scholarly journals “Validating silicon polytrodes with paired juxtacellular recordings: method and dataset”

2016 ◽  
Author(s):  
Joana P. Neto ◽  
Gonçalo Lopes ◽  
João Frazão ◽  
Joana Nogueira ◽  
Pedro Lacerda ◽  
...  
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Cross Validation ◽  
Electrode Materials ◽  
Validation Dataset ◽  
Neural Signals ◽  
Neural Structure ◽  
New Methods ◽  
New Algorithms

AbstractCross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micron resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals.

scholarly journals Validating silicon polytrodes with paired juxtacellular recordings: method and dataset

2016 ◽  
Vol 116 (2) ◽  
pp. 892-903 ◽  
Author(s):  
Joana P. Neto ◽  
Gonçalo Lopes ◽  
João Frazão ◽  
Joana Nogueira ◽  
Pedro Lacerda ◽  
...  
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Cross Validation ◽  
Electrode Materials ◽  
Validation Dataset ◽  
Neural Signals ◽  
Neural Structure ◽  
New Methods ◽  
New Algorithms

Cross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo “paired-recordings” such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micropipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micrometer resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals.

scholarly journals Graphene-Based Electrode Materials for Neural Activity Detection

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6170
Author(s):  
Weichen Wei ◽  
Xuejiao Wang
Keyword(s):  
Neural Activity ◽  
High Performance ◽  
Electrode Materials ◽  
Neurological Diseases ◽  
Activity Detection ◽  
Neural Signals ◽  
Neural Electrode ◽  
Neural Electrodes ◽  
Wide Range

The neural electrode technique is a powerful tool for monitoring and regulating neural activity, which has a wide range of applications in basic neuroscience and the treatment of neurological diseases. Constructing a high-performance electrode–nerve interface is required for the long-term stable detection of neural signals by electrodes. However, conventional neural electrodes are mainly fabricated from rigid materials that do not match the mechanical properties of soft neural tissues, thus limiting the high-quality recording of neuroelectric signals. Meanwhile, graphene-based nanomaterials can form stable electrode–nerve interfaces due to their high conductivity, excellent flexibility, and biocompatibility. In this literature review, we describe various graphene-based electrodes and their potential application in neural activity detection. We also discuss the biological safety of graphene neural electrodes, related challenges, and their prospects.

iTSP-PseAAC: Identify Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC

2021 ◽  
Vol 15 ◽  
Author(s):  
Muhammad Awais ◽  
Waqar Hussain ◽  
Nouman Rasool ◽  
Yaser Daanial Khan
Keyword(s):  
Tumor Suppressor ◽  
Cross Validation ◽  
Control Cell ◽  
Normal Function ◽  
In Vivo Studies ◽  
Tumor Suppressor Proteins ◽  
Proposed Model ◽  
Self Consistency

Background: The uncontrolled growth due to accumulation of genetic and epigenetic changes as a result of loss or reduction in the normal function of Tumor Suppressor Genes (TSGs) and Pro-oncogenes is known as cancer. TSGs control cell division and growth by repairing of DNA mistakes during replication and restrict the unwanted proliferation of a cell or activities, those are the part of tumor production. Objectives: This study aims to propose a novel, accurate, user-friendly model to predict tumor suppressor proteins, which would be freely available to experimental molecular biologists to assist them using in vitro and in vivo studies. Methods: The predictor model has used the input feature vector (IFV) calculated from the physicochemical properties of proteins based on FCNN to compute the accuracy, sensitivity, specificity, and MCC. The proposed model was validated against different exhaustive validation techniques i.e. self-consistency and cross-validation. Results: Using self-consistency, the accuracy is 99%, for cross-validation and independent testing has 99.80% and 100% accuracy respectively. The overall accuracy of the proposed model is 99%, sensitivity value 98% and specificity 99% and F1-score was 0.99. Conclusion: It concludes, the proposed model for prediction of the tumor suppressor proteins can predict the tumor suppressor proteins efficiently, but it still has space for improvements in computational ways as the protein sequences may rapidly increase, day by day.

scholarly journals Facile sonochemical-assisted synthesis of orthorhombic phase black phosphorus/rGO hybrids for effective photothermal therapy

Nanophotonics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 3023-3034
Author(s):  
Weiyuan Liang ◽  
Dou Wang ◽  
Xiaohui Ren ◽  
Chenchen Ge ◽  
Hanyue Wang ◽  
...  
Keyword(s):  
Electrode Materials ◽  
Orthorhombic Phase ◽  
Cost Effective ◽  
Black Phosphorus ◽  
Antitumor Effects ◽  
Covalent Bonds ◽  
Preparation Conditions ◽  
Reduced Graphene

AbstractTwo-dimensional black phosphorus (BP) has been demonstrated to be promising in photoelectronic devices, electrode materials, and biomedicine owing to its outstanding properties. However, the application of BP has been hindered by harsh preparation conditions, high costs, and easy degradation in ambient condition. Herein, we report a facile and cost-effective strategy for synthesis of orthorhombic phase BP and a kind of BP-reduced graphene oxide (BP/rGO) hybrids in which BP remains stable for more than 4 weeks ascribed to the formation of phosphorus-carbon covalent bonds between BP and rGO as well as the protection effect of the unique wrinkle morphology of rGO nanosheets. Surface modification BP/rGO hybrids (PEGylated BP/rGO) exhibit excellent photothermal performance with photothermal conversion efficiency as high as 57.79% at 808 nm. The BP/rGO hybrids exhibit enhanced antitumor effects both in vitro and in vivo, showing promising perspectives in biomedicine.

scholarly journals Deep ConvNet: Non-Random Weight Initialization for Repeatable Determinism, Examined with FSGM

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4772
Author(s):  
Richard N. M. Rudd-Orthner ◽  
Lyudmila Mihaylova
Keyword(s):  
Cross Validation ◽  
Color Image ◽  
Validation Dataset ◽  
Number Sequence ◽  
Random Weight ◽  
Fast Gradient ◽  
Weight Initialization ◽  
Fitting In ◽  
Sequence Substitution ◽  
Sign Method

A repeatable and deterministic non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM). Using the FSGM approach as a technique to measure the initialization effect with controlled distortions in transferred learning, varying the dataset numerical similarity. The focus is on convolutional layers with induced earlier learning through the use of striped forms for image classification. Which provided a higher performing accuracy in the first epoch, with improvements of between 3–5% in a well known benchmark model, and also ~10% in a color image dataset (MTARSI2), using a dissimilar model architecture. The proposed method is robust to limit optimization approaches like Glorot/Xavier and He initialization. Arguably the approach is within a new category of weight initialization methods, as a number sequence substitution of random numbers, without a tether to the dataset. When examined under the FGSM approach with transferred learning, the proposed method when used with higher distortions (numerically dissimilar datasets), is less compromised against the original cross-validation dataset, at ~31% accuracy instead of ~9%. This is an indication of higher retention of the original fitting in transferred learning.

scholarly journals Omitted Variable Bias in GLMs of Neural Spiking Activity

2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Linear Models ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
History Effects ◽  
Omitted Variable Bias ◽  
Wide Range ◽  
Variable Bias

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables, as well as the dynamics of single neurons. However, in any given experiment, many variables that have an impact on neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how postspike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single-neuron firing. Omitted variable bias can appear in any model with confounders—where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.

scholarly journals Neural structure mapping in human probabilistic reward learning

2018 ◽  
Author(s):  
F. Luyckx ◽  
H. Nili ◽  
B. Spitzer ◽  
C. Summerfield
Keyword(s):  
Structural Alignment ◽  
Learning Task ◽  
Number Line ◽  
Visual Images ◽  
Abstract Concepts ◽  
Structure Mapping ◽  
Neural Signals ◽  
Neural Structure ◽  
Relational Patterns ◽  
Guided Learning

AbstractHumans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented by a single dimension (i.e. on a mental line), for example according to their cardinality, size or value. Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their pay-out probability in a way that suggested they were encoded onto the same mental number line. Our findings suggest that in humans, learning about values is accompanied by structural alignment of value representations with neural codes for the concept of magnitude.

scholarly journals Interpersonal Comparison of Utility by Measuring Neural Activity

2021 ◽  
Author(s):  
Kaosu Matsumori ◽  
Kazuki Iijima ◽  
Yukihito Yomogida ◽  
Kenji Matsumoto
Keyword(s):  
Neural Activity ◽  
Optimal Taxation ◽  
Cost Benefit ◽  
Third Party ◽  
Impossibility Theorem ◽  
Interpersonal Comparison ◽  
Neural Signals ◽  
Collective Decisions ◽  
Economic Goods ◽  
Household Incomes

Aggregating welfare across individuals to reach collective decisions is one of the most fundamental problems in our society. Interpersonal comparison of utility is pivotal and inevitable for welfare aggregation, because if each person's utility is not interpersonally comparable, there is no rational aggregation procedure that simultaneously satisfies even some very mild conditions for validity (Arrow's impossibility theorem). However, scientific methods for interpersonal comparison of utility have thus far not been available. Here, we have developed a method for interpersonal comparison of utility based on brain signals, by measuring the neural activity of participants performing gambling tasks. We found that activity in the medial frontal region was correlated with changes in expected utility, and that, for the same amount of money, the activity evoked was larger for participants with lower household incomes than for those with higher household incomes. Furthermore, we found that the ratio of neural signals from lower-income participants to those of higher-income participants coincided with estimates of their psychological pleasure by "impartial spectators", i.e. disinterested third-party participants satisfying specific conditions. Finally, we derived a decision rule based on aggregated welfare from our experimental data, and confirmed that it was applicable to a distribution problem. These findings suggest that our proposed method for interpersonal comparison of utility enables scientifically reasonable welfare aggregation by escaping from Arrow's impossibility and has implications for the fair distribution of economic goods. Our method can be further applied for evidence-based policy making in nations that use cost-benefit analyses or optimal taxation theory for policy evaluation.

scholarly journals Gold (III) Derivatives in Colon Cancer Treatment

2022 ◽  
Vol 23 (2) ◽  
pp. 724
Author(s):  
Agata Gurba ◽  
Przemysław Taciak ◽  
Mariusz Sacharczuk ◽  
Izabela Młynarczuk-Biały ◽  
Magdalena Bujalska-Zadrożny ◽  
...  
Keyword(s):  
Protein Structures ◽  
Thioredoxin Reductase ◽  
Structural Similarity ◽  
In Vivo Studies ◽  
Antitumor Effects ◽  
Solubility In Water ◽  
Physiological Conditions ◽  
Drug Candidates ◽  
New Methods

Cancer is one of the leading causes of morbidity and mortality worldwide. Colorectal cancer (CRC) is the third most frequently diagnosed cancer in men and the second in women. Standard patterns of antitumor therapy, including cisplatin, are ineffective due to their lack of specificity for tumor cells, development of drug resistance, and severe side effects. For this reason, new methods and strategies for CRC treatment are urgently needed. Current research includes novel platinum (Pt)- and other metal-based drugs such as gold (Au), silver (Ag), iridium (Ir), or ruthenium (Ru). Au(III) compounds are promising drug candidates for CRC treatment due to their structural similarity to Pt(II). Their advantage is their relatively good solubility in water, but their disadvantage is an unsatisfactory stability under physiological conditions. Due to these limitations, work is still underway to improve the formula of Au(III) complexes by combining with various types of ligands capable of stabilizing the Au(III) cation and preventing its reduction under physiological conditions. This review summarizes the achievements in the field of stable Au(III) complexes with potential cytotoxic activity restricted to cancer cells. Moreover, it has been shown that not nucleic acids but various protein structures such as thioredoxin reductase (TrxR) mediate the antitumor effects of Au derivatives. The state of the art of the in vivo studies so far conducted is also described.

scholarly journals A method for single-neuron chronic recording from the retina in awake mice

Science ◽  
2018 ◽  
Vol 360 (6396) ◽  
pp. 1447-1451 ◽  
Author(s):  
Guosong Hong ◽  
Tian-Ming Fu ◽  
Mu Qiao ◽  
Robert D. Viveros ◽  
Xiao Yang ◽  
...  
Keyword(s):  
Retinal Ganglion Cells ◽  
Visual Information ◽  
Single Neuron ◽  
Ganglion Cells ◽  
Ex Vivo ◽  
Neural Circuitry ◽  
Retinal Ganglion ◽  
Chronic Recording ◽  
The Brain

The retina, which processes visual information and sends it to the brain, is an excellent model for studying neural circuitry. It has been probed extensively ex vivo but has been refractory to chronic in vivo electrophysiology. We report a nonsurgical method to achieve chronically stable in vivo recordings from single retinal ganglion cells (RGCs) in awake mice. We developed a noncoaxial intravitreal injection scheme in which injected mesh electronics unrolls inside the eye and conformally coats the highly curved retina without compromising normal eye functions. The method allows 16-channel recordings from multiple types of RGCs with stable responses to visual stimuli for at least 2 weeks, and reveals circadian rhythms in RGC responses over multiple day/night cycles.

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