Miracle-dz/UniFAR-FTUs huggingface dataset:
Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.
Settable Polymeric Autograft Extenders in a Rabbit Radius Model of Bone Formation

Autograft (AG) is the gold standard for bone grafts, but limited quantities and patient morbidity are associated with its use. AG extenders have been proposed to minimize the volume of AG while maintaining the osteoinductive properties of the implant. In this study, poly(ester urethane) (PEUR) and poly(thioketal urethane) (PTKUR) AG extenders were implanted in a 20-mm rabbit radius defect model to evaluate new bone formation and graft remodeling. Outcomes including µCT and histomorphometry were measured at 12 weeks and compared to an AG (no polymer) control. AG control examples exhibited new bone formation, but inconsistent healing was observed. The implanted AG control was resorbed by 12 weeks, while AG extenders maintained implanted AG throughout the study. Bone growth from the defect interfaces was observed in both AG extenders, but residual polymer inhibited cellular infiltration and subsequent bone formation within the center of the implant. PEUR-AG extenders degraded more rapidly than PTKUR-AG extenders. These observations demonstrated that AG extenders supported new bone formation and that polymer composition did not have an effect on overall bone formation. Furthermore, the results indicated that early cellular infiltration is necessary for harnessing the osteoinductive capabilities of AG.
Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images

The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.
Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment

Nowadays, gait assessment in the real life environment is gaining more attention. Therefore, it is desirable to know how some factors, such as surfaces (natural, artificial) or dual-tasking, influence real life gait pattern. The aim of this study was to assess gait variability and gait complexity during single and dual-task walking on different surfaces in an outdoor environment. Twenty-nine healthy young adults aged 23.31 ± 2.26 years (18 females, 11 males) walked at their preferred walking speed on three different surfaces (asphalt, cobbles, grass) in single-task and in two dual-task conditions (manual task—carrying a cup filled with water, cognitive task—subtracting the number 7). A triaxial inertial sensor attached to the lower trunk was used to record trunk acceleration during gait. From 15 strides, sample entropy (SampEn) as an indicator of gait complexity and root mean square (RMS) as an indicator of gait variability were computed. The findings demonstrate that in an outdoor environment, the surfaces significantly impacted only gait variability, not complexity, and that the tasks affected both gait variability and complexity in young healthy adults.
The Differentiation Status of Hair Cells That Regenerate Naturally in the Vestibular Inner Ear of the Adult Mouse

Aging, disease, and trauma can lead to loss of vestibular hair cells and permanent vestibular dysfunction. Previous work showed that, following acute destruction of ∼95% of vestibular hair cells in adult mice, ∼20% regenerate naturally (without exogenous factors) through supporting cell transdifferentiation. There is, however, no evidence for the recovery of vestibular function. To gain insight into the lack of functional recovery, we assessed functional differentiation in regenerated hair cells for up to 15 months, focusing on key stages in stimulus transduction and transmission: hair bundles, voltage-gated conductances, and synaptic contacts. Regenerated hair cells had many features of mature type II vestibular hair cells, including polarized mechanosensitive hair bundles with zone-appropriate stereocilia heights, large voltage-gated potassium currents, basolateral processes, and afferent and efferent synapses. Regeneration failed, however, to recapture the full range of properties of normal populations, and many regenerated hair cells had some properties of immature hair cells, including small transduction currents, voltage-gated sodium currents, and small or absent HCN (hyperpolarization-activated cyclic nucleotide-gated) currents. Furthermore, although mouse vestibular epithelia normally have slightly more type I hair cells than type II hair cells, regenerated hair cells acquired neither the low-voltage-activated potassium channels nor the afferent synaptic calyces that distinguish mature type I hair cells from type II hair cells and confer distinctive physiology. Thus, natural regeneration of vestibular hair cells in adult mice is limited in total cell number, cell type diversity, and extent of cellular differentiation, suggesting that manipulations are needed to promote full regeneration with the potential for recovery of vestibular function. SIGNIFICANCE STATEMENT Death of inner ear hair cells in adult mammals causes permanent loss of hearing and balance. In adult mice, the sudden death of most vestibular hair cells stimulates the production of new hair cells but does not restore balance. We investigated whether the lack of systems-level function reflects functional deficiencies in the regenerated hair cells. The regenerated population acquired mechanosensitivity, voltage-gated channels, and afferent synapses, but did not reproduce the full range of hair cell types. Notably, no regenerated cells acquired the distinctive properties of type I hair cells, a major functional class in amniote vestibular organs. To recover vestibular system function in adults, we may need to solve how to regenerate the normal variety of mature hair cells.
General Anesthesia Disrupts Complex Cortical Dynamics in Response to Intracranial Electrical Stimulation in Rats

Visual Abstract The capacity of human brain to sustain complex cortical dynamics appears to be strongly associated with conscious experience and consistently drops when consciousness fades. For example, several recent studies in humans found a remarkable reduction of the spatiotemporal complexity of cortical responses to local stimulation during dreamless sleep, general anesthesia, and coma. However, this perturbational complexity has never been directly estimated in non-human animals in vivo previously, and the mechanisms that prevent neocortical neurons to engage in complex interactions are still unclear. Here, we quantify the complexity of electroencephalographic (EEG) responses to intracranial electrical stimulation in rats, comparing wakefulness to propofol, sevoflurane, and ketamine anesthesia. The evoked activity changed from highly complex in wakefulness to far simpler with propofol and sevoflurane. The reduced complexity was associated with a suppression of high frequencies that preceded a reduced phase-locking, and disruption of functional connectivity and pattern diversity. We then showed how these parameters dissociate with ketamine and depend on intensity and site of stimulation. Our results support the idea that brief periods of activity-dependent neuronal silence can interrupt complex interactions in neocortical circuits, and open the way for further mechanistic investigations of the neuronal basis for consciousness and loss of consciousness across species.
Machine learning with convolutional neural networks for clinical cardiologists

© Author(s) (or their employer(s)) 2021. No commercial reuse. See rights and permissions. Published by BMJ. INTRODUCTION Machine learning (ML) is a revolution in computer science and is set to change the face of cardiology practice. In ML, humans no longer need to convert an understanding of a problem into a stepwise algorithmic solution; instead, the computer learns to solve a task for itself. While ML can seem intimidating, the underlying principles build on familiar and established techniques. The recent revolution that made ML so effective, however, was the recognition that numerous sequential layers of simple arithmetic, termed neural networks, become surprisingly effective at solving difficult problems. This ‘deep learning’ has been startlingly effective across a variety of problems, and a particular type, the convolutional neural network (CNN) has revolutionised image analysis. CNNs are inspired by the human visual cortex and have been used successfully in cardiology to process data that are onedimensional (1D) (ECGs, pressure waveforms), twodimensional (2D) (Xrays, MRIs) and threedimensional (3D) (echocardiography videos, cardiac magnetic resonance cine videos and CT volumes). We are now entering the stage where these CNNs’ performances are starting to equal that of cardiologists in some domains. 2 In this review, we will cover the basics of ML, before explaining the workings of neural networks, and particularly CNNs. ML will play an increasing role in medical practice and, as with any diagnostic test or piece of medical equipment, an understanding of these systems will better equip medical staff to interpret these systems’ results.
Structure, Distribution, Chemical Composition, and Gene Expression Pattern of Glandular Trichomes on the Leaves of Rhus potaninii Maxim

Rhus potaninii Maxim is an economically and medicinally important tree species in China. It produces galls (induced by aphids) with a high abundance of tannins. Here, we discuss the histology, cellular structures and their distribution, and the macromolecular components of secretive glandular trichomes on the leaves of R. potaninii. A variation in the density of glandular trichomes and tomenta was found between the adaxial and abaxial sides of a leaf in different regions and stages of the leaf. The glandular trichomes on R. potaninii trees comprise a stalk with no cellular structure and a head with 8–15 cells. Based on staining, we found that the secretion of glandular trichomes has many polysaccharides, phenolic compounds, and acidic lipids but very few neutral lipids. The dense glandular trichomes provide mechanical protection for young tissues; additionally, their secretion protects the young tissues from pathogens by a special chemical component. According to transcriptome analysis, we found enhanced biosynthetic and metabolism pathways of glycan, lipids, toxic amino acids, and phenylpropanoids. This shows a similar tendency to the staining. The numbers of differentially expressed genes were large or small; the averaged range of upregulated genes was greater than that of the downregulated genes in most subpathways. Some selectively expressed genes were found in glandular trichomes, responsible for the chitinase activity and pathogenesis-related proteins, which all have antibacterial activity and serve for plant defense. To our knowledge, this is the first study showing the components of the secretion from glandular trichomes on the leaf surface of R. potaninii.
A Thermal Skin Model for Comparing Contact Skin Temperature Sensors and Assessing Measurement Errors

To improve the measurement and subsequent use of human skin temperature (Tsk) data, there is a need for practical methods to compare Tsk sensors and to quantify and better understand measurement error. We sought to develop, evaluate, and utilize a skin model with skin-like thermal properties as a tool for benchtop Tsk sensor comparisons and assessments of local temperature disturbance and sensor bias over a range of surface temperatures. Inter-sensor comparisons performed on the model were compared to measurements performed in vivo, where 14 adult males completed an experimental session involving rest and cycling exercise. Three types of Tsk sensors (two of them commercially available and one custom made) were investigated. Skin-model-derived inter-sensor differences were similar (within ±0.4 °C) to the human trial when comparing the two commercial Tsk sensors, but not for the custom Tsk sensor. Using the skin model, all surface Tsk sensors caused a local temperature disturbance with the magnitude and direction dependent upon the sensor and attachment and linearly related to the surface-to-environment temperature gradient. Likewise, surface Tsk sensors also showed bias from both the underlying disturbed surface temperature and that same surface in its otherwise undisturbed state. This work supports the development and use of increasingly realistic benchtop skin models for practical Tsk sensor comparisons and for identifying potential measurement errors, both of which are important for future Tsk sensor design, characterization, correction, and end use.
Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.
Genotoxicity and oxidative stress induction by polystyrene nanoparticles in the colorectal cancer cell line HCT116

The potential risks of environmental nanoparticles (NPs), in particular Polystyrene Nanoparticles (PNPs), is an emerging problem; specifically, the interaction of PNPs with intestinal cells has not been characterized so far. The mechanism by which polystyrene particles are transferred to humans has not yet been clarified, whether directly through ingestion from contaminated food. We evaluated the interaction between PNPs and colorectal adenocarcinoma cells (HCT116). Cells were exposed to different concentrations of PNPs, metabolic activity and the consequent cytotoxic potential were assessed through viability test; we evaluated the PNP genotoxic potential through the Cytokinesis-Block Micronucleus cytome (CBMN cyt) assay. Finally, we detected Reactive Oxygen Species (ROS) production after NPs exposure and performed Western Blot analysis to analyze the enzymes (SOD1, SOD2, Catalase, Glutathione Peroxidase) involved in the cell detoxification process that comes into play during the cell-PNPs interaction. This work analyzes the cyto and genotoxicity of PNPs in the colorectal HCT116 cell line, in particular the potential damage from oxidative stress produced by PNPs inside the cells related to the consequent nuclear damage. Our results show moderate toxicity of PNPs both in terms of ROS production and DNA damage. Further studies will be needed on different cell lines to have a more complete picture of the impact of environmental pollution on human health in terms of PNPs cytotoxicity and genotoxicity.
Structures and implications of TBP–nucleosome complexes

Significance The TATA box-binding protein (TBP) is highly conserved throughout eukaryotes and plays a central role in the assembly of the transcription preinitiation complex (PIC) at the promoter of genes. We show here that TBP can interact with nucleosomes containing the Widom-601 sequence at two different positions. One position comprises a TATA box-like sequence near the end of nucleosomal DNA, and the other position is GC-rich and located near the nucleosome dyad. TBP–nucleosome complexes must be resolved before PIC assembly can occur but may persist during mitosis when chromatin is condensed, possibly explaining how TBP can act as a bookmarking factor that transfers transcriptional memory. The TATA box-binding protein (TBP) is highly conserved throughout eukaryotes and plays a central role in the assembly of the transcription preinitiation complex (PIC) at gene promoters. TBP binds and bends DNA, and directs adjacent binding of the transcription factors TFIIA and TFIIB for PIC assembly. Here, we show that yeast TBP can bind to a nucleosome containing the Widom-601 sequence and that TBP–nucleosome binding is stabilized by TFIIA. We determine three cryo-electron microscopy (cryo-EM) structures of TBP–nucleosome complexes, two of them containing also TFIIA. TBP can bind to superhelical location (SHL) –6, which contains a TATA-like sequence, but also to SHL +2, which is GC-rich. Whereas binding to SHL –6 can occur in the absence of TFIIA, binding to SHL +2 is only observed in the presence of TFIIA and goes along with detachment of upstream terminal DNA from the histone octamer. TBP–nucleosome complexes are sterically incompatible with PIC assembly, explaining why a promoter nucleosome generally impairs transcription and must be moved before initiation can occur.
Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor

Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R2 values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor.
Digital light processing of liquid crystal elastomers for self-sensing artificial muscles

Digital light processing 3D prints liquid crystal elastomers for artificial muscle-like actuation and optomechanical self-sensing. Artificial muscles based on stimuli-responsive polymers usually exhibit mechanical compliance, versatility, and high power-to-weight ratio, showing great promise to potentially replace conventional rigid motors for next-generation soft robots, wearable electronics, and biomedical devices. In particular, thermomechanical liquid crystal elastomers (LCEs) constitute artificial muscle-like actuators that can be remotely triggered for large stroke, fast response, and highly repeatable actuations. Here, we introduce a digital light processing (DLP)–based additive manufacturing approach that automatically shear aligns mesogenic oligomers, layer-by-layer, to achieve high orientational order in the photocrosslinked structures; this ordering yields high specific work capacity (63 J kg−1) and energy density (0.18 MJ m−3). We demonstrate actuators composed of these DLP printed LCEs’ applications in soft robotics, such as reversible grasping, untethered crawling, and weightlifting. Furthermore, we present an LCE self-sensing system that exploits thermally induced optical transition as an intrinsic option toward feedback control.