Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased sensitivity and variability of phytotoxicity responses in Arctic soils to a reference toxicant, boric acid

Industrial and human activities in the Arctic regions may pose a risk to terrestrial Arctic ecosystem functions. One of the most common terrestrial toxicological end points, primary productivity, typically is assessed using a plant phytotoxicity test. Because of cryoturbation, a soil mixing process common in polar regions, we hypothesized that phytotoxicity test results in Arctic soils would be highly variable compared to other terrestrial ecosystems. The variability associated with phytotoxicity tests was evaluated using Environment Canada's standardized plant toxicity test in three cryoturbated soils from Canada's Arctic exposed to a reference toxicant, boric acid. Northern wheatgrass (Elymus lanceolatus) not only was more sensitive to toxicants in Arctic soils, its response to toxicants was more variable compared to that in temperate soils. The phytotoxicity of boric acid in cryosols was much greater than commonly reported in other soils, with a boric acid concentration of less than 150 μg/g soil needed to inhibit root and shoot growth by 20%. Large variability also was found in the phytotoxicity test results, with coefficients of variation for 10 samples ranging from 160 to 79%. The increased toxicity of boric acid in cryosols and variability in test response was not explained by soil properties. Based on our admittedly limited data set of three different Arctic soils, we recommend that more than 30 samples be taken from each control and potentially impacted area to accurately assess contaminant effects at sites in northern Canada. Such intensive sampling will insure that false‐negative results for toxicant impacts in Arctic soils are minimized.
Impact of HIV Antiretroviral Therapy on Depression and Mental Health Among Clients With HIV in Uganda

Objective With wide-reaching harmful effects of depression, and the absence of psychiatric treatment in most HIV care programs in sub-Saharan Africa, we examined the effects of antiretroviral therapy (ART) on depression and other mental health indicators. Methods 602 patients (302 non-ART, 300 ART) were followed for the first 12 months of HIV care in Uganda, with assessments at entry into care and Months 6 and 12. Mental health was assessed with measures of depression, hopelessness, and internalized HIV stigma; physical health functioning was assessed as an explanatory variable. Results Thirteen percent had clinical depression, 57% had elevated depressive symptoms, and CD4 cell count was negatively correlated with measures of depression at baseline. Significant reductions in elevated depressive symptoms (time: odds ratio [95% confidence interval] = 0.53 [0.43–0.64]) and hopelessness (time: &bgr; = −0.12, p <.001) were observed in both the ART and non-ART groups, but the drop in depression was greater among ART patients in intention-to-treat multivariate analysis (ART × time: p <.001). When added to the regression models, change in physical health functioning predicted positive longitudinal change on measures of depression, hopelessness, and internalized stigma (all p values <.001), yet ART status remained a significant independent predictor of each (ART × time: p values ranged from <.05 to <.001). Most mental health benefits of ART were experienced in the first 6 months of care. Conclusions These findings demonstrate the mental health benefits of HIV care and ART. However, in some people, mental health problems persist once physical health is stabilized, in which case mental health treatment may be needed.
Sleep/Sedation in Children Undergoing EEG Testing: A Comparison of Chloral Hydrate and Music Therapy

ABSTRACT. This study included a total of 60 pediatric patients ranging from 1 month through 5 years of age. The effects of chloral hydrate and music therapy were evaluated and compared as means of safe and effective ways to achieve sleep/sedation in infants and toddlers undergoing EEG testing. The results of the study indicate that music therapy may be a cost-effective, risk-free alternative to pharmacological sedation.
Measuring and Evaluating Changes in State Opinion Across Eight Issues

Is state opinion stable or dynamic? Do states exhibit similar or divergent trends? I answer these questions by creating dynamic measures of state opinion using multilevel regression, imputation, and post-stratification (MRP) techniques across eight issues: two global indicators, partisanship and ideology; and six specific issues, including spending on education and welfare, abortion, the death penalty, presidential approval and consumer sentiment. I find that patterns of dynamism and heterogeneity vary. State opinion on some issues, such as consumer sentiment, is highly dynamic but follow closely with national trends. On other issues, such as education spending, states exhibit more stability with heterogeneous trends. And still other issues, such as abortion, exhibit both stability and homogeneity. The results have implications for longstanding debates concerning the nature and origins of mass opinion, the way that state opinions are translated into policies, and how to measure state opinion. Finally, the extensive data set of longitudinal state opinion is publicly available.
Human Action Recognition Bases on Local Action Attributes

Human action recognition received many interest in the computer vision community. Most of the existing methods focus on either construct robust descriptor from the temporal domain, or computational method to exploit the discriminative power of the descriptor. In this paper we explore the idea of using local action attributes to form an action descriptor, where an action is no longer characterized with the motion changes in the temporal domain but the local semantic description of the action. We propose an novel framework where introduces local action attributes to represent an action for the final human action categorization. The local action attributes are defined for each body part which are independent from the global action. The resulting attribute descriptor is used to jointly model human action to achieve robust performance. In addition, we conduct some study on the impact of using body local and global low-level feature for the aforementioned attributes. Experiments on the KTH dataset and the MV-TJU dataset show that our local action attribute based descriptor improve action recognition performance.
Estimation of the wild‐type minimum inhibitory concentration value distribution

Antimicrobial resistance has become one of the main public health burdens of the last decades, and monitoring the development and spread of non‐wild‐type isolates has therefore gained increased interest. Monitoring is performed based on the minimum inhibitory concentration (MIC) values, which are collected through the application of dilution experiments. In order to account for the unobserved population heterogeneity of wild‐type and non‐wild‐type isolates, mixture models are extremely useful. Instead of estimating the entire mixture globally, it was our major aim to provide an estimate for the wild‐type first component only. The characteristics of this first component are not expected to change over time, once the wild‐type population has been confidently identified for a given antimicrobial. With this purpose, we developed a new method based on the multinomial distribution, and we carry out a simulation study to study the properties of the new estimator. Because the new approach fits within the likelihood framework, we can compare distinct distributional assumptions in order to determine the most suitable distribution for the wild‐type population. We determine the optimal parameters based on the AIC criterion, and attention is also paid to the model‐averaged approach using the Akaike weights. The latter is thought to be very suitable to derive specific characteristics of the wild‐type distribution and to determine limits for the wild‐type MIC range. In this way, the new method provides an elegant means to compare distinct distributional assumptions and to quantify the wild‐type MIC distribution of specific antibiotic–bacterium combinations. Copyright © 2013 John Wiley & Sons, Ltd.
Effects of Exercise on EEG Activity and Standard Tools Used to Assess Concussion

A variety of cognitive assessment tools are used to determine the functional status of the brain before and after injury in athletes. Questionnaires, neuropsychological tests, and electroencephalographic (EEG) measures have been recently used to directly assess brain function on and near the playing field. However, exercise can affect cognitive performance and EEG measures of cortical activity. To date, little empirical evidence exists on the effects of acute exercise on these measures of neurological function. We therefore quantified athlete performance on a standardized battery of concussion assessment tools and EEG measurements immediately before and after acute exercise to simulate conditions of athletic competition. Heart rate and arterial oxygen levels were collected before and after the exercise challenge consisting of a 1-mile run. Together these data, from a gender-balanced cohort of collegiate athletes, demonstrated that moderate to hard levels of acute exercise improved performance on the King-Devick test (K-D test) and Standardized Assessment of Concussion (SAC) component of the Sport Concussion Assessment Tool (SCAT3). Gender played an important role in these effects, and performance was most affected by exercise in female athletes. EEG activity in the theta band (4–8 Hz) was decreased during periods of quiet resting with eyes open or eyes closed. Additionally, exercise produced a slowing of the EEG during the K-D test and a shift to higher frequencies during the balance assessment of the SCAT3. Together, these data indicate that exercise alone can influence outcome measures of cognitive assessment tools used to assess brain function in athletes. Finally, care must be taken to acquire postinjury measurements during a comparable physiologic state to that in which baseline assessment data were measured, and further research is needed into the factors influencing outcome measures of these tests.
Survey: Internet of Thing Using FPGA

Everything in its way to be computerized and most of the objects are coming to be smart in present days. Modern Internet of Thing (IoT) allows these objects to be on the network by using IoT platforms. IoT is a smart information society that consists of smart devices; these devices can communicate with each other without human's intervention. IoT systems require flexible platforms. Through the use of Field Programmable Gate Array (FPGA), IoT devices can interface with the outside world easily with low power consumption, low latency, and best determinism. FPGAs provide System on Chip (SoC) technique due to FPGAs scalability which enables the designer to implement and integrate large number of hardware clocks at single chip. FPGA can be deemed as a special purpose reprogrammable processor since it can process signals at its input pins, manipulate them, and give off signals on the output pins. In this paper, using FPGA for IoT is the limelight.
Macrophage inhibitory cytokine‐1 is associated with cognitive impairment and predicts cognitive decline – the Sydney Memory and Aging Study

Higher levels of macrophage inhibitory cytokine‐1, also known as growth differentiation factor 15 (MIC‐1/GDF15), are associated with adverse health outcomes and all‐cause mortality. The aim of this study was to examine the relationships between MIC‐1/GDF15 serum levels and global cognition, five cognitive domains, and mild cognitive impairment (MCI), at baseline (Wave 1) and prospectively at 2 years (Wave 2), in nondemented participants aged 70–90 years. Analyses were controlled for age, sex, education, Framingham risk score, history of cerebrovascular accident, acute myocardial infarction, angina, cancer, depression, C‐reactive protein, tumor necrosis factor‐α, interleukins 6 and 12, and apolipoprotein ε4 genotype. Higher MIC‐1/GDF15 levels were significantly associated with lower global cognition at both waves. Cross‐sectional associations were found between MIC‐1/GDF15 and all cognitive domains in Wave 1 (all P < 0.001) and between processing speed, memory, and executive function in Wave 2 (all P < 0.001). Only a trend was found for the prospective analyses, individuals with high MIC‐1/GDF15 at baseline declined in global cognition, executive function, memory, and processing speed. However, when categorizing MIC‐1/GDF15 by tertiles, prospective analyses revealed statistically significant lower memory and executive function in Wave 2 in those in the upper tertile compared with the lower tertile. Receiver operating characteristics (ROC) analysis was used to determine MIC‐1/GDF15 cutoff values associated with cognitive decline and showed that a MIC‐1/GDF15 level exceeding 2764 pg/ml was associated with a 20% chance of decline from normal to MCI or dementia. In summary, MIC‐1/GDF15 levels are associated with cognitive performance and cognitive decline. Further research is required to determine the pathophysiology of this relationship.
Industry-based equity premium forecasts

Purpose This paper aims to study whether the industry indexes predict the evolution of the broad stock market in the USA. Design/methodology/approach The study uses industry indexes to predict the equity premium in the USA. It considers several types of predictive models: constant coefficients and constant volatility, drifting coefficients and constant volatility, constant coefficients and stochastic volatility and drifting coefficients and stochastic volatility. The models are estimated through the particle learning algorithm, which is suitable for dealing with the problem that an investor faces in practice, given that it allows the investor to revise the parameters as new information arrives. The individual forecasts are combined based on their past performance. Findings The results reveal that models exhibit significant predictive ability. The models with constant volatility exhibit better performance, at the statistical level, but the models with stochastic volatility generate higher gains for a mean–variance investor. Practical implications This study’s findings are valuable not only for finance researchers but also for private investors and mutual fund managers, who can use these forecasts to improve the performance of their portfolios. Originality/value To the best of the knowledge of the author, this is the first paper that uses particle learning and combination of forecasts to predict the equity premium in the USA based on industry indexes. The study shows that the models generate valuable forecasts over the long time span that is considered.
Eating disorders: a hidden phenomenon in outpatient mental health?

BACKGROUND Eating disorders are common but underdiagnosed illnesses. Help-seeking for co-occurring issues, such as anxiety and depression, are common. OBJECTIVES To identify the prevalence of eating problems, using the SCOFF, and eating disorders when screening positive on the SCOFF (i.e., ≥2), among patients seeking help for anxiety and depression at a community-based mental health service. METHOD Patients (N = 260) consecutively referred and assessed for anxiety and depression treatment were administered the SCOFF screening questionnaire and a semi-structured standardized diagnostic interview during routine intake. RESULTS 18.5% (48/260) scored ≥2 on the SCOFF, indicating eating problems. Of these, 41% (19/48) met criteria for an eating disorder. Thus, overall, 7.3% (19/260) of the sample met criteria for a DSM-IV eating disorder. Those scoring ≥2 on the SCOFF were more likely to: be female (p = 0.001), younger (p = 0.003), and have a history of self-harm (p < 0.001). DISCUSSION This study confirms that eating disorders are a hidden phenomenon in general outpatient mental health. By using a standardized diagnostic interview to establish diagnosis rather than self- or staff-report, the study builds on limited previous findings. The naturalistic study setting shows that screening for eating disorders can be easily built into routine intake practice, and successfully identifies treatment need.
Livestock Disease Indemnity Design When Moral Hazard is Followed by Adverse Selection

Averting or limiting the outbreak of infectious disease in domestic livestock herds is an economic and potential human health issue that involves the government and individual livestock producers. Producers have private information about preventive biosecurity measures they adopt on their farms prior to outbreak (ex ante moral hazard), and following outbreak they possess private information about whether or not their herd is infected (ex post adverse selection). We investigate how indemnity payments can be designed to provide incentives to producers to invest in biosecurity and report infection to the government in the presence of asymmetric information. We compare the relative magnitude of the first- and second-best levels of biosecurity investment and indemnity payments to demonstrate the tradeoff between risk sharing and efficiency, and we discuss the implications for status quo U.S. policy.
Optimal monetary policy in response to shifts in the beveridge curve

I build a dynamic stochastic general equilibrium model with search and matching frictions in the labor market and analyze the optimal monetary policy response to an outward shift in the Beveridge curve. The results cover several cases depending on the reason for the shift. If the shift is due to a fall in the efficiency of matching, then the optimal response of the central bank is to stabilize inflation. On the other hand, if the shift arises from an increase in the elasticity of employment matches with respect to vacancies, then the policy maker faces a trade off between stabilizing inflation and unemployment. The optimal policy response to the efficient labor market shock changes when real wages are sticky but remains unchanged when home and market goods are imperfect substitutes, compared to the case when they are not. When contrasted to a Taylor rule that targets inflation and output growth, the optimal monetary policy is more aggressive in pursuit of its objectives.
Public/Private Transportation Partnerships and American Space Transportation

Public/private partnerships and intergovernmental interdependencies have played a critical role in the development of all transportation modes that are linked together to form the American transportation system. Transportation system development has been a slow and incremental process shaped by national security, private sector economic demands, and availability of technology, population density and geography. Whether the initial transportation mode began at the private or public level, all sustainable transportation modes evolved through shifting patterns of public/private involvement. The purpose of this paper is to use this pattern to analyze and understand how the formation of public/private relationships have contributed to American transportation development and how the absence of these historical partnerships has inhibited the evolution of American commercial space transportation. The essay provides an overview of the public and private partnerships in the formation of new transportation mode development, explains how the lack of these historical partnerships have hampered American space transportation growth and provides recommendations for space transportation policy change.
RECORDING APPROACH OF HERITAGE SITES BASED ON MERGING POINT CLOUDS FROM HIGH RESOLUTION PHOTOGRAMMETRY AND TERRESTRIAL LASER SCANNING

Different approaches and tools are required in Cultural Heritage Documentation to deal with the complexity of monuments and sites. The documentation process has strongly changed in the last few years, always driven by technology. Accurate documentation is closely relied to advances of technology (imaging sensors, high speed scanning, automation in recording and processing data) for the purposes of conservation works, management, appraisal, assessment of the structural condition, archiving, publication and research (Patias et al., 2008). We want to focus in this paper on the recording aspects of cultural heritage documentation, especially the generation of geometric and photorealistic 3D models for accurate reconstruction and visualization purposes. The selected approaches are based on the combination of photogrammetric dense matching and Terrestrial Laser Scanning (TLS) techniques. Both techniques have pros and cons and recent advances have changed the way of the recording approach. The choice of the best workflow relies on the site configuration, the performances of the sensors, and criteria as geometry, accuracy, resolution, georeferencing, texture, and of course processing time. TLS techniques (time of flight or phase shift systems) are widely used for recording large and complex objects and sites. Point cloud generation from images by dense stereo or multi-view matching can be used as an alternative or as a complementary method to TLS. Compared to TLS, the photogrammetric solution is a low cost one, as the acquisition system is limited to a high-performance digital camera and a few accessories only. Indeed, the stereo or multi-view matching process offers a cheap, flexible and accurate solution to get 3D point clouds. Moreover, the captured images might also be used for models texturing. Several software packages are available, whether web-based, open source or commercial. The main advantage of this photogrammetric or computer vision based technology is to get at the same time a point cloud (the resolution depends on the size of the pixel on the object), and therefore an accurate meshed object with its texture. After matching and processing steps, we can use the resulting data in much the same way as a TLS point cloud, but in addition with radiometric information for textures. The discussion in this paper reviews recording and important processing steps as geo-referencing and data merging, the essential assessment of the results, and examples of deliverables from projects of the Photogrammetry and Geomatics Group (INSA Strasbourg, France).
Reducing barriers to breast cancer care through Avon patient navigation programs.

CONTEXT Avon Foundation for Women grantees provide breast cancer services through patient navigation (PN) in an effort to alleviate barriers to care among underserved women. OBJECTIVE To gain a better understanding of how PN programs function, this study explores variations in the use of navigators, types of services offered, description of clients they serve, tracking of treatment completion, and evaluation mechanisms. PARTICIPANTS Fifty-six Avon PN programs funded since 2008 throughout the United States were contacted. DESIGN An online survey was distributed to the grantees of which 44 (81%) complete responses were collected and analyzed. RESULTS Clients were racially and ethnically diverse, mostly in the 40- to 64-year old age range (64%) and 91.6% with an average income of less than $30 000. Women were either uninsured (50.7%) or receiving Medicaid (32.4%). PN programs were both community and hospital-based (22.5%); many hospitals (35.2%) were described as safety nets (eg, provide a significant level of care to low-income, uninsured, vulnerable populations). On-site services included breast screening (eg, mammography and breast ultrasound) and treatment (eg, breast surgery and radiation therapy). Some barriers to care identified by the programs included transportation, access to appointments, language, and financial issues (eg, cost of screening and treatment specifically for those uninsured). More than 39% of programs provided care across the cancer continuum. CONCLUSIONS Many Avon PN programs incorporated navigation services that span the cancer care continuum. They addressed disparities by offering navigation and on-site medical services to reduce multiple systems barriers and social issues related to breast care.