Torque-coupled thermodynamic model for F_oF_1-ATPase

F_oF_1-ATPase is a motor protein complex that utilizes transmembrane ion flow to drive the synthesis of adenosine triphosphate (ATP) from adenosine diphosphate (ADP) and phosphate (Pi). While many theoretical models have been proposed to account for its rotary activity, most of them focus on the F_o or F_1 portions separately rather than the complex as a whole. Here, we propose a simple but new torque-coupled thermodynamic model of F_oF_1-ATPase. Solving this model at steady state, we find that the monotonic variation of each portion's efficiency becomes much more robust over a wide range of parameters when the F_o and F_1 portions are coupled together, as compared to cases when they are considered separately. Furthermore, the coupled model predicts the dependence of each portion's kinetic behavior on the parameters of the other. Specifically, the power and efficiency of the F_1 portion are quite sensitive to the proton gradient across the membrane, while those of the F_o portion as well as the related Michaelis constants for proton concentrations respond insensitively to concentration changes in the reactants of ATP synthesis. The physiological proton gradient across the membrane in the F_o portion is also shown to be optimal for the Michaelis constants of ADP and phosphate in the F_1 portion during ATP synthesis. Together, our coupled model is able to predict key dynamic and thermodynamic features of the F_oF_1-ATPase in vivo semiquantitatively, and suggests that such coupling approach could be further applied to other biophysical systems

Effect Analysis on the Industrial Upgrading and Economic Growth

Using literature review and data analysis, this paper elaborates the connotation and the necessity of industrial upgrading, and focuses on interactive effect of the industrial upgrading and economic growth in Ji’nan by analyzing its growth and the proportion of each industry. According to the results, industrial upgrading is the strong driving force of economic development, similarly, industrial upgrading is also included in the economic development. The industrial upgrading of Ji’nan has made some achievements but problems still exist, for example, how to create the industry system with Ji’nan characteristics, promote industrial upgrading, and realize the sustainable development of Ji’nan economy

Cerebral Falx Mature Teratoma with Rare Imaging in an Adult

Intracranial mature teratoma is a rare lesion in adults. Despite several intracranial mature teratomas had been reported not to be located at the midline region, no one was found to be within cerebral falx. Herein, we reported a 37-year-old female patient with an intracranial mature teratoma confined within frontal cerebral falx. Her main complaint was intermitted headache, which could not be relieved recently by taking painkiller. Excepting for mild papilledema, we did not find positive neurological signs on physical examination. CT scanning showed it was a round homogenously hypodense lesion with hyperdense signal at its rim. MRI revealed the lesion was 3.5cm×3.6cm×4.5cm in volume, with uniformed hypointensity on T1WI, hyperintensity on T2WI and enhancement in the capsule. It was totally removed via inter-hemispheric approach, and we found the lesion was confined within the frontal cerebral falx. Postoperatively, it was proved histologically to be a mature teratoma. At three years of fellow up, neither neurological deficits nor recurrent sings on MRI was found. To our best knowledge, this is the first case of intracranial mature teratoma within cerebral falx

Domain Adaptation and Image Classification via Deep Conditional Adaptation Network

Unsupervised domain adaptation aims to generalize the supervised model trained on a source domain to an unlabeled target domain. Marginal distribution alignment of feature spaces is widely used to reduce the domain discrepancy between the source and target domains. However, it assumes that the source and target domains share the same label distribution, which limits their application scope. In this paper, we consider a more general application scenario where the label distributions of the source and target domains are not the same. In this scenario, marginal distribution alignment-based methods will be vulnerable to negative transfer. To address this issue, we propose a novel unsupervised domain adaptation method, Deep Conditional Adaptation Network (DCAN), based on conditional distribution alignment of feature spaces. To be specific, we reduce the domain discrepancy by minimizing the Conditional Maximum Mean Discrepancy between the conditional distributions of deep features on the source and target domains, and extract the discriminant information from target domain by maximizing the mutual information between samples and the prediction labels. In addition, DCAN can be used to address a special scenario, Partial unsupervised domain adaptation, where the target domain category is a subset of the source domain category. Experiments on both unsupervised domain adaptation and Partial unsupervised domain adaptation show that DCAN achieves superior classification performance over state-of-the-art methods. In particular, DCAN achieves great improvement in the tasks with large difference in label distributions (6.1\% on SVHN to MNIST, 5.4\% in UDA tasks on Office-Home and 4.5\% in Partial UDA tasks on Office-Home)

Big Data Analytics on Traditional HPC Infrastructure Using Two-Level Storage

Data-intensive computing has become one of the major workloads on traditional high-performance computing (HPC) clusters. Currently, deploying data-intensive computing software framework on HPC clusters still faces performance and scalability issues. In this paper, we develop a new two-level storage system by integrating Tachyon, an in-memory file system with OrangeFS, a parallel file system. We model the I/O throughputs of four storage structures: HDFS, OrangeFS, Tachyon and two-level storage. We conduct computational experiments to characterize I/O throughput behavior of two-level storage and compare its performance to that of HDFS and OrangeFS, using TeraSort benchmark. Theoretical models and experimental tests both show that the two-level storage system can increase the aggregate I/O throughputs. This work lays a solid foundation for future work in designing and building HPC systems that can provide a better support on I/O intensive workloads with preserving existing computing resources.Comment: Submitted to SC15, 8 pages, 7 figures, 3 table

Duszność spowodowana gruźliczakami powstającymi w rdzeniu przedłużonym w czasie leczenia gruźliczego zapalenia opon mózgowo-rdzeniowych

Formation of tuberculoma is a rare response of neurotuberculosis in patients regularly and adequately treated with anti-tuberculous drugs. We report a 13-year-old girl with two tuberculomas which formed in the dorsal part of the medulla oblongata during chemotherapy for tuberculous meningitis. The tuberculomas were both removed via a suboccipital midline approach and were demonstrated by pathological findings but the girl died of cardiac arrest that was thought to be caused by postoperative medulla oblongata oedema. In combination with a literature review, we discuss the clinical features and treatment options of brainstem tuberculomas.Tworzenie się gruźliczaka jest rzadką reakcją w przebiegu właściwie leczonej gruźlicy układu nerwowego. W pracy autorzy opisują przypadek 13-letniej dziewczynki z dwoma gruźliczakami, które utworzyły się w grzbietowej części rdzenia przedłużonego w czasie farmakologicznego leczenia gruźliczego zapalenia opon mózgowo-rdzeniowych. Oba gruźliczaki usunięto z dostępu podpotylicznego w linii środkowej i potwierdzono ich rozpoznanie w badaniu histopatologicznym, ale pacjentka zmarła w wyniku zatrzymania krążenia, przypuszczalnie wskutek pooperacyjnego obrzęku rdzenia przedłużonego. Na podstawie przedstawionego przypadku i przeglądu piśmiennictwa omówiono objawy kliniczne i możliwości leczenia gruźliczaków pnia mózgu