Synthesis, Infra-red, Raman, NMR and structural characterization by X-ray Diffraction of [C12H17N2]2CdCl4 and [C6H10N2]2Cd3Cl10 compounds

The synthesis, infra-red, Raman and NMR spectra and crystal structure of 2, 4, 4- trimethyl-4, 5- dihydro-3H-benzo[b] [1, 4] diazepin-1-ium tetrachlorocadmate, [C12H17N2]2CdCl4 and benzene-1,2-diaminium decachlorotricadmate(II) [C6H10N2]2Cd3Cl10 are reported. The [C12H17N2]2CdCl4 compound crystallizes in the triclinic system (P-1 space group) with Z = 2 and the following unit cell dimensions: a = 9.6653(8) angstrom, b = 9.9081(9) angstrom, c = 15.3737(2) angstrom, alpha = 79.486(1)degrees, beta = 88.610(8)degrees and gamma = 77.550(7)degrees. The structure was solved by using 4439 independent reflections down to R value of 0.029. In crystal structure, the tetrachlorocadmiate anion is connected to two organic cations through N-H...Cl hydrogen bonds and Van Der Waals interaction as to build cation-anion-cation cohesion. The [C6H10N2]2Cd3Cl10 crystallizes in the triclinic system (P-1 space group). The unit cell dimensions are a = 6.826 (5)angstrom, b = 9.861 (7)angstrom, c = 10.344 (3)angstrom, alpha = 103.50 (1)degrees, beta = 96.34 (4)degrees and gamma = 109.45 (3)degrees, Z=2. The final R value is 0.053 (Rw=0.128). Its crystal structure consists of organic cations and polymeric chains of [Cd3Cl10]4- anions running along the [011] direction, In The [C6H10N2]2Cd3Cl10 compounds hydrogen bond interactions between the inorganic chains and the organic cations, contribute to the crystal packing. PACS Codes: 61.10.Nz, 61.18.Fs, 78.30.-jComment: 19 pages, 10 figure

Big Data: Managing the Future\u27s Agriculture and Natural Resource Systems

Big Data: Managing the Future\u27s Agriculture and Natural Resource Systems Big data is the incredible flow of information that surrounds each of us, every day. Big data tools identify patterns and habits, not only in research, but in manufacturing, logistics–even ordering items online

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte

A Biological Global Positioning System: Considerations for Tracking Stem Cell Behaviors in the Whole Body

Many recent research studies have proposed stem cell therapy as a treatment for cancer, spinal cord injuries, brain damage, cardiovascular disease, and other conditions. Some of these experimental therapies have been tested in small animals and, in rare cases, in humans. Medical researchers anticipate extensive clinical applications of stem cell therapy in the future. The lack of basic knowledge concerning basic stem cell biology-survival, migration, differentiation, integration in a real time manner when transplanted into damaged CNS remains an absolute bottleneck for attempt to design stem cell therapies for CNS diseases. A major challenge to the development of clinical applied stem cell therapy in medical practice remains the lack of efficient stem cell tracking methods. As a result, the fate of the vast majority of stem cells transplanted in the human central nervous system (CNS), particularly in the detrimental effects, remains unknown. The paucity of knowledge concerning basic stem cell biology—survival, migration, differentiation, integration in real-time when transplanted into damaged CNS remains a bottleneck in the attempt to design stem cell therapies for CNS diseases. Even though excellent histological techniques remain as the gold standard, no good in vivo techniques are currently available to assess the transplanted graft for migration, differentiation, or survival. To address these issues, herein we propose strategies to investigate the lineage fate determination of derived human embryonic stem cells (hESC) transplanted in vivo into the CNS. Here, we describe a comprehensive biological Global Positioning System (bGPS) to track transplanted stem cells. But, first, we review, four currently used standard methods for tracking stem cells in vivo: magnetic resonance imaging (MRI), bioluminescence imaging (BLI), positron emission tomography (PET) imaging and fluorescence imaging (FLI) with quantum dots. We summarize these modalities and propose criteria that can be employed to rank the practical usefulness for specific applications. Based on the results of this review, we argue that additional qualities are still needed to advance these modalities toward clinical applications. We then discuss an ideal procedure for labeling and tracking stem cells in vivo, finally, we present a novel imaging system based on our experiments

Assessing Well-Integrity Risk: A Qualitative Model

Summary For successful delivery of well integrity (WI), there needs to be an understanding of the risks that can cause undesirable events such as safety hazards or loss of containment. Performing a risk assessment (RA) on a well, or type of well, will help determine and rank the potential risks and provide information that allows limited resources to be applied in the most effective manner. The main objectives of performing a risk assessment include (a) following a formal process to assess risk consistently and to enable comparison between well-barrier failure-mode scenarios; (b) qualitatively assessing well-barrier failure risk for every segment of a well; (c) documenting suggestions that are offered by the riskassessment team for mitigating well-barrier failure risk; and (d) providing a report of the methodology, failure-mode scenarios, risk ranking, and potential mitigation actions for use as a reference tool for managing WI on a routine basis. Our WI/RA model follows a common qualitative risk-assessment process—a team-based, structured brainstorming format, using the "What-If Methodology" to identify potential hazards associated with well-barrier failure modes. In addition, the model has the following attributes: It incorporates a unique method to segment well barriers into discrete sections, successively "failing" each section for evaluation. The list of analyzed well-barrier failure modes, along with their risk ranking, becomes the risk register for the well or type of well.It is adaptable for assessing well-barrier failure modes on a single well, or a group of wells, having the same general design parameters. An entire well portfolio can be assessed quickly by analyzing types of wells rather than individual wells.It can be used to assess well-barrier failure risk for any type of well.The model can easily be modified to conform to any company's risk model.The WI/RA model has been proven toSuccessfully assess well-barrier failure risk for thousands of wellsFocus specifically on well-barrier failure modes, and as a result be an effective tool that should be incorporated into a "best-in-class" WI programBe used as a management tool to provide guidance for how limited resources can be used effectively to continuously deliver WI.</jats:p