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Semantic representations are processed along a posterior-to-anterior gradient reflecting a shift from perceptual (e.g., it has eight legs) to conceptual (e.g., venomous spiders are rare) information. One critical region is the anterior temporal lobe (ATL): patients with semantic variant primary progressive aphasia (svPPA), a clinical syndrome associated with ATL neurodegeneration, manifest a deep loss of semantic knowledge. We test the hypothesis that svPPA patients perform semantic tasks by over-recruiting areas implicated in perceptual processing. We compared MEG recordings of svPPA patients and healthy controls during a categorization task. While behavioral performance did not differ, svPPA patients showed indications of greater activation over bilateral occipital cortices and superior temporal gyrus, and inconsistent engagement of frontal regions. These findings suggest a pervasive reorganization of brain networks in response to ATL neurodegeneration: the loss of this critical hub leads to a dysregulated (semantic) control system, and defective semantic representations are seemingly compensated via enhanced perceptual processing.

This article explores the development of a new observation research tool called the EMC-PK2, designed to capture coherent mathematics teaching and learning practices in preschool through second-grade classrooms. There is widespread interest in improving early math instruction and moving from traditional didactic instructional methods to a more problem-solving approach. However, there are few observational tools appropriate for research supporting high-quality mathematics teaching and learning practices that can inform what is happening during math lessons and that are appropriate across preschool and elementary school environments. This tool was developed to try and meet that need. It was piloted and first used in a longitudinal study in two large U.S. public school districts, across Pre-K through second grade. Analysis of the observational data offers insights into the psychometrics of the tool, showing reliable use and capturing several key dimensions of practice: at the activity level, teacher facilitation and student engagement; and at the observation level, differentiation and classroom environment. Although costly in both time and resources to implement at a large scale, the EMC-PK2 can offer much-needed understanding for researchers concerned with early math teaching and learning.

En Diferencia y repetición, Gilles Deleuze propone un enfoque novedoso de la pregunta trascendental clásica: explicar la génesis de los objetos en el mundo. Para ello son claves los conceptos duales de lo actual y lo virtual -a los que Deleuze define como “dos mitades del ser”-. Este artículo proveerá una presentación inicial del debate sobre los detalles de esta pareja conceptual en la bibliografía secundaria en el tema. Específicamente, existe un debate sobre los detalles de la relación entre lo actual y lo virtual trascendental. Exploraré las dos posturas respecto a esta distinción. En primer lugar, los que leen en Deleuze una “prioridad virtual”, donde lo actual se empobrece debido al flujo unidireccional de lo virtual. En segundo lugar, los filósofos que consideran que la relación entre lo virtual y lo actual tiene una naturaleza más recíproca. Además, voy a mostrar de qué manera el concepto de intensidad es crucial para comprender lo virtual y lo actual, lo que también es un tema que ha generado bastante debate. Finalmente, se sacarán algunas conclusiones a partir de una lectura en detalle de estos conceptos tal como aparecen en Diferencia y repetición.

Abstract The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single‐gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high‐throughput genetics. We characterized YETI using growth phenotyping and BAR‐seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter‐perturbing screens, including CRISPRi. We engineered a second expression system, Z3EB42, that gives lower expression than Z3EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

A concept for a smart material morphing building surface tile that would utilize adaptive surface wrinkle patterns to improve solar interaction is explored. The effect of the wrinkle patterns is numerically investigated in the context of an objective to reduce solar irradiance entering buildings by changing the shape of the surface (i.e., surface topography) so that the facade is self-shading, thereby reducing energy costs of the building for temperature control. A generally applicable algorithm was utilized and is presented to quantify the area of an arbitrarily shaped/oriented surface that is in shade for any given date/time and geographic location. Numerical case studies are shown that utilize the surface shading algorithm to evaluate the capabilities of various basic wrinkle patterns, both static and dynamically changing, to self-shade a building surface over the course of a day. The results indicate that a morphing wrinkle pattern can substantially increase the amount and duration of surface area in shade over time in comparison to any of the static (non-morphing) patterns, although it is noted that there is an expected tradeoff in the energy cost to change the surface pattern. Furthermore, it is shown that as the location of the proposed tile on the building facade changes, the optimal wrinkle pattern changes as well.

The human sensorimotor system has a remarkable ability to quickly and efficiently learn movements from sensory experience. A prominent example is sensorimotor adaptation, learning that characterizes the sensorimotor system's response to persistent sensory errors by adjusting future movements to compensate for those errors. Despite being essential for maintaining and fine-tuning motor control, mechanisms underlying sensorimotor adaptation remain unclear. A component of sensorimotor adaptation is implicit (i.e., the learner is unaware of the learning process) which has been suggested to result from sensory prediction errors-the discrepancies between predicted sensory consequences of motor commands and actual sensory feedback. However, to date no direct neurophysiological evidence that sensory prediction errors drive adaptation has been demonstrated. Here, we examined prediction errors via magnetoencephalography (MEG) imaging of the auditory cortex (n = 34) during sensorimotor adaptation of speech to altered auditory feedback, an entirely implicit adaptation task. Specifically, we measured how speaking-induced suppression (SIS)--a neural representation of auditory prediction errors--changed over the trials of the adaptation experiment. SIS refers to the suppression of auditory cortical response to speech onset (in particular, the M100 response) to self-produced speech when compared to the response to passive listening to identical playback of that speech. SIS was reduced (reflecting larger prediction errors) during the early learning phase compared to the initial unaltered feedback phase. Furthermore, reduction in SIS positively correlated with behavioral adaptation extents, suggesting that larger prediction errors were associated with more learning. In contrast, such a reduction in SIS was not found in a control experiment in which participants heard unaltered feedback and thus did not adapt. In addition, in some participants who reached a plateau in the late learning phase, SIS increased (reflecting smaller prediction errors), demonstrating that prediction errors were minimal when there was no further adaptation. Together, these findings provide the first neurophysiological evidence for the hypothesis that prediction errors drive human sensorimotor adaptation.

Alzheimer's disease (AD) is a neurodegenerative disease involving cognitive impairment and abnormalities in speech and language. Here, we examine how AD affects the fidelity of auditory feedback predictions during speaking. We focus on the phenomenon of speaking-induced suppression (SIS), the auditory cortical responses' suppression during auditory feedback processing. SIS is determined by subtracting the magnitude of auditory cortical responses during speaking from listening to playback of the same speech. Our state feedback control (SFC) model of speech motor control explains SIS as arising from the onset of auditory feedback matching a prediction of that feedback onset during speaking, a prediction that is absent during passive listening to playback of the auditory feedback. Our model hypothesizes that the auditory cortical response to auditory feedback reflects the mismatch with the prediction: small during speaking, large during listening, with the difference being SIS. Normally, during speaking, auditory feedback matches its predictions, then SIS will be large. Any reductions in SIS will indicate inaccuracy in auditory feedback prediction not matching the actual feedback. We investigated SIS in AD patients [ n  =   20; mean (SD) age, 60.77 (10.04); female (%), 55.00] and healthy controls [ n  =   12; mean (SD) age, 63.68 (6.07); female (%), 83.33] through magnetoencephalography (MEG)-based functional imaging. We found a significant reduction in SIS at ∼100 ms in AD patients compared with healthy controls (linear mixed effects model, F (1,57.5)  = 6.849, p  =   0.011). The results suggest that AD patients generate inaccurate auditory feedback predictions, contributing to abnormalities in AD speech., Competing Interests: The authors declare no competing financial interests., (Copyright © 2023 Kim et al.)

Objective: Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction. Approach: We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI-T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components-cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image. Main results: Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction. Significance: Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value., (Creative Commons Attribution license.)

Objectives: Auditory cortical activation of the two hemispheres to monaurally presented tonal stimuli has been shown to be asynchronous in normal hearing (NH) but synchronous in the extreme case of adult-onset asymmetric hearing loss (AHL) with single-sided deafness. We addressed the wide knowledge gap between these two anchoring states of interhemispheric temporal organization. The objectives of this study were as follows: (1) to map the trajectory of interhemispheric temporal reorganization from asynchrony to synchrony using magnitude of interaural threshold difference as the independent variable in a cross-sectional study and (2) to evaluate reversibility of interhemispheric synchrony in association with hearing in noise performance by amplifying the aidable poorer ear in a repeated measures, longitudinal study., Design: The cross-sectional and longitudinal cohorts were comprised of 49 subjects (AHL; N = 21; 11 male, 10 female; mean age = 48 years) and NH (N = 28; 16 male, 12 female; mean age = 45 years). The maximum interaural threshold difference of the two cohorts spanned from 0 to 65 dB. Magnetoencephalography analyses focused on latency of the M100 peak response from auditory cortex in both hemispheres between 50 msec and 150 msec following monaural tonal stimulation at the frequency (0.5, 1, 2, 3, or 4 kHz) corresponding to the maximum and minimum interaural threshold difference for better and poorer ears separately. The longitudinal AHL cohort was drawn from three subjects in the cross-sectional AHL cohort (all male; ages 49 to 60 years; varied AHL etiologies; no amplification for at least 2 years). All longitudinal study subjects were treated by monaural amplification of the poorer ear and underwent repeated measures examination of the M100 response latency and quick speech in noise hearing in noise performance at baseline, and postamplification months 3, 6, and 12., Results: The M100 response peak latency values in the ipsilateral hemisphere lagged those in the contralateral hemisphere for all stimulation conditions. The mean (SD) interhemispheric latency difference values (ipsilateral less contralateral) to better ear stimulation for three categories of maximum interaural threshold difference were as follows: NH (≤ 10 dB)-8.6 (3.0) msec; AHL (15 to 40 dB)-3.0 (1.2) msec; AHL (≥ 45 dB)-1.4 (1.3) msec. In turn, the magnitude of difference values were used to define interhemispheric temporal organization states of asynchrony, mixed asynchrony and synchrony, and synchrony, respectively. Amplification of the poorer ear in longitudinal subjects drove interhemispheric organization change from baseline synchrony to postamplification asynchrony and hearing in noise performance improvement in those with baseline impairment over a 12-month period., Conclusions: Interhemispheric temporal organization in AHL was anchored between states of asynchrony in NH and synchrony in single-sided deafness. For asymmetry magnitudes between 15 and 40 dB, the intermediate mixed state of asynchrony and synchrony was continuous and reversible. Amplification of the poorer ear in AHL improved hearing in noise performance and restored normal temporal organization of auditory cortices in the two hemispheres. The return to normal interhemispheric asynchrony from baseline synchrony and improvement in hearing following monoaural amplification of the poorer ear evolved progressively over a 12-month period., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 The Authors. Ear & Hearing is published on behalf of the American Auditory Society, by Wolters Kluwer Health, Inc.)

Neurodynamic Utility Toolbox for Magnetoencephalo- and Electroencephalography (NUTMEG) is an open-source MATLAB-based toolbox for the analysis and reconstruction of magnetoencephalography/electroencephalography data in source space. NUTMEG includes a variety of options for the user in data import, preprocessing, source reconstruction, and functional connectivity. A group analysis toolbox allows the user to run a variety of inferential statistics on their data in an easy-to-use GUI-driven format. Importantly, NUTMEG features an interactive five-dimensional data visualization platform. A key feature of NUTMEG is the availability of a large menu of interference cancelation and source reconstruction algorithms. Each NUTMEG operation acts as a stand-alone MATLAB function, allowing the package to be easily adaptable and scripted for the more advanced user for interoperability with other software toolboxes. Therefore, NUTMEG enables a wide range of users access to a complete "sensor-to- source-statistics" analysis pipeline., (Copyright © 2020 Hinkley, Dale, Cai, Zumer, Dalal, Findlay, Sekihara and Nagarajan.)