United States Department of Defense

We introduce the DET Curve as a means of representing performance on detection tasks that involve a tradeoff of error types. We discuss why we prefer it to the traditional ROC Curve and offer several examples of its use in speaker recognition and language recognition. We explain why it is likely to produce approximately linear curves. We also note special points that may be included on these curves, how they are used with multiple targets, and possible further applications.

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD-abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, Question, BACKCHANNEL, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.

OBJECTIVE: To evaluate the effectiveness of training and institutionalizing teamwork behaviors, drawn from aviation crew resource management (CRM) programs, on emergency department (ED) staff organized into caregiver teams. STUDY SETTING: Nine teaching and community hospital EDs. STUDY DESIGN: A prospective multicenter evaluation using a quasi-experimental, untreated control group design with one pretest and two posttests of the Emergency Team Coordination Course (ETCC). The experimental group, comprised of 684 physicians, nurses, and technicians, received the ETCC and implemented formal teamwork structures and processes. Assessments occurred prior to training, and at intervals of four and eight months after training. Three outcome constructs were evaluated: team behavior, ED performance, and attitudes and opinions. Trained observers rated ED staff team behaviors and made observations of clinical errors, a measure of ED performance. Staff and patients in the EDs completed surveys measuring attitudes and opinions. DATA COLLECTION: Hospital EDs were the units of analysis for the seven outcome measures. Prior to aggregating data at the hospital level, scale properties of surveys and event-related observations were evaluated at the respondent or case level. PRINCIPAL FINDINGS: A statistically significant improvement in quality of team behaviors was shown between the experimental and control groups following training (p = .012). Subjective workload was not affected by the intervention (p = .668). The clinical error rate significantly decreased from 30.9 percent to 4.4 percent in the experimental group (p = .039). In the experimental group, the ED staffs' attitudes toward teamwork increased (p = .047) and staff assessments of institutional support showed a significant increase (p = .040). CONCLUSION: Our findings point to the effectiveness of formal teamwork training for improving team behaviors, reducing errors, and improving staff attitudes among the ETCC-trained hospitals.

This review summarizes evidence for the effectiveness of technology use in foreign language (FL) learning and teaching, with a focus on empirical studies that compare the use of newer technologies with more traditional methods or materials. The review of over 350 studies (including classroom-based technologies, individual study tools, network-based social computing, and mobile and portable devices) revealed that, in spite of an abundance of publications available on the topic of technology use in FL learning and teaching, evidence of efficacy is limited. However, strong support for the claim that technology made a measurable impact in FL learning came from studies on computer-assisted pronunciation training, in particular, automatic speech recognition (ASR). These studies demonstrated that ASR can facilitate the improvement of pronunciation and can provide feedback effectively. Additional studies provided strong support for the use of chat in FL learning. These studies showed that, with chat, both the amount of learners’ language production and its complexity significantly increased. The literature revealed moderate support for claims that technology enhanced learners’ output and interaction, affect and motivation, feedback, and metalinguistic knowledge.

Toll-like receptors (TLRs) and RIG-I-like receptors (RLRs) constitute distinct families of pattern-recognition receptors that sense nucleic acids derived from viruses and trigger antiviral innate immune responses. TLR3, TLR7, and TLR9 are membrane proteins localized to the endosome that recognize viral double-stranded RNA, single-stranded RNA, and DNA, respectively, while RLRs, including RIG-I, Mda5, and LGP2, are cytoplasmic proteins that recognize viral RNA. Upon recognition of these nucleic acid species, TLRs and RLRs recruit specific intracellular adaptor proteins to initiate signaling pathways culminating in activation of NF-kappaB, MAP kinases, and IRFs that control the transcription of genes encoding type I interferon and other inflammatory cytokines, which are important for eliminating viruses. Here, we review recent insights into the signaling pathways initiated by TLR and RLR and their roles in innate and adaptive immune responses.

Conservation scientists generally agree that many types of protected areas will be needed to protect tropical forests. But little is known of the comparative performance of inhabited and uninhabited reserves in slowing the most extreme form of forest disturbance: conversion to agriculture. We used satellite-based maps of land cover and fire occurrence in the Brazilian Amazon to compare the performance of large (> 10,000 ha) uninhabited (parks) and inhabited (indigenous lands, extractive reserves, and national forests) reserves. Reserves significantly reduced both deforestation and fire. Deforestation was 1.7 (extractive reserves) to 20 (parks) times higher along the outside versus the inside of the reserve perimeters and fire occurrence was 4 (indigenous lands) to 9 (national forests) times higher. No strong difference in the inhibition of deforestation (p = 0. 11) or fire (p = 0.34) was found between parks and indigenous lands. However, uninhabited reserves tended to be located away from areas of high deforestation and burning rates. In contrast, indigenous lands were often created in response to frontier expansion, and many prevented deforestation completely despite high rates of deforestation along their boundaries. The inhibitory effect of indigenous lands on deforestation was strong after centuries of contact with the national society and was not correlated with indigenous population density. Indigenous lands occupy one-fifth of the Brazilian Amazon-five times the area under protection in parks--and are currently the most important barrier to Amazon deforestation. As the protected-area network expands from 36% to 41% of the Brazilian Amazon over the coming years, the greatest challenge will be successful reserve implementation in high-risk areas of frontier expansion as indigenous lands are strengthened. This success will depend on a broad base of political support.

This paper provides a tutorial introduction to the constant modulus (CM) criterion for blind fractionally spaced equalizer (FSE) design via a (stochastic) gradient descent algorithm such as the constant modulus algorithm (CMA). The topical decisions utilized in this tutorial can be used to help catalog the emerging literature on the CM criterion and on the behavior of (stochastic) gradient descent algorithms used to minimize it.

Governments are increasingly adopting behavioral science techniques for changing individual behavior in pursuit of policy objectives. The types of "nudge" interventions that governments are now adopting alter people's decisions without coercion or significant changes to economic incentives. We calculated ratios of impact to cost for nudge interventions and for traditional policy tools, such as tax incentives and other financial inducements, and we found that nudge interventions often compare favorably with traditional interventions. We conclude that nudging is a valuable approach that should be used more often in conjunction with traditional policies, but more calculations are needed to determine the relative effectiveness of nudging.

Steroid-resistant asthma comprises an important source of morbidity in patient populations. T(H)17 cells represent a distinct population of CD4(+) Th cells that mediate neutrophilic inflammation and are characterized by the production of IL-17, IL-22, and IL-6. To investigate the function of T(H)17 cells in the context of Ag-induced airway inflammation, we polarized naive CD4(+) T cells from DO11.10 OVA-specific TCR-transgenic mice to a T(H)2 or T(H)17 phenotype by culturing in conditioned medium. In addition, we also tested the steroid responsiveness of T(H)2 and T(H)17 cells. In vitro, T(H)17 cytokine responses were not sensitive to dexamethasone (DEX) treatment despite immunocytochemistry confirming glucocorticoid receptor translocation to the nucleus following treatment. Transfer of T(H)2 cells to mice challenged with OVA protein resulted in lymphocyte and eosinophil emigration into the lung that was markedly reduced by DEX treatment, whereas T(H)17 transfer resulted in increased CXC chemokine secretion and neutrophil influx that was not attenuated by DEX. Transfer of T(H)17 or T(H)2 cells was sufficient to induce airway hyperresponsiveness (AHR) to methacholine. Interestingly, AHR was not attenuated by DEX in the T(H)17 group. These data demonstrate that polarized Ag-specific T cells result in specific lung pathologies. Both T(H)2 and T(H)17 cells are able to induce AHR, whereas T(H)17 cell-mediated airway inflammation and AHR are steroid resistant, indicating a potential role for T(H)17 cells in steroid-resistant asthma.

MIPSGAL is a 278 deg 2 survey of the inner Galactic plane using the Multiband Infrared Photometer for Spitzer aboard the Spitzer Space Telescope. The survey field was imaged in two passbands, 24 and 70 m with resolutions of 6 and 18, respectively. The survey was designed to provide a uniform, well-calibrated and well-characterized data set for general inquiry of the inner Galactic plane and as a longer-wavelength complement to the shorter-wavelength Spitzer survey of the Galactic plane: Galactic Plane Infrared Mapping Survey Extraordinaire. The primary science drivers of the current survey are to identify all high-mass (M > 5 M ) protostars in the inner Galactic disk and to probe the distribution, energetics, and properties of interstellar dust in the Galactic disk. The observations were planned to minimize data artifacts due to image latents at 24 m and to provide full coverage at 70 m. Observations at ecliptic latitudes within 15of the ecliptic plane were taken at multiple epochs to help reject asteroids. The data for the survey were collected in three epochs, 2005 September-October, 2006 April, and 2006 October with all of the data available to the public. The estimated point-source sensitivities of the survey are 2 and 75 mJy (3 ) at 24 and 70 m, respectively. Additional data processing was needed to mitigate image artifacts due to bright sources at 24 m and detector responsivity variations at 70 m due to the large dynamic range of the Galactic plane. Enhanced data products including artifact-mitigated mosaics and point-source catalogs are being produced with the 24 m mosaics already publicly available from the NASA/IPAC Infrared Science Archive. Some preliminary results using the enhanced data products are described.

Novel, noble-metal-free, solid-state all-titanium carbide (Ti₃C₂T_x) MXene microsupercapacitors are fabricated, which exhibit high areal capacitance, excellent rate-capability, and are transferable to any surface.

OBJECTIVE: To describe new onset and persistence of self reported post-traumatic stress disorder symptoms in a large population based military cohort, many of whom were deployed in support of the wars in Iraq and Afghanistan. DESIGN: Prospective cohort analysis. SETTING AND PARTICIPANTS: Survey enrolment data from the millennium cohort (July 2001 to June 2003) obtained before the wars in Iraq and Afghanistan. Follow-up (June 2004 to February 2006) data on health outcomes collected from 50 184 participants. MAIN OUTCOME MEASURES: Self reported post-traumatic stress disorder as measured by the posttraumatic stress disorder checklist-civilian version using Diagnostic and Statistical Manual of Mental Disorders, fourth edition criteria. RESULTS: More than 40% of the cohort were deployed between 2001 and 2006; between baseline and follow-up, 24% deployed for the first time in support of the wars in Iraq and Afghanistan. New incidence rates of 10-13 cases of post-traumatic stress disorder per 1000 person years occurred in the millennium cohort. New onset self reported post-traumatic stress disorder symptoms or diagnosis were identified in 7.6-8.7% of deployers who reported combat exposures, 1.4-2.1% of deployers who did not report combat exposures, and 2.3-3.0% of non-deployers. Among those with self reported symptoms of post-traumatic stress disorder at baseline, deployment did not affect persistence of symptoms. CONCLUSIONS: After adjustment for baseline characteristics, these prospective data indicate a threefold increase in new onset self reported post-traumatic stress disorder symptoms or diagnosis among deployed military personnel who reported combat exposures. The findings define the importance of post-traumatic stress disorder in this population and emphasise that specific combat exposures, rather than deployment itself, significantly affect the onset of symptoms of post-traumatic stress disorder after deployment.

Abstract The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique—Subtype and Stage Inference (SuStaIn)—able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer’s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype ( p = 7.18 × 10 −4 ) or temporal stage ( p = 3.96 × 10 −5 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

A language-independent means of gauging topical similarity in unrestricted text is described. The method combines information derived from n-grams (consecutive sequences of n characters) with a simple vector-space technique that makes sorting, categorization, and retrieval feasible in a large multilingual collection of documents. No prior information about document content or language is required. Context, as it applies to document similarity, can be accommodated by a well-defined procedure. When an existing document is used as an exemplar, the completeness and accuracy with which topically related documents are retrieved is comparable to that of the best existing systems. The results of a formal evaluation are discussed, and examples are given using documents in English and Japanese.

IMPORTANCE: Glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1) have been widely studied and show promise for clinical usefulness in suspected traumatic brain injury (TBI) and concussion. Understanding their diagnostic accuracy over time will help translate them into clinical practice. OBJECTIVES: To evaluate the temporal profiles of GFAP and UCH-L1 in a large cohort of trauma patients seen at the emergency department and to assess their diagnostic accuracy over time, both individually and in combination, for detecting mild to moderate TBI (MMTBI), traumatic intracranial lesions on head computed tomography (CT), and neurosurgical intervention. DESIGN, SETTING, AND PARTICIPANTS: This prospective cohort study enrolled adult trauma patients seen at a level I trauma center from March 1, 2010, to March 5, 2014. All patients underwent rigorous screening to determine whether they had experienced an MMTBI (blunt head trauma with loss of consciousness, amnesia, or disorientation and a Glasgow Coma Scale score of 9-15). Of 3025 trauma patients assessed, 1030 met eligibility criteria for enrollment, and 446 declined participation. Initial blood samples were obtained in 584 patients enrolled within 4 hours of injury. Repeated blood sampling was conducted at 4, 8, 12, 16, 20, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156, 168, and 180 hours after injury. MAIN OUTCOMES AND MEASURES: Diagnosis of MMTBI, presence of traumatic intracranial lesions on head CT scan, and neurosurgical intervention. RESULTS: A total of 1831 blood samples were drawn from 584 patients (mean [SD] age, 40 [16] years; 62.0% [362 of 584] male) over 7 days. Both GFAP and UCH-L1 were detectible within 1 hour of injury. GFAP peaked at 20 hours after injury and slowly declined over 72 hours. UCH-L1 rose rapidly and peaked at 8 hours after injury and declined rapidly over 48 hours. Over the course of 1 week, GFAP demonstrated a diagnostic range of areas under the curve for detecting MMTBI of 0.73 (95% CI, 0.69-0.77) to 0.94 (95% CI, 0.78-1.00), and UCH-L1 demonstrated a diagnostic range of 0.30 (95% CI, 0.02-0.50) to 0.67 (95% CI, 0.53-0.81). For detecting intracranial lesions on CT, the diagnostic ranges of areas under the curve were 0.80 (95% CI, 0.67-0.92) to 0.97 (95% CI, 0.93-1.00)for GFAP and 0.31 (95% CI, 0-0.63) to 0.77 (95% CI, 0.68-0.85) for UCH-L1. For distinguishing patients with and without a neurosurgical intervention, the range for GFAP was 0.91 (95% CI, 0.79-1.00) to 1.00 (95% CI, 1.00-1.00), and the range for UCH-L1 was 0.50 (95% CI, 0-1.00) to 0.92 (95% CI, 0.83-1.00). CONCLUSIONS AND RELEVANCE: GFAP performed consistently in detecting MMTBI, CT lesions, and neurosurgical intervention across 7 days. UCH-L1 performed best in the early postinjury period.

Sport-related concussion (SRC) is typically followed by clinical recovery within days, but reports of prolonged symptoms are common. We investigated the incidence of prolonged recovery in a large cohort (n = 18,531) of athlete seasons over a 10-year period. A total of 570 athletes with concussion (3.1%) and 166 controls who underwent pre-injury baseline assessments of symptoms, neurocognitive functioning and balance were re-assessed immediately, 3 hr, and 1, 2, 3, 5, 7, and 45 or 90 days after concussion. Concussed athletes were stratified into typical (within 7 days) or prolonged (> 7 days) recovery groups based on symptom recovery time. Ten percent of athletes (n = 57) had a prolonged symptom recovery, which was also associated with lengthier recovery on neurocognitive testing (p < .001). At 45-90 days post-injury, the prolonged recovery group reported elevated symptoms, without deficits on cognitive or balance testing. Prolonged recovery was associated with unconsciousness [odds ratio (OR), 4.15; 95% confidence interval (CI) 2.12-8.15], posttraumatic amnesia (OR, 1.81; 95% CI, 1.00-3.28), and more severe acute symptoms (p < .0001). These results suggest that a small percentage of athletes may experience symptoms and functional impairments beyond the typical window of recovery after SRC, and that prolonged recovery is associated with acute indicators of more severe injury.

As the nature of work becomes more complex, teams have become necessary to ensure effective functioning within organizations. The healthcare industry is no exception. As such, the prevalence of training interventions designed to optimize teamwork in this industry has increased substantially over the last 10 years (Weaver, Dy, & Rosen, 2014). Using Kirkpatrick's (1956, 1996) training evaluation framework, we conducted a meta-analytic examination of healthcare team training to quantify its effectiveness and understand the conditions under which it is most successful. Results demonstrate that healthcare team training improves each of Kirkpatrick's criteria (reactions, learning, transfer, results; d = .37 to .89). Second, findings indicate that healthcare team training is largely robust to trainee composition, training strategy, and characteristics of the work environment, with the only exception being the reduced effectiveness of team training programs that involve feedback. As a tertiary goal, we proposed and found empirical support for a sequential model of healthcare team training where team training affects results via learning, which leads to transfer, which increases results. We find support for this sequential model in the healthcare industry (i.e., the current meta-analysis) and in training across all industries (i.e., using meta-analytic estimates from Arthur, Bennett, Edens, & Bell, 2003), suggesting the sequential benefits of training are not unique to medical teams. Ultimately, this meta-analysis supports the expanded use of team training and points toward recommendations for optimizing its effectiveness within healthcare settings. (PsycINFO Database Record

Satellite networks provide global coverage and support a wide range of services, low Earth orbit (LEO) satellites provide short round-trip delays and are becoming increasingly important. One of the challenges in LEO satellite networks is the development of specialized and efficient routing algorithms. In this work, a datagram routing algorithm for LEO satellite networks is introduced. The algorithm generates minimum propagation delay paths. The performance of the algorithm is evaluated through simulations. The robustness issues of the algorithm are also discussed.

The diagnosis and treatment of mild traumatic brain injury (MTBI)have historically been hampered by an incomplete base of scientific evidence to guide clinicians. One question has been most elusive to clinicians and researchers alike: What is the true natural history of MTBI? Fortunately, the science of MTBI has advanced more in the last decade than in the previous 50 years, and now reaches a maturity point at which the science can drive an evidence-based approach to clinical management. In particular, technological advances in functional neuroimaging have created a powerful bridge between the clinical and basic science of MTBI in humans. Collectively, findings from clinical, basic science, and functional neuroimaging studies now establish a foundation on which to build integrative theories and testable hypotheses around a comprehensive model of MTBI recovery. We review the current scientific literature on postconcussion symptom recovery, neuropsychological outcome, and neurophysiological healing after MTBI. Special emphasis is placed on how the new evidence base can help guide clinicians in the evaluation and management of military-related MTBI.