Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulation of Flowering Time by Improving Leaf Health Markers and Expansion by Salicylic Acid Treatment: A New Approach to Induce Flowering in Malus domestica

In the external coincidence model, internal and external molecular signals, provided by the circadian clock and sunlight, respectively, are required to induce flowering. Salicylic acid (SA) applications during floral induction have multiple effects. In the current study, Malus × domestica plants were exposed to SA during the flower-induction stage to analyze the effect on various health markers and flowering. A total of 56 equal-sized Fuji/M9 trees that were about 7 years old were randomly divided into two groups. The first group (SA-treated) was sprayed with 4 mM SA solution, while the second group was sprayed with distilled water which served as control (CK). The SA applications increased various leaf pigments. Abiotic stress markers were increased in CK during the flower-induction stage. In the SA-treated group, non-enzymatic antioxidants increased, whereas in the control group, enzymatic antioxidants increased during the flower-induction stage. Histo-morphometric properties of leaves were significantly improved in the SA-treated group. The relative expression of the mRNA levels of MdMED80, −81, −3, and −41 were significantly increased in SA-treated leaves, leading to an early and increased flowering phenotype. Thus, SA increased leaf expansion and health-related marker levels, which lead to early induction of flowering in M. domestica. Overall, our work established a role for leaf health assessments in the regulation of flowering in M. domestica.
The Bilingual Home Language Boost Through the Lens of the COVID-19 Pandemic

Usage-based accounts of language acquisition suggest that bilingual language proficiency is dynamic and susceptible to changes in language use. The COVID-19 pandemic led to unprecedented modifications in the language learning environment of developing bilinguals. Drawing on this unique opportunity, we analyzed existing data of two matched groups of Mandarin-English bilingual children (ages 4 to 8 years, n = 38), one tested before (pre-COVID group) and the other during (COVID group) the pandemic. The dataset comprises responses to a language environment questionnaire, and scores on a sentence comprehension task and a sentence recall task in the bilinguals’ two languages. Questionnaire data revealed a richer Mandarin language environment for children in the COVID group compared to peers in the pre-COVID group. On both comprehension and production tasks, the two groups performed comparably in English but the COVID group showed better performance in Mandarin than the pre-COVID group. Within the pre-COVID group, English was stronger than Mandarin in both comprehension and production. Within the COVID group, the two languages were balanced in comprehension and Mandarin was stronger than English in production. Moreover, language use variables were correlated with production performance in both languages. These patterns illustrate the intimate relationships between language use and bilingual language proficiency through the lens of COVID-19 induced language environment modification.
Development and Validation of an Italian Adaptation of the Psychosocial Aspects of Hereditary Cancer Questionnaire

Individuals that attend cancer genetic counseling may experience test-related psychosocial problems that deserve clinical attention. In order to provide a reliable and valid first-line screening tool for these issues, Eijzenga and coworkers developed the Psychosocial Aspects of Hereditary Cancer (PAHC) questionnaire. The aim of this work was to develop an Italian adaptation of the PAHC (I-PACH). This prospective multicenter observational study included three stages: (1) development of a provisional version of the I-PAHC; (2) pilot studies aimed at testing item readability and revising the questionnaire; and (3) a main study aimed at testing the reliability and validity of the final version of the I-PAHC with the administration of a battery comprising measures of depression, anxiety, worry, stress, and life problems to 271 counselees from four cancer genetic clinics. Adapting the original PAHC to the Italian context involved adding two further domains and expanding the emotions domain to include positive emotions. While most of the items were found to be easy to understand and score, some required revision to improve comprehensibility; others were considered irrelevant or redundant and therefore deleted. The final version showed adequate reliability and validity. The I-PAHC provides comprehensive content coverage of cancer genetic-specific psychosocial problems, is well accepted by counselees, and can be considered a sound assessment tool for psychosocial issues related to cancer genetic counseling and risk assessment in Italy.
Pathogen Challenge and Dietary Shift Alter Microbiota Composition and Activity in a Mucin-Associated in vitro Model of the Piglet Colon (MPigut-IVM) Simulating Weaning Transition

Enterotoxigenic Escherichia coli (ETEC) is the principal pathogen responsible for post-weaning diarrhea in newly weaned piglets. Expansion of ETEC at weaning is thought to be the consequence of various stress factors such as transient anorexia, dietary change or increase in intestinal inflammation and permeability, but the exact mechanisms remain to be elucidated. As the use of animal experiments raise more and more ethical concerns, we used a recently developed in vitro model of piglet colonic microbiome and mucobiome, the MPigut-IVM, to evaluate the effects of a simulated weaning transition and pathogen challenge at weaning. Our data suggested that the tested factors impacted the composition and functionality of the MPigut-IVM microbiota. The simulation of weaning transition led to an increase in relative abundance of the Prevotellaceae family which was further promoted by the presence of the ETEC strain. In contrast, several beneficial families such as Bacteroidiaceae or Ruminococcaceae and gut health related short chain fatty acids like butyrate or acetate were reduced upon simulated weaning. Moreover, the incubation of MPigut-IVM filtrated effluents with porcine intestinal cell cultures showed that ETEC challenge in the in vitro model led to an increased expression of pro-inflammatory genes by the porcine cells. This study provides insights about the etiology of a dysbiotic microbiota in post-weaning piglets.
Comparative Transcriptome and Endophytic Bacterial Community Analysis of Morchella conica SH

The precious rare edible fungus Morchella conica is popular worldwide for its rich nutrition, savory flavor, and varieties of bioactive components. Due to its high commercial, nutritional, and medicinal value, it has always been a hot spot. However, the molecular mechanism and endophytic bacterial communities in M. conica were poorly understood. In this study, we sequenced, assembled, and analyzed the genome of M. conica SH. Transcriptome analysis reveals significant differences between the mycelia and fruiting body. As shown in this study, 1,329 and 2,796 genes were specifically expressed in the mycelia and fruiting body, respectively. The Gene Ontology (GO) enrichment showed that RNA polymerase II transcription activity-related genes were enriched in the mycelium-specific gene cluster, and nucleotide binding-related genes were enriched in the fruiting body-specific gene cluster. Further analysis of differentially expressed genes in different development stages resulted in finding two groups with distinct expression patterns. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment displays that glycan degradation and ABC transporters were enriched in the group 1 with low expressed level in the mycelia, while taurine and hypotaurine metabolismand tyrosine metabolism-related genes were significantly enriched in the group 2 with high expressed level in mycelia. Moreover, a dynamic shift of bacterial communities in the developing fruiting body was detected by 16S rRNA sequencing, and co-expression analysis suggested that bacterial communities might play an important role in regulating gene expression. Taken together, our study provided a better understanding of the molecular biology of M. conica SH and direction for future research on artificial cultivation.
3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities

Accurate, automated quantitative segmentation of anatomical structures in radiological scans, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), can produce significant biomarkers and can be integrated into computer-aided diagnosis (CADx) systems to support the interpretation of medical images from multi-protocol scanners. However, there are serious challenges towards developing robust automated segmentation techniques, including high variations in anatomical structure and size, varying image spatial resolutions resulting from different scanner protocols, and the presence of blurring artefacts. This paper presents a novel computing approach for automated organ and muscle segmentation in medical images from multiple modalities by harnessing the advantages of deep learning techniques in a two-part process. (1) a 3D encoder-decoder, Rb-UNet, builds a localisation model and a 3D Tiramisu network generates a boundary-preserving segmentation model for each target structure; (2) the fully trained Rb-UNet predicts a 3D bounding box encapsulating the target structure of interest, after which the fully trained Tiramisu model performs segmentation to reveal organ or muscle boundaries for every protrusion and indentation. The proposed approach is evaluated on six different datasets, including MRI, Dynamic Contrast Enhanced (DCE) MRI and CT scans targeting the pancreas, liver, kidneys and iliopsoas muscles. We achieve quantitative measures of mean Dice similarity coefficient (DSC) that surpasses or are comparable with the state-of-the-art and demonstrate statistical stability. A qualitative evaluation performed by two independent experts in radiology and radiography verified the preservation of detailed organ and muscle boundaries.
TSM-Based Adaptive Fuzzy Control of Robotic Manipulators with Output Constraints

This paper proposes an adaptive control scheme based on terminal sliding mode (TSM) for robotic manipulators with output constraints and unknown disturbances. The fuzzy logic system (FLS) is developed to approximate unknown dynamics of robotic manipulators. An error transformation technique is used in the process of controller design to ensure that the output constraints are not violated. The advantage of the error transformation compared to traditional barrier Lyapunov functions (BLFs) is that there is no need to design a virtual controller. Thus, the design complexity of the controller is reduced. Through Lyapunov stability analysis, the system state can be proved to converge to the neighborhood near the balanced point in finite time. Extensive simulation results illustrated the validity of the proposed controller.
3D M-Net: Object-Specific 3D Segmentation Network Based on a Single Projection

The internal assembly correctness of industrial products directly affects their performance and service life. Industrial products are usually protected by opaque housing, so most internal detection methods are based on X-rays. Since the dense structural features of industrial products, it is challenging to detect the occluded parts only from projections. Limited by the data acquisition and reconstruction speeds, CT-based detection methods do not achieve real-time detection. To solve the above problems, we design an end-to-end single-projection 3D segmentation network. For a specific product, the network adopts a single projection as input to segment product components and output 3D segmentation results. In this study, the feasibility of the network was verified against data containing several typical assembly errors. The qualitative and quantitative results reveal that the segmentation results can meet industrial assembly real-time detection requirements and exhibit high robustness to noise and component occlusion.
From Nanometer Aggregates to Micrometer Crystals: Insight into the Coarsening Mechanism of Calcite

Grain size increases when crystals respond to dynamic equilibrium in a saturated solution. The pathway to coarsening is generally thought to be driven by Ostwald ripening, that is, simultaneous dissolution and reprecipitation, but models to describe Ostwald ripening neglect solid–solid interactions and crystal shapes. Grain coarsening of calcite, CaCO3, is relevant for biomineralization and commercial products and is an important process in diagenesis of sediments to rock during geological time. We investigated coarsening of pure, synthetic calcite powder of sub-micrometer diameter crystals and aged it in saturated solutions at 23, 100, and 200 °C for up to 261 days. Scanning electron microscopy (SEM) and Brunauer–Emmett–Teller (BET) surface area analysis showed rapid coarsening at 100 and 200 °C. Evidence of particle growth at 23 °C was not visible by SEM, but high resolution X-ray diffraction (XRD) data demonstrated steady growth of nanometer crystallites. The results can be described by theory where gr.
Characterization of Monodispersed γ-Al2O3 Particles, Synthesized by Homogeneous Precipitation under Reflux Boiling

Abstract In this study, micron size uniform particles of aluminum compounds were synthesized by the homogeneous precipitation method under reflux boiling, using aluminum nitrate, urea and potassium sulfate as starting materials. In this method, varying amounts of the reactants were heated in a Pyrex glass round bottom flask at the boiling temperature for various periods of time, which led to the production of precipitated particles in the reactant mixtures. Scanning electron microscopic (SEM) examination of the precipitated particles revealed that uniformity in particle morphology of the precipitated particles was dependent on the applied experimental conditions. As such, the latter were extensively optimized for obtaining particles of uniform morphology and size. Some of the batches of the precipitated particles were calcined at the elevated temperature (800 and 1000 °C) for transformation into crystalline solids. Similarly, the as prepared and calcined particles were characterized by SEM, XRD, TGA/DTA and FTIR. XRD results revealed that at 1000 °C of calcinations, the as prepared amorphous alumina transformed in to crystalline state, while below this temperature, i.e. at 800 °C calcination, the powder was amorphous in nature. The point of zero charge (PZC) of the desired sample was estimated by the salt addition method.
Construction of mRNA Regulatory Networks Reveals the Key Genes in Atrial Fibrillation

Atrial fibrillation (AF), the most familiar heart rhythm disorder, is a major cause of stroke in the world, whereas the mechanism behind AF remains largely unclear. In the current study, we used the RNA-seq method to identify 275 positively regulated mRNAs and 117 negatively regulated mRNAs in AF compared to healthy controls. Through bioinformatic analysis, it indicated that these distinctively expressed genes took part in regulating multiple AF-related biological processes and pathways, such as platelet aggregation, platelet activation, pri-miRNA transcription, and transforming growth factor-beta (TGF-β) receptor signaling pathway. Protein-protein interaction (PPI) network analysis identified ITGB5, SRC, ACTG1, ILK, ITGA2B, ITGB3, TUBB4B, CDK11A, PAFAH1B1, CDK11B, and TUBG1 as hub regulators in AF. Moreover, the quantitative real-time PCR (qRT-PCR) assay was conducted and revealed that these hub genes were remarkably overexpressed in AF samples compared to normal samples. We believed that this study would enrich the understanding of the pathogenesis of AF and enable further research on the pathogenesis of AF.
A self-learning teacher-student framework for gastrointestinal image classification

We present a semi-supervised teacher-student framework to improve classification performance on gastrointestinal image data. As labeled data is scarce in medical settings, this framework is built specifically to take advantage of vast amounts of unlabeled data. It consists of three main steps: (1) train a teacher model with labeled data, (2) use the teacher model to infer pseudo labels with unlabeled data, and (3) train a new and larger student model with a combination of labeled images and inferred pseudo labels. These three steps are repeated several times by treating the student as a teacher to relabel the unlabeled data and consequently train a new student. We demonstrate that our framework can classify both video capsule endoscopy (VCE) and standard endoscopy images. Our results indicate that our teacher-student framework can significantly increase the performance compared to traditional supervised-learning-based models, i.e., an overall increase in the $F_{1}$-score of 4.7% for the Kvasir-Capsule VCE dataset and 3.2% for the HyperKvasir colonoscopy dataset. We believe that our framework can use more of the data collected at hospitals without the need for expert labels, contributing to overall better models for medical multimedia systems for automatic disease detection.
Voice-Leading Schema Recognition Using Rhythm and Pitch Features

Musical schemata constitute important structural building blocks used across historical styles and periods. They consist of two or more melodic lines that are combined to form speciﬁc successions of intervals. This paper tackles the problem of recognizing voice-leading schemata in polyphonic music. Since schema types and subtypes can be realized in a wide variety of ways on the musical surface, ﬁnding schemata in an automated fashion is a challenging task. To perform schema inference we employ a skipgram model that computes schema candidates, which are then classiﬁed using a binary classiﬁer on musical features related to pitch and rhythm. This model is evaluated on a novel dataset of schema annotations in Mozart’s pi-ano sonatas produced by expert annotators, which is published alongside this paper. The features are chosen to encode music-theoretically predicted properties of schema instances. We assess the relevance of each feature for the classiﬁcation task, thus contributing to the theoretical understanding of complex musical objects.
Thermal Compensation Method for Piezoresistive Pressure Transducer

Piezoresistive pressure sensors are employed in a wide range of applications, but their output signal shows a substantial dependence on the working temperature. Usually, there is a need for an additional component to ensure a proper temperature-compensated signal for the sensors. In this article, we describe a method for the sensor thermal compensation that uses only the electrical signal produced by the pressure sensors themselves, without the need of additional measurements. The method proved to be effective while also ensuring a significant reduction in the measurement error.
EffUnet-SpaGen: An Efficient and Spatial Generative Approach to Glaucoma Detection

Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings.
On the identification of chronodisruption-based biomarkers to estimate pregnancy attempt time

It is well known that chronodisruption alters circadian rhythms, with negative consequences on different pathologies and mental disorders. The objective of this work is to study the effect of factors related to chronodisruption, i.e. chronic disruption of circadian rhythms leading to disease, in pregnancy attempt time. The methodology followed to reach this objective is based on using the well known random forest as feature selector together with a consensus ranking measure. The data are composed by a cohort of 211 women health professionals. In addition to some variables related to physiological issues as Body Mass Index (BMI), age and menarche, variables related to sleep quality are considered. These variables are those related to Pittsburg questionnaire. Preliminary experiments highlight some interesting and non obvious relations between some factors related to sleep hygiene.
Towards Clustering Human Behavioral Patterns based on Digital Phenotyping

Mental health professionals use clinical evidence and information self-reported by patients for diagnosing mental disorders. However, self-reports and questionnaires are approaches affected by cognitive biases due to imprecision in the report. Machine learning has been used along with sensor data embedded in mobile devices (e.g., smartphones) to identify patterns correlated to mental disorders. This study proposes a solution that uses a clustering algorithm to analyze group behaviors over time. We believe that our solution can help mental health professionals to continuously monitor patients, identify similar behaviors, and changes in behaviors. We present an analysis of different clustering algorithms and show group dynamics. We conclude that the Birch algorithm has the best performance for grouping behaviors in our experiments.
Combining musical features for cover detection

Recent works have addressed the automatic cover detection problem from a metric learning perspective. They employ different input representations, aiming to exploit melodic or harmonic characteristics of songs and yield promising performances. In this work, we propose a comparative study of these different representations and show that systems combining melodic and harmonic features drastically outperform those relying on a single input representation. We illustrate how these features complement each other with both quantitative and qualitative analyses. We finally investigate various fusion schemes and propose methods yielding state-of-the-art performances on two publicly-available large datasets.