Experimentally induced diabetes causes glial activation, glutamate toxicity and cellular damage leading to changes in motor function

Behavioral impairments are the most empirical consequence of diabetes mellitus documented in both humans and animal models, but the underlying causes are still poorly understood. As the cerebellum plays a major role in coordination and execution of the motor functions, we investigated the possible involvement of glial activation, cellular degeneration and glutamate transportation in the cerebellum of rats, rendered diabetic by a single injection of streptozotocin (STZ; 45 mg/kg body weight; intraperitoneally). Motor function alterations were studied using Rotarod test (motor coordination) and grip strength (muscle activity) at 2nd, 4th, 6th, 8th, 10th, and 12th week post-diabetic confirmation. Scenario of glial (astroglia and microglia) activation, cell death and glutamate transportation was gaged using immunohistochemistry, histological study and image analysis. Cellular degeneration was clearly demarcated in the diabetic cerebellum. Glial cells were showing sequential and marked activation following diabetes in terms of both morphology and cell number. Bergmann glial cells were hypertrophied and distorted. Active caspase-3 positive apoptotic cells were profoundly present in all three cerebellar layers. Reduced co-labeling of GLT-1 and GFAP revealed the altered glutamate transportation in cerebellum following diabetes. These results, exclusively derived from histology, immunohistochemistry and cellular quantification, provide first insight over the associative reciprocity between the glial activation, cellular degeneration and reduced glutamate transportation, which presumably lead to the behavioral alterations following STZ-induced diabetes

Determinants of the Size and Structure of Corporate Boards: 1935-2000

We argue that the size and composition of corporate boards are determined by tradeoffs involving the information that directors bring to boards versus the coordination costs and free rider problems associated with their additions to boards. Our hypotheses lead to predictions that firm size and growth opportunities are important determinants of these board characteristics. Using a sample of 82 U.S. firms that survived over the period of 1935 through 2000, we find strong support for the hypotheses. The hypotheses also find support in the relation between changes in board size and firms' merger and divestiture activity, and changes in the geographical diversification of firms. We find no robust relation between firm performance and either board size or composition after accounting for the determinants of these board characteristics.Board size, board composition, mergers and acquisitions, firm size, growth opportunities, diversification, geographical diversification, firm performance, endogeneity

CFD Simulation of Mixing And Segregation in a Tapered Fluidized Bed

Fluidization of different materials results either in a well-mixed or a segregated bed. Depending upon the operating conditions, smaller particles (floatsam) tend to rise to the bed, and larger particles (jetsam) tend to sink to the bottom of the bed. The tapered fluidized bed can be used to overcome certain draw backs of the gas-solid system because of the fact that a velocity gradient exists along the axial direction of the bed with increase in cross-sectional area. To study the dynamic characteristics of the homogenous mixture of regular and irregular particles several experiments have been carried out with varying compositions.The particle flow pattern and granule segregation in tapered fluidized bed have been studied by first fluidizing the beds of varying total mass and granule fractions and then defluidize them suddenly to freeze the composition, section the bed in layers, and determine the composition in each layer by sieving. A series of unsteady, three-fluid CFD simulations were performed using FLUENTTM 6.2. Simulation parameters viz. solution technique, grid, maximum packing fraction and operating conditions like gas velocity were each investigated for the relative effects on particle mixing and segregation. Good agreement of solid volume fraction profile was obtained between the experimental results and simulation results for regular particle

Battery case shear

Hand operated shear removes a battery case without disturbing the internal components which are to be tested. It consists of three tool-steel elements, the cutter blade, and a hand lever that provides the mechanical advantage required to cut steel

Computationally Comparing Biological Networks and Reconstructing Their Evolution

Biological networks, such as protein-protein interaction, regulatory, or metabolic networks, provide information about biological function, beyond what can be gleaned from sequence alone. Unfortunately, most computational problems associated with these networks are NP-hard. In this dissertation, we develop algorithms to tackle numerous fundamental problems in the study of biological networks. First, we present a system for classifying the binding affinity of peptides to a diverse array of immunoglobulin antibodies. Computational approaches to this problem are integral to virtual screening and modern drug discovery. Our system is based on an ensemble of support vector machines and exhibits state-of-the-art performance. It placed 1st in the 2010 DREAM5 competition. Second, we investigate the problem of biological network alignment. Aligning the biological networks of different species allows for the discovery of shared structures and conserved pathways. We introduce an original procedure for network alignment based on a novel topological node signature. The pairwise global alignments of biological networks produced by our procedure, when evaluated under multiple metrics, are both more accurate and more robust to noise than those of previous work. Next, we explore the problem of ancestral network reconstruction. Knowing the state of ancestral networks allows us to examine how biological pathways have evolved, and how pathways in extant species have diverged from that of their common ancestor. We describe a novel framework for representing the evolutionary histories of biological networks and present efficient algorithms for reconstructing either a single parsimonious evolutionary history, or an ensemble of near-optimal histories. Under multiple models of network evolution, our approaches are effective at inferring the ancestral network interactions. Additionally, the ensemble approach is robust to noisy input, and can be used to impute missing interactions in experimental data. Finally, we introduce a framework, GrowCode, for learning network growth models. While previous work focuses on developing growth models manually, or on procedures for learning parameters for existing models, GrowCode learns fundamentally new growth models that match target networks in a flexible and user-defined way. We show that models learned by GrowCode produce networks whose target properties match those of real-world networks more closely than existing models

Ethics and professional development: a primer for doctors in training

While dealing with colleagues, patients and relatives of patients, doctors have to practice ethical behavior. This paper describes some basics on etiquette, ethics, people management skills, team work, politeness, etc which will give doctors 3 C’s: Competence, Confidence and Compassion, which in turn will enable doctors ascend rapidly in health care organizations. It is suggested that principles of etiquette and ethics be acquired by medical students while in training itself so that they can implement them and use them from day one in the hospital they join. Whether these principles should be part of medical curriculum is a matter to be decided by appropriate authorities

Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations

To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users.Comment: In the proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Please cite the conference versio