Miracle-dz/UniFAR-FTUs huggingface dataset:
Intracellular selection of trans-cleaving hammerhead ribozymes

Abstract Hammerhead ribozyme is the smallest and best characterized catalytic RNA-cleaving ribozyme. It has been reported as potential therapeutic tools to manipulate the expression of target genes. However, most of naturally occurring hammerhead ribozymes process self-cleavage rather than cleave substrate RNA in trans, and its high intracellular activity relies on the tertiary interaction of Loop II and steam I bulge, resulting in decreased performance as applied in gene silencing. We described a direct intracellular selection method to evolve hammerhead variants based on trans-cleavage mode via using a toxin gene as the reporter. And a dual fluorescence proteins system has also been established to quantitatively evaluate the efficiency of selected ribozymes in the cell. Based on this selection strategy, we obtained three mutants with enhanced intracellular cleaving activity compared to wide type hammerhead ribozyme. The best one, TX-2 was revealed to possess better and consistent gene knockdown ability at different positions on diverse targeted mRNA either in prokaryotic or eukaryotic cells than wild-type hammerhead ribozyme. These observations imply the efficiency of the intracellular selection method of the trans-acting ribozyme and the potentials of improved ribozyme variants for research and therapeutic purposes.
Detection of the conformational changes of Discosoma red fluorescent proteins adhered on silver nanoparticles-based nanocomposites via surface-enhanced Raman scattering

Description of the relationship between protein structure and function remains a primary focus in molecular biology, biochemistry, protein engineering and bioelectronics. Moreover, the investigation of the protein conformational changes after adhesion and dehydration is of importance to tackle problems related to the interaction of proteins with solid surfaces. In this paper the conformational changes of wild-type Discosoma recombinant red fluorescent proteins (DsRed) adhered on silver nanoparticles (AgNPs)-based nanocomposites are explored via surface-enhanced Raman scattering (SERS). Originality in the present approach is to work on dehydrated DsRed thin protein layers in link with natural conditions during drying. To enable the SERS effect, plasmonic substrates consisting of a single layer of AgNPs encapsulated by an ultra-thin silica cover layer were elaborated by plasma process. The achieved enhancement of the electromagnetic field in the vicinity of the AgNPs is as high as 105. This very strong enhancement factor allowed detecting Raman signals from discontinuous layers of DsRed issued from solution with protein concentration of only 80 nM. Three different conformations of the DsRed proteins after adhesion and dehydration on the plasmonic substrates were identified. It was found that the DsRed chromophore structure of the adsorbed proteins undergoes optically assisted chemical transformations when interacting with the optical beam, which leads to reversible transitions between the three different conformations. The proposed time-evolution scenario endorses the dynamical character of the relationship between protein structure and function. It also confirms that the conformational changes of proteins with strong internal coherence, like DsRed proteins, are reversible.
Modified Mindfulness-Based Cognitive Therapy for Depressive Symptoms in Parkinson's Disease: a Pilot Trial

Background: Mindfulness-based cognitive therapy (MBCT) has evidence of efficacy in a range of populations, but few studies to date have reported on MBCT for treatment of anxious and depressive symptoms in Parkinson's disease (PD). Aims: The aim of this study was to examine the efficacy of modified MBCT in reducing symptoms of anxiety and depression and improving quality of life in PD. Method: Thirty-six individuals with PD were randomly assigned to either modified MBCT or a waitlist control. Changes in symptoms of anxiety, depression and quality of life were compared at group level using generalized linear mixed models and at individual level using reliable change analysis. Results: At post-treatment, there was a significant reduction in depressive symptoms for people undertaking modified MBCT at both group and individual levels compared with controls. There was no significant effect on anxiety or quality of life at the group level, although significantly more people had reliable improvement in anxiety after modified MBCT than after waitlist. Significantly more waitlist participants had reliable deterioration in symptoms of anxiety and depression than those completing modified MBCT. Most participants stayed engaged in modified MBCT, with only three drop-outs. Discussion: This proof-of-concept study demonstrates the potential efficacy of modified MBCT as a treatment for depressive symptoms in Parkinson's disease and suggests further research is warranted.
Simulation of heterogeneous tumour genomes with HeteroGenesis and in silico whole exome sequencing

Abstract Summary Tumour evolution results in progressive cancer phenotypes such as metastatic spread and treatment resistance. To better treat cancers, we must characterize tumour evolution and the genetic events that confer progressive phenotypes. This is facilitated by high coverage genome or exome sequencing. However, the best approach by which, or indeed whether, these data can be used to accurately model and interpret underlying evolutionary dynamics is yet to be confirmed. Establishing this requires sequencing data from appropriately heterogeneous tumours in which the exact trajectory and combination of events occurring throughout its evolution are known. We therefore developed HeteroGenesis: a tool to generate realistically evolved tumour genomes, which can be sequenced using weighted-Wessim (w-Wessim), an in silico exome sequencing tool that we have adapted from previous methods. HeteroGenesis simulates more complex and realistic heterogeneous tumour genomes than existing methods, can model different evolutionary dynamics, and enables the creation of multi-region and longitudinal data. Availability and implementation HeteroGenesis and w-Wessim are freely available under the GNU General Public Licence from https://github.com/GeorgetteTanner, implemented in Python and supported on linux and MS Windows. Supplementary information Supplementary data are available at Bioinformatics online.
Two‐Microphone Binary Mask Speech Enhancement in Diffuse and Directional Noise Fields

Two‐microphone binary mask speech enhancement (2mBMSE) has been of particular interest in recent literature and has shown promising results. Current 2mBMSE systems rely on spatial cues of speech and noise sources. Although these cues are helpful for directional noise sources, they lose their efficiency in diffuse noise fields. We propose a new system that is effective in both directional and diffuse noise conditions. The system exploits two features. The first determines whether a given time–frequency (T‐F) unit of the input spectrum is dominated by a diffuse or directional source. A diffuse signal is certainly a noise signal, but a directional signal could correspond to a noise or speech source. The second feature discriminates between T‐F units dominated by speech or directional noise signals. Speech enhancement is performed using a binary mask, calculated based on the proposed features. In both directional and diffuse noise fields, the proposed system segregates speech T‐F units with hit rates above 85%. It outperforms previous solutions in terms of signal‐to‐noise ratio and perceptual evaluation of speech quality improvement, especially in diffuse noise conditions.
The endothelial glycocalyx: Barrier functions versus red cell hemodynamics: A model of steady state ultrafiltration through a bi-layer formed by a porous outer layer and more selective membrane-associated inner layer.

BACKGROUND Ultrastructural investigations of the endothelial glycocalyx reveal a layer adjacent to the cell surface with a structure consistent with the primary ultrafilter of vascular walls. Theory predicts this layer can be no greater than 200-300 nm thick, a result to be reconciled with observations that red cells and large macromolecules are excluded from a region 1 micrometer or more from the cell membrane. OBJECTIVE To determine whether this apparent inconsistency might be accounted for by a model of steady state water and protein transport through a glycocalyx bi-layer formed by a porous outer layer in series with a more selective inner layer. METHODS Expressions for coupled water and albumin fluxes through the two layers were used to describe steady state ultra-filtration though the bi-layer model. RESULTS Albumin accumulates at the interface between the porous layer and the selective inner layer. The osmotic pressure of accumulated albumin significantly modifies the observed permeability properties of the microvessel wall by an effective unstirred layer effect. CONCLUSIONS The model places significant constraints on the outer layer permeability properties . The only outer layer properties that are consistent with measured steady state filtration rates and models of red cell flux through microvessels are an albumin permeability coefficient and hydraulic conductivity more than an order of magnitude larger than the those of the inner layer.
Spatial–Temporal Discriminative Restricted Boltzmann Machine for Event-Related Potential Detection and Analysis

Detecting event-related potential (ERP) is a challenging problem because of its low signal-to-noise ratio and complex spatial–temporal features. Conventional detection methods usually rely on the ensemble averaging technique, which may eliminate subtle but important information in ERP signals and lead to poor detection performance. Inspired by the good performance of discriminative restricted Boltzmann machine (DRBM) in feature extraction and classification, we propose a spatial–temporal DRBM (ST-DRBM) to extract spatial and temporal features for ERP detection. The experimental results and statistical analyses demonstrate that the proposed method is able to achieve state-of-the-art ERP detection performance. The ST-DRBM is not only an effective ERP detector, but also a practical tool for ERP analysis. Based on the proposed model, similar scalp distribution and temporal variations were found in the ERP signals of different sessions, which indicated the feasibility of cross-session ERP detection. Given its state-of-the-art performance and effective analytical technique, ST-DRBM is promising for ERP-based brain–computer interfaces and neuroscience research.
Sensitivity of Crosswise Model to Simplistic Selection of Nonsensitive Questions: An Application to Estimate Substance Use, Alcohol Consumption and Extramarital Sex Among Iranian College Students

Abstract Background: Nonrandomized response (NRR) models are a new generation of surveys for sensitive issues. This study aims to evaluate the validity of estimates from the crosswise model (as one of the efficient models) through employing different response probabilities of nonsensitive questions. Methods: This cross-sectional study was conducted during October and November 2015 among 1777 students of Tabriz University of Medical Sciences. Estimates of monthly alcohol consumption, and at least one instance of illicit drug use and extramarital sex over the last year were determined using direct questioning (DQ) and the Crosswise model (CM). In the last model, the probability of positive response to the nonsensitive questions was determined by using five different methods: uniform distribution (I), Benford’s law (II), and estimations based on data from three other studies (III, IV, V). Results: Crosswise estimates of sensitive behaviors with different probabilities of a positive response to nonsensitive questions differed significantly. For example, estimates of history of using illegal opioids at least once in the last year among men varied significantly from 5.0% to 16.1% with different crosswise models based on the probability of being born in Spring using method I (0.250), III (0.287), IV (0.248), and V (0.310). The model based on Benford’s law (II) was applied to estimate alcohol and cannabis consumption, and its estimates showed significant discrepancy with results of crosswise models I and V. Conclusion: Estimates from crosswise model is highly sensitive to the response probability of nonsensitive questions. It seems that if this question is not selected carefully, the mentioned models will provide overestimates or underestimates, and the more-is-better hypothesis is not always valid. To achieve valid estimates, the exact probability of a positive response to the nonsensitive question must be known for the studied population.
Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks.

Background Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer's disease (AD) and its prodromal phase, mild cognitive impairment (MCI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application. Methods In this study, the baseline MR images and follow-up information during 3 years of 150 normal controls (NC), 150 patients with stable MCI (sMCI) and 157 converted MCI (cMCI) were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The deep convolutional neural networks (CNNs) were adopted to distinguish different stages of MCI from the NC group, and predict the conversion time from MCI to AD. Two CNN architectures including GoogleNet and CaffeNet were explored and evaluated in multiple classifications and estimations of conversion risk using transfer learning from pre-trained ImageNet (via fine-tuning) and five-fold cross-validation. A novel data augmentation approach using random views aggregation was applied to generate abundant image patches from the original MR scans. Results The GoogleNet acquired accuracies with 97.58%, 67.33% and 84.71% in three-way discrimination among the NC, sMCI and cMCI groups respectively, whereas the CaffeNet obtained promising accuracies of 98.71%, 72.04% and 92.35% in the NC, sMCI and cMCI classifications. Furthermore, the accuracy measures of conversion risk of patients with cMCI ranged from 71.25% to 83.25% in different time points using GoogleNet, whereas the CaffeNet achieved remarkable accuracy measures from 95.42% to 97.01% in conversion risk prediction. Conclusions The experimental results demonstrated that the proposed methods had prominent capability in classification among the 3 groups such as sMCI, cMCI and NC, and exhibited significant ability in conversion risk prediction of patients with MCI.
A toolbox of IgG subclass-switched recombinant monoclonal antibodies for enhanced multiplex immunolabeling of brain

Generating recombinant monoclonal antibodies (R-mAbs) from mAb-producing hybridomas offers numerous advantages that increase the effectiveness, reproducibility, and transparent reporting of research. We report here the generation of a novel resource in the form of a library of recombinant R-mAbs validated for neuroscience research. We cloned immunoglobulin G (IgG) variable domains from cryopreserved hybridoma cells and input them into an integrated pipeline for expression and validation of functional R-mAbs. To improve efficiency over standard protocols, we eliminated aberrant Sp2/0-Ag14 hybridoma-derived variable light transcripts using restriction enzyme treatment. Further, we engineered a plasmid backbone that allows for switching of the IgG subclasses without altering target binding specificity to generate R-mAbs useful in simultaneous multiplex labeling experiments not previously possible. The method was also employed to rescue IgG variable sequences and generate functional R-mAbs from a non-viable cryopreserved hybridoma. All R-mAb sequences and plasmids will be archived and disseminated from open source suppliers.
Investigation of the Origin of Voltage Generation in Potentized Homeopathic Medicine through Raman Spectroscopy

Abstract Background For the study of homeopathic medicines in proper perspective, emerging techniques in material science are being used. Vibrational spectroscopy is one such tool for providing information on different states of hydrogen bonding as an effect of potentization. The associated change in electrical properties is also correlated with this effect. Objective From the vibrational spectra, the changes in hydrogen bonding due to dilution followed by unidirectional vigorous shaking (together termed potentization) of 91% ethanol and two homeopathic medicines Chininum purum and Acidum benzoicum have been studied. The aim was to correlate the result with the change in the electrical properties of the system. Methods Raman spectroscopy was used to study the vibrational spectra. A U-shaped glass tube (electrochemical cell), where one arm contained bi-distilled water and the other arm alcohol/homeopathic medicine (the arms being separated by a platinum foil), was used to measure the voltage generated across two symmetrically placed platinum electrodes. Results For all samples, it was observed that potentization affected the intensity of OH stretching bands at the frequencies 3240 cm−1, 3420 cm−1 and 3620 cm−1, corresponding to strong hydrogen bond, weak hydrogen bond and broken hydrogen bond, respectively. With the increase in potency, in the presence and absence of the two medicines in ethanol, the number of OH groups linked by strong hydrogen bonds decreased, while the number of OH groups with weak hydrogen bonds increased. With the increase in potentization, the number of OH groups with broken hydrogen bonds showed a difference in the presence and absence of the medicine. The voltage measurements for ethanol show that, with succussion, the magnitude of voltage increased with the two medicines at lower potencies, but not at higher potency where the voltage is lower. Acidum benzoicum, which is acidic in nature, had higher voltage values (113mV, 130 mV and 118 mV at 6C, 30C and 200C, respectively), compared with Chininum purum, which is basic in nature (20 mV, 85 mV and 65 mV at 6C, 30C and 200C, respectively). Conclusion The experimental results indicate a correlation between the vibrational and electrical properties of the homeopathic medicines Acidum benzoicum and Chininum purum at different potencies.
Generating Blues Solos with Variational Autoencoders

This thesis describes the design and implementation of a variational autoencoder that generates blues solos. The architecture of the variational autoencoder is capable of capturing long-term dependencies in musical data, which is verified in experiments. A dataset of MIDI solos was manually extracted from a corpus of MIDI songs in the genre of blues and used to train a Long Short-Term Memory Recurrent Neural Network. Tools for extracting musical information from MIDI files into a format that can be used for training a network were designed, implemented, and verified. Results show that the network is able to generate solos that have significant variations from the training data. Some of the generated solos are capable of being mistaken for real solos, and a few even outperform real solos in certain aspects. However, in most cases limitations on the system that lead to losses in musical information, and a limited dataset, inhibits the system’s ability to produce solos that are perceived as blues.
Induced pluripotent stem cell‐based modeling of mutant LRRK2‐associated Parkinson's disease

Recent advances in cell reprogramming have enabled assessment of disease‐related cellular traits in patient‐derived somatic cells, thus providing a versatile platform for disease modeling and drug development. Given the limited access to vital human brain cells, this technology is especially relevant for neurodegenerative disorders such as Parkinson's disease (PD) as a tool to decipher underlying pathomechanisms. Importantly, recent progress in genome‐editing technologies has provided an ability to analyze isogenic induced pluripotent stem cell (iPSC) pairs that differ only in a single genetic change, thus allowing a thorough assessment of the molecular and cellular phenotypes that result from monogenetic risk factors. In this review, we summarize the current state of iPSC‐based modeling of PD with a focus on leucine‐rich repeat kinase 2 (LRRK2), one of the most prominent monogenetic risk factors for PD linked to both familial and idiopathic forms. The LRRK2 protein is a primarily cytosolic multi‐domain protein contributing to regulation of several pathways including autophagy, mitochondrial function, vesicle transport, nuclear architecture and cell morphology. We summarize iPSC‐based studies that contributed to improving our understanding of the function of LRRK2 and its variants in the context of PD etiopathology. These data, along with results obtained in our own studies, underscore the multifaceted role of LRRK2 in regulating cellular homeostasis on several levels, including proteostasis, mitochondrial dynamics and regulation of the cytoskeleton. Finally, we expound advantages and limitations of reprogramming technologies for disease modeling and drug development and provide an outlook on future challenges and expectations offered by this exciting technology.
Preoperative Opioids and 1-year Patient-reported Outcomes After Spine Surgery.

STUDY DESIGN Longitudinal Cohort Study. OBJECTIVE Determine 1-year patient-reported outcomes associated with preoperative chronic opioid therapy and high-preoperative opioid dosages in patients undergoing elective spine surgery. SUMMARY OF BACKGROUND DATA Back pain is the most disabling condition worldwide and over half of patients presenting for spine surgery report using opioids. Preoperative dosage has been correlated with poor outcomes, but published studies have not assessed the relationship of both preoperative chronic opioids and opioid dosage with patient-reported outcomes. METHODS For patients undergoing elective spine surgery between 2010 and 2017, our prospective institutional spine registry data was linked to opioid prescription data collected from our state's Prescription Drug Monitoring Program to analyze outcomes associated with preoperative chronic opioid therapy and high-preoperative opioid dosage, while adjusting for confounders through multivariable regression analyses. Outcomes included 1-year meaningful improvements in pain, function, and quality of life. Additional outcomes included 1-year satisfaction, return to work, 90-day complications, and postoperative chronic opioid use. RESULTS Of 2128 patients included, preoperative chronic opioid therapy was identified in 21% and was associated with significantly higher odds (adjusted odds ratio [95% confidence interval]) of not achieving meaningful improvements at 1-year in extremity pain (aOR:1.5 [1.2-2]), axial pain (aOR:1.7 [1.4-2.2]), function (aOR:1.7 [1.4-2.2]), and quality of life (aOR:1.4 [1.2-1.9]); dissatisfaction (aOR:1.7 [1.3-2.2]); 90-day complications (aOR:2.9 [1.7-4.9]); and postoperative chronic opioid use (aOR:15 [11.4-19.7]). High-preoperative opioid dosage was only associated with postoperative chronic opioid use (aOR:4.9 [3-7.9]). CONCLUSION Patients treated with chronic opioids prior to spine surgery are significantly less likely to achieve meaningful improvements at 1-year in pain, function, and quality of life; and less likely to be satisfied at 1-year with higher odds of 90-day complications, regardless of dosage. Both preoperative chronic opioid therapy and high-preoperative dosage are independently associated with postoperative chronic opioid use. LEVEL OF EVIDENCE 2.