154 research outputs found

    5 Key Takeaways from “Creative Commons for Academic Librarians”

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    This lightning talk will cover five key takeaways from the ten week certificate course on open licensing for academic librarians offered by the Creative Commons organization. The talk will cover basic copyright principles necessary to understand the function and application of open licenses such as creative commons. The five key takeaways will be presented by a medical librarian working at an academic medical library, so the lens of the lightning talk will be focused on the needs of medical libraries. Examples for each takeaway will incorporate challenges, needs, or use cases of institutional repositories at medical institutions. During the lightning talk, the presenter will share free resources to learn more about open licensing. Medical librarians and those supporting medical institutional repositories will be better able to contribute to the Open Access (OA) movement if they are comfortable with open licensing and able to guide patrons and depositors through the open licensing selection and interpretation process. By the end of this lightning talk, attendees will have a basic understanding of creative commons licenses and be able to articulate why they are important to incorporate into institutional repositories

    A Commission on a Cyber Mission

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    Human Embryonic Stem Cells and Induced Pluripotent Stem Cells: The Promising Tools for Insulin-Producing Cell Generation

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    Diabetes mellitus, a disease with abnormally high level of blood glucose, can cause a wide range of chronic complications that affect almost every parts of the body. The major goal of diabetes treatments is to control elevated blood glucose without causing abnormally low levels of blood glucose. Despite islet transplantation provided endogenous insulin secretion in individuals with diabetes, the scarcity of cadaveric donors for pancreatic β-cell still remains a major obstacle. In this regard, the needs for an unlimited supply for cell replacement strategy have led to explore the way of generating insulin-producing cells to use in the disease treatment. Human embryonic stem cells (hESCs) offer a source to produce the desired kind of cell. Currently, several researchers achieved insulin-producing cells from hESCs using a multistep differentiation protocols, growth factors, and/or chemical compounds. In this review, we summarized the hESCs derivation, culture methods, and characteristics of hESCs. We also emphasized on the current methods for direct differentiation of hESCs into embryoid bodies (EBs) and toward insulin-producing cells, characterization of these insulin-producing cells, and the limitation of hESCs. Since the discovery of induced pluripotent stem cells (iPSCs), which have similar properties to hESCs but less ethical issues than hESCs, can be created directly from somatic cells that hold great promise as the therapeutic source for developing cell-based therapy. Herein, the methods to produce iPSC-derived insulin-producing cells are also discussed. Moreover, the encapsulation technology which is a powerful tool for accelerate hESCs and iPSCs applications in medicine which provide a new avenue for diabetes treatment in the future is also included in this review. Understanding the basic knowledge of hESCs and iPSCs, their differentiation capability toward insulin-producing cells will stimulate more therapeutic value of hESCs and iPSCs for diabetic treatments, drug screening, and regenerative medicine

    Starvation promotes autophagy-associated maturation of the ovary in the giant freshwater prawn, Macrobrachium rosenbergii

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    Limitation of food availability (starvation) is known to influence the reproductive ability of animals. Autophagy is a lysosomal driven degradation process that protects the cell under metabolic stress conditions, such as during nutrient shortage. Whether, and how starvation-induced autophagy impacts on the maturation and function of reproductive organs in animals are still open questions. In this study, we have investigated the effects of starvation on histological and cellular changes that may be associated with autophagy in the ovary of the giant freshwater prawn, Macrobachium rosenbergii. To this end, the female prawns were daily fed (controls) or unfed (starvation condition) for up to 12 days, and the ovary tissue was analyzed at different time-points. Starvation triggered ovarian maturation, and concomitantly increased the expression of autophagy markers in vitellogenic oocytes. The immunoreactivities for autophagy markers, including Beclin1, LC3-II, and Lamp1, were enhanced in the late oocytes within the mature ovaries, especially at the vitellogenic stages. These markers co-localized with vitellin in the yolk granules within the oocytes, suggesting that autophagy induced by starvation could drive vitellin utilization, thus promoting ovarian maturation

    Dual-Purpose Photometric-Conductivity Detector for Simultaneous and Sequential Measurements in Flow Analysis

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    This work presents a new dual-purpose detector for photometric and conductivity measurements in flow-based analysis. The photometric detector is a paired emitter-detector diode (PEDD) device, whilst the conductivity detection employs a capacitively coupled contactless conductivity detector (C4D). The flow-through detection cell is a rectangular acrylic block (ca. 2 x 2 x 1.5 cm) with cylindrical channels in Z-configuration. For the PEDD detector, the LED light source and detector are installed inside the acrylic block. The two electrodes of the C4D are silver conducting ink painted on the PEEK inlet and outlet tubing of the Z-flow cell. The dual-purpose detector is coupled with a sequential injection analysis (SIA) system for simultaneous detection of the absorbance of the orange dye and conductivity of the dissolved oral rehydration salt powder. The detector was also used for sequential measurements of creatinine and the conductivity of human urine samples. The creatinine analysis is based on colorimetric detection of the Jaffe reaction using the PEDD detector, and the conductivity of the urine, as measured by the C4D detector, is expressed in millisiemens (mS cm(-1))

    Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus

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    The aim of this study is to explore the relationship between hematological parameters and glycemic status in the establishment of quantitative population-health relationship (QPHR) model for identifying individuals with or without diabetes mellitus (DM). Methods: A cross-sectional investigation of 190 participants residing in Nakhon Pathom, Thailand in January-March, 2013 was used in this study. Individuals were classified into 3 groups based on their blood glucose levels (normal, Pre-DM and DM). Hematological (white blood cell (WBC), red blood cell (RBC), hemoglobin (Hb) and hematocrite (Hct)) and glucose parameters were used as input variables while the glycemic status was used as output variable. Support vector machine (SVM) and artificial neural network (ANN) are machine learning approaches that were employed for identifying the glycemic status while association analysis (AA) was utilized in discovery of health parameters that frequently occur together. Results: Relationship amongst hematological parameters and glucose level indicated that the glycemic status (normal, Pre-DM and DM) was well correlated with WBC, RBC, Hb and Hct. SVM and ANN achieved accuracy of more than 98 % in classifying the glycemic status. Furthermore, AA analysis provided association rules for defining individuals with or without DM. Interestingly, rules for the Pre-DM group are associated with high levels of WBC, RBC,Hb and Hct. Conclusion: This study presents the utilization of machine learning approaches for identification of DM status as well as in the discovery of frequently occurring parameters. Such predictive models provided high classification accuracy as well as pertinent rules in defining DM
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