The First Catalog of Archaeomagnetic Directions From Israel With 4,000 Years of Geomagnetic Secular Variations

The large and well-studied archaeological record of Israel offers a unique opportunity for collecting high resolution archaeomagnetic data from the past several millennia. Here, we initiate the first catalog of archaeomagnetic directions from Israel, with data covering the past four millennia. The catalog consists of 76 directions, of which 47 fulfill quality selection criteria with Fisher precision parameter (k) ≥ 60, 95% cone of confidence (α95) < 6° and number of specimens per site (n) ≥ 8. The new catalog complements our published paleointensity data from the Levant and enables testing the hypothesis of a regional geomagnetic anomaly in the Levant during the Iron Age proposed by Shaar et al. (2016, 2017). Most of the archaeomagnetic directions show < 15° angular deviations from an axial dipole field. However, we observe in the tenth and ninth century BCE short intervals with field directions that are 19°-22° different from an axial dipole field and inclinations that are 20°-22° steeper than an axial dipole field. The beginning of the first millennium BCE is also characterized with fast secular variation rates. The new catalog provides additional support to the Levantine Iron Age Anomaly hypothesis

Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction

IntroductionEpilepsy is a neurological disease characterized by sudden, unprovoked seizures. The unexpected nature of epileptic seizures is a major component of the disease burden. Predicting seizure onset and alarming patients may allow timely intervention, which would improve clinical outcomes and patient quality of life. Currently, algorithms aiming to predict seizures suffer from a high false alarm rate, rendering them unsuitable for clinical use.MethodsWe adopted here a risk-controllingprediction calibration method called Learn then Test to reduce false alarm rates of seizure prediction. This method calibrates the output of a “black-box” model to meet a specified false alarm rate requirement. The method was initially validated on synthetic data and subsequently tested on publicly available electroencephalogram (EEG) records from 15 patients with epilepsy by calibrating the outputs of a deep learning model.Results and discussionValidation showed that the calibration method rigorously controlled the false alarm rate at a user-desired level after our adaptation. Real data testing showed an average of 92% reduction in the false alarm rate, at the cost of missing four of nine seizures of six patients. Better-performing prediction models combined with the proposed method may facilitate the clinical use of real-time seizure prediction systems

Gene Essentiality Analyzed by In Vivo Transposon Mutagenesis and Machine Learning in a Stable Haploid Isolate of Candida albicans

This work was supported by European Research Council Advanced Award 340087 (RAPLODAPT) to J.B., the Dahlem Centre of Plant Sciences (DCPS) of the Freie Universität Berlin (R.K.), Israel Science Foundation grant no. 715/18 (R.S.), the Wellcome Trust (grants 086827, 075470, 101873, and 200208) and the MRC Centre for Medical Mycology (N006364/1) (N.A.R.G.). Data availability.All of the code and required dependencies for analysis of the TnSeq data are available at https://github.com/berman-lab/transposon-pipeline. Library insertion sequences are available at NCBI under project PRJNA490565 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA490565). Datasets S1 through S9 are available at https://doi.org/10.6084/m9.figshare.c.4251182.Peer reviewedPublisher PD

DeepFry: Identifying Vocal Fry Using Deep Neural Networks

Vocal fry or creaky voice refers to a voice quality characterized by irregular glottal opening and low pitch. It occurs in diverse languages and is prevalent in American English, where it is used not only to mark phrase finality, but also sociolinguistic factors and affect. Due to its irregular periodicity, creaky voice challenges automatic speech processing and recognition systems, particularly for languages where creak is frequently used. This paper proposes a deep learning model to detect creaky voice in fluent speech. The model is composed of an encoder and a classifier trained together. The encoder takes the raw waveform and learns a representation using a convolutional neural network. The classifier is implemented as a multi-headed fully-connected network trained to detect creaky voice, voicing, and pitch, where the last two are used to refine creak prediction. The model is trained and tested on speech of American English speakers, annotated for creak by trained phoneticians. We evaluated the performance of our system using two encoders: one is tailored for the task, and the other is based on a state-of-the-art unsupervised representation. Results suggest our best-performing system has improved recall and F1 scores compared to previous methods on unseen data.Comment: under submission to Interspeech 202

Floating macro litter in European rivers - top items

The JRC exploratory project RIMMEL provides information about litter, mainly plastic waste, entering the European Seas through river systems. RIMMEL has collected data on riverine floating macro litter inputs to the sea. Data acquisition was based on the Riverine Litter Observation Network (RiLON) activities, which collected data from rivers in the European marine basins over a period of one year (September 2016 – September 2017). Data was collected by visual observations and documented with the JRC Floating Litter Monitoring Application for mobile devices, allowing a harmonized reporting, compatible with the MSFD Master List of Categories for Litter Items. This report includes the Top Items lists of riverine floating macro litter, based on the total amount of litter items identified during RiLON activities and ranked by abundance. Top Items lists have been elaborated considering the whole database for the European Seas and further detailed for each individual European regional sea: Baltic Sea, Black Sea, Mediterranean Sea and North-East Atlantic. The North-East Atlantic and the Mediterranean Sea regions showed similar litter categories in their Top 20 Items. These two regions provided most of the available data, influencing the general Top Items list. In the Black Sea and Baltic Sea regions, where data availability was limited, the Top Items lists showed more differences among the different regions. Overall, the general Top Items list for the European Seas showed a predominance of plastic item categories (artificial polymer materials). As a whole, plastic items made up to 80.8% of all objects, with plastic and polystyrene fragments comprising 45% of the identified items in the database. Additionally, Single Use Plastics such as bottles, cover/packaging and bags were also ranked among the most frequently found floating litter. The similarities in the Top 10 and Top 20 items for the different regions, and the appearance of Single Use Plastics scoring high in the ranking, support the need for common actions against plastic pollution at EU level.JRC.D.2-Water and Marine Resource

Distinct PKA Signaling in Cytosolic and Mitochondrial Compartments in Electrically Paced Atrial Myocytes

Protein kinase A (PKA) is a key nodal signaling molecule that regulates a wide range of cellular functions in the cytosol and mitochondria. The distribution of A-kinase anchoring proteins that tether PKA, the local interaction with degradation molecules, and regulation by Ca2+, may lead to distinct spatiotemporal cAMP/PKA signaling in these compartments. In this work, FRET-based sensors were used to investigate PKA signaling in the cytosol, outer mitochondrial membrane (OMM), and mitochondrial matrix (MM) and its crosstalk with Ca2+ in response to electrical stimulation of cultured rabbit atrial cells. A gradual decrease in PKA activity eliminating the ability of the atrial cells to respond to physiological electrical stimulation, was observed upon treatment of cells with H-89. Chelation of intracellular Ca2+ by BAPTA reduced PKA activity and diminished its response to forskolin, an AC stimulator. Under basal conditions, PKA activity in response to forskolin was lower in the OMM compared to the cytosol and MM. In response to electrical stimulation in the presence of ISO, distinct compartmentalization of PKA activity was observed, with higher activity in the cytosol and MM than in the OMM. Thus, distinct Ca2+-dependent spatiotemporal cAMP/PKA signaling exists in atrial cells, likely mediating its excitation and mitochondrial function

Speech characteristics yield important clues about motor function: Speech variability in individuals at clinical high-risk for psychosis

Abstract Background and hypothesis: Motor abnormalities are predictive of psychosis onset in individuals at clinical high risk (CHR) for psychosis and are tied to its progression. We hypothesize that these motor abnormalities also disrupt their speech production (a highly complex motor behavior) and predict CHR individuals will produce more variable speech than healthy controls, and that this variability will relate to symptom severity, motor measures, and psychosis-risk calculator risk scores. Study design: We measure variability in speech production (variability in consonants, vowels, speech rate, and pausing/timing) in N = 58 CHR participants and N = 67 healthy controls. Three different tasks are used to elicit speech: diadochokinetic speech (rapidly-repeated syllables e.g., papapa…, pataka…), read speech, and spontaneously-generated speech. Study results: Individuals in the CHR group produced more variable consonants and exhibited greater speech rate variability than healthy controls in two of the three speech tasks (diadochokinetic and read speech). While there were no significant correlations between speech measures and remotely-obtained motor measures, symptom severity, or conversion risk scores, these comparisons may be under-powered (in part due to challenges of remote data collection during the COVID-19 pandemic). Conclusion: This study provides a thorough and theory-driven first look at how speech production is affected in this at-risk population and speaks to the promise and challenges facing this approach moving forward