Kenji Osafune
Keyword(s):  
Regenerative Medicine ◽  
Cell Therapy ◽  
Kidney Development ◽  
Disease Modeling ◽  
Kidney Diseases ◽  
Collecting Duct ◽  
Current Status ◽  
Renal Tubules ◽  
Discovery Research ◽  
Drug Discovery Research

AbstractWith few curative treatments for kidney diseases, increasing attention has been paid to regenerative medicine as a new therapeutic option. Recent progress in kidney regeneration using human-induced pluripotent stem cells (hiPSCs) is noteworthy. Based on the knowledge of kidney development, the directed differentiation of hiPSCs into two embryonic kidney progenitors, nephron progenitor cells (NPCs) and ureteric bud (UB), has been established, enabling the generation of nephron and collecting duct organoids. Furthermore, human kidney tissues can be generated from these hiPSC-derived progenitors, in which NPC-derived glomeruli and renal tubules and UB-derived collecting ducts are interconnected. The induced kidney tissues are further vascularized when transplanted into immunodeficient mice. In addition to the kidney reconstruction for use in transplantation, it has been demonstrated that cell therapy using hiPSC-derived NPCs ameliorates acute kidney injury (AKI) in mice. Disease modeling and drug discovery research using disease-specific hiPSCs has also been vigorously conducted for intractable kidney disorders, such as autosomal dominant polycystic kidney disease (ADPKD). In an attempt to address the complications associated with kidney diseases, hiPSC-derived erythropoietin (EPO)-producing cells were successfully generated to discover drugs and develop cell therapy for renal anemia. This review summarizes the current status and future perspectives of developmental biology of kidney and iPSC technology-based regenerative medicine for kidney diseases.

scholarly journals Spine impairment in mice high-expressing neuregulin 1 due to LIMK1 activation

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Peng Chen ◽  
Hongyang Jing ◽  
Mingtao Xiong ◽  
Qian Zhang ◽  
Dong Lin ◽  
...  
Keyword(s):  
Neural Development ◽  
Protein Level ◽  
Brain Regions ◽  
Postmortem Brain ◽  
Pathophysiological Mechanism ◽  
Brain Disorders ◽  
Spine Density ◽  
Genes Encoding ◽  
High Level ◽  
And Function

AbstractThe genes encoding for neuregulin1 (NRG1), a growth factor, and its receptor ErbB4 are both risk factors of major depression disorder and schizophrenia (SZ). They have been implicated in neural development and synaptic plasticity. However, exactly how NRG1 variations lead to SZ remains unclear. Indeed, NRG1 levels are increased in postmortem brain tissues of patients with brain disorders. Here, we studied the effects of high-level NRG1 on dendritic spine development and function. We showed that spine density in the prefrontal cortex and hippocampus was reduced in mice (ctoNrg1) that overexpressed NRG1 in neurons. The frequency of miniature excitatory postsynaptic currents (mEPSCs) was reduced in both brain regions of ctoNrg1 mice. High expression of NRG1 activated LIMK1 and increased cofilin phosphorylation in postsynaptic densities. Spine reduction was attenuated by inhibiting LIMK1 or blocking the NRG1–LIMK1 interaction, or by restoring NRG1 protein level. These results indicate that a normal NRG1 protein level is necessary for spine homeostasis and suggest a pathophysiological mechanism of abnormal spines in relevant brain disorders.

scholarly journals Development of a Selection Method for Discovering Irreversible (Covalent) Binders from a DNA-Encoded Library

2018 ◽  
Vol 24 (2) ◽  
pp. 169-174 ◽  
Author(s):  
Zhengrong Zhu ◽  
LaShadric C. Grady ◽  
Yun Ding ◽  
Kenneth E. Lind ◽  
Christopher P. Davie ◽  
...  
Keyword(s):  
Magnetic Beads ◽  
Chemical Probes ◽  
Lead Discovery ◽  
3C Protease ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Affinity Resin ◽  
Covalent Ligands ◽  
Developing Method ◽  
Unique Mechanism

DNA-encoded libraries (DELs) have been broadly applied to identify chemical probes for target validation and lead discovery. To date, the main application of the DEL platform has been the identification of reversible ligands using multiple rounds of affinity selection. Irreversible (covalent) inhibition offers a unique mechanism of action for drug discovery research. In this study, we report a developing method of identifying irreversible (covalent) ligands from DELs. The new method was validated by using 3C protease (3CP) and on-DNA irreversible tool compounds (rupintrivir derivatives) spiked into a library at the same concentration as individual members of that library. After affinity selections against 3CP, the irreversible tool compounds were specifically enriched compared with the library members. In addition, we compared two immobilization methods and concluded that microscale columns packed with the appropriate affinity resin gave higher tool compound recovery than magnetic beads.

scholarly journals Modern Approaches in the Discovery and Development of Plant-Based Natural Products and Their Analogues as Potential Therapeutic Agents

Molecules ◽  
2022 ◽  
Vol 27 (2) ◽  
pp. 349
Author(s):  
Asim Najmi ◽  
Sadique A. Javed ◽  
Mohammed Al Bratty ◽  
Hassan A. Alhazmi
Keyword(s):  
Natural Products ◽  
Drug Discovery ◽  
Natural Product ◽  
Therapeutic Agents ◽  
Throughput Capacity ◽  
Scientific Interest ◽  
Comprehensive Overview ◽  
Natural Sources ◽  
Discovery Research ◽  
Drug Discovery Research

Natural products represents an important source of new lead compounds in drug discovery research. Several drugs currently used as therapeutic agents have been developed from natural sources; plant sources are specifically important. In the past few decades, pharmaceutical companies demonstrated insignificant attention towards natural product drug discovery, mainly due to its intrinsic complexity. Recently, technological advancements greatly helped to address the challenges and resulted in the revived scientific interest in drug discovery from natural sources. This review provides a comprehensive overview of various approaches used in the selection, authentication, extraction/isolation, biological screening, and analogue development through the application of modern drug-development principles of plant-based natural products. Main focus is given to the bioactivity-guided fractionation approach along with associated challenges and major advancements. A brief outline of historical development in natural product drug discovery and a snapshot of the prominent natural drugs developed in the last few decades are also presented. The researcher’s opinions indicated that an integrated interdisciplinary approach utilizing technological advances is necessary for the successful development of natural products. These involve the application of efficient selection method, well-designed extraction/isolation procedure, advanced structure elucidation techniques, and bioassays with a high-throughput capacity to establish druggability and patentability of phyto-compounds. A number of modern approaches including molecular modeling, virtual screening, natural product library, and database mining are being used for improving natural product drug discovery research. Renewed scientific interest and recent research trends in natural product drug discovery clearly indicated that natural products will play important role in the future development of new therapeutic drugs and it is also anticipated that efficient application of new approaches will further improve the drug discovery campaign.

scholarly journals Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation

2018 ◽  
Author(s):  
D. Kuhner ◽  
L.D.J. Fiederer ◽  
J. Aldinger ◽  
F. Burget ◽  
M. Völker ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Study ◽  
Human Robot Interaction ◽  
Service Robots ◽  
Referring Expressions ◽  
Non Invasive ◽  
Neuronal Signal ◽  
High Level ◽  
User Friendly ◽  
Robotic Tasks

AbstractAs autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of the robotic tasks and the environment. Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users. While non-expert users can make the effort to familiarize themselves with a robotic system, paralyzed users may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: non-invasive neuronal signal recording and co-adaptive deep learning which form the brain-computer interface (BCI), high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level planning using referring expressions and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

scholarly journals THREE –WAY SECURITY USING IMAGE BASED AUTHENTICATION SYSTEM

2016 ◽  
pp. 46-47
Author(s):  
SARIKA KHALADKAR ◽  
SARITA MALUNJKAR ◽  
POOJA SHINGOTE
Keyword(s):  
Instant Messaging ◽  
Image Authentication ◽  
Control Mechanisms ◽  
Authentication System ◽  
One Time Password ◽  
Personal Accounts ◽  
Brute Force Attack ◽  
The One ◽  
High Level ◽  
User Friendly

Secure environments protect their resources against unauthorized access by enforcing access control mechanisms. So when increasing security is an issue text based passwords are not enough to counter such problems. The need for something more secure along with being user friendly is required. This is where Image Based Authentication (IBA) comes into play. This helps to eliminate tempest attack, shoulder attack, Brute-force attack. Using the instant messaging service available in internet, user will obtain the One Time Password (OTP) after image authentication. This OTP then can be used by user to access their personal accounts. The image based authentication method relies on the user’s ability to recognize pre-chosen categories from a grid of pictures. This paper integrates Image based authentication and HMAC based one time password to achieve high level of security in authenticating the user over the internet.

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