scholarly journals Spatiotemporal patterns of rodent hippocampal field potentials uncover spatial representations

2019 ◽  
Author(s):  
Liang Cao ◽  
Viktor Varga ◽  
Zhe S. Chen
Keyword(s):  
Large Scale ◽  
Scale Up ◽  
Field Potential ◽  
Spatiotemporal Patterns ◽  
Spatial Representations ◽  
Learning Approaches ◽  
Field Potentials ◽  
Hippocampal Field ◽  
Low Dimensional ◽  
Array Recordings

AbstractSpatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze run, immobility and sleep. Here, we showed that multi-site hippocampal field potential amplitude at ultra-high frequency band (FPAuhf) provides not only a fast and reliable reconstruction of the rodent’s position in wake, but also a readout of replay content during sharp wave ripples. This FPAuhf feature may serve as robust real-time decoding strategy from large-scale (up to 100,000 electrodes) recordings in closed-loop experiments. Furthermore, we developed unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods, and to infer latent dynamical structures from lower-rank FPAuhf features. We also developed a novel optical flow-based method to identify propagating spatiotemporal LFP patterns from multi-site array recordings, which can be used for decoding application. Finally, we developed a prospective decoding strategy to predict animal’s future decision in goal-directed navigation.

scholarly journals Fronto-striatal oscillations predict vocal output in bats

2019 ◽  
Author(s):  
Kristin Weineck ◽  
Francisco García-Rosales ◽  
Julio C. Hechavarría
Keyword(s):  
Large Scale ◽  
Phase Locking ◽  
Field Potential ◽  
Dorsal Striatum ◽  
Field Potentials ◽  
Action Prediction ◽  
Theta Band ◽  
Motor Action ◽  
Echolocation Calls ◽  
General Aspects

SummaryThe ability to vocalize is ubiquitous in vertebrates, but neural networks leading to vocalization production remain poorly understood. Here we performed simultaneous, large scale, neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus) during the production of echolocation and non-echolocation calls in bats. This approach allows to assess the general aspects underlying vocalization production in mammals and the unique evolutionary adaptations of bat echolocation. Our findings show that distinct intra-areal brain rhythms in the beta (12-30 Hz) and gamma (30-80 Hz) bands of the local field potential can be used to predict the bats’ vocal output and that phase locking between spikes and field potentials occurs prior vocalization production. Moreover, the fronto-striatal network is differentially coupled in the theta-band during the production of echolocation and non-echolocation calls. Overall, our results present evidence for fronto-striatal network oscillations in motor action prediction in mammals.

scholarly journals Chronic, Multi-Site Recordings Supported by Two Low-Cost, Stationary Probe Designs Optimized to Capture Either Single Unit or Local Field Potential Activity in Behaving Rats

2021 ◽  
Vol 12 ◽  
Author(s):  
Miranda J. Francoeur ◽  
Tianzhi Tang ◽  
Leila Fakhraei ◽  
Xuanyu Wu ◽  
Sidharth Hulyalkar ◽  
...  
Keyword(s):  
Local Field ◽  
Single Unit ◽  
Large Scale ◽  
Brain Activity ◽  
Low Cost ◽  
Local Field Potentials ◽  
Field Potential ◽  
Brain Regions ◽  
Field Potentials ◽  
Brain Target

Rodent models of cognitive behavior have greatly contributed to our understanding of human neuropsychiatric disorders. However, to elucidate the neurobiological underpinnings of such disorders or impairments, animal models are more useful when paired with methods for measuring brain function in awake, behaving animals. Standard tools used for systems-neuroscience level investigations are not optimized for large-scale and high-throughput behavioral battery testing due to various factors including cost, time, poor longevity, and selective targeting limited to measuring only a few brain regions at a time. Here we describe two different “user-friendly” methods for building extracellular electrophysiological probes that can be used to measure either single units or local field potentials in rats performing cognitive tasks. Both probe designs leverage several readily available, yet affordable, commercial products to facilitate ease of production and offer maximum flexibility in terms of brain-target locations that can be scalable (32–64 channels) based on experimental needs. Our approach allows neural activity to be recorded simultaneously with behavior and compared between micro (single unit) and more macro (local field potentials) levels of brain activity in order to gain a better understanding of how local brain regions and their connected networks support cognitive functions in rats. We believe our novel probe designs make collecting electrophysiology data easier and will begin to fill the gap in knowledge between basic and clinical research.

Solid Lipid Nanoparticles: A Promising Drug Delivery Technology

2009 ◽  
Vol 2 (2) ◽  
pp. 509-516
Author(s):  
S. Pragati ◽  
S. Kuldeep ◽  
S. Ashok ◽  
M. Satheesh
Keyword(s):  
Drug Delivery ◽  
Solid Lipid Nanoparticles ◽  
Large Scale ◽  
Colloidal Particles ◽  
Scale Up ◽  
Lipid Nanoparticles ◽  
Scale Production ◽  
Research Activity ◽  
New Approach ◽  
Solid Lipid

One of the situations in the treatment of disease is the delivery of efficacious medication of appropriate concentration to the site of action in a controlled and continual manner. Nanoparticle represents an important particulate carrier system, developed accordingly. Nanoparticles are solid colloidal particles ranging in size from 1 to 1000 nm and composed of macromolecular material. Nanoparticles could be polymeric or lipidic (SLNs). Industry estimates suggest that approximately 40% of lipophilic drug candidates fail due to solubility and formulation stability issues, prompting significant research activity in advanced lipophile delivery technologies. Solid lipid nanoparticle technology represents a promising new approach to lipophile drug delivery. Solid lipid nanoparticles (SLNs) are important advancement in this area. The bioacceptable and biodegradable nature of SLNs makes them less toxic as compared to polymeric nanoparticles. Supplemented with small size which prolongs the circulation time in blood, feasible scale up for large scale production and absence of burst effect makes them interesting candidates for study. In this present review this new approach is discussed in terms of their preparation, advantages, characterization and special features.

Recombinant Protein Production in Microalgae: Emerging Trends

2020 ◽  
Vol 27 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Niaz Ahmad ◽  
Muhammad Aamer Mehmood ◽  
Sana Malik
Keyword(s):  
Chloroplast Genome ◽  
Recombinant Proteins ◽  
Large Scale ◽  
Higher Plants ◽  
Scale Up ◽  
Chloroplast Transformation ◽  
Point Of View ◽  
Nuclear Transformation ◽  
Scale Production ◽  
Algal Chloroplast

: In recent years, microalgae have emerged as an alternative platform for large-scale production of recombinant proteins for different commercial applications. As a production platform, it has several advantages, including rapid growth, easily scale up and ability to grow with or without the external carbon source. Genetic transformation of several species has been established. Of these, Chlamydomonas reinhardtii has become significantly attractive for its potential to express foreign proteins inexpensively. All its three genomes – nuclear, mitochondrial and chloroplastic – have been sequenced. As a result, a wealth of information about its genetic machinery, protein expression mechanism (transcription, translation and post-translational modifications) is available. Over the years, various molecular tools have been developed for the manipulation of all these genomes. Various studies show that the transformation of the chloroplast genome has several advantages over nuclear transformation from the biopharming point of view. According to a recent survey, over 100 recombinant proteins have been expressed in algal chloroplasts. However, the expression levels achieved in the algal chloroplast genome are generally lower compared to the chloroplasts of higher plants. Work is therefore needed to make the algal chloroplast transformation commercially competitive. In this review, we discuss some examples from the algal research, which could play their role in making algal chloroplast commercially successful.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Expanding the boundaries of learning

2021 ◽  
Vol 102 (8) ◽  
pp. 8-13
Author(s):  
Thomas Hatch
Keyword(s):  
Instructional Practices ◽  
New Hampshire ◽  
Large Scale ◽  
Educational Experiences ◽  
Scale Up ◽  
Project Based Learning

Taking advantage of the possibilities for learning outside of school requires us to build on what we know about why it is so hard to sustain and scale up unconventional educational experiences within conventional schools. To illustrate the opportunities and challenges, Thomas Hatch describes a large-scale approach to project-based learning developed in a camp in New Hampshire and incorporated in a Brooklyn school, a trip-based program in Detroit, and Singapore’s systemic embrace of learning outside school. By understanding the conditions that can sustain alternative instructional practices, educators can find places to challenge the boundaries of schooling and create visions of the possible that exceed current constraints.

scholarly journals Scaling Up Delivery of Biofortified Staple Food Crops Globally: Paths to Nourishing Millions

2021 ◽  
pp. 037957212098250
Author(s):  
Jennifer K. Foley ◽  
Kristina D. Michaux ◽  
Bho Mudyahoto ◽  
Laira Kyazike ◽  
Binu Cherian ◽  
...  
Keyword(s):  
Pearl Millet ◽  
Large Scale ◽  
Scale Up ◽  
Lessons Learned ◽  
Raw Material ◽  
Micronutrient Deficiencies ◽  
Seed Systems ◽  
Public And Private ◽  
Context Specific ◽  
Public And Private Sector

Background: Micronutrient deficiencies affect over one quarter of the world’s population. Biofortification is an evidence-based nutrition strategy that addresses some of the most common and preventable global micronutrient gaps and can help improve the health of millions of people. Since 2013, HarvestPlus and a consortium of collaborators have made impressive progress in the enrichment of staple crops with essential micronutrients through conventional plant breeding. Objective: To review and highlight lessons learned from multiple large-scale delivery strategies used by HarvestPlus to scale up biofortification across different country and crop contexts. Results: India has strong public and private sector pearl millet breeding programs and a robust commercial seed sector. To scale-up pearl millet, HarvestPlus established partnerships with public and private seed companies, which facilitated the rapid commercialization of products and engagement of farmers in delivery activities. In Nigeria, HarvestPlus stimulated the initial acceptance and popularization of vitamin A cassava using a host of creative approaches, including “crowding in” delivery partners, innovative promotional programs, and development of intermediate raw material for industry and novel food products. In Uganda, orange sweet potato (OSP) is a traditional subsistence crop. Due to this, and the lack of formal seed systems and markets, HarvestPlus established a network of partnerships with community-based nongovernmental organizations and vine multipliers to popularize and scale-up delivery of OSP. Conclusions: Impact of biofortification ultimately depends on the development of sustainable markets for biofortified seeds and products. Results illustrate the need for context-specific, innovative solutions to promote widespread adoption.

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

2019 ◽  
Vol 78 (5) ◽  
pp. 617-628 ◽  
Author(s):  
Erika Van Nieuwenhove ◽  
Vasiliki Lagou ◽  
Lien Van Eyck ◽  
James Dooley ◽  
Ulrich Bodenhofer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Adaptive Immune System ◽  
Healthy Children ◽  
Learning Approaches ◽  
Data Set ◽  
Immune Signature ◽  
Systemic Jia

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

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