A spatial memory signal shows that the parietal cortex has access to a craniotopic representation of space

Humans effortlessly establish a gist-like memory of their environment whenever they enter a new place, a memory that can guide action even in the absence of vision. Neurons in the lateral intraparietal area (LIP) of the monkey exhibit a form of this environmental memory. These neurons respond when a monkey makes a saccade that brings the spatial location of a stimulus that appeared on a number of prior trials, but not on the present trial, into their receptive fields (RFs). The stimulus need never have appeared in the neuron’s RF. This memory response is usually weaker, with a longer latency than the neuron’s visual response. We suggest that these results demonstrate that LIP has access to a supraretinal memory of space, which is activated when the spatial location of the vanished stimulus can be described by a retinotopic vector from the center of gaze to the remembered spatial location

Analysis of the Changing Functional Structure of Major Urban Centers of Ethiopia

The study attempted to analyze the patterns of the changing functional structure of major urban centers of Ethiopia over the period of 2009 to 2012. An economic base, export employment multiplier and shift share analysis were used to identify the basic sectors, the export employment and expanding and declining industries in fifteen major urban centers. The location quotient results showed that there have been changes in economic bases, for twelve out of fifteen urban centers. The major sources of export employment were construction, distributive and social services for most of the towns considered. Export employment analysis has indicated that some towns of leading regions have relatively higher percentage of export employment compared to others. In general, the major drivers of the urban employment growth in many urban centers over the study period were found to be distributive, social and extractive sectors. However, the role of the transformative economic sectors, particularly the manufacturing sector is not as such encouraging for many towns. Against this back drop, the analysis surfaced local economic development issue such as how the towns included in this study could capitalize more on the transformative economic sectors through various incentive mechanisms.Keywords: Urban Functions, Basic Employment, Employment Multiplier, Location Quotient, Shift Share Analysi

Adsorptive removal of fluoride from water using nanoscale aluminium oxide hydroxide (AlOOH)

In this study the fluoride removal potential of nanoscale aluminium oxide hydroxide (nano-AlOOH) has been investigated. The material was produced using aluminium nitrate (Al(NO3)3.9H2O, 95%), and ammonium bicarbonate (NH4HCO3, 98%) and its density and mineralogy were investigated. A series of batch adsorption experiments were carried out to assess parameters that influence the adsorption process. The parameters considered were contact time and adsorbent dose, initial fluoride concentration, and pH. Results showed that most of the adsorption took place during the first 30 min; and equilibrium was reached at one hour contact time with an optimum adsorbent dose of 1.6 g L-1 for initial fluorideconcentration of 20 mg L-1. The removal efficiency of fluorideincreased with increase in adsorbent dosage. The fluoride removal efficiency was increased as the pH of the solution increases from pH 3 to 8, but any further increase in pH led to a decrease in fluoride removal efficiency. Maximum adsorption occurred at around pH 7 with initial fluoride concentration of 20 mg L-1. The adsorption data were well fitted to the Langmuir isotherm model with a maximum adsorption capacity of 62.5 mg F- g-1. The kinetic studies showed that the adsorption of fluoride by nano-AlOOH obeys a pseudo-second order rate equation. The intraparticle diffusion was not a rate-controlling step for the adsorption process. Thus, the overall study indicates that nano-AlOOH is an efficient defluoridating material. KEY WORDS: Nanoscale AlOOH, Defluoridation, Fluoride removal efficiency, Adsorption capacity, Adsorption kinetics, Adsorption mechanism Bull. Chem. Soc. Ethiop. 2014, 28(2), 215-227.DOI: http://dx.doi.org/10.4314/bcse.v28i2.

Mendelian randomization case-control PheWAS in UK Biobank shows evidence of causality for smoking intensity in 28 distinct clinical conditions.

Background: Smoking is one of the greatest threats to public health worldwide. We integrated phenome-wide association study (PheWAS) and Mendelian randomization (MR) approaches to explore causal effects of genetically predicted smoking intensity across the human disease spectrum. Methods: We conducted PheWAS case-control analyses in 152,483 ever smokers of White-British ancestry, aged 39-73 years. Disease diagnoses were based on hospital inpatient and mortality registrations. Smoking intensity was instrumented by four genetic variants, and disease risks estimated for one cigarette per day heavier intakes. Associations passing the FDR threshold (p<0•0025) were assessed for causality using several complementary MR approaches. Findings: Genetically instrumented smoking intensity was associated with 48 conditions, with MR supporting a possible causal effect for 28 distinct outcomes. Each cigarette smoked per day elevated the odds of respiratory diseases by 5% to 33% (nine distinct diseases, including pneumonia, emphysema, obstructive chronic bronchitis, pleurisy, pulmonary collapse, respiratory failure) and the odds of circulatory disease by 5% to 23% (seven diseases, including atherosclerosis, myocardial infarction, congestive heart failure, arterial embolisms). Further effects were seen for cancer within the respiratory system and other neoplasms, renal failure, septicaemia, and retinal disorders. No associations were observed in sensitivity analyses on 185,002 never smokers. Interpretation: These genetic data demonstrate the substantial adverse health impacts by smoking intensity and suggest notable increases in the risks of several diseases. Public health initiatives should highlight the damage caused by smoking intensity and the potential benefits of reducing or ideally quitting smoking

Musculoskeletal Modeling Component of the NASA Digital Astronaut Project

The NASA Digital Astronaut Project s (DAP) objective is to provide computational tools that support research of the physiological response to low gravity environments and analyses of how changes cause health and safety risks to the astronauts and to the success of the mission. The spaceflight risk associated with muscle atrophy is impaired performance due to reduced muscle mass, strength and endurance. Risks of early onset of osteoporosis and bone fracture are among the spaceflight risks associated with loss of bone mineral density. METHODS: Tools under development include a neuromuscular model, a biomechanical model and a bone remodeling model. The neuromuscular model will include models of neuromuscular drive, muscle atrophy, fiber morphology and metabolic processes as a function of time in space. Human movement will be modeled with the biomechanical model, using muscle and bone model parameters at various states. The bone remodeling model will allow analysis of bone turnover, loss and adaptation. A comprehensive trade study was completed to identify the current state of the art in musculoskeletal modeling. The DAP musculoskeletal models will be developed using a combination of existing commercial software and academic research codes identified in the study, which will be modified for use in human spaceflight research. These individual models are highly dependent upon each other and will be integrated together once they reach sufficient levels of maturity. ANALYSES: The analyses performed with these models will include comparison of different countermeasure exercises for optimizing effectiveness and comparison of task requirements and the state of strength and endurance of a crew member at a particular time in a mission. DISCUSSION: The DAP musculoskeletal model has the potential to complement research conducted on spaceflight induced changes to the musculoskeletal system. It can help with hypothesis formation, identification of causative mechanisms and supplementing small data samples

Fitting parametric random effects models in very large data sets with application to VHA national data

<p>Abstract</p> <p>Background</p> <p>With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data.</p> <p>Data</p> <p>We use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years.</p> <p>Methods and results</p> <p>The outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous.</p> <p>Conclusion</p> <p>When the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs.</p

Analysis of the Ethio-Sudan cross-border cattle trade: the case of Amhara Regional State

This study focuses on the Ethio-Sudan cross-border cattle trade along the border between Sudan and the Amhara Region. It was initiated by the Integrated Livestock Development Project (ILDP) which operates in the North Gondar Zone of the Amhara Regional State. Legal and illegal livestock marketing systems are examined where small farmer exporters and traders are the major actors in the illegal cattle marketing system while medium- to large-scale licensed exporters are dominantly operating in the legal system. Some of the topics discussed are - volume of export; characterization of the system, factors contributing to the marketing system, pricing mechanism; tariff rate; marketing fee; and health certification

Overview and Evaluation of a Computational Bone Physiology Modeling Toolchain and Its Application to Testing of Exercise Countermeasures

Prolonged microgravity exposure disrupts natural bone remodeling processes and can lead to a significant loss of bone strength, increasing injury risk during missions and placing astronauts at a greater risk of bone fracture later in life. Resistance-based exercise during missions is used to combat bone loss, but current exercise countermeasures do not completely mitigate the effects of microgravity. To address this concern, we present work to develop a personalizable, site-specific computational modeling toolchain of bone remodeling dynamics to understand and estimate changes in volumetric bone mineral density (BMD) in response to microgravity-induced bone unloading and in-flight exercise. The toolchain is evaluated against data collected from subjects in a 70-day bedrest study and is found to provide insight into the amount of exercise stimulus needed to minimize bone loss, quantitatively predicting post-study volumetric BMD of control subjects who did not perform exercise, and qualitatively predicting the effects of exercise. Results suggest that, with additional data, the toolchain could be improved to aid in developing customized in-flight exercise regimens and predict exercise effectiveness

The Digital Astronaut Project Bone Remodeling Model

Under the conditions of microgravity, astronauts lose bone mass at a rate of 1% to 2% a month, particularly in the lower extremities such as the proximal femur: (1) The most commonly used countermeasure against bone loss has been prescribed exercise, (2) However, current exercise countermeasures do not completely eliminate bone loss in long duration, 4 to 6 months, spaceflight, (3,4) leaving the astronaut susceptible to early onset osteoporosis and a greater risk of fracture later in their lives. The introduction of the Advanced Resistive Exercise Device, coupled with improved nutrition, has further minimized the 4 to 6 month bone loss. But further work is needed to implement optimal exercise prescriptions, and (5) In this light, NASA's Digital Astronaut Project (DAP) is working with NASA physiologists to implement well-validated computational models that can help understand the mechanisms of bone demineralization in microgravity, and enhance exercise countermeasure development