Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of oncolytic virotherapy on immune microenvironment in immune subtypes identified in gastric cancer and hepatocellular carcinoma

Tumor occurrence and progression are significantly influenced by immunity, and the immune infiltration and immune-related gene expression in solid tumors are closely correlated to the response of patients to immunotherapy. In this study, the level of tumor infiltrating immune cells in gastric cancer and hepatocellular carcinoma samples from the TCGA database were assessed using ssGSEA, and the tumor samples were divided into two subtypes (Imm_H and Imm_L) with different immune cell infiltration level. The differences in immune cell percentage and immune checkpoint gene expression between the two subtypes indicated that the Imm_H group had higher levels of immune infiltration, but also more infiltrated immunosuppressive cells and higher mRNA levels of immune checkpoint genes. Then the immune subtype-specific gene network was built and the main modules representing the genes and functions that differ between the two immune subtypes were identified. To explore the effect of oncolytic virus on tumor immune microenvironment, we constructed the previously developed synthetic adenovirus containing the synthetic sensory switch gene circuit, assessed the antitumor effect in mouse models, and measured the proportion of different cell types by single-cell RNA sequencing. The results showed that synthetic oncolytic virus inhibited tumor development and altered the proportion of infiltrating immune cells, suggesting that synthetic oncolytic virus may have different mechanism on the two immune subtypes. Graphical abstract
Self-supervised vision transformers accurately decode cellular state heterogeneity

Characterising cellular phenotypic heterogeneity is essential to understand the relationship between the molecular and morphological determinants of cellular state. Here we report that publicly available self-supervised vision transformers (ss-ViTs) accurately elucidate phenotypic stem cell heterogeneity out-of-the-box. Moreover, we introduce scDINO, an adapted ss-ViT trained on five-channel automated microscopy data, attaining excellent performance in delineating peripheral blood immune cell identity. Thus, ss-ViTs represent a leap forward in the unsupervised analysis of phenotypic heterogeneity.
Hippocampal area CA2 controls seizure dynamics, interictal EEG abnormalities and social comorbidity in mouse models of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is characterized by spontaneous recurrent seizures, abnormal activity between seizures, and impaired behavior. CA2 pyramidal neurons (PNs) are potentially important because inhibiting them with a chemogenetic approach reduces seizure frequency in a mouse model of TLE. However, whether seizures could be stopped by timing inhibition just as a seizure begins is unclear. Furthermore, whether inhibition would reduce the cortical and motor manifestations of seizures are not clear. Finally, whether interictal EEG abnormalities and TLE comorbidities would be improved are unknown. Therefore, real-time optogenetic silencing of CA2 PNs during seizures, interictal activity and behavior were studied in 2 mouse models of TLE. CA2 silencing significantly reduced seizure duration and time spent in convulsive behavior. Interictal spikes and high frequency oscillations were significantly reduced, and social behavior was improved. Therefore, brief focal silencing of CA2 PNs reduces seizures, their propagation, and convulsive manifestations, improves interictal EEG, and ameliorates social comorbidities. HIGHLIGHTS Real-time CA2 silencing at the onset of seizures reduces seizure duration When CA2 silencing reduces seizure activity in hippocampus it also reduces cortical seizure activity and convulsive manifestations of seizures Interictal spikes and high frequency oscillations are reduced by real-time CA2 silencing Real-time CA2 silencing of high frequency oscillations (>250Hz) rescues social memory deficits of chronic epileptic mice
Plant selection and ecological microhabitat drive domestications of shrub-associated microbiomes in a revegetated shrub ecosystem

Shrubs are used for revegetation of degraded dryland ecosystem worldwide and could recruit large numbers of microbes from the soil; however, the plant-associated microbiome assembly and the effect of plant introduction on the soil microbiomes are not fully understood. We detected shrub-associated microbes from five ecological microhabitats, including the leaves, litter, roots, rhizosphere, and root zone, across four xeric shrub plantations (Artemisia ordosica, Caragana korshinskii, Hedysarum mongolicum, and Salix psammophila). To detect the patterns of shrub-associated microbiome assembly, 16S and ITS2 rRNA gene sequencing was performed. PERMANOVA and differential abundance analysis demonstrated that changes in the bacterial and fungal communities were more dependent on the microhabitats rather than on the plant species, with distinct niche differentiation. Moreover, source tracking and nestedness analysis showed that shrub-associated bacteria were primarily derived from bulk soils and slightly pruned in different microhabitats; however, a similar pattern was not found for fungi. Furthermore, the surrounding zone of roots was a hotpot for microbial recruitments of revegetated shrubs. Null model analysis indicated that homogeneous selection of determinism dominated the bacterial communities, whereas dispersal limitation and undominated process of stochasticity drove the assembly of fungal communities. Our findings indicate that ecological microhabitat of revegetated shrublands was the main predictor of the bacterial and fungal compositional variances. This study will help advance our understanding of the mechanism underlying the plant-soil microbiome feedbacks during the initial plant-establishment period in a dryland ecosystem. Further, this work provides theoretical reference for establishment and sustainable management of shrublands in drylands.
Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarised and incorporated into patient outcome prediction models in several ways, however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integration approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using each single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalisation when using multiple datasets as the model input. Summary key points This work assesses and compares the performance of three categories of workflow consisting of 350 analytical combinations for outcome prediction using multi-sample, multi-conditions single-cell studies. We observed that using ensemble of feature types performs better than using individual feature type We found that in the current data, all learning approaches including deep learning exhibit similar predictive performance. When combining multiple datasets as the input, our study found that integrating multiple datasets at the cell level performs similarly to simply concatenating the patient representation without modification.
Morphological profiling by Cell Painting in human neural progenitor cells classifies hit compounds in a pilot drug screen for Alzheimer’s disease

Alzheimer’s disease (AD) accounts for 60-70% of dementia cases. Current treatments are inadequate and there is a need to develop new approaches to AD drug discovery. We chose to develop a cell phenotype-based drug screen centred on the AD-risk gene, SORL1, which encodes the protein SORLA. Increased AD risk has been repeatedly linked to variants in SORL1, particularly those that confer loss of, or decreased, SORLA. This is consistent with the lower SORL1 levels observed in post-mortem brain samples from individuals with AD. Consistent with its role in the endolysosomal pathway, deletion of SORL1 is associated with enlarged endosomes in neural progenitor cells (NPCs) and neurons. We, therefore, hypothesised that multiparametric, image-based phenotyping would identify features characteristic of SORL1 deletion. An automated morphological profiling assay (known as Cell Painting) was adapted to wild-type and SORL1-/- NPCs. This methodology was used to determine the phenotypic response of SORL1-/- NPCs to treatment with compounds from a small FDA/internationally-approved drug library (TargetMol, 330 compounds). We detected distinct phenotypic signatures for SORL1-/- NPCs compared to isogenic wild-type controls. Furthermore, we identified 16 approved drugs that reversed the mutant morphological signatures in NPCs derived from 3 SORL1-/- subclonal iPSC lines. Network pharmacology analysis revealed the 16 compounds belonged to five mechanistic groups: 20S proteasome, aldehyde dehydrogenase, topoisomerase I and II, and DNA synthesis inhibitors. Enrichment analysis confirmed targeting to gene sets associated with these annotated targets, and to pathways/biological processes associated with DNA synthesis/damage/repair, Proteases/proteasome and metabolism. _Prediction of novel targets for some compounds revealed enrichment in pathways associated with neural cell function and AD. The findings suggest that image-based phenotyping by morphological profiling distinguishes SORL1-/- NPCs from isogenic wild-type lines, and predicts treatment responses that rescue SORL1-/--associated cellular signatures that are relevant to both SORLA function and AD. Overall, this work suggests that i) a quantitative phenotypic metric can distinguish iPSC-derived SORL1-/- NPCs from isogenic wild-type control and ii) phenotypic screening combined with multiparametric high-content image analysis is a viable option for drug repurposing and discovery in this human neural cell model of Alzheimer’s disease.
PLOD2, a key factor for MRL MSC metabolism and chondroprotective properties

Background Initially discovered for its ability to regenerate ear holes, the MRL mouse has been the subject of multiple research studies aimed at evaluating its ability to regenerate other body tissues and at deciphering the mechanisms underlying it. These enhanced abilities to regenerate, retained in the adult, protect the MRL mouse from degenerative diseases such as osteoarthritis (OA). Here, we hypothesized that MSC derived from the regenerative MRL mouse could be involved in their regenerative potential through the release of pro-regenerative mediators. Method To address this hypothesis, we compared the secretome of MRL and BL6 MSC and identified several candidate molecules produced at significantly higher levels by MRL MSC than by BL6 MSC. We selected one candidate and performed functional in vitro assays to evaluate its role on MRL MSC properties including metabolic profile, migration, and chondroprotective effects. Using an experimental model for osteoarthritis (OA) induced by collagenase (CiOA), we assessed its contribution to MRL MSC protection from OA. Results Among the candidate molecules highly expressed by MRL MSC, we focused our attention on procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 (PLOD2), coding for the lysyl hydrolase LH2 in charge of post-translational modifications of collagen for its stability and stiffness. PLOD2 is induced by hypoxia-inducible factor 1-alpha (HIF-1a) involved in the regeneration process of adult MRL mice. Plod2 silencing induced a decrease in the glycolytic function of MRL MSC, resulting in the alteration of their migratory and chondroprotective abilities in vitro. In vivo, we showed that plod2 silencing in MRL MSC significantly impaired their capacity to protect mouse from developing OA. Conclusion Our results demonstrate that the chondroprotective and therapeutic properties of MRL MSC in the CiOA experimental model are in part mediated by PLOD2.
WomboCombo results for OAEI 2022

This paper presents the results of the WomboCombo Matcher in the Ontology Alignment Evaluation Initiative (OAEI) 2022. WomboCombo is an ontology matching tool that finds node pairs starting out from simpler exact string matching based steps through more complex neural Language Model based steps. We also train a classifier to differentiate between entities with the same and entities with similar meaning.
From POMDP Executions to Probabilistic Axioms

Partially Observable Markov Decision Processes (POMDPs) allow modeling systems with uncertain state using probability distributions over states (called beliefs). However, in complex domains, POMDP solvers must explore large belief spaces, which is computationally intractable. One solution is to introduce domain knowledge to drive exploration, in the form of logic specifications. However, defining effective specifications may be challenging even for domain experts. We propose an approach based on inductive logic programming to learn specifications with confidence level from observed POMDP executions. We show that the learning approach converges to robust specifications as the number of examples increases.
Gender Knowledge and Artificial Intelligence (short paper)

Among the various types of biases that can be recognised in the behaviour of algorithms learning from data, gender-related biases assume particular importance in certain contexts, such as the Italian one, traditionally linked to a patriarchal vision of society. This becomes even more true considering the context of university education, where there is a strong under-representation of female students in STEM Faculties, and, particularly, in Computer Science Courses. After a brief review of gender biases reported in Machine Learning-based systems, the experience of the teaching “Gender Knowledge and Ethics in Artificial Intelligence” active since A.Y. 2021-22 at the School of Engineering of the University of Padova is presented.
Physiosmart: a Preliminary Study About the Quality of Rehabilitation Using a Computer Vision Approach

For chronic patients, rehabilitation can reduce disability-related pain and improve functional capacity and quality of life. Every day, chronic patients should perform rehabilitation exercises, at home under the therapist’s guidance. There are many problems related to the home rehabilitation scenario; most are connected to the patient experience. Sometimes, patients need to plug in components or have to face tricky procedures before the system run. The idea behind the work is to build a home rehabilitation system using a smartphone or tablet PCs ready to be used. Smartphone-based computer vision tools have shown potential for practical application in the field of telerehabilitation. To provide greater accessibility, there is a need to reduce the use of sensors and ensure the accuracy of monitoring without compromising the user experience. In this paper, we propose the validation of a novel smartphone-based pose detection tool during the performance of a rehabilitation exercise in which the elbow extension angle needs to be calculated as a metric. The tool allows real-time analysis of the exercise, although further efforts are needed to improve its accuracy.
Narrative Objects

In this work we operate on the view that the pragmatic employment of objects by an agent is manifested via their affordances for that specific agent. What affordances can manifest themselves in a particular situation depends, in part, on the dispositions offered by the particular objects involved. The contribution of this work is to construct and deploy a large scale formal model of dispositions for physical objects that can be employed to constrain and describe the roles they can play in narratives.
Towards Conflictual Narrative Mechanics

We propose a five steps methodology to retrieve, reconstruct and analyse conflict related narratives in a standardized and automated way. Our methodology combines AI and network analysis techniques to build a visual representation of key agents and entities involved in a conflict and to characterize their relations. Unlike the majority of existing methods, ours can be applied to any type of conflict, as, through two data downloading phases, it first generates a bird’s-eye representation and then a fine-grained map of any conflict. Given the broad applicability of the proposed methodology, we believe that this work moves the first steps towards a better understanding of conflictual narrative mechanics.
BiodivTab: semantic table annotation benchmark construction, analysis, and new additions

Systems that annotate tabular data semantically have witnessed increasing attention from the community in recent years; this process is commonly known as Semantic Table Annotation (STA). Its objective is to map individual table elements to their counterparts from a Knowledge Graph (KG). Individual cells and columns are assigned to KG entities and classes to disambiguate their meaning. STA-systems achieve high scores on the existing, synthetic benchmarks but often struggle on real-world datasets. Thus, realistic evaluation benchmarks are needed to enable the advancement of the field. In this paper, we detail the construction pipeline of BiodivTab, the first benchmark based on real-world data from the biodiversity domain. In addition, we compare it with the existing benchmarks. Moreover, we highlight common data characteristics and challenges in the field. BiodivTab is publicly available 1 and has 50 tables as a mixture of real and augmented samples from biodiversity datasets. It has been applied during the SemTab 2021 challenge, and participants achieved F1-scores of at most ∼ 60% across individual annotation tasks. Such results show that domain-specific benchmarks are more challenging for state-of-the-art systems than synthetic datasets.
A Seven-Level Inverter With Natural Balance and Boosting Capability

This paper introduces a new active-neutral-point clamped (ANPC) seven-level inverter with a switched-capacitor technique. The introduced inverter generates a seven-level ac output voltage with a maximum voltage-boosting gain of 1.5. The higher output voltage does not imply high voltage across the switches, as the maximum blocking voltage of the switches does not exceed the input voltage. The dc-link capacitor voltages are naturally balanced, and the flying capacitor achieves self-balancing through the series-parallel connection of the input voltage source, without the need for auxiliary control methods. Additionally, compared to major traditional seven-level inverters such as NPC, FC, and hybrid topologies, the proposed topology reduces the dc voltage requirement by 33.3%, eliminating the need for an intermediate dc-boosting stage and making the introduced inverter a good candidate for applications such as photovoltaic panels and full-cells systems, both characterized by a low input voltage. The operation and performance of the proposed topology are validated through simulation and experimental results.
Audible and Subaudible Components of the First and Second Heart Sounds

Cardiovascular disease (CVD) has been a pressing medical issue in the United States for over a century and has been a leading cause of death [1], [2] with a great impact on mortality, morbidity, and healthcare cost. The Centers for Disease Control and Prevention (CDC) [3] reported that CVD is responsible for one death every 34 seconds and approximately 697,000 deaths in 2020 alone. Additionally, between 2017 to 2018, CVD directly and indirectly cost the United States economy approximately 378 billion dollars [2].
Comparative Analysis of Functional Connectivity Metrics in EEG Datasets

Analysis of functional connectivity helps to determine how brain regions interact with one another and to understand neurological diseases better. In this study, we compare functional connectivity networks derived from electroencephalogram (EEG) data using Pearson's correlation and mutual information. The TUH EEG Epilepsy Corpus (TUEP) dataset is analysed with methods from Graph Theory, Statistics and Machine Learning. Our findings can be used to develop features for predictive models. Specifically, we show that with just 19 channels, a convolutional neural network model achieves 94% and 95% area under the receiver operating characteristic (ROC) curve (AUC) for correlation and mutual information, respectively. Thus, we provide evidence that application of Machine Learning methods to EEG data not containing seizures can help to accurately identify individuals with epilepsy. This may have considerable implications on diagnosing the pathology.
Comprehensible Alarm Message Generation for Natural Disaster

Nowadays, people are confronted with destructive natural disasters (such as earthquakes, hurricanes, floods, and wildfires) of increasing frequency and damage, which consists of an intensive amount of ecological, social, and economical losses. Existing research works on the role of disaster observation mainly focus on developing a system that can only detect and postassess disaster damages. However, one of the key drawbacks of these systems is that post-assessment does not have much effect in alleviating the damage of natural disasters. Secondly, the most immediate need of people in the sphere of disaster influence is receiving an intelligible message from news broadcasters. Therefore, it is necessary for a holistic system to better manage and assess during natural disasters. In this work, we conduct an adequate analysis on available data resources, feasible approaches, and finally, we propose our technique for better disaster monitoring and generating a comprehensible alarm message utilizing disaster assessment methods.
Magnitude Prediction Model for Japan Seismic Tremors Using Artificial Neural Network

The artificial neural network (ANN) model for predicting seismic tremor magnitudes for Japan is portrayed in this paper. The authors have retrieved the earthquake dataset from European-Mediterranean Seismological Center for this study. The dataset comprises a list of 5000 quake events that occurred from 1st July 2010 to 14th April 2016 in the region of Japan. Different neural network structures and ANN configurations exemplify the ANN model construction. The experiment is carried out by fine-tuning network variables such as type, transfer function, training function, and hidden neurons. The forecast accuracies of each of these network configurations are compared. The resultant ANN model features Levenberg-Marquardt backpropagation method for training the model, the nonlinear sigmoid activation function for the hidden layer, and the model's performance is evaluated concerning Mean Squared Error (MSE) and Gradient (g).
MarbleNet: A Deep Neural Network Solution for Vietnamese Voice Activity Detection

Voice activity detection in the wild is considered to be challenging work, especially when applied to the Vietnamese language as many proposed approaches are not extensive enough. In this paper, we aim to solve this problem by using MarbleNet, a model built on top of previous successful applications of using ID CNNs to solve conventional problems. We compiled a dataset, a combination of the VIVOS dataset for speech labelling and audios collected from Freesound.org for background noise. We present the performance of MarbleNet on the chosen dataset and perform experiments that compare the performance of MarbleNet and two other CNN-based architectures to measure the efficiency of our solution. Experiments show that MarbleNet, with a smaller size, can outperform other CNN-based models in clean and many noisy environments.