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The article offers information on several conferences including of 2013 American Association for Applied Linguistics (AAAL) Conference to be held in Dallas, Texas from March 16-19, the 2013 International Conference on English Education to be held in Taiwan from April 20-21, and the 11th Asia Teaching English as a Foreign Language (TEFL) International Conference to be held in the Philippines from October 26-28, 2013.

A continuous monitoring of neurological status can help in reporting the physical and mental health of a person. This can be capitalized for building a healthcare tracking system using a wearable device and a handheld mobile device. In this paper, we have used the non-EEG physiological biosignals dataset which gives practicability among subjects for acquiring data easily from wearable device sensors linearly and comfortably rather than the way of putting the subjects in a cumbersome setup laboratory. This paper proposes a custom fully convolutional-LSTM (FCN-LSTM) network to identify the neurological status of a subject using multivariate time series physiological sensor data. The proposed architecture uses parallel stacks of the convolutional layers and LSTM cells. This combination of different network types is significant for the selected problem as the fully convolutional section of the model extracts the local spatial features in the data, while the LSTM network handles the high-level features and temporal dependencies. The proposed FCN-LSTM model yielded a high accuracy of 98.6% and a precision of 98% on the non-EEG dataset from UT-Dallas. The average accuracy of single-subject results of the dataset using the proposed model was observed to be 99.26%. The results from the proposed model are significantly improved when compared with various state-of-the-art works on this problem. These results strongly suggest that this model, when put on a wearable device, can be effectively used to detect the neurological status or stress that the subject may be going through in real time. [ABSTRACT FROM AUTHOR]

This paper presents an equilibrium-based modeling framework for emergency response (ER) workload balancing for robust operations in metropolitan areas. The problem is formulated as a non-linear mathematical program (NLP), which determines the optimal maximum workload for each ER station such that the weighted sum of the area-wide expected response time and its variation is minimized. The concept of Marginal Cost of Uncertainty (MCU) is introduced to measure the impact of a station's workload increase on the robustness of the area-wide service performance. The solution of the NLP is proved to be equivalent to a state of equilibrium in which all stations have a minimum MCU. An iterative solution methodology is developed, which adopts a modified version of the Frank-Wolfe decomposition algorithm for convex optimization. The workload is iteratively balanced among adjacent stations until the state of equilibrium is achieved. At equilibrium, no station can reduce its MCU value by unilaterally shifting a part of its workload to any other station(s) in the area. The developed framework is applied to determine the optimal workload balancing strategy for 58 fire stations serving the City of Dallas. The framework is shown to enhance the robustness of the ER service performance especially in situations with imbalanced workloads. [ABSTRACT FROM AUTHOR]

Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions. [ABSTRACT FROM AUTHOR]

Objectives: Understanding if police malfeasance might be "contagious" is vital to identifying efficacious paths to police reform. Accordingly, we investigate whether an officer's propensity to engage in misconduct is associated with her direct, routine interaction with colleagues who have themselves engaged in misbehavior in the past. Methods: Recognizing the importance of analyzing the actual social networks spanning a police force, we use data on collaborative responses to 1,165,136 "911" calls for service by 3475 Dallas Police Department (DPD) officers across 2013 and 2014 to construct daily networks of front-line interaction. And we relate these cooperative networks to reported and formally sanctioned misconduct on the part of the DPD officers during the same time period using repeated-events survival models. Results: Estimates indicate that the risk of a DPD officer engaging in misconduct is not associated with the disciplined misbehavior of her ad hoc, on-the-scene partners. Rather, a greater risk of misconduct is associated with past misbehavior, officer-specific proneness, the neighborhood context of patrol, and, in some cases, officer race, while departmental tenure is a mitigating factor. Conclusions: Our observational findings—based on data from one large police department in the United States—ultimately suggest that actor-based and ecological explanations of police deviance should not be summarily dismissed in favor of accounts emphasizing negative socialization, where our study design also raises the possibility that results are partly driven by unobserved trait-based variation in the situations that officers find themselves in. All in all, interventions focused on individual officers, including the termination of deviant police, may be fruitful for curtailing police misconduct—where early interventions focused on new offenders may be key to avoiding the escalation of deviance. [ABSTRACT FROM AUTHOR]

The study examines racial differences in crime victimization rates among Black, Hispanic and White individuals during the stay-at-home order in Dallas city, TX. The study is based on sample of 85,958 calls for service recorded by Dallas Police Department between February 13, 2019, and April 30, 2020, where victims have been identified and their race is known. The findings suggest that Blacks were less likely to experience unintended drop in crime rates during the lockdown and also less likely to see decline in crimes against property when compared to both White and Hispanic victims. At the same time Blacks were more likely to experience increase in crimes committed at the apartments during the lockdown with Black females experiencing higher increase than Black males. There were no differences in crime victimization for incidents occurred in public places and crimes against persons. [ABSTRACT FROM AUTHOR]

Large, low-cost entrants can have a disrupting impact on incumbents. Supercenters may influence competing supermarkets' prices, but their effects on competition between supermarkets' private labels and manufacturer brands remain poorly understood. This study examines the impact of Wal-Mart Supercenters on supermarkets' strategic use of private labels and the control they may exercise over the pricing of manufacturer brands. We use a structural model to investigate alternative pricing scenarios in which the supermarkets have varying control over manufacturer-brand retail prices. The analysis is applied to the milk market in the Dallas/Fort Worth metroplex in an early period of Supercenter growth: 1996–2001. We find that Wal-Mart induces supermarkets to price their private label more competitively and has a very small effect on the pricing of the manufacturer brand. We also identify the type of consumers Wal-Mart Supercenters can attract from supermarkets, leaving incumbents to face a different composition of consumers. Consumers that continue shopping at traditional supermarkets are found to be less price-sensitive, view incumbents as less heterogeneous, and experience the manufacturer brand as more differentiated from private labels. [ABSTRACT FROM AUTHOR]

Overall trends of academic staffing in the US have indicated declines in tenure, increased use of contingent faculty, and stratification of teaching and research efforts. However, little is known about academic staffing patterns in professional programs, and little research has been done using program-level data. The purposes of this study were to identify faculty staffing patterns, determine if patterns change over time, and identify predictors of staffing patterns in US-based physical therapy programs. Yearly program-level accreditation data from 2008 to 2017 were analyzed. A finite mixture model analysis identified staffing patterns. Logistic regression analyses were run to predict category membership and observe change over time. Two academic staffing categories emerged: (1) Broad staffing patterns (90.4% of programs) composed of more core faculty on tenure track and who held academic degrees and (2) Flexible programs (9.7% of observations) with higher numbers of core faculty, fewer faculty on tenure track, and fewer faculty with an academic doctoral degree. There was little change over time from one category to another. Programs were more likely to adopt a Flexible pattern when budget and faculty teaching time rose. Membership to one of two staffing models appears to be predicted by institutional reliance on professional programs for revenues. Either programs lower expenses by having tenured faculty balance teaching and research, or they rely on non-tenured faculty positions and higher enrollments. [ABSTRACT FROM AUTHOR]

Objectives: We illustrate how a machine learning algorithm, Random Forests, can provide accurate long-term predictions of crime at micro places relative to other popular techniques. We also show how recent advances in model summaries can help to open the 'black box' of Random Forests, considerably improving their interpretability. Methods: We generate long-term crime forecasts for robberies in Dallas at 200 by 200 feet grid cells that allow spatially varying associations of crime generators and demographic factors across the study area. We then show how using interpretable model summaries facilitate understanding the model's inner workings. Results: We find that Random Forests greatly outperform Risk Terrain Models and Kernel Density Estimation in terms of forecasting future crimes using different measures of predictive accuracy, but only slightly outperform using prior counts of crime. We find different factors that predict crime are highly non-linear and vary over space. Conclusions: We show how using black-box machine learning models can provide accurate micro placed based crime predictions, but still be interpreted in a manner that fosters understanding of why a place is predicted to be risky. [ABSTRACT FROM AUTHOR]

Copyright of Gesunde Pflanzen is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Properly extracting patterns of individual mobility with high resolution data sources such as the one extracted from smartphone applications offers important opportunities. Potential opportunities not offered by call detailed records (CDRs), which offer resolutions triangulated from antennas, are route choices, travel modes detection and close encounters. Nowadays, there is not a standard and large scale data set collected over long periods that allows us to characterize these. In this work we thoroughly examine the use of data from smartphone applications, also referred to as location-based services (LBS) data, to extract and understand the vehicular route choice behavior. Taking the Dallas-Fort Worth metroplex as an example, we first extract the vehicular trips with simple rules and reconstruct the origin-destination matrix by coupling the extracted vehicular trips of the active LBS users and the United States census data. We then present a method to derive the commonly used routes by individuals from the LBS traces with varying sample rate intervals. We further inspect the relation between the number of routes and the trip characteristics, including the departure time, trip length and travel time. Specifically, we consider the travel time index and buffer index for the LBS users taking different number of routes. Empirical results demonstrate that during the peak hours, travelers tend to reduce the impact of traffic congestion by taking alternative routes. Overall, the proposed data analysis framework is cost-effective to treat sparse data generated from the use of smartphones to inform routing behavior. The potential in practice is to inform demand management strategies, by targeting individual users while generating large scale estimates of congestion mitigation. [ABSTRACT FROM AUTHOR]

The article focuses on the critical testing of a new apoptosis drug in the U.S. A startup company Joyant Pharmaceuticals in Dallas, Texas, licensed some anticancer compounds from the University of Texas-Southwestern (UTSW). In a September 2004 paper in the journal "Science", UTSW investigators described how one of them killed cancer cell lines (in combination with another compound) by inducing apoptosis, or programmed cell death, at spectacularly low concentrations. Embryologists, over a hundred years ago, were the first to recognize cell death as a necessary process, but the critical importance of apoptosis in eliminating unwanted adult cells only became accepted with the isolation of its molecular components beginning in the 1980s.

Abstract COVID-19 has wreaked havoc on the lives of persons around the world and social scientists are just beginning to understand its consequences on human behavior. One policy that public health officials put in place to help stop the spread of the virus were stay-at-home/shelter-in-place lockdown-style orders. While designed to protect people from the coronavirus, one potential and unintended consequence of such orders could be an increase in domestic violence – including abuse of partners, elders or children. Stay-at-home orders result in perpetrators and victims being confined in close quarters for long periods of time. In this study, we use data from Dallas, Texas to examine the extent to which a local order was associated with an increase in domestic violence. Our results provide some evidence for a short-term spike in the 2 weeks after the lockdown was instituted but a decrease thereafter. We note that it is difficult to determine just how much the lockdown was the cause of this increase as the domestic violence trend was increasing prior to the order. [ABSTRACT FROM AUTHOR]

Macroinvertebrate community structure and assemblages associated with the planted, native submerged aquatic vegetation (SAV) species Heteranthera dubia (Jacq.) MacMillan and Potamogeton nodosus Poiret were examined in a series of constructed urban floodway wetlands, the Dallas Floodway Extension Lower Chain of Wetlands, Dallas, TX, USA. Macroinvertebrate community metrics, including abundance, richness, diversity, and evenness associated with SAV and three different wetlands of varying construction completion dates, water sources (direct or wetland-channeled wastewater effluent), and ecosystem management stage (established/reference or developing) were compared and analyzed. Assemblages at sampling sites were also classified and related to vegetation and wetland physicochemical parameters. Plant species affected only macroinvertebrate abundance, with the less-dissected P. nodosus supporting higher counts than H. dubia. Wetland age and water-effluent type had the most substantial effect on macroinvertebrate communities. The older, longer-managed wetland and wetland-channeled effluent habitat consistently demonstrated higher quality metrics and biodiversity than newly constructed, direct effluent wetland habitat. Increased vegetation cover and wetland age, coupled with moderate water temperature, pH, and DO levels were characteristics of more rich and diverse macroinvertebrate communities, including pollutant-sensitive taxa, such as Ephemeroptera and Trichoptera. [ABSTRACT FROM AUTHOR]

Purpose: Chronic low back pain (cLBP) affects a quarter of a population during its lifetime. The most severe cases include patients not responding to interventions such as 5-week-long in-hospital multi-disciplinary protocols. This document reports on a pilot study offering an alpha-phase synchronization (APS) brain rehabilitation intervention to a population of n = 16 multi-resistant cLBP patients.Methods: The intervention consists of 20 sessions of highly controlled electroencephalography (EEG) APS operant conditioning (neurofeedback) paradigm delivered in the form of visual feedback. Visual analogue scale for pain, Dallas, Hamilton, and HAD were measured before, after, at 6-month and 12-month follow-up. Full-scalp EEG data were analyzed to study significant changes in the brain's electrical activity.Results: The intervention showed a great and lasting response of most measured clinical scales. The clinical improvement was lasting beyond the 6-month follow-up endpoints. The EEG data confirm that patients did control (intra-session trends) and learned to better control (intersession trends) their APS neuromarker resulting in (nonsignificant) baseline changes in their resting state activity. Last and most significantly, the alpha-phase concentration (APC) neuromarker, specific to phase rather than amplitude, was found to correlate significantly with the reduction in clinical symptoms in a typical dose-response effect.Conclusion: This first experiment highlights the role of the APC neuromarker in relation to the nucleus accumbens activity and its role on nociception and the chronicity of pain. This study suggests APC rehabilitation could be used clinically for the most severe cases of cLBP. Its excellent safety profile and availability as a home-use intervention makes it a potentially disruptive tool in the context of nonsteroidal anti-inflammatory drugs and opioid abuses. These slides can be retrieved under Electronic Supplementary Material. [ABSTRACT FROM AUTHOR]

The article discusses the challenges faced by researcher Ellen Vitetta of the University of Texas Southwestern Medical Center about her effort to develop an immunotoxin. Vitetta and her team genetically engineered the ricin A chain in a region where vascular leakage occur. Her method proves to be successful when it was able to solve the vascular leakage side effect of the drug. However, the development of the drug encounters many obstacles like the costliness of the process and the delay in the time of testing a drug's effectiveness.

Objectives: To examine whether information on where the police patrol drawn from automatic vehicle location (AVL) systems can be used to increase the amount of directed patrol time at high-crime police beats and crime hot spots, and whether such increases would lead to reductions in crime. Methods: In an experimental study with a block-randomized design, 232 police beats were randomly allocated to an experimental or control condition. In the experimental condition, the police commanders knew the amount of time that police spent in beats and crime hot spots. This information was not provided to commanders in the control condition. Over a 13-week period, assigned patrol time, unallocated patrol time, total patrol time, and crime were tracked at both police beats and crime hot spots ( N = 1006). Results: Knowledge of where police officers patrolled did not affect directed patrol at the beat level. At the hot spots level, the treatment group experienced meaningful increases in unallocated patrol time and total patrol time, and a decrease in crime. Conclusions: A key finding of the study is that information generated from AVL can be used to increase directed patrol time at crime hot spots, and that these increased levels of patrol will lead to reductions in crime. At the same time, our study points to the fact that only a small proportion of unallocated time in Dallas is actually focused on hot spots policing. We suggest that this is the reason why crime went down significantly at the hot spots but not in beats overall in Dallas. [ABSTRACT FROM AUTHOR]

The article reports on the research conducted by Christopher Cowan of the University of Texas Southwestern Medical Center regarding the implication of cocaine to the brain cells of human beings in Dallas, Texas. Researchers found that repeated exposure to cocaine increases the density of connections among the nerve cells in a brain region that is central to motivation and reward. When they applied the cocaine in laboratory mice and they discovered that the substance suppresses the protein MEF2, thus encouraging medium-sized spiny neurons in the nucleus accumbents to form more links.

Congenital pseudarthrosis is an uncommon anomaly poorly referred to in the pediatric literature. To describe congenital pseudarthrosis in children. We discuss the presentation, clinical symptomatology, and treatment. We reviewed the records of four children with congenital pseudarthrosis from 1990 to 2009 at the radiology department of Texas Scottish Rite Hospital for Children. Four girls ages 4 through 14 were identified. Three children presented with a bulge in the mid-clavicle as a chief complaint. The fourth child (14 years old) had bluish discoloration of the right upper extremity with venous distention. Three of the four did not have other abnormalities, while the 14-year-old had exostosis on the left hand. The mid-portion of the right clavicle was involved in all four children. Three of the four were treated surgically. Congenital pseudarthrosis of the clavicle is an uncommon anomaly found mostly in girls and in the mid-right clavicle. [ABSTRACT FROM AUTHOR]

The Dallas-Fort Worth region is subject to a severe expansive soils hazard, resulting in costly damage to many thousands of residential foundations each year. A GIS is used to examine relationships between a sample of approximately 10,000 foundation repair addresses and geologic and soil conditions. The study results show that foundation repairs are concentrated in areas underlain by bedrock, surficial deposits and soils that promote high shrink–swell potentials. A linear extensibility index, designed as an index of overall shrink–swell potential explains about 48% of the variation in the prevalence of repairs. Repairs are concentrated on soils that underlay many areas of new urban growth in the DFW region, suggesting that the incidence of foundation repairs in these areas will probably increase in the future. [ABSTRACT FROM AUTHOR]

Combined magnetic and geochemical studies were conducted on sediments from White Rock Lake, a reservoir in suburban Dallas (USA), to investigate how land use has affected sediment and water quality since the reservoir was filled in 1912. The chronology of a 167-cm-long core is constrained by the recognition of the pre-reservoir surface and by 137Cs results. In the reservoir sediments, magnetic susceptibility (MS) and isothermal remanent magnetization (IRM) are largely carried by detrital titanomagnetite that originally formed in igneous rocks. Titanomagnetite and associated hematite are the dominant iron oxides in a sample from the surficial deposit in the watershed but are absent in the underlying Austin Chalk. Therefore, these minerals were transported by wind into the watershed. After about 1960, systematic decreases in Ti, Fe, and Al suggest diminished input of detrital Fe-Ti oxides from the surficial deposits. MS and IRM remain constant over this interval, however, implying compensation by an increase in strongly magnetic material derived from human activity. Anthropogenic magnetite in rust and ferrite spherules (from fly ash?) are more common in sediment deposited after about 1970 than before and may account for the constant magnetization despite the implied decrease in detrital Fe-Ti oxides. An unexpected finding is the presence of authigenic greigite (Fe3S4), the abundance of which is at least partly controlled by climate. Greigite is common in sediments that predate about 1975, with zones of concentration indicated by relatively high IRM/MS. High greigite contents in sediment deposited during the early to mid-1950s and during the mid-1930s correspond to several-year periods of below-average precipitation and drought from historical records. Relatively long water-residence times in the reservoir during these periods may have led to elevated levels of sulfate available for bacterial sulfate reduction. The sulfate was probably derived via the oxidation of pyrite that is common in the underlying Austin Chalk. These results provide a basis for the paleoenvironmental interpretation of greigite occurrence in older lake sediments. The results also indicate that greigite formed rapidly and imply that it can be preserved in the amounts produced over a short time span (in this lake, only a few years). This finding thus suggests that, in some lacustrine settings, greigite is capable of recording paleomagnetic secular variation. [ABSTRACT FROM AUTHOR]

This article examines spatial autocorrelation in transaction prices of single-family properties in Dallas, Texas. The empirical analysis is conducted using a semilog hedonic house price equation and a spherical autocorrelation function with data for over 5000 transactions of homes sold between 1991:4 and 1993:1. Properties are geocoded and assigned to separate housing submarkets within metropolitan Dallas. Hedonic and spherical autocorrelation parameters are estimated separately for each submarket using estimated generalized least squares (EGLS). We find strong evidence of spatial autocorrelation in transaction prices within submarkets. Results for spatially autocorrelated residuals are mixed. In four of eight submarkets, there is evidence of spatial autocorrelation in the hedonic residuals for single-family properties located within a 1200 meter radius. In two submarkets, the hedonic residuals are spatially autocorrelated throughout the submarket, while the hedonic residuals are spatially uncorrelated in the remaining two submarkets. Finally, we compare OLS and kriged EGLS predicted values for properties sold during 1993:1. Kriged EGLS predictions are more accurate than OLS in six of eight submarkets, while OLS has smaller prediction errors in submarkets where the residuals are spatially uncorrelated and the estimated semivariogram has a large variance. [ABSTRACT FROM AUTHOR]