The ASL-CDI 2.0: an updated, normed adaptation of the MacArthur Bates Communicative Development Inventory for American Sign Language

Vocabulary is a critical early marker of language development. The MacArthur Bates Communicative Development Inventory has been adapted to dozens of languages, and provides a bird’s-eye view of children’s early vocabularies which can be informative for both research and clinical purposes. We present an update to the American Sign Language Communicative Development Inventory (the ASL-CDI 2.0, https://www.aslcdi.org), a normed assessment of early ASL vocabulary that can be widely administered online by individuals with no formal training in sign language linguistics. The ASL-CDI 2.0 includes receptive and expressive vocabulary, and a Gestures and Phrases section; it also introduces an online interface that presents ASL signs as videos. We validated the ASL-CDI 2.0 with expressive and receptive in-person tasks administered to a subset of participants. The norming sample presented here consists of 120 deaf children (ages 9 to 73 months) with deaf parents. We present an analysis of the measurement properties of the ASL-CDI 2.0. Vocabulary increases with age, as expected. We see an early noun bias that shifts with age, and a lag between receptive and expressive vocabulary. We present these findings with indications for how the ASL-CDI 2.0 may be used in a range of clinical and research settingsAccepted manuscrip

Optimizing human pulmonary perfusion measurement using an in silico model of arterial spin labeling magnetic resonance imaging.

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an imaging methodology that uses blood as an endogenous contrast agent to quantify flow. One limitation of this method of capillary blood quantification when applied in the lung is the contribution of signals from non-capillary blood. Intensity thresholding is one approach that has been proposed for minimizing the non-capillary blood signal. This method has been tested in previous in silico modeling studies; however, it has only been tested under a restricted set of physiological conditions (supine posture and a cardiac output of 5 L/min). This study presents an in silico approach that extends previous intensity thresholding analysis to estimate the optimal "per-slice" intensity threshold value using the individual components of the simulated ASL signal (signal arising independently from capillary blood as well as pulmonary arterial and pulmonary venous blood). The aim of this study was to assess whether the threshold value should vary with slice location, posture, or cardiac output. We applied an in silico modeling approach to predict the blood flow distribution and the corresponding ASL quantification of pulmonary perfusion in multiple sagittal imaging slices. There was a significant increase in ASL signal and heterogeneity (COV = 0.90 to COV = 1.65) of ASL signals when slice location changed from lateral to medial. Heterogeneity of the ASL signal within a slice was significantly lower (P = 0.03) in prone (COV = 1.08) compared to in the supine posture (COV = 1.17). Increasing stroke volume resulted in an increase in ASL signal and conversely an increase in heart rate resulted in a decrease in ASL signal. However, when cardiac output was increased via an increase in both stroke volume and heart rate, ASL signal remained relatively constant. Despite these differences, we conclude that a threshold value of 35% provides optimal removal of large vessel signal independent of slice location, posture, and cardiac output

DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

Detection of major ASL sign types in continuous signing for ASL recognition

In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker

Fructose transport-deficient Staphylococcus aureus reveals important role of epithelial glucose transporters in limiting sugar-driven bacterial growth in airway surface liquid.

Hyperglycaemia as a result of diabetes mellitus or acute illness is associated with increased susceptibility to respiratory infection with Staphylococcus aureus. Hyperglycaemia increases the concentration of glucose in airway surface liquid (ASL) and promotes the growth of S. aureus in vitro and in vivo. Whether elevation of other sugars in the blood, such as fructose, also results in increased concentrations in ASL is unknown and whether sugars in ASL are directly utilised by S. aureus for growth has not been investigated. We obtained mutant S. aureus JE2 strains with transposon disrupted sugar transport genes. NE768(fruA) exhibited restricted growth in 10 mM fructose. In H441 airway epithelial-bacterial co-culture, elevation of basolateral sugar concentration (5-20 mM) increased the apical growth of JE2. However, sugar-induced growth of NE768(fruA) was significantly less when basolateral fructose rather than glucose was elevated. This is the first experimental evidence to show that S. aureus directly utilises sugars present in the ASL for growth. Interestingly, JE2 growth was promoted less by glucose than fructose. Net transepithelial flux of D-glucose was lower than D-fructose. However, uptake of D-glucose was higher than D-fructose across both apical and basolateral membranes consistent with the presence of GLUT1/10 in the airway epithelium. Therefore, we propose that the preferential uptake of glucose (compared to fructose) limits its accumulation in ASL. Pre-treatment with metformin increased transepithelial resistance and reduced the sugar-dependent growth of S. aureus. Thus, epithelial paracellular permeability and glucose transport mechanisms are vital to maintain low glucose concentration in ASL and limit bacterial nutrient sources as a defence against infection

An Effective Ratner Equidistribution Result for ASL(2,R)

Let G=ASL(2,R) be the affine special linear group of the plane, and set Gamma=ASL(2,Z). We prove a polynomially effective asymptotic equidistribution result for the orbits of a 1-dimensional, non-horospherical unipotent flow on Gamma\G.Comment: A few typos corrected. Some added text in Sections 1.3 and 6, prompted by the referees' comments. To appear in Duke Math

Spin correlations in the algebraic spin liquid - implications for high Tc superconductors

We propose that underdoped high $T_c$ superconductors are described by an algebraic spin liquid (ASL) at high energies, which undergoes a spin-charge recombination transition at low energies. The spin correlation in the ASL is calculated via its effective theory - a system of massless Dirac fermions coupled to a U(1) gauge field. We find that without fine tuning any parameters the gauge interaction strongly enhances the staggered spin correlation even in the presence of a large single particle pseudo-gap. This allows us to show that the ASL plus spin-charge recombination picture can explain many highly unusual properties of underdoped high $T_c$ superconductors.Comment: 22 pages, 18 figures, submitted to PR