Transcription Impacts the Efficiency of mRNA Translation via Co-transcriptional N6-adenosine Methylation.

Transcription and translation are two main pillars of gene expression. Due to the different timings, spots of action, and mechanisms of regulation, these processes are mainly regarded as distinct and generally uncoupled, despite serving a common purpose. Here, we sought for a possible connection between transcription and translation. Employing an unbiased screen of multiple human promoters, we identified a positive effect of TATA box on translation and a general coupling between mRNA expression and translational efficiency. Using a CRISPR-Cas9-mediated approach, genome-wide analyses, and in vitro experiments, we show that the rate of transcription regulates the efficiency of translation. Furthermore, we demonstrate that m6A modification of mRNAs is co-transcriptional and depends upon the dynamics of the transcribing RNAPII. Suboptimal transcription rates lead to elevated m6A content, which may result in reduced translation. This study uncovers a general and widespread link between transcription and translation that is governed by epigenetic modification of mRNAs.

The poly(A)-binding protein nuclear 1 suppresses alternative cleavage and polyadenylation sites.

Alternative cleavage and polyadenylation (APA) is emerging as an important layer of gene regulation. Factors controlling APA are largely unknown. We developed a reporter-based RNAi screen for APA and identified PABPN1 as a regulator of this process. Genome-wide analysis of APA in human cells showed that loss of PABPN1 resulted in extensive 3' untranslated region shortening. Messenger RNA transcription, stability analyses, and in vitro cleavage assays indicated enhanced usage of proximal cleavage sites (CSs) as the underlying mechanism. Using Cyclin D1 as a test case, we demonstrated that enhanced usage of proximal CSs compromises microRNA-mediated repression. Triplet-repeat expansion in PABPN1 (trePABPN1) causes autosomal-dominant oculopharyngeal muscular dystrophy (OPMD). The expression of trePABPN1 in both a mouse model of OPMD and human cells elicited broad induction of proximal CS usage, linked to binding to endogenous PABPN1 and its sequestration in nuclear aggregates. Our results elucidate a novel function for PABPN1 as a suppressor of APA.

A lithium-isotope perspective on the evolution of carbon and silicon cycles.

The evolution of the global carbon and silicon cycles is thought to have contributed to the long-term stability of Earth's climate1-3. Many questions remain, however, regarding the feedback mechanisms at play, and there are limited quantitative constraints on the sources and sinks of these elements in Earth's surface environments4-12. Here we argue that the lithium-isotope record can be used to track the processes controlling the long-term carbon and silicon cycles. By analysing more than 600 shallow-water marine carbonate samples from more than 100 stratigraphic units, we construct a new carbonate-based lithium-isotope record spanning the past 3 billion years. The data suggest an increase in the carbonate lithium-isotope values over time, which we propose was driven by long-term changes in the lithium-isotopic conditions of sea water, rather than by changes in the sedimentary alterations of older samples. Using a mass-balance modelling approach, we propose that the observed trend in lithium-isotope values reflects a transition from Precambrian carbon and silicon cycles to those characteristic of the modern. We speculate that this transition was linked to a gradual shift to a biologically controlled marine silicon cycle and the evolutionary radiation of land plants13,14.

Condylar Shave For the Treatment of Active Condylar Growth Excess.

The authors present a novel approach for addressing excessive condylar growth in individuals exhibiting asymmetric mandibular growth patterns.

End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks.

BACKGROUND: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas- cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. METHODS: We used the CODE cohort to develop and test a model for AF risk prediction for individual patients from the raw ECG recordings without the use of additional digital biomarkers. The cohort is a collection of ECG recordings and annotations by the Telehealth Network of Minas Gerais, in Brazil. A convolutional neural network based on a residual network architecture was implemented to produce class probabilities for the classification of AF. The probabilities were used to develop a Cox proportional hazards model and a Kaplan-Meier model to carry out survival analysis. Hence, our model is able to perform risk prediction for the development of AF in patients without the condition. RESULTS: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being >0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have >85% chance of remaining AF free up until after seven years. CONCLUSION: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision- making and patient management processes.

eRNAs are required for p53-dependent enhancer activity and gene transcription.

Binding within or nearby target genes involved in cell proliferation and survival enables the p53 tumor suppressor gene to regulate their transcription and cell-cycle progression. Using genome-wide chromatin-binding profiles, we describe binding of p53 also to regions located distantly from any known p53 target gene. Interestingly, many of these regions possess conserved p53-binding sites and all known hallmarks of enhancer regions. We demonstrate that these p53-bound enhancer regions (p53BERs) indeed contain enhancer activity and interact intrachromosomally with multiple neighboring genes to convey long-distance p53-dependent transcription regulation. Furthermore, p53BERs produce, in a p53-dependent manner, enhancer RNAs (eRNAs) that are required for efficient transcriptional enhancement of interacting target genes and induction of a p53-dependent cell-cycle arrest. Thus, our results ascribe transcription enhancement activity to p53 with the capacity to regulate multiple genes from a single genomic binding site. Moreover, eRNA production from p53BERs is required for efficient p53 transcription enhancement.

Distinct mechanisms mediate pacemaker dysfunction associated with catecholaminergic polymorphic ventricular tachycardia mutations: Insights from computational modeling.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a stress-induced ventricular arrhythmia associated with rhythm disturbance and impaired sinoatrial node cell (SANC) automaticity (pauses). Mutations associated with dysfunction of Ca2+-related mechanisms have been shown to be present in CPVT. These dysfunctions include impaired Ca2+ release from the ryanodine receptor (i.e., RyR2R4496C mutation) or binding to calsequestrin 2 (CASQ2). In SANC, Ca2+ signaling directly and indirectly mediates pacemaker function. We address here the following research questions: (i) what coupled-clock mechanisms and pathways mediate pacemaker mutations associated with CPVT in basal and in response to ß-adrenergic stimulation? (ii) Can different mechanisms lead to the same CPVT-related pacemaker pauses? (iii) Can the mutation-induced deteriorations in SANC function be reversed by drug intervention or gene manipulation? We used a numerical model of mice SANC that includes membrane and intracellular mechanisms and their interconnected signaling pathways. In the basal state of RyR2R4496C SANC, the model predicted that the Na+-Ca2+ exchanger current (INCX) and T-type Ca2+ current (ICaT) mediate between changes in Ca2+ signaling and SANC dysfunction. Under ß-adrenergic stimulation, changes in cAMP-PKA signaling and the sodium currents (INa), in addition to INCX and ICaT, mediate between changes in Ca2+ signaling and SANC automaticity pauses. Under basal conditions in Casq2-/-, the same mechanisms drove changes in Ca2+ signaling and subsequent pacemaker dysfunction. However, SANC automaticity pauses in response to ß-AR stimulation were mediated by ICaT and INa. Taken together, distinct mechanisms can lead to CPVT-associated SANC automaticity pauses. In addition, we predict that specifically increasing SANC cAMP-PKA activity by either a pharmacological agent (IBMX, a phosphodiesterase (PDE) inhibitor), gene manipulation (overexpression of adenylyl cyclase 1/8) or direct manipulation of the SERCA phosphorylation target through changes in gene expression, compensate for the impairment in SANC automaticity. These findings suggest new insights for understanding CPVT and its therapeutic approach.

Noninvasive fetal electrocardiography for the detection of fetal arrhythmias.

OBJECTIVE: To assess whether noninvasive fetal electrocardiography (NI-FECG) enables the diagnosis of fetal arrhythmias. METHODS: A total of 500 echocardiography and NI-FECG recordings were collected from pregnant women during a routine medical visit in this multicenter study. All the cases with fetal arrhythmias (n = 12) and a matching number of control (n = 14) were used. Two perinatal cardiologists analyzed the extracted NI-FECG while blinded to the echocardiography. The NI-FECG-based diagnosis was compared with the reference fetal echocardiography diagnosis. RESULTS: NI-FECG and fetal echocardiography agreed on all cases (Ac = 100%) on the presence of an arrhythmia or not. However, in one case, the type of arrhythmia identified by the NI-FECG was incorrect because of the low resolution of the extracted fetal P-wave, which prevented resolving the mechanism (2:1 atrioventricular conduction) of the atrial tachycardia. CONCLUSION: It is possible to diagnose fetal arrhythmias using the NI-FECG technique. However, this study identifies that improvement in algorithms for reconstructing the P-wave is critical to systematically resolve the mechanisms underlying the arrhythmias. The elaboration of a NI-FECG Holter device will offer new opportunities for fetal diagnosis and remote monitoring of problematic pregnancies because of its low-cost, noninvasiveness, portability, and minimal setup requirements.

Electronic structure dynamics in a low bandgap polymer studied by time-resolved photoelectron spectroscopy.

Means to measure the temporal evolution following a photo-excitation in conjugated polymers are a key for the understanding and optimization of their function in applications such as organic solar cells. In this paper we study the electronic structure dynamics by direct pump-probe measurements of the excited electrons in such materials. Specifically, we carried out a time-resolved photoelectron spectroscopy (TRPES) study of the polymer PCPDTBT by combining an extreme ultraviolet (XUV) high harmonic generation source with a time-of-flight spectrometer. After excitation to either the 1st excited state or to a higher excited state, we follow how the electronic structure develops and relaxes on the electron binding energy scale. Specifically, we follow a less than 50 fs relaxation of the higher exited state and a 10 times slower relaxation of the 1st excited state. We corroborate the results using DFT calculations. Our study demonstrates the power of TRPES for studying photo-excited electron energetics and dynamics of solar cell materials.

Immune response to Cystoisospora suis in piglets: local and systemic changes in T-cell subsets and selected mRNA transcripts in the small intestine.

Infections of neonatal piglets with Cystoisospora suis are responsible for substantial economic losses in pig production. To investigate kinetics of T-cell populations, which are possibly involved in this infection, lymphocytes from blood, spleen, mesenteric lymph nodes and the jejunal mucosa of infected and noninfected piglets were investigated by flow cytometry and immunohistochemistry at five time points during the acute phase of primary infection. Additionally, mRNA expression levels of pattern recognition receptors and immunomodulatory cytokines in the jejunum were investigated. T-cell receptor-γδ(+) T cells were found to be increased in the gut mucosa 4 days after infection and were most likely involved in the primary local immune response. Five to eleven days later, cytotoxic T cells peaked in this location, which was preceded by an expansion of this lymphocyte population in the mesenteric lymph nodes. In intestines of infected piglets, mRNA expressions of TLR-2, NOD2 and TNF-α were significantly upregulated, suggesting an involvement in parasite recognition, immune response and possibly also in immunopathology. Taken together, this study identifies cellular and molecular players involved in the early immune responses against C. suis, but their precise role in the pathogenesis and control of this neonatal disease requires further investigation.

pyPPG: a Python toolbox for comprehensive photoplethysmography signal analysis.

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. METHODS: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. RESULTS: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91-0.94 in RBDB and 0.93 in SHDB, compared to 0.89-0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.

Leuven-Haifa High-Resolution Fundus Image Dataset for Retinal Blood Vessel Segmentation and Glaucoma Diagnosis.

The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by a senior annotator. All senior segmentation corrections are provided as well as the junior segmentations of the test set. An open-source toolbox for the parametrization of segmentations was developed. Diagnosis, age, sex, vascular parameters as well as a quality score are provided as metadata. Potential reuse is envisioned as the development or external validation of blood vessels segmentation algorithms or study of the vasculature in glaucoma and the development of glaucoma diagnosis algorithms. The dataset is available on the KU Leuven Research Data Repository (RDR).

A Review on Atrial Fibrillation Detection From Ambulatory ECG.

Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential. Internet of Things (IoT) and artificial intelligence (AI) technologies have driven the development of wearable electrocardiograph (ECG) devices used for health monitoring, which are an effective means of AF detection. The main challenges of AF analysis using ambulatory ECG include ECG signal quality assessment to select available ECG, the robust and accurate detection of QRS complex waves to monitor heart rate, and AF identification under the interference of abnormal ECG rhythm. Through ambulatory ECG measurement and intelligent detection technology, the probability of postoperative recurrence of AF can be reduced, and personalized treatment and management of patients with AF can be realized. This work describes the status of AF monitoring technology in terms of devices, algorithms, clinical applications, and future directions.

LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images.

Objective.This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health.Approach.We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation.Main Results.LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators.Significance.The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.

Robust peak detection for photoplethysmography signal analysis.

Efficient and accurate evaluation of long-term photoplethysmography (PPG) recordings is essential for both clinical assessments and consumer products. In 2021, the top opensource peak detectors were benchmarked on the Multi-Ethnic Study of Atherosclerosis (MESA) database consisting of polysomnography (PSG) recordings and continuous sleep PPG data, where the Automatic Beat Detector (Aboy) had the best accuracy. This work presents Aboy++, an improved version of the original Aboy beat detector. The algorithm was evaluated on 100 adult PPG recordings from the MESA database, which contains more than 4.25 million reference beats. Aboy++ achieved an F1-score of 85.5%, compared to 80.99% for the original Aboy peak detector. On average, Aboy++ processed a 1 hour-long recording in less than 2 seconds. This is compared to 115 seconds (i.e., over 57-times longer) for the open-source implementation of the original Aboy peak detector. This study demonstrated the importance of developing robust algorithms like Aboy++ to improve PPG data analysis and clinical outcomes. Overall, Aboy++ is a reliable tool for evaluating long-term wearable PPG measurements in clinical and consumer contexts. The open-source algorithm is available on the physiozoo.com website (on publication of this proceeding).

Using beat-to-beat heart signals for age-independent biometric verification.

Use of non-stationary physiological signals for biometric verification, reduces the ability to forge. Such signals should be simple to acquire with inexpensive equipment. The beat-to-beat information embedded within the time intervals between consecutive heart beats is a non-stationary physiological signal; its potential for biometric verification has not been studied. This work introduces a biometric verification method termed "CompaRR". Heartbeat was extracted from longitudinal recordings from 30 mice ranging from 6 to 24 months of age (equivalent to ~ 20-75 human years). Fifty heartbeats, which is close to resting human heartbeats in a minute, were sufficient for the verification task, achieving a minimal equal error rate of 0.21. When trained on 6-month-old mice and tested on unseen mice up to 18-months of age (equivalent to ~ 50 human years), no significant change in the verification performance was noted. Finally, when the model was trained on data from drug-treated mice, verification was still possible.

Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.

Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.

Characteristics of patients with positional OSA according to ethnicity and the identification of a novel phenotype-lateral positional patients: a Multi-Ethnic Study of Atherosclerosis (MESA) study.

STUDY OBJECTIVES: We investigated the characteristics of obstructive sleep apnea (OSA) positional patients' (PP) phenotypes among different ethnic groups in the Multi-Ethnic Study of Atherosclerosis (MESA) dataset. Moreover, we hypothesized the existence of a new OSA PP phenotype we coined "Lateral PP," for whom the lateral apnea-hypopnea index is at least double the supine apnea-hypopnea index. METHODS: From 2,273 adults with sleep information, we analyzed data of 1,323 participants who slept more than 4 hours and had at least 30 minutes of sleep in both the supine and the nonsupine positions. Demographics and clinical information were compared for the different PP and ethnic groups. RESULTS: 861 (65.1%) patients had OSA, and 35 (4.1%) were Lateral PP. Lateral PP patients were mainly females (62.9%), obese (median body mass index: 31.4 kg/m2), had mild-moderate OSA (94.3%), and mostly were non-Chinese American (97.1%). Among all patients with OSA, 550 (63.9%) were Supine PP and 17.7% were supine-isolated OSA. Supine PP and Lateral PP were present in 73.1% and 1.0% of Chinese Americans, 61.0% and 3.4% of Hispanics, 68.3% and 4.7% of White/Caucasian, and 56.2% and 5.2% of Black/African-American patients with OSA. CONCLUSIONS: Chinese Americans have the highest prevalence of Supine PP, whereas Black/African-American patients lean toward less Supine PP and higher Lateral PP. Lateral PP appears to be a novel OSA phenotype. However, Lateral PP was observed in a small group of patients with OSA and thus its existence should be further validated. CITATION: Ben Sason Y, Oksenberg A, Sobel JA, Behar JA. Characteristics of patients with positional OSA according to ethnicity and the identification of a novel phenotype-lateral positional patients: a Multi-Ethnic Study of Atherosclerosis (MESA) study. J Clin Sleep Med. 2023;19(3):529-538.

Positional sleep apnea phenotyping using machine learning and digital oximetry biomarkers.

Study Objectives. To examine the feasibility of using digital oximetry biomarkers (OBMs) and body position to identify positional obstructive sleep apnea (POSA) phenotypes.Methods. A multiclass extreme gradient boost (XGBoost) was implemented to classify between three POSA phenotypes, i.e., positional patients (PP), including supine-predominant OSA (spOSA), and supine-isolated OSA (siOSA), and non-positional patients (NPP). A total of 861 individuals with OSA from the multi ethnic study of atherosclerosis (MESA) dataset were included in the study. Overall, 43 OBMs were computed for supine and non-supine positions and used as input features together with demographic and clinical information (META). Feature selection, using mRMR, was implemented, and nested cross validation was used for the model's performance evaluation.Results. The best performance for the multiclass classification yielded a median weighted F1 of 0.79 with interquartile range (IQR) of 0.06. Binary classification between PP to NPP achieved weighted F1 of 0.87 (0.04).Conclusion. Using OBMs computed in PP and NPP with OSA, it is possible to distinguish between the different phenotypes of POSA. This data-driven algorithm may be embedded in portable home sleep tests.