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Purpose: Previous research has demonstrated that typically developing children, verbal children with a diagnosis of autism, children with Down syndrome, children with developmental language disorder, and children with dyslexia can all benefit from orthographic support during word learning tasks. This study sought to determine if minimally speaking or nonspeaking children with a diagnosis of autism would also demonstrate an orthographic facilitation effect during a computer-based remote word learning task. Method: Twenty-two school-age children with a diagnosis of autism and little to no spoken language learned four novel words by contrasting the words with known objects. Two novel words were taught with orthographic support present, and two were taught without orthographic support. Participants were exposed to the words a total of 12 times and then given an immediate posttest to assess identification. Parent report measures of receptive vocabulary, expressive vocabulary, autism symptomatology, and reading skills were also collected. Results: During learning tasks, participants performed equally well whether orthographic support was given or not. For the posttest, however, participants performed significantly better for words that were taught with orthographic support. The presence of orthography improved accuracy and supported a greater number of participants to reach the passing criterion compared to the absence of orthography. Orthographic representations aided the word learning of those with lower expressive language significantly more than those with higher expressive language. Conclusions: Minimally speaking or nonspeaking children with a diagnosis of autism benefit from orthographic support when learning new words. Further investigation is warranted to determine if this effect holds during face-to-face interactions using augmentative and alternative communication systems. Supplemental Material: https://doi.org/10.23641/asha.22465492

To examine the outcomes and cost effectiveness of expectant management versus immediate delivery of women who experience preterm premature rupture of membranes (PPROM) at 34 weeks.A cost-effectiveness model was built using TreeAge software to compare outcomes in a theoretical cohort of 37,455 women with PPROM at 34 weeks undergoing expectant management until 37 weeks versus immediate delivery. Outcomes included fetal death, neonatal sepsis, neonatal death, neonatal neurodevelopmental delay, healthy neonate, maternal sepsis, maternal death, cost, and quality-adjusted life years. Probabilities were derived from the literature, and a cost-effectiveness threshold was set at $100,000 per quality-adjusted life year.In our theoretical cohort of 37,455 women, expectant management yielded 58 fewer neonatal deaths and 164 fewer cases of neonatal neurodevelopmental delay. However, it resulted in 407 more cases of neonatal sepsis and 2.7 more cases of maternal sepsis. Expectant management resulted in 3,531 more quality-adjusted life years and a cost savings of $71.9 million per year, making it a dominant strategy. Univariate sensitivity analysis demonstrated expectant management was cost effective until the weekly cost of antepartum admission exceeded $17,536 (baseline estimate: $12,520) or the risk of maternal sepsis following intraamniotic infection exceeded 20%.Our model demonstrated that expectant management of PPROM at 34 weeks yielded better outcomes on balance at a lower cost than immediate delivery. This analysis is important and timely in light of recent studies suggesting improved neonatal outcomes with expectant management. However, individual risks and preferences must be considered in making this clinical decision as expectant management may increase the risk of adverse perinatal outcomes when the risk of puerperal infection increases.

Background The Qanuinngitsiarutiksait study aimed to develop detailed profiles of Inuit health service utilization in Manitoba, by Inuit living in Manitoba (approximately 1,500) and by Inuit from the Kivalliq region of Nunavut who travel to Manitoba to access care not available in Nunavut (approximately 16,000 per year). Methods We used health administrative data routinely collected in Manitoba for all services provided and developed an algorithm to identify Inuit in the dataset. This paper focused on health services used by Inuit from the Kivalliq for prenatal care and birthing. Results Our study found that approximately 80 percent of births to women from the Kivalliq region occur in Manitoba, primarily in Winnipeg. When perinatal care and birthing are combined, they constitute one third of all consults happening by Kivalliq residents in Manitoba. For scale, hospitalizations for childbirths to Kivalliq women about to only 5 percent of all childbirth-related hospitalizations in Manitoba. Conclusions The practice of evacuating women from the Kivalliq for perinatal care and birthing is rooted in colonialism, rationalized as ensuring that women whose pregnancy is at high risk have access to specialized care not available in Nunavut. While defendable, this practice is costly, and does not provide Inuit women a choice as to where to birth. Attempts at relocating birthing to the north have proven complex to operationalize. Given this, there is an urgent need to develop Inuit-centric and culturally appropriate perinatal and birthing care in Manitoba.

Treatment of gestational diabetes mellitus has been demonstrated to improve perinatal outcomes. However, the role of the Special Supplemental Nutrition Program for Women, Infants, and Children in maternal and neonatal outcomes for qualifying patients with gestational diabetes mellitus is less understood.The objective of this study is to observe the relationship of enrollment in the Special Supplemental Nutrition Program for Women, Infants, and Children with pregnancy outcomes in patients with gestational diabetes.This was a retrospective cohort study using National Vital Statistics Birth Data of pregnant persons diagnosed with gestational diabetes mellitus between 2014 and 2018. The study population was composed of patients who had Medicaid coverage for maternity care; patients with Medicaid are automatically qualified for the Special Supplemental Nutrition Program for Women, Infants, and Children. The study groups were defined as those who enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children vs those who did not enroll. In addition, maternal and neonatal outcomes for these groups were analyzed. Univariate and multivariable logistic regression analyses adjusted for significant covariates were performed.Of 460,377 pregnant persons with pregnancies complicated by gestational diabetes mellitus, 73% were enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children, and 27% were not. Pregnant persons with gestational diabetes mellitus enrolled in the Special Supplemental Nutrition Program for Women, Infants, and Children had decreased odds of preterm delivery before 34 and 37 weeks of gestation. Although the Special Supplemental Nutrition Program for Women, Infants, and Children group had higher odds of large-for-gestational-age neonates and cesarean delivery, the overall rates of these outcomes in both cohorts were high.The Special Supplemental Nutrition Program for Women, Infants, and Children provides a resource for perinatal support, supplemental food, and nutritional education. The decrease in the rates of preterm deliveries in pregnant persons with gestational diabetes mellitus that enroll in the Special Supplemental Nutrition Program for Women, Infants, and Children, Infants, and Children relative to those that qualified for the program but did not enroll suggested that having access to available education and food sources may influence perinatal outcomes.

Purpose A systematic review was performed to determine the extent to which orthographic facilitation, a strategy to improve word learning, has been demonstrated in the literature for children and adolescents from clinical categories such as developmental language disorders (DLD), autism spectrum disorders (ASD), Down syndrome, dyslexia, hearing impairment, intellectual disability, and cerebral palsy. Method Five databases were searched for all studies published through December 2019. Eligible studies included participants from a clinical population (DLD, ASD, dyslexia, cerebral palsy, Down syndrome, hearing impairment, etc.) and compared word learning with and without orthography. Selected studies were extracted for pertinent information. In addition, assessment of the methodological rigor was performed for each study. Results The review yielded five studies that targeted word learning with orthographic facilitation for children from various clinical populations including DLD, verbal children with autism, Down syndrome, and dyslexia. All studied populations showed a benefit for word learning in picture naming posttests when words were trained in the presence of orthography. Conclusions For the studied populations, training words in the presence of orthography will improve word learning accuracy and retention. The review highlights the need for more research in this area across other clinical populations. Supplemental Material https://doi.org/10.23641/asha.14632791

BackgroundAlthough there is a growing body of literature on cognitive and language processing in bilingual children with developmental language disorder (DLD), there is a major gap in the evidence for language intervention. Critically, speech-language therapists are often required to make clinical decisions for language intervention on specific domains, such as phonology, vocabulary, morphosyntax and literacy. AimsTo examine evidence for language intervention and cross-language transfer effects in bilingual children with DLD. Specifically, the study aimed to review intervention evidence targeting non-linguistic cognitive skills and six areas of language: phonology, vocabulary, morphosyntax, pragmatics, narrative skills and literacy. Methods & ProceduresWe carried out searches in five electronic databases: CINAHL, Scopus, Psychinfo, Proquest and Sciencedirect. Data from selected papers were extracted and organized into the three following categories: study information, participant information and intervention information. Critical appraisal for selected papers was conducted using a quality assessment tool (QAT). Outcomes & ResultsWe included 14 papers in the review. The majority indicated evidence for vocabulary intervention. There was limited evidence for intervention targeting phonology or morphosyntax. Cross-language generalization effects were evident for vocabulary, but in some instances also reported for morphosyntax and literacy. Conclusions & ImplicationsThe present review indicates that there is a significant gap in the literature regarding language intervention for several key language areas such as morphosyntax, narrative skills and literacy. There are only limited data for the effects of cross-language generalization indicating that more research is needed in this area specifically for skills beyond vocabulary. What this paper addsWhat is already known on the subjectPrevious studies have examined the effects of bi- and monolingual intervention in bilingual children with DLD. Although the results indicated superior effects for bilingual compared with monolingual intervention, language intervention evidence in specific language domains (e.g., vocabulary, literacy) has not been investigated. What this paper adds to existing knowledgeThis study will add intervention evidence specific to language domains such as phonology, vocabulary, morphosyntax, pragmatics, narrative skills and literacy. Additionally, we have synthesized intervention evidence on non-linguistic cognition given that these skills are often impaired in bilingual children with DLD. The review has also demonstrated evidence for the effects of cross-language transfer beyond vocabulary skills, especially when the intervention was provided in the home language. What are the potential or actual clinical implications of this work?Although there was a lack of intervention evidence in language domains such as pragmatics, the results indicated some evidence for intervention targeting vocabulary. However, positive effects of cross-language generalization were not constrained to vocabulary but were also reported for intervention targeting mean length of utterance and literacy in the home language. This result indicates an interactive nature of the two languages, as well as provides further evidence for supporting home language(s) in intervention. Finally, intervention targeting non-linguistic cognition may yield additional cross-domain generalization to language skills specifically for bilingual children with DLD.

An unmanned ground vehicle (UGV) network operates as an ad hoc autonomous tactical network. In this paper, we study the deployment of a UGV network and its ability to use location estimation to provide reliable data forwarding. This analysis involves (1) location estimation to obtain realistic estimates of the node’s (UGV) spatial positions and (2) real-time path planning to obtain reliable routing among nodes using the estimates of the node positions. Conventional approaches commonly use the popular Random Waypoint (RWP) model (or its variants) as the mobility model. The RWP approach uses randomly selected node positions and has no way to cope with nodes moving in and out of transmission range. These deficiencies cause the route reliability to suffer. Our approach (1) uses an Extended Kalman Filter (EKF) for node mobility/location estimation and (2) uses a modified Ad Hoc On Demand Distance Vector (AODV) algorithm for route selection using a new routing parameter called contact time. We refer to this combined approach as the AODV-LocPred algorithm. We compare the AODV-LocPred algorithm with AODV using the RWP model. This is referred to as AODV-RWP. Our simulations show that the AODV-LocPred algorithm significantly outperforms the AODV-RWP algorithm in terms of packet delivery ratio (PDR), because it provides realistic node position estimates and it copes in a reasonable way with nodes moving in and out of transmission range. However, the AODV-LocPred incurs an increase in end-to-end delay. Nonetheless, we expect that in many real-world settings, this tradeoff is acceptable.

An 11 yr old female spayed golden retriever weighing 30.3 kg presented for evaluation of progressive lethargy, anorexia, tachypnea, stiff gait, and nonlocalized pain. On physical exam, the patient was febrile and tachycardic, and an arrhythmia with pulse deficits was noted. Clinicopathological abnormalities included thrombocytopenia, leukocytosis, nonregenerative anemia, and mild hypoalbuminemia. The patient progressed overnight to develop a productive cough, and an echocardiogram performed the next morning revealed irregular proliferative lesions of the pulmonic valve with moderate pulmonic regurgitation. Subsequent blood cultures grew two organisms: alpha-hemolytic streptococci spp. and Empedobacter brevis. The dog was treated with appropriate intravenous antibiotics for 2 wk and then switched to oral therapy. The clinicopathologic abnormalities, fever, and clinical signs resolved with oral antibiotic treatment. To the authors' knowledge, this case report represents the first detailed published case of bacterial endocarditis with E brevis bacteremia involving the pulmonic valve. The clinical presentation, diagnosis, treatment, and follow-up are discussed.

The goal of “as-built” computational modeling is to incorporate the most representative geometry and material information for an (fabricated or legacy) object into simulations. While most engineering finite element simulations are based on an object’s idealized “as-designed” configuration with information obtained from technical drawings or computer-aided design models, as-built modeling uses nondestructive characterization and metrology techniques to provide the feature information. By incorporating more representative geometry and material features as initial conditions, the uncertainty in the simulation results can be reduced, providing a more realistic understanding of the event and object being modeled. In this paper, key steps and technology areas in the as-built modeling framework are: (1) inspection using nondestructive characterization and metrology techniques; (2) data reduction (signal and image processing including artifact removal, data sensor fusion, and geometric feature extraction); and (3) engineering and physics analysis using finite element codes. We illustrate the process with a cylindrical phantom and include a discussion of the key concepts and areas that need improvement. Our results show that reasonable as-built initial conditions based on a volume overlap criteria can be achieved and that notable differences between simulations of the as-built and as-designed configurations can be observed for a given load case. Specifically, a volume averaged difference of accumulated plastic strain of 3% and local spatially varying differences up to 10%. The example presented provides motivation and justification to engineering teams for the additional effort required in the as-built modeling of high value parts. Further validation of the approach has been proposed as future work.

An ad hoc unmanned ground vehicle (UGV) network operates as an intermittently connected mobile delay tolerant network (DTN). In this paper, we develop a mobility estimation algorithm that can be coupled with a cooperative communication routing algorithm to provide a basis for real time path planning in UGV-DTNs. A Gauss-Markov state space model is used for the node dynamics. The nonlinear measurement signals are constant-power RSSI (Received Signal Strength Indicator) signals transmitted from fixed-position base stations. An extended Kalman filter (EKF) is derived for estimating the position, velocity and acceleration of aUGV node in a two-dimensional spatial grid environment. We use Matlab to simulate a single mobile node traveling along a trajectory that includes abrupt maneuvers. Estimation performance is measured using zero-mean whiteness tests on the innovations sequences, root mean square error (RSME) of the state estimates, weighted sum squared residuals (WSSRs), and the posterior Cramer-Rao lower bound (PCRLB). Under these performance indices, we demonstrate that the mobility estimation algorithm performs effectively.

We describe algorithms for automating the process of picking seismic events in prestack migrated common depth image gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal/image processing algorithms. No model assumption is made, such as hyperbolic moveout. We train a probabilistic neural network for voxel classification using event times, subsurface points, and offsets (ground truth information) picked manually by expert interpreters. The key to success is using effective features that capture the important behavior of the measured signals. We test a variety of features calculated in a local neighborhood about the voxel under analysis. Selection algorithms ensure that we use only the features that maximize class separability. This event‐picking algorithm has the potential to reduce significantly the cycle time and cost of 3‐D prestack depth migration while making the velocity model inversion more robust.

A cognitive signal processing system (for example, in radar or sonar) is one that observes and learns from the environment; then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional systems. Current cognitive radar algorithms are designed only for target impulse responses that are Gaussian distributed to achieve mathematical tractability. Our research generalizes the cognitive radar target classifier to deal effectively with arbitrary non-Gaussian distributed target responses. Given exemplars of target impulse responses, our Bayesian illumination waveform design algorithm requires the ability to draw complex correlated samples from a target distribution specified by both an arbitrary desired probability density function and a desired power spectral density. This capability is realized using kernel density estimation and an extension of a new simple and efficient nonlinear sampling algorithm by Nichols et al. Simu...

Statistical sampling algorithms are widely used in Bayesian signal processing for drawing real-valued independent, identically distributed (i.i.d.) samples from a desired distribution. This paper focuses on the more difficult problem of how to draw complex correlated samples from a distribution specified by both an arbitrary desired probability density function and a desired power spectral density. This problem arises in cognitive signal processing. A cognitive signal processing system (for example, in radar or sonar) is one that observes and learns from the environment; then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional systems. Current cognitive radar algorithms focus only on target impulse responses that are Gaussian distributed to achieve mathematical tractability. This research generalizes the cognitive radar target classifier to deal effectively with arbitrary non-Gaussian distributed target response...

We describe algorithms for automating the process of picking seismic events in pre-stack migrated gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal-image processing algorithms. We train a probabilistic neural network (PNN) for pixel classification using event times and offsets (ground truth information) picked manually by expert interpreters. The key to success is in using effective features that capture the important behavior of the measured signals. We use a variety of features calculated in a local neighborhood about the pixel under analysis. Feature selection algorithms are used to ensure that we use only the features that maximize class separability. The novelty of the work lies in (a) the use of pre-stack migrated gathers rather than stacked data, (b) the use of two-dimensional statistical and wavelet features, and (c) the use of a PNN for classification. 8 refs., 3 figs

A cognitive radar (CR) system is one that observes and learns from the environment; then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional radar systems. Romero et al. recently developed a CR system that performs multiple hypothesis target classification and exploits the spectral sparsity of correlated narrowband target responses to achieve significant performance improvements over traditional radars that use wideband illumination pulses. This CR system was designed for Gaussian target responses. This research generalizes the CR system to deal effectively with arbitrary non-Gaussian distributed target responses via two key contributions: (1) an important statistical expected value operation that is usually evaluated in closed form is evaluated numerically using an ensemble averaging operation, and (2) a powerful new statistical sampling algorithm and a kernel density estimator are applied to draw complex targe...

The proposed approaches to animal sound characterization are motivated by the philosophical theme of this special session; diverse problems, similar solutions. The literature primarily makes use of some fundamental algorithms. This paper proposes some powerful techniques that have the potential to effect significant advances in animal acoustics. Several classes of algorithms are discussed, along with examples of how they have been used to solve similar problems in diverse applications. The key algorithm areas discussed are (1) statistical feature extraction from two‐dimensional or multi‐dimensional hierarchical transforms, such as wavelet transforms, higher‐order poly‐spectral features, histogram features, texture features, etc.; (2) feature selection algorithms to choose a subset of most useful features and overcome the curse of dimensionality; (3) advanced classification algorithms; (4) performance measures that include estimates of statistical confidence for small sample sizes; and (5) sensor fusion al...

This work addresses the "As‐Built" modeling problem in ultrasonic nondestructive evaluation (NDE), in which one is given measurements of a mechanical part before and after use. The condition of the "as‐built" part (prior to use) is known, so that after the part has been used, it can be tested for damage. This enables a two‐step model‐based approach: (1) Given input‐output measurements (A‐Scans), estimate a dynamic prediction‐error model of the "as‐built" measurement(s) using system identification algorithms. The model is validated by testing the innovations (residuals) for statistical whiteness and then stored for future use. (2) Later, when testing the part for damage, the error between the measurement and the output of the stored model is tested against a short‐term whiteness confidence interval test statistic. If the part passes the test, this implies that the model remains valid and the part is declared undamaged. If the part fails the test, this indicates a model mismatch, which means that the part's...

Two methods for isolating Salmonella and Arizona organisms from turtles, blending and excretion, were evaluated, and the percentage of isolates obtained by each method was compared with the percentage of isolates obtained by culture of turtle organs. The blending and excretion methods were equally effective in detecting the overall incidence of Salmonella and Arizona infections in turtles. The percentage of isolates obtained by specific organ culture, however, was less than the percentage obtained by the other two methods. The blending method detected a greater number of turtles with Arizona infections than did the excretion method, but there was no difference in the number of Salmonella infections detected by the two methods. The frequency of isolation of Arizona organisms from organs other than the small intestine and colon was higher than that of Salmonella .