Miracle-dz/UniFAR-FTUs huggingface dataset:
Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as support vector machines, k-nearest neighbours and various variations of neural networks for the detection of diabetes in patient samples and simulated artificial breath samples.
Relation of circulating lncRNA GAS5 and miR‐21 with biochemical indexes, stenosis severity, and inflammatory cytokines in coronary heart disease patients

Long noncoding RNA GAS5 (lnc‐GAS5) and its target microRNA‐21 (miR‐21) regulate blood lipid, macrophages, Th cells, vascular smooth muscle cells to participate in atherosclerosis, and related coronary heart disease (CHD). The study aimed to further explore the linkage of their circulating expressions with common biochemical indexes, stenosis severity and inflammatory cytokines in CHD patients.
Complete Sets of Material Constants of [001]-Poled 0.72Pb(Mg1/3Nb2/3)O3- 0.28PbTiO3 Single Crystals Using Alternating Current Poling

Alternating current poling (ACP) is an effective method to improve the piezoelectric performance of relaxor-PbTiO3 (PT) ferroelectric single crystal. 0.72Pb(Mg1/3Nb2/3)O3-0.28PbTiO3 (PMN-PT) single crystals have been used to fabricate piezoelectric transducers for medical imaging. Up to date, there are no reports about the full matrix material constants of PMN-0.28PT single crystals poled by ACP. Here, we report the complete sets of elastic, dielectric, and piezoelectric properties of [001]-poled PMN-0.28PT single crystals by direct current poling (DCP) and ACP through the resonance method. The results show that [001]-poled rhombohedral PMN-0.28PT single crystals exhibit the enhancement of longitudinal and transverse piezoelectric properties (<inline-formula> <tex-math notation="LaTeX">${d}_{33} \sim 2000$ </tex-math></inline-formula> pC/N, <inline-formula> <tex-math notation="LaTeX">${d}_{31} \sim -1010$ </tex-math></inline-formula> pC/N) after ACP. Compared with DCP samples (<inline-formula> <tex-math notation="LaTeX">${d}_{33} \sim 1660$ </tex-math></inline-formula> pC/N, <inline-formula> <tex-math notation="LaTeX">${d}_{31} \sim -780$ </tex-math></inline-formula> pC/N), the values of <inline-formula> <tex-math notation="LaTeX">${d}_{33}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${d}_{31}$ </tex-math></inline-formula> increase 20% and 29%, respectively. While the <inline-formula> <tex-math notation="LaTeX">${d}_{15}$ </tex-math></inline-formula> value decrease from 110 pC/N for DCP sample to 90 pC/N for ACP sample, showing the decrease in transverse shear piezoelectric properties. In addition, the elastic stiffness coefficients <inline-formula> <tex-math notation="LaTeX">${c}_{11}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${c}_{12}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${c}_{13}$ </tex-math></inline-formula>, the elastic compliance coefficients <inline-formula> <tex-math notation="LaTeX">${s}_{11}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${s}_{12}$ </tex-math></inline-formula>, and the dielectric constants <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{11}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\varepsilon _{33}$ </tex-math></inline-formula> have great change compared with DCP and ACP samples. This variation of the property matrices provides a reference for high-performance piezoelectric device design.
The influence of distance learning during COVID-19 pandemic on student's self-regulated learning in higher education: A qualitative study

While the topic on self-regulated learning (SRL) in the online environment has been investigated considerably, there are few researches on SRL in large-scale distance learning during coronavirus disease 2019 (COVD-19). This research investigated six strategies involved in Zimmerman's SRL framework: goal setting and self-efficacy in forethought phase, task strategies and metacognitive monitoring in performance phase, and self-evaluation and self-satisfaction in self-reflection phase to study the influence of large-scale distance learning on students’ self-regulated learning during COVID-19 pandemic. By in-depth interviews with five Chinese graduates studying in different universities in Japan, it was found that some students’ SRL were negatively influenced in the distance learning during COVID-19 pandemic while some with reported higher level of SRL were less affected. This paper discussed the implication of the findings and concluded with suggestions to promote learners' self-regulated learning in distance learning.
Mandatory coupling of zygotic transcription to DNA replication in early Drosophila embryos

Collisions between transcribing RNA polymerases and DNA replication forks are disruptive. The threat of collisions is particularly acute during the rapid early embryonic cell cycles of Drosophila when S phase occupies the entirety of interphase. We hypothesized that collision-avoidance mechanisms safeguard the onset of zygotic transcription in these cycles. To explore this hypothesis, we used real-time imaging of transcriptional events at the onset of each interphase. Endogenously tagged RNA polymerase II (RNAPII) abruptly formed clusters before nascent transcripts accumulated, indicating recruitment prior to transcriptional engagement. Injection of inhibitors of DNA replication prevented RNAPII clustering, blocked formation of foci of the pioneer factor Zelda, and largely prevented expression of transcription reporters. Knockdown of Zelda or the histone acetyltransferase CBP prevented RNAPII cluster formation except at the replication-dependent (RD) histone gene locus. We suggest a model in which the passage of replication forks allows Zelda and a distinct pathway at the RD histone locus to reconfigure chromatin to nucleate RNAPII clustering and promote transcriptional initiation. The replication dependency of these events defers initiation of transcription and ensures that RNA polymerases transcribe behind advancing replication forks. The resulting coordination of transcription and replication explains how early embryos circumvent collisions and promote genome stability.
Electrophysiological properties of intrinsic cardiac neurons: the foundation of neurocardiology

Neurocardiology is becoming increasingly relevant to the management of heart disease. This book presents novel concepts about how neurons from the level of the heart to the sensorium exert dynamic control over cardiac electrical and mechanical events over a lifetime. The book's major strength is in the tying together of basic scientific and clinical information on how the various components of neural control of the heart remodel during the evolution of cardiac pathologies - information essential for future development of integrated and targeted.
Resonant waves drive long-range correlations in fMRI signals

The fundamental principles driving spontaneous long-range correlations between distant brain areas - known as intrinsic functional connectivity - remain unclear. To investigate this, we develop an ultrafast functional Magnetic Resonance Imaging (fMRI) approach with unprecedented temporal resolution (38 milliseconds) in the rat brain. We detect a repertoire of principal components exhibiting standing wave properties, i.e., with phase relationships varying gradually across space and oscillating in time, driving in- and anti-phase synchronization across distinct cortical and subcortical structures. The spatial configuration, stability and peak frequency of these standing waves is found to depend on the sedation/anaesthesia state, with medetomidine sedation revealing the most stable (i.e., less damped) standing waves, resonating at frequencies extending up to 0.25 Hz. Our findings show that the complex activity patterns observed in resting-state fMRI signals result from the superposition of standing waves, supporting the hypothesis that intrinsic functional connectivity is inherently associated to resonance phenomena.
GFAP: ultra-fast and accurate gene functional annotation software for plants

Sequence alignment is the basis of gene functional annotation for unknow sequences. Selecting closely related species as the reference species should be an effective way to improve the accuracy of gene annotation for plants, compared with only based on one or some model plants. Therefore, limited species number in previous software or website is disadvantageous for plant gene annotation. Here, we collected the protein sequences of 236 plant species with known genomic information from 63 families. After that, these sequences were annotated by pfam, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to construct our databases. Furthermore, we developed the software, Gene Annotation Software for Plants (GFAP), to perform gene annotation using our databases. GFAP, an open-source software running on Windows and MacOS systems, is an efficient and network independent tool. GFAP can search the protein domain, GO and KEGG information for 43000 genes within 4 minutes. In addition, GFAP can also perform the sequence alignment, statistical analysis and drawing. The website of https://gitee.com/simon198912167815/gfap-database provides the software, databases, testing data and video tutorials for users. GFAP contained large amount of plant-species information. We believe that it will become a powerful tool in gene annotation using closely related species for phytologists.
Three amphioxus reference genomes reveal gene and chromosome evolution of chordates

The slow-evolving invertebrate amphioxus has an irreplaceable role in advancing our understanding into the vertebrate origin and innovations. Here we resolve the nearly complete chromosomal genomes of three amphioxus species, one of which best recapitulates the 17 chordate ancestor linkage groups. We reconstruct the fusions, retention or rearrangements between descendants of whole genome duplications (WGDs), which gave rise to the extant microchromosomes likely existed in the vertebrate ancestor. Similar to vertebrates, the amphioxus genome gradually establishes its 3D chromatin architecture at the onset of zygotic activation, and forms two topologically associated domains at the Hox gene cluster. We find that all three amphioxus species have ZW sex chromosomes with little sequence differentiation, and their putative sex-determining regions are nonhomologous to each other. Our results illuminate the unappreciated interspecific diversity and developmental dynamics of amphioxus genomes, and provide high-quality references for understanding the mechanisms of chordate functional genome evolution.
An emergent temporal basis set robustly supports cerebellar time-series learning

Learning plays a key role in the function of many neural circuits. The cerebellum is considered a ‘learning machine’ essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum’s input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning outcomes has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from naturalistic inputs, in contrast to traditional classification tasks. The model robustly produced temporal basis sets from naturalistic inputs, and the resultant GCL output supported learning of temporally complex target functions. Learning favored surprisingly dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. Moreover, different cerebellar tasks were supported by diverse pattern separation features that matched the demands of the tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that population statistics of granule cell activity may differ across cerebellar regions to support distinct behaviors.
A CNN model with feature integration for MI EEG subject classification in BMI

Objective Electroencephalogram (EEG) based motor imagery (MI) classification is an important aspect in brain-machine interfaces (BMIs) which bridges between neural system and computer devices decoding brain signals into recognizable machine commands. However, the MI classification task is challenging due to inherent complex properties, inter-subject variability, and low signal-to-noise ratio (SNR) of EEG signals. To overcome the above-mentioned issues, the current work proposes an efficient multi-scale convolutional neural network (MS-CNN). Approach In the framework, discriminant user-specific features have been extracted and integrated to improve the accuracy and performance of the CNN classifier. Additionally, different data augmentation methods have been implemented to further improve the accuracy and robustness of the model. Main results The model achieves an average classification accuracy of 93.74% and Cohen’s kappa-coefficient of 0.92 on the BCI competition IV2b dataset outperforming several baseline and current state-of-the-art EEG-based MI classification models. Significance The proposed algorithm effectively addresses the shortcoming of existing CNN-based EEG-MI classification models and significantly improves the classification accuracy.
Assessing Bayesian Phylogenetic Information Content of Morphological Data Using Knowledge From Anatomy Ontologies

Morphology remains a primary source of phylogenetic information for many groups of organisms, and the only one for most fossil taxa. Organismal anatomy is not a collection of randomly assembled and independent ‘parts’, but instead a set of dependent and hierarchically nested entities resulting from ontogeny and phylogeny. How do we make sense of these dependent and at times redundant characters? One promising approach is using ontologies—structured controlled vocabularies that summarize knowledge about different properties of anatomical entities, including developmental and structural dependencies. Here we assess whether the proximity of ontology-annotated characters within an ontology predicts evolutionary patterns. To do so, we measure phylogenetic information across characters and evaluate if it is hierarchically structured by ontological knowledge—in much the same way as phylogeny structures across-species diversity. We implement an approach to evaluate the Bayesian phylogenetic information (BPI) content and phylogenetic dissonance among ontology-annotated anatomical data subsets. We applied this to datasets representing two disparate animal groups: bees (Hexapoda: Hymenoptera: Apoidea, 209 chars) and characiform fishes (Actinopterygii: Ostariophysi: Characiformes, 463 chars). For bees, we find that BPI is not substantially structured by anatomy since dissonance is often high among morphologically related anatomical entities. For fishes, we find substantial information for two clusters of anatomical entities instantiating concepts from the jaws and branchial arch bones, but among-subset information decreases and dissonance increases substantially moving to higher level subsets in the ontology. We further applied our approach to address particular evolutionary hypotheses with an example of morphological evolution in miniature fishes. While we show that ontology does indeed structure phylogenetic information, additional relationships and processes, such as convergence, likely play a substantial role in explaining BPI and dissonance, and merit future investigation. Our work demonstrates how complex morphological datasets can be interrogated with ontologies by allowing one to access how information is spread hierarchically across anatomical concepts, how congruent this information is, and what sorts of processes may structure it: phylogeny, development, or convergence.
Synaptic plasticity of inhibitory synapses onto medial olivocochlear efferent neurons

The descending auditory system modulates the ascending system at every level. The final descending, or efferent stage, is comprised of lateral olivocochlear (LOC) and medial olivocochlear (MOC) neurons. MOC somata in the ventral brainstem project axons to the cochlea to synapse onto outer hair cells (OHC), inhibiting OHC-mediated cochlear amplification. MOC suppression of OHC function is implicated in cochlear gain control with changing sound intensity, detection of salient stimuli, attention, and protection against acoustic trauma. Thus, sound excites MOC neurons to provide negative feedback of the cochlea. Sound also inhibits MOC neurons via medial nucleus of the trapezoid body (MNTB) neurons. However, MNTB-MOC synapses exhibit short-term depression, suggesting reduced MNTB-MOC inhibition during sustained stimuli. Further, due to high rates of both baseline and sound-evoked activity in MNTB neurons in vivo, MNTB-MOC synapses may be tonically depressed. To probe this, we characterized short-term plasticity of MNTB-MOC synapses in mouse brain slices. We mimicked in vivo-like temperature and extracellular calcium conditions, and in vivo-like activity patterns of fast synaptic activation rates, sustained activation, and prior tonic activity. Synaptic depression was sensitive to extracellular calcium concentration and temperature. During rapid MNTB axon stimulation, post-synaptic currents (PSCs) in MOC neurons summated but with concurrent depression, resulting in smaller, sustained currents, suggesting tonic inhibition of MOC neurons during rapid circuit activity. Low levels of baseline MNTB activity did not significantly reduce responses to subsequent rapid activity that mimics sound stimulation, indicating that, in vivo, MNTB inhibition of MOC neurons persists despite tonic synaptic depression. Key points summary Inhibitory synapses from MNTB onto MOC neurons exhibit short-term plasticity that is sensitive to calcium and temperature, with enhanced synaptic depression occurring at higher calcium concentrations and at room temperature High rates of background synaptic activity that mimic the upper limits of spontaneous MNTB activity cause tonic synaptic depression of MNTB-MOC synapses that limits further synaptic inhibition High rates of activity at MNTB-MOC synapses cause synaptic summation concurrent depression to yield a response with an initial large amplitude that decays to a tonic inhibition
A novel method for multi-scale recording of intracranial EEG through dynamic alteration of electrode surface area

Intracranial EEG (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson’s disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulation studies and experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain. We first present a theoretical model and an in vitro validation of the method. We then report the results of an in vivo implementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e., epileptic spikes. We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation increased with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in medium or large electrodes. This likely depends on the precise location and spatial spread of each spike. Overall, this new method enables multi-scale measurements of electrical activity in the human brain that facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.
Lessons from Frankenstein on Technology and Society

The lesson of Frankenstein is about the need to socialize our creations and, more generally, to care for our society. Service technicians work to make technology function in society; however, their work is poorly understood and little acknowledged. The circumstances of their job, their relationships with both customers and their employers, and even the tools chosen for them by their employers all indicate that the lesson of Frankenstein has not been learned. An effort to create a suitable (and well-socialized) tool for the technicians is described to show how hard it is to create technology with appropriate care and understanding.
Designing Human-AI Collaborative Systems for Historical Photo Identiﬁcation

Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. My dissertation addresses this challenge by leveraging the combined strengths of crowdsourcing and AI. Speciﬁcally, I study 1) how crowdsourced human expertise and facial recognition can be combined to support historical photo identiﬁcation, 2) how novice crowds and face recognition can assist experts in picking the correct match from a set of similar-looking candidates, 3) how to design a quality assessment framework for verifying historical photo IDs, and 4) how the underlying AI model affects user behavior in the context of historical photo identiﬁcation