The lncRNAs at X Chromosome Inactivation Center: Not Just a Matter of Sex Dosage Compensation

Non‐coding RNAs (ncRNAs) constitute the majority of the transcriptome, as the result of pervasive transcription of the mammalian genome. Different RNA species, such as lncRNAs, miRNAs, circRNA, mRNAs, engage in regulatory networks based on their reciprocal interactions, often in a competitive manner, in a way denominated “competing endogenous RNA (ceRNA) networks” (“ceRNET”): miRNAs and other ncRNAs modulate each other, since miRNAs can regulate the expression of lncRNAs, which in turn regulate miRNAs, titrating their availability and thus competing with the binding to other RNA targets. The unbalancing of any network component can derail the entire regulatory circuit acting as a driving force for human diseases, thus assigning “new” functions to “old” molecules. This is the case of XIST, the lncRNA characterized in the early 1990s and well known as the essential molecule for X chromosome inactivation in mammalian females, thus preventing an imbalance of X‐linked gene expression between females and males. Currently, literature concerning XIST biology is becoming dominated by miRNA associations and they are also gaining prominence for other lncRNAs produced by the X‐inactivation center. This review discusses the available literature to explore possible novel functions related to ceRNA activity of lncRNAs produced by the X‐inactivation center, beyond their role in dosage compensation, with prospective implications for emerging gender‐biased functions and pathological mechanisms

Human MicroRNAs Interacting With SARS-CoV-2 RNA Sequences: Computational Analysis and Experimental Target Validation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel RNA virus affecting humans, causing a form of acute pulmonary respiratory disorder named COVID-19, declared a pandemic by the World Health Organization. MicroRNAs (miRNA) play an emerging and important role in the interplay between viruses and host cells. Although the impact of host miRNAs on SARS-CoV-2 infection has been predicted, experimental data are still missing. This study started by a bioinformatics prediction of cellular miRNAs potentially targeting viral RNAs; then, a number of criteria also based on experimental evidence and virus biology were applied, giving rise to eight promising binding miRNAs. Their interaction with viral sequences was experimentally validated by transfecting luciferase-based reporter plasmids carrying viral target sequences or their inverted sequences into the lung A549 cell line. Transfection of the reporter plasmids resulted in a reduction of luciferase activity for five out of the eight potential binding sites, suggesting responsiveness to endogenously expressed miRNAs. Co-transfection of the reporter plasmids along with miRNA mimics led to a further and strong reduction of luciferase activity, validating the interaction between miR-219a-2-3p, miR-30c-5p, miR-378d, miR-29a-3p, miR-15b-5p, and viral sequences. miR-15b was also able to repress plasmid-driven Spike expression. Intriguingly, the viral target sequences are fully conserved in more recent variants such as United Kingdom variant B.1.1.7 and South Africa 501Y.V2. Overall, this study provides a first experimental evidence of the interaction between specific cellular miRNAs and SARS-CoV-2 sequences, thus contributing to understanding the molecular mechanisms underlying virus infection and pathogenesis to envisage innovative therapeutic interventions and diagnostic tools

An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition

We propose an ensemble learning framework with Poisson sub-sampling to effectively train a collection of teacher models to issue some differential privacy (DP) guarantee for training data. Through boosting under DP, a student model derived from the training data suffers little model degradation from the models trained with no privacy protection. Our proposed solution leverages upon two mechanisms, namely: (i) a privacy budget amplification via Poisson sub-sampling to train a target prediction model that requires less noise to achieve a same level of privacy budget, and (ii) a combination of the sub-sampling technique and an ensemble teacher-student learning framework that introduces DP-preserving noise at the output of the teacher models and transfers DP-preserving properties via noisy labels. Privacy-preserving student models are then trained with the noisy labels to learn the knowledge with DP-protection from the teacher model ensemble. Experimental evidences on spoken command recognition and continuous speech recognition of Mandarin speech show that our proposed framework greatly outperforms existing state-of-the-art DP-preserving algorithms in both ASR tasks

A novel ceRNA regulatory network involving the long non-coding antisense RNA SPACA6P-AS, miR-125a and its mRNA targets in hepatocarcinoma cells

MicroRNAs (miRNA), and more recently long non-coding RNAs (lncRNA), are emerging as a driving force for hepatocellular carcinoma (HCC), one of the leading causes of cancer-related death. In this work, we investigated a possible RNA regulatory network involving two oncosuppressive miRNAs, miR-125a and let-7e, and a long non-coding antisense RNA, SPACA6P-AS (SP-AS), all transcribed from the same locus, with SP-AS in the opposite direction and thus carrying complementary sequences to the miRNAs. In vitro experiments validated the binding of the miRNAs to SP-AS. Then, the boosting of either the miRNAs or SP-AS levels demonstrated their reciprocal inhibition. In addition, overexpression of SP-AS resulted in a reduced silencing activity of miR-125a and let-7e toward their key oncogenic targets, i.e., Lin28b, MMP11, SIRT7, Zbtb7a, Cyclin D1, CDC25B, HMGA2, that resulted significantly upregulated. Finally, the analysis of 374 HCC samples in comparison to 50 normal liver tissues showed an upregulation of SP-AS and a reverse expression of miR-125a, not observed for let-7e; consistently, miR-125a oncogenic targets were upregulated. Overall, the data depict a novel competing endogenous RNA (ceRNA) network, ceRNET, whereby miR-125a can regulate the expression of SP-AS, which in turn regulates the miRNA by competing with the binding to the mRNA targets. We speculate that the unbalancing of any network component may contribute to hepatocarcinogenesis

A multimodal retina-iris biometric system using the levenshtein distance for spatial feature comparison

The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and retina, and the fusion is obtained relying upon the comparison score via the Levenshtein distance. We evaluated our approach by testing several combinations of publicly available biometric databases, namely one for retina images and three for iris images. To provide comprehensive results, detection error trade-off-based metrics, as well as statistical analyses for assessing the authentication performance, were considered. The best achieved False Acceptation Rate and False Rejection Rate indices were and 3.33%, respectively, for the multimodal retina-iris biometric approach that overall outperformed the unimodal systems. These results draw the potential of the proposed approach as a multimodal authentication framework using multiple static biometric traits

HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models

Advancements in deep neural networks have allowed automatic speech recognition (ASR) systems to attain human parity on several publicly available clean speech datasets. However, even state-of-the-art ASR systems experience performance degradation when confronted with adverse conditions, as a well-trained acoustic model is sensitive to variations in the speech domain, e.g., background noise. Intuitively, humans address this issue by relying on their linguistic knowledge: the meaning of ambiguous spoken terms is usually inferred from contextual cues thereby reducing the dependency on the auditory system. Inspired by this observation, we introduce the first open-source benchmark to utilize external large language models (LLMs) for ASR error correction, where N-best decoding hypotheses provide informative elements for true transcription prediction. This approach is a paradigm shift from the traditional language model rescoring strategy that can only select one candidate hypothesis as the output transcription. The proposed benchmark contains a novel dataset, “HyPoradise” (HP), encompassing more than 334, 000 pairs of N-best hypotheses and corresponding accurate transcriptions across prevalent speech domains. Given this dataset, we examine three types of error correction techniques based on LLMs with varying amounts of labeled hypotheses-transcription pairs, which gains a significant word error rate (WER) reduction. Experimental evidence demonstrates the proposed technique achieves a breakthrough by surpassing the upper bound of traditional re-ranking based methods. More surprisingly, LLM with reasonable prompt and its generative capability can even correct those tokens that are missing in N-best list. We make our results publicly accessible for reproducible pipelines with released pre-trained models, thus providing a new evaluation paradigm for ASR error correction with LLMs

microRNA-377-3p downregulates the oncosuppressor T-cadherin in colorectal adenocarcinoma cells

Colorectal cancer (CRC) is the second leading cause of death of malignant tumors worldwide. Recent studies point to a role for the adiponectin-receptor axis in colorectal carcinogenesis, and in particular to the oncosuppressive properties of the T-cadherin receptor. In addition, the loss of T-cadherin expression in tumor tissues has been linked to cancer progression and attributed to aberrant methylation of its promoter. Recognizing the pivotal role of microRNAs in CRC, this study explores their possible contribution to the downregulation of T-cadherin. A systematic bioinformatics analysis, restricted by microRNA expression data in the colon or in cultured colorectal cell lines, predicted twelve top-ranking target miRNA sites within the 3ʹ UTR of T-cadherin. Experimental validation analyses based on luciferase reporter constructs and miRNA mimic or miRNA inhibitor transfections toward colorectal adenocarcinoma cell lines indicated that miR-377-3p was able to directly bind to the T-cadherin sequence, and thus downregulating its expression. Given the oncogenic activity of miR-377 and the oncosuppressive activity of T-cadherin in CRC, the regulatory circuit highlighted in this study may add new insights into molecular mechanisms driving colorectal carcinogenesis, and perspectively it could be exploited to identify novel biomarkers and therapeutic targets

Electrical resistivity tomography investigations in the ufita Valley (southern Italy).

Several Electrical Resistivity Tomography (ERT) surveys have been carried out to study the subsurface structural and sedimentary settings of the upper Ufita River valley, and to evaluate their efficiency to distinguish the geological boundary between shallow Quaternary sedimentary deposits and clayey bedrock characterized by moderate resistivity contrast. Five shallow ERTs were carried out across a morphological scarp running at the foot of the northeastern slope of the valley. This valley shoulder is characterized by a set of triangular facets, that some authors associated to the presence of a SW-dipping normal fault. The geological studies allow us to interpret the shallow ERTs results obtaining a resistivity range for each Quaternary sedimentary deposit. The tomographies showed the geometrical relationships of alluvial and slope deposits, having a maximum thickness of 30-40 m, and the morphology of the bedrock. The resistivity range obtained for each sedimentary body has been used for calibrating the tomographic results of one 3560m-long deep ERT carried out across the deeper part of the intramountain depression with an investigation depth of about 170 m. The deep resistivity result highlighted the complex alluvial setting, characterized by alternating fine grained lacustrine deposits and coarser gravelly fluvial sediments

Initiation of dendritic failure of LLZTO via sub-surface lithium deposition

The occurrence of lithium deposition in occluded spaces within ceramic electrolytes due to electronic leakage currents can jeopardise the commercialization of power-dense solid-state batteries. Here, we utilize plasma-FIB serial sectioning to visualize the surface and sub-surface of a garnet solid electrolyte (LLZTO) after lithium plating. We study the morphology of surface spallation cracks, which represent the initial stage of dendrite formation. Employing a LiMg anode, we track the magnesium diffusion around these surface cracks with EDS. The absence of magnesium in early-stage cracks suggests they form due to the pressure build-up from the deposition of pure lithium in occluded pores near the electrolyte surface. These spallation cracks act as current focusing and stress concentration hot spots. Electron beam induced current imaging demonstrates that short-circuiting lithium dendrites grow from the spallations during plating. Thus, the sub-surface deposition of lithium is a possible explanation for the initiation of lithium dendrites in LLZTO

Using the ERT method in tectonically active areas: hints from Southern Apennine (Italy)

Abstract. Electrical Resistivity Tomography (ERT) method has been used to study two tectonically active areas of southern Apennine (Caggiano Faults and Ufita Basin). The main aim of this job was to study the structural setting of the investigated areas, i.e. the geometry of the basins at depth, the location of active faults at surface, and their geometrical characterization. The comparison between ERT and trench/drilling data allowed us to evaluate the efficacy of the ERT method in studying active faults and the structural setting of seismogenic areas. In the Timpa del Vento intermontane basin, high resolution ERT across the Caggiano Fault scarps, with different arrays, electrode spacing (from 1 to 10 m) and penetration depth (from about 5 to 40 m) was carried out. The obtained resistivity models allowed us to locate the fault planes along the hillslope and to gather information at depth, as later confirmed by paleoseismological trenches excavated across the fault trace. In the Ufita River Valley a 3560-m-long ERT was carried out across the basin, joining 11 roll-along multi-channel acquisition system with an electrode spacing of 20 m and reaching an investigation depth of about 170 m. The ERT allowed us to reconstruct the geometry and thickness of the Quaternary deposits filling the Ufita Valley. Our reconstruction of the depositional setting is in agreement with an interpretative geological section based on borehole data