ARIES: Acquisition of Requirements and Incremental Evolution of Specifications

This paper describes a requirements/specification environment specifically designed for large-scale software systems. This environment is called ARIES (Acquisition of Requirements and Incremental Evolution of Specifications). ARIES provides assistance to requirements analysts for developing operational specifications of systems. This development begins with the acquisition of informal system requirements. The requirements are then formalized and gradually elaborated (transformed) into formal and complete specifications. ARIES provides guidance to the user in validating formal requirements by translating them into natural language representations and graphical diagrams. ARIES also provides ways of analyzing the specification to ensure that it is correct, e.g., testing the specification against a running simulation of the system to be built. Another important ARIES feature, especially when developing large systems, is the sharing and reuse of requirements knowledge. This leads to much less duplication of effort. ARIES combines all of its features in a single environment that makes the process of capturing a formal specification quicker and easier

Empowering Students Through the Application of Self-Efficacy Theory in School Social Work: An Intervention Model

Self-efficacy is a construct well suited for social workers in the educational setting. Among the various job functions that school social workers assume, a large portion of their time is directed toward providing counseling and clinical services. Perceptions of self-efficacy are based upon the extent students expect to successfully attain their goals. Self-efficacious students with strong beliefs in their abilities will choose activities and social situations where they believe that they will be successful. Thus, they will be motivated to devote more time and effort toward accomplishing related goals. Conversely, inefficacious students of similar intelligence and capabilities may choose to abandon a challenging activity or social situation when they believe they will not be successful. The purpose of this paper is to describe a psycho-educational intervention model that school social workers can use to increase students’ self-efficacy. This article will first provide an overview of the theoretical foundations of Bandura’s self-efficacy theory. Included will be the four sources of influences used in formulating a person’s self-efficacy beliefs. Second, a self-efficacy intervention model will be introduced in which school social workers can counsel students to increase their self-efficacy. This intervention plan incorporates the four sources of Bandura’s self-efficacy training – enactive mastery experience, vicarious experience, verbal persuasion and physiologic and affective states. Third, a case example will be presented that illustrates the application of this intervention model with 12 deaf and hard of hearing high school students who experienced bullying in one California school district

Striatal cholinergic interneurons generate beta and gamma oscillations in the corticostriatal circuit and produce motor deficits

Cortico-basal ganglia-thalamic (CBT) neural circuits are critical modulators of cognitive and motor function. When compromised, these circuits contribute to neurological and psychiatric disorders, such as Parkinson's disease (PD). In PD, motor deficits correlate with the emergence of exaggerated beta frequency (15-30 Hz) oscillations throughout the CBT network. However, little is known about how specific cell types within individual CBT brain regions support the generation, propagation, and interaction of oscillatory dynamics throughout the CBT circuit or how specific oscillatory dynamics are related to motor function. Here, we investigated the role of striatal cholinergic interneurons (SChIs) in generating beta and gamma oscillations in cortical-striatal circuits and in influencing movement behavior. We found that selective stimulation of SChIs via optogenetics in normal mice robustly and reversibly amplified beta and gamma oscillations that are supported by distinct mechanisms within striatal-cortical circuits. Whereas beta oscillations are supported robustly in the striatum and all layers of primary motor cortex (M1) through a muscarinic-receptor mediated mechanism, gamma oscillations are largely restricted to the striatum and the deeper layers of M1. Finally, SChI activation led to parkinsonian-like motor deficits in otherwise normal mice. These results highlight the important role of striatal cholinergic interneurons in supporting oscillations in the CBT network that are closely related to movement and parkinsonian motor symptoms.DP2 NS082126 - NINDS NIH HHS; R01 NS081716 - NINDS NIH HHS; R21 NS078660 - NINDS NIH HHShttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4896681/Published versio

ARE THERE ELECTRICAL DEVICES THAT CAN MEASURE THE BODY'S ENERGY STATE CHANGE TO AN ACUPUNCTURE TREATMENT? Part l, The Meridian Stress Assessment (MSA-21J Device

The general field of energy medicine is growing strongly but is still in great need of reliable monitoring instruments to assess the relative energetic state of humans with respect to a health/pathology ratio. Two commercial instruments: Bio-Meridian's MSA-21 and Korotkov's GDV were selected for an in-parallel study of the following question, "Can they meaningfully discriminate the effects of acupuncture treatment on the body's energy state?" In this part 1 paper we discuss the results obtained by Bio-Meridian' MSA-21. the experimental design was to determine if the MSA-21 and the GDV could discern a quantifiable difference between an authentic acupuncture session and a sham acupuncture session for 34 subjects. The single research hypothesis was, "If energy is added to andlor redistributed in the body via true acupuncture needling, as contrasted with sham acupuncture needling, a worthy measurement instrument must (at least) be able to discriminate this energy change contrast in a statistically significant fashion." Indeed, the MSA-21 instrument passed this test in good order and provided much useful adjunct information as well

Mitochondrial dysfunction and oxidative stress in patients with chronic kidney disease.

Mitochondria abnormalities in skeletal muscle may contribute to frailty and sarcopenia, commonly present in patients with chronic kidney disease (CKD). Dysfunctional mitochondria are also a major source of oxidative stress and may contribute to cardiovascular disease in CKD We tested the hypothesis that mitochondrial structure and function worsens with the severity of CKD Mitochondrial volume density, mitochondrial DNA (mtDNA) copy number, BNIP3, and PGC1α protein expression were evaluated in skeletal muscle biopsies obtained from 27 subjects (17 controls and 10 with CKD stage 5 on hemodialysis). We also measured mtDNA copy number in peripheral blood mononuclear cells (PBMCs), plasma isofurans, and plasma F2-isoprostanes in 208 subjects divided into three groups: non-CKD (eGFR>60 mL/min), CKD stage 3-4 (eGFR 60-15 mL/min), and CKD stage 5 (on hemodialysis). Muscle biopsies from patients with CKD stage 5 revealed lower mitochondrial volume density, lower mtDNA copy number, and higher BNIP3 content than controls. mtDNA copy number in PBMCs was decreased with increasing severity of CKD: non-CKD (6.48, 95% CI 4.49-8.46), CKD stage 3-4 (3.30, 95% CI 0.85-5.75, P = 0.048 vs. non-CKD), and CKD stage 5 (1.93, 95% CI 0.27-3.59, P = 0.001 vs. non-CKD). Isofurans were higher in patients with CKD stage 5 (median 59.21 pg/mL, IQR 41.76-95.36) compared to patients with non-CKD (median 49.95 pg/mL, IQR 27.88-83.46, P = 0.001), whereas F2-isoprostanes did not differ among groups. Severity of CKD is associated with mitochondrial dysfunction and markers of oxidative stress. Mitochondrial abnormalities, which are common in skeletal muscle from patients with CKD stage 5, may explain the muscle dysfunction associated with frailty and sarcopenia in CKD Further studies are required to evaluate mitochondrial function in vivo in patients with different CKD stages

The Effects of Continuous Improvement Practices Using State Value-Added Data for Reading Educator Program Enhancement

Louisiana’s value-added evaluation of teacher preparation programs has provided a salient impetus for program improvement; however, due to the nature of the assessment, teacher preparation programs need to use additional sources of data to identify actionable responses to the value-added results. This paper describes one teacher preparation program’s approach to continuous program improvement in reading education and describes some of the limitations and benefits of value-added assessment results for that purpose

Reflections on the journey: six short stories

One of the goals of the 2011 International Year of Chemistry is to celebrate the contributions of women to science. A question that has been frequently asked in this regard is... Why is it necessary to highlight women in the "age of equality"? The reasons are varied but the facts are that many women scientists worked in obscurity throughout the 19th and even well into the 20th century, sometimes publishing anonymously to be heard. This celebration of Women in Science is one way to recognize both the resiliency and passion of these women. As part of this celebration, Chemistry Central Journal's Thematic Series of "Women in Chemistry" includes this article describing the path several women took as they pursued chemistry careers spanning the latter part of the 20th century and into the early 21st century. Sharon Haynie, Nancy Jones, Cheryl Martin, Paula Olsiewski, Mary Roberts and Amber Hinkle each have unique story of their personal journey from childhood to adulthood. As you read these stories, listen generously, and feel free to share your own stories, comments and thoughts

DynaSim: a MATLAB toolbox for neural modeling and simulation

[EN] DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. DynaSim is freely available at http://dynasimtoolbox.org. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.This material is based upon research supported by the U.S. Army Research Office under award number ARO W911NF-12-R-0012-02, the U.S. Office of Naval Research under award number ONR MURI N00014-16-1-2832, and the National Science Foundation under award number NSF DMS-1042134 (Cognitive Rhythms Collaborative: A Discovery Network)Sherfey, JS.; Soplata, AE.; Ardid-Ramírez, JS.; Roberts, EA.; Stanley, DA.; Pittman-Polletta, BR.; Kopell, NJ. (2018). DynaSim: a MATLAB toolbox for neural modeling and simulation. Frontiers in Neuroinformatics. 12:1-15. https://doi.org/10.3389/fninf.2018.00010S11512Bokil, H., Andrews, P., Kulkarni, J. E., Mehta, S., & Mitra, P. P. (2010). Chronux: A platform for analyzing neural signals. Journal of Neuroscience Methods, 192(1), 146-151. doi:10.1016/j.jneumeth.2010.06.020Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., … Destexhe, A. (2007). Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience, 23(3), 349-398. doi:10.1007/s10827-007-0038-6Börgers, C., & Kopell, N. (2005). Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons. Neural Computation, 17(3), 557-608. doi:10.1162/0899766053019908Ching, S., Cimenser, A., Purdon, P. L., Brown, E. N., & Kopell, N. J. (2010). Thalamocortical model for a propofol-induced  -rhythm associated with loss of consciousness. Proceedings of the National Academy of Sciences, 107(52), 22665-22670. doi:10.1073/pnas.1017069108Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134(1), 9-21. doi:10.1016/j.jneumeth.2003.10.009Durstewitz, D., Seamans, J. K., & Sejnowski, T. J. (2000). Neurocomputational models of working memory. Nature Neuroscience, 3(S11), 1184-1191. doi:10.1038/81460EatonJ. W. BatemanD. HaubergS. WehbringR. GNU Octave Version 4.2.0 Manual: A High-Level Interactive Language for Numerical Computations2016Ermentrout, B. (2002). Simulating, Analyzing, and Animating Dynamical Systems. doi:10.1137/1.9780898718195FitzHugh, R. (1955). Mathematical models of threshold phenomena in the nerve membrane. The Bulletin of Mathematical Biophysics, 17(4), 257-278. doi:10.1007/bf02477753Gewaltig, M.-O., & Diesmann, M. (2007). NEST (NEural Simulation Tool). Scholarpedia, 2(4), 1430. doi:10.4249/scholarpedia.1430Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., … Silver, R. A. (2010). NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail. PLoS Computational Biology, 6(6), e1000815. doi:10.1371/journal.pcbi.1000815Goodman, D. (2008). Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics, 2. doi:10.3389/neuro.11.005.2008Goodman, D. F. M. (2009). The Brian simulator. Frontiers in Neuroscience, 3(2), 192-197. doi:10.3389/neuro.01.026.2009Hines, M. L., & Carnevale, N. T. (1997). The NEURON Simulation Environment. Neural Computation, 9(6), 1179-1209. doi:10.1162/neco.1997.9.6.1179Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., … Wang. (2003). The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524-531. doi:10.1093/bioinformatics/btg015Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569-1572. doi:10.1109/tnn.2003.820440Kopell, N., Ermentrout, G. B., Whittington, M. A., & Traub, R. D. (2000). Gamma rhythms and beta rhythms have different synchronization properties. Proceedings of the National Academy of Sciences, 97(4), 1867-1872. doi:10.1073/pnas.97.4.1867Kramer, M. A., Roopun, A. K., Carracedo, L. M., Traub, R. D., Whittington, M. A., & Kopell, N. J. (2008). Rhythm Generation through Period Concatenation in Rat Somatosensory Cortex. PLoS Computational Biology, 4(9), e1000169. doi:10.1371/journal.pcbi.1000169Lorenz, E. N. (1963). Deterministic Nonperiodic Flow. Journal of the Atmospheric Sciences, 20(2), 130-141. doi:10.1175/1520-0469(1963)0202.0.co;2Markram, H., Meier, K., Lippert, T., Grillner, S., Frackowiak, R., Dehaene, S., … Saria, A. (2011). Introducing the Human Brain Project. Procedia Computer Science, 7, 39-42. doi:10.1016/j.procs.2011.12.015McDougal, R. A., Morse, T. M., Carnevale, T., Marenco, L., Wang, R., Migliore, M., … Hines, M. L. (2016). Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience. Journal of Computational Neuroscience, 42(1), 1-10. doi:10.1007/s10827-016-0623-7Meng, L., Kramer, M. A., Middleton, S. J., Whittington, M. A., & Eden, U. T. (2014). A Unified Approach to Linking Experimental, Statistical and Computational Analysis of Spike Train Data. PLoS ONE, 9(1), e85269. doi:10.1371/journal.pone.0085269Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical Journal, 35(1), 193-213. doi:10.1016/s0006-3495(81)84782-0Rudolph, M., & Destexhe, A. (2007). How much can we trust neural simulation strategies? Neurocomputing, 70(10-12), 1966-1969. doi:10.1016/j.neucom.2006.10.138Stimberg, M., Goodman, D. F. M., Benichoux, V., & Brette, R. (2014). Equation-oriented specification of neural models for simulations. Frontiers in Neuroinformatics, 8. doi:10.3389/fninf.2014.00006Traub, R. D., Buhl, E. H., Gloveli, T., & Whittington, M. A. (2003). Fast Rhythmic Bursting Can Be Induced in Layer 2/3 Cortical Neurons by Enhancing Persistent Na+Conductance or by Blocking BK Channels. Journal of Neurophysiology, 89(2), 909-921. doi:10.1152/jn.00573.200

Effluent Organic Nitrogen (EON): Bioavailability and Photochemical and Salinity-Mediated Release

The goal of this study was to investigate three potential ways that the soluble organic nitrogen (N) fraction of wastewater treatment plant (WWTP) effluents, termed effluent organic N (EON), could contribute to coastal eutrophication - direct biological removal, photochemical release of labile compounds, and salinity-mediated release of ammonium (NH4+). Effluents from two WWTPs were used in the experiments. For the bioassays, EON was added to water from four salinities (∼0 to 30) collected from the James River (VA) in August 2008, and then concentrations of N and phosphorus compounds were measured periodically over 48 h. Bioassay results, based on changes in DON concentrations, indicate that some fraction of the EON was removed and that the degree of EON removal varied between effluents and with salinity. Further, we caution that bioassay results should be interpreted within a broad context of detailed information on chemical characterization. EON from both WWTPs was also photoreactive, with labile NH4+ and dissolved primary amines released during exposure to sunlight. We also present the first data that demonstrate that when EON is exposed to higher salinities, increasing amounts of NH4+ are released, further facilitating EON use as effluent transits from freshwater through estuaries to the coast

Introducing the national COPD resources and outcomes project

<p>Abstract</p> <p>Background</p> <p>We report baseline data on the organisation of COPD care in UK NHS hospitals participating in the National COPD Resources and Outcomes Project (NCROP).</p> <p>Methods</p> <p>We undertook an initial survey of participating hospitals in 2007, looking at organisation and performance indicators in relation to general aspects of care, provision of non-invasive ventilation (NIV), pulmonary rehabilitation, early discharge schemes, and oxygen. We compare, where possible, against the national 2003 audit.</p> <p>Results</p> <p>100 hospitals participated. These were typically larger sized Units. Many aspects of COPD care had improved since 2003. Areas for further improvement include organisation of acute care, staff training, end-of-life care, organisation of oxygen services and continuation of pulmonary rehabilitation.</p> <p>Conclusion</p> <p>Key Points: positive change occurs over time and repeated audit seems to deliver some improvement in services. It is necessary to assess interventions such as the Peer Review used in the NCROP to achieve more comprehensive and rapid change.</p