Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Molecule Atomic Force Microscopy Reveals Clustering of the Yeast Plasma-Membrane Sensor Wsc1

Signalling is a key feature of living cells which frequently involves the local clustering of specific proteins in the plasma membrane. How such protein clustering is achieved within membrane microdomains (“rafts”) is an important, yet largely unsolved problem in cell biology. The plasma membrane of yeast cells represents a good model to address this issue, since it features protein domains that are sufficiently large and stable to be observed by fluorescence microscopy. Here, we demonstrate the ability of single-molecule atomic force microscopy to resolve lateral clustering of the cell integrity sensor Wsc1 in living Saccharomyces cerevisiae cells. We first localize individual wild-type sensors on the cell surface, revealing that they form clusters of ∼200 nm size. Analyses of three different mutants indicate that the cysteine-rich domain of Wsc1 has a crucial, not yet anticipated function in sensor clustering and signalling. Clustering of Wsc1 is strongly enhanced in deionized water or at elevated temperature, suggesting its relevance in proper stress response. Using in vivo GFP-localization, we also find that non-clustering mutant sensors accumulate in the vacuole, indicating that clustering may prevent endocytosis and sensor turnover. This study represents the first in vivo single-molecule demonstration for clustering of a transmembrane protein in S. cerevisiae. Our findings indicate that in yeast, like in higher eukaryotes, signalling is coupled to the localized enrichment of sensors and receptors within membrane patches.
Adherence to the American Diabetes Association standards of care among patients with type 2 diabetes in primary care in Saudi Arabia

Objectives: To assess adherence to 11 American Diabetes Association (ADA) standards of diabetic care. Methods: We conducted this one-year historical prospective study between October 2010 and September 2011 on 450 adult type 2 diabetes patients in a primary care center in Saudi Arabia. We used the definitions of the 2010 ADA standards of diabetic care processes and targets. Results: Four-hundred and fifty medical files were valid. The adherence to ADA process standards of measurement of glycated hemoglobin (HbA1c) was 68.7%, 92.9% for blood pressure, and 80.2% for serum lipids. Screening was lowest for nephropathy (35.6%), and highest for diabetic foot (72%). Adherence to medications ranged between 82.2% for antiplatelets, and 92.4% for dyslipidemia. For outcome standards, 24.2% of the patients had an HbA1c <7%, and 32.2% had controlled blood pressure (<130/80 mm Hg); and 58.5% achieved targeted low-density lipoproteins (LDL). Only 7.2% had glycemic control in addition to controlled blood pressure and targeted LDL level. An increasing trend of patients achieving glycemic control (<7%) was shown throughout follow-up (p=0.003). Conclusions: We found suboptimal adherence with many ADA standards of diabetic care among patients with type 2 diabetes treated at a primary care center in Saudi Arabia. The achievement of outcome standards, either singly or combined, is lower than the adherence rates. However, the figures show improvement in adherence during the follow-up period.
Aging and functional reorganization of striatum- and medial-temporal lobe-dependent memory systems

Unterschrift Datum ACKNOWLEDGMENTS This dissertation was conducted within the research project " Neuromodulation of Lifespan Cognition " at the Center for Lifespan Psychology of the Max Planck Institute for Human Development in Berlin. I was also a member of the General Psychology research group at Humboldt-Universität zu Berlin and a fellow of the International Max Planck Research School " The Life Course: Evolutionary and Ontogenetic Dynamics (LIFE) ". All research was funded by the Max Planck Society, except the experiments reported in Paper II, which were funded by the Princeton Center for Health and Well-being. I am obliged to all these sources of funding who made this dissertation possible. I am most grateful to my supervisor Shu-Chen Li. Shu-Chen, your innumerable efforts, your continued guidance, your enthusiasm and supervision were the foundation of my dissertation. I am deeply grateful for all the time you invested in this research and your excellent support in all intellectual and everyday matters. I am also thankful to my supervisor Peter Frensch, who has always been an inspiration and who has provided me with many opportunities already very early in my academic career. These opportunities ignited my passion for the science of the mind. Furthermore, I want to express my gratitude to Ulman Lindenberger for providing me with an outstanding and unique research environment at the Max Planck Institute and for inspiring and challenging discussions. I am very obliged to Torsten Schubert for giving me the opportunity to continue to be a member of the General Psychology research group and for investing the time and effort to evaluate this thesis. I also would like to thank Lars Bäckman, whose work has inspired many aspects of this dissertation. It is a great honor that you are a member of my dissertation committee. I am very thankful for the support from all members of the Neuromodulation project. Thank you Ben for all the inspiring and challenging discussions we had. It was great working with you and I hope we will continue to collaborate. Goran, thanks for listening and advising. Dorothea, thanks you for letting me help working on your dataset. I am also indebted to all other members of the Neuromodulation group who helped with data acquisition. Among the many important intellectual influences, I would like to highlight Christian Doeller and Robert Gaschler. Christian, I admire your work and I enjoyed visiting you. I hope we will …
The Unicast and Multicast Capacity Regions of Large Wireless Networks

We consider the question of determining the scalingof then-dimensional unicast and the n×2n-dimensional multicast capacity regions of a wireless network with n nodes placed uniformly at random in a square region of arean and communicating over Gaussian fading channels. We identi fy this scaling of both the unicast and multicast capacity regions in terms of Θ(n), out of 2n total possible, cuts. These cuts only depend on the geometry of the locations of the source nodes and their destination no des and the traffic demands between them, and thus can be readily evaluated. Our results are constructive and p rovide optimal (in the scaling sense) communication schemes. We illustrate the strength of these results by comp uting the capacity scaling in a number of scenarios with non-uniform traffic patterns for which no such results h ave been available before.
POLYAMINE DEPLETION INDUCES NPM MODULATING STABILITY AND TRANSCRIPTIONAL ACTIVITY OF p 53 IN INTESTINAL EPITHELIAL CELLS

Our previous studies have shown that polyamines are required for normal intestinal mucosal growth and decreased levels of polyamines inhibit intestinal epithelial cell proliferation by stabilizing p53 and other growth-inhibiting proteins. Nucleophosmin (NPM) is a multifunctional protein and has been recently shown to regulate p53 activity. The current study sought to determine whether polyamine depletion increases NPM modulating the stability and transcriptional activity of p53 in normal intestinal epithelial cells (IEC-6 line). Depletion of cellular polyamines by α-difluoromethylornithine (DFMO), the specific inhibitor of polyamine biosynthesis, stimulated expression of the NPM gene and induced nuclear translocation of NPM protein. Polyamine depletion stimulated NPM expression primarily by increasing NPM gene transcription and its mRNA stability, and induced NPM nuclear translocation through activation of phosphorylation of mitogen-activated protein kinase kinase (MEK). Increased NPM interacted with p53 and formed a NPM/p53 complex in polyamine-deficient cells. Inhibition of NPM expression by small interfering RNA targeting NPM (siNPM) not only destabilized p53 as indicated by a decrease in its protein half-life but also prevented the increased p53-dependent transactivation as shown by a suppression of the p21-promoter activity. Decreased expression of NPM by siNPM also promoted cell growth in polyamine-deficient cells. These results indicate that 1) polyamine depletion increases expression of the NPM gene and enhances NPM nuclear translocation and 2) increased NPM interacts with and stabilizes p53 leading to inhibition of IEC-6 cell proliferation.
Context Matters: Contextual Factors Informing Mentoring in Art Initial Teacher Education

This article explores the contextual factors that shape mentoring practice in Art Initial Teacher Education. Based on in-depth interviews, nonparticipant observations and stimulated recall interviews with participants, we examine how various factors related to the context of mentors’ work influence their approaches to subject matter mentoring. Adopting a discursive stance to mentoring, we use critical discourse analysis to expose connections between mentors’ language, ideas, and beliefs and the broader context of subject matter mentoring. In each mentoring setting studied, the analysis surfaces distinctive contextual factors that are grounded in mentors’ interpretations of the roles and functions of their subject matter domains. We show how these factors inform mentors’ perceptions of the purposes and processes of mentoring and their enactments in practice. Our findings offer an extended perspective to subject matter mentoring and new directions for thinking about context in mentoring. Implications for mentor preparation and selection are discussed.
A Survey of Semi-Supervised Learning Methods

In traditional machine learning approaches to classification, one uses only a labelled set to train the classifier. Labelled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labelled data, to build better classifiers. Because semi-supervised learning requires less human effort and gives higher accuracy, it is of great interest both in theory and in practice. The paper discusses various important approaches to semi-supervised learning such as self-training, co-training(CO), expectation maximization (EM), CO-EM, Then how graph-based methods are useful is explained. All semi-supervised learning methods are classified into generative and discriminative methods. But experimental results show that the hybrid algorithm gives better classification accuracy.
Comparison between two types of large sample covariance matrices

Let {X ij }, i, j = · · · , be a double array of independent and identically distributed (i.i.d.) real random variables with EX 11 = µ, E|X 11 − µ| 2 = 1 and E|X 11 | 4 < ∞. Consider sample covariance matrices (with/without empirical centering) S = 1 n n j=1 (s j − ¯ s)(s j − ¯ s) T and S = 1 n n j=1 s j s T j , where ¯ s = 1 n n j=1 s j and s j = T 1/2 n (X 1j , · · · , X pj) T with (T 1/2 n) 2 = T n , non-random symmetric non-negative definite matrix. It is proved that central limit theorems of eigenvalue statistics of S and S are different as n → ∞ with p/n approaching a positive constant. Moreover , it is also proved that such a different behavior is not observed in the average behavior of eigenvectors.
Does compassion go viral? Social media, caring, and the Fort McMurray wildfire

ABSTRACT In May 2016, an enormous wildfire threatened the city of Fort McMurray, Alberta and forced the evacuation of all of the city’s residents. Outpourings of support teemed in from all across Canada and over the world, prompting the largest charitable response in Canadian Red Cross history. This paper examines Albertans’ response to the wildfire by exploring caring and helping behaviors as well as the role of social media in facilitating these remarkable charitable efforts. The paper uses mixed methods including an analysis of the most popular Tweets related to the wildfire and an Alberta survey collected months after the disaster. The analysis of tweets reveals that care, concern, and invitations to help were prominent in social media discourse about the wildfire. The analysis of survey data demonstrates that those who followed news about the wildfire on social media express higher overall levels of care and concern for those affected, which led to helping those impacted by the wildfire. The findings provide important insights about the role of social media in disaster relief and recovery as well as citizens’ civic engagement.
Randomized Comparison of Strategies for Type B Aortic Dissection: The INvestigation of STEnt Grafts in Aortic Dissection (INSTEAD) Trial

Background— Thoracic endovascular aortic repair (TEVAR) represents a novel concept for type B aortic dissection. Although life-saving in acute emergencies, outcomes and survival of TEVAR in stable dissection are unknown. Methods and Results— One hundred forty patients in stable clinical condition at least 2 weeks after index dissection were randomly subjected to elective stent-graft placement in addition to optimal medical therapy (n=72) or to optimal medical therapy alone (n=68) with surveillance (arterial pressure according to World Health Organization guidelines ≤120/80 mm Hg). The primary end point was all-cause death at 2 years, whereas aorta-related death, progression (with need for conversion or additional endovascular or open surgery), and aortic remodeling were secondary end points. There was no difference in all-cause deaths, with a 2-year cumulative survival rate of 95.6±2.5% with optimal medical therapy versus 88.9±3.7% with TEVAR (P=0.15); the trial, however, turned out to be underpowered. Moreover, the aorta-related death rate was not different (P=0.44), and the risk for the combined end point of aorta-related death (rupture) and progression (including conversion or additional endovascular or open surgery) was similar (P=0.65). Three neurological adverse events occurred in the TEVAR group (1 paraplegia, 1 stroke, and 1 transient paraparesis), versus 1 case of paraparesis with medical treatment. Finally, aortic remodeling (with true-lumen recovery and thoracic false-lumen thrombosis) occurred in 91.3% of patients with TEVAR versus 19.4% of those who received medical treatment (P<0.001), which suggests ongoing aortic remodeling. Conclusions— In the first randomized study on elective stent-graft placement in survivors of uncomplicated type B aortic dissection, TEVAR failed to improve 2-year survival and adverse event rates despite favorable aortic remodeling. Clinical Trial Registration— URL: http://www.clinicaltrials. gov. Unique identifier: NCT00525356.
A study of the role of Ohio school psychologists

3. When a map, drawing or chart, etc., was part of the material being photographed the photographer followed a definite method in "sectioning" the material. It is customary to begin photoing at the upper left hand corner of a large sheet and to continue photoing from left to right in equal sections with a small overlap. If necessary, sectioning is continued again — beginning below the first row and continuing on until complete.
Long-Range Navigation on Complex Networks using Lévy Random Walks

We introduce a strategy of navigation in undirected networks, including regular, random, and complex networks, that is inspired by Lévy random walks, generalizing previous navigation rules. We obtained exact expressions for the stationary probability distribution, the occupation probability, the mean first passage time, and the average time to reach a node on the network. We found that the long-range navigation using the Lévy random walk strategy, compared with the normal random walk strategy, is more efficient at reducing the time to cover the network. The dynamical effect of using the Lévy walk strategy is to transform a large-world network into a small world. Our exact results provide a general framework that connects two important fields: Lévy navigation strategies and dynamics on complex networks.
Eecient Network Qos Provisioning Based on per Node Traac Shaping

This paper addresses the problem of providing per connection end to end delay guarantees in a high speed network We assume that the network is connection oriented and enforces some admission control which ensures that the source tra c conforms to speci ed tra c characteristics We concentrate on the class of Rate Controlled Service Disciplines in which tra c from each connection is reshaped at every hop and develop end to end delay bounds for the general case where di erent reshapers are used at each hop In addition we establish that these bounds can also be achieved when the shapers at each hop have the same minimal envelope The main disadvantage of this class of service disciplines is that the end to end delay guarantees are obtained as the sum of the worst case delays at each node but we show that this problem can be alleviated through proper reshaping of the tra c to an envelope which is in general di erent from the original envelope of the source tra c We illustrate the impact of this reshaping by demonstrating its use in designing Rate Controlled Service disciplines that outperform GPS based service disciplines Furthermore we show that we can restrict the space of good shapers to a family which is characterized by only one parameter We also describe extensions to the service discipline that make it work conserving and as a result reduce the average end to end delays
A Blueprint for the Spatiotemporal Origins of Mouse Hippocampal Interneuron Diversity

Although vastly outnumbered, inhibitory interneurons critically pace and synchronize excitatory principal cell populations to coordinate cortical information processing. Precision in this control relies upon a remarkable diversity of interneurons primarily determined during embryogenesis by genetic restriction of neuronal potential at the progenitor stage. Like their neocortical counterparts, hippocampal interneurons arise from medial and caudal ganglionic eminence (MGE and CGE) precursors. However, while studies of the early specification of neocortical interneurons are rapidly advancing, similar lineage analyses of hippocampal interneurons have lagged. A “hippocampocentric” investigation is necessary as several hippocampal interneuron subtypes remain poorly represented in the neocortical literature. Thus, we investigated the spatiotemporal origins of hippocampal interneurons using transgenic mice that specifically report MGE- and CGE-derived interneurons either constitutively or inducibly. We found that hippocampal interneurons are produced in two neurogenic waves between E9–E12 and E12–E16 from MGE and CGE, respectively, and invade the hippocampus by E14. In the mature hippocampus, CGE-derived interneurons primarily localize to superficial layers in strata lacunosum moleculare and deep radiatum, while MGE-derived interneurons readily populate all layers with preference for strata pyramidale and oriens. Combined molecular, anatomical, and electrophysiological interrogation of MGE/CGE-derived interneurons revealed that MGE produces parvalbumin-, somatostatin-, and nitric oxide synthase-expressing interneurons including fast-spiking basket, bistratified, axo-axonic, oriens-lacunosum moleculare, neurogliaform, and ivy cells. In contrast, CGE-derived interneurons contain cholecystokinin, calretinin, vasoactive intestinal peptide, and reelin including non-fast-spiking basket, Schaffer collateral-associated, mossy fiber-associated, trilaminar, and additional neurogliaform cells. Our findings provide a basic blueprint of the developmental origins of hippocampal interneuron diversity.
Discrete gap probabilities and discrete Painlevé equations

We prove that Fredholm determinants of the form det(1-K_s), where K_s is the restriction of either the discrete Bessel kernel or the discrete {}_2F_1 kernel to {s,s+1,. }, can be expressed through solutions of discrete Painleve II and V equations, respectively. These Fredholm determinants can also be viewed as distribution functions of the first part of the random partitions distributed according to a poissonized Plancherel measure and a z-measure, or as normalized Toeplitz determinants with symbols exp(\eta(u+1/u)) and (1+u)^z(1+\xi/u)^{z'}. The proofs are based on a general formalism involving discrete integrable operators and discrete Riemann-Hilbert problem. A continuous version of the formalism has been worked out in math-ph/0111007.
Anytime Prediction: Efficient Ensemble Methods for Any Computational Budget

Abstract : A modern practitioner of machine learning must often consider trade-offs between accuracy and complexity when selecting from available machine learning algorithms. Prediction tasks can range from requiring real-time performance to being largely unconstrained in their use of computational resources. In each setting, an ideal algorithm utilizes as much of the available computation as possible to provide the most accurate result. This issue is further complicated by applications where the computational constraints are not fixed in advance. In many applications predictions are often needed in time to allow for adaptive behaviors which respond to real-time events. Such constraints often rely on a number of factors at prediction time, making it difficult to select a fixed prediction algorithm a priori. In these situations an ideal approach is to use an anytime prediction algorithm. Such an algorithm rapidly produces an initial prediction and then continues to refine the result as time allows, producing final results which dynamically improve to fit any computational budget. Our approach uses a greedy, cost-aware extension of boosting which fuses the disparate areas of functional gradient descent and greedy sparse approximation algorithms. By using a cost-greedy selection procedure our algorithms provide an intuitive and effective way to trade-off computational cost and accuracy for any computational budget. This approach learns a sequence of predictors to apply as time progresses, using each new result to update and improve the current prediction as time allows. Furthermore, we present theoretical work in the different areas we have brought together, and show that our anytime approach is guaranteed to achieve near-optimal performance with respect to unknown prediction time budgets. We also present the results of applying our algorithms to a number of problem domains such as classification and object detection that indicate that our approach to anytime prediction is more e