In the Supplementary Information initially published online for this Letter, the x axis labels were missing in Supplementary Fig. 5a–c; they should have been ‘t (μs)’, ‘ν (MHz)’ and ‘t (μs)’, ...respectively. These labels have now been added.
The authors report that the labels indicating the symptom types and no symptom lines in the original version of Figure 2 were missing. The correct version of Figure 2 with the labels included is ...provided below.
Suppose that T is a plane tree without vertices of degree 2 and with at least one vertex of at least degree 3, and C is the cycle obtained by connecting the leaves of T in a cyclic order. Set G=T∪C, ...which is called a Halin graph. A k-L(2,1)-labeling of a graph G=(V,E) is a mapping f:V(G)→{0,1,...,k} such that, for any xsub.1,xsub.2∈V(G), it holds that |f(xsub.1)−f(xsub.2)|≥2 if xsub.1xsub.2∈E(G), and |f(xsub.1)−f(xsub.2)|≥1 if the distance between xsub.1 and xsub.2 is 2 in G. The L(2,1)-labeling number, denoted λ(G), of G is the least k for which G is k-L(2,1)-labelable. In this paper, we prove that every Halin graph G with Δ=8 has λ(G)≤10. This improves a known result, which states that every Halin graph G with Δ≥9 satisfies λ(G)≤Δ+2. This result, together with some known results, shows that every Halin graph G satisfies λ(G)≤Δ+6.
The aim of the study was to evaluate the labeling yield of .sup.44Sc-DOTATATE radiobioconjugate when the labeling is performed in the presence of various amounts of competing metallic impurities. In ...the case of Ca.sup.2+ and Al.sup.3+ the effect is irrelevant, which is understandable considering the low stability constant of Ca.sup.2+-DOTA and Al.sup.3+-DOTA complexes. However, the presence of Fe.sup.2+/3+, Zn.sup.2+ and Cu.sup.2+ cations very strongly influences the efficiency of the .sup.44Sc-DOTATATE formation. Surprisingly, while the Zn.sup.2+-DOTA stability constants is the smallest, Zn.sup.2+ cations competes more strongly with Sc.sup.3+ than Fe.sup.2+,3+ and Cu.sup.2+ at the DOTATATE coordination site.
It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that ...the label correlations are local and shared only by a data subset. In fact, in the real-world applications, both cases may occur that some label correlations are globally applicable and some are shared only in a local group of instances. Moreover, it is also a usual case that only partial labels are observed, which makes the exploitation of the label correlations much more difficult. That is, it is hard to estimate the label correlations when many labels are absent. In this paper, we propose a new multi-label approach GLOCAL dealing with both the full-label and the missing-label cases, exploiting global and local label correlations simultaneously, through learning a latent label representation and optimizing label manifolds. The extensive experimental studies validate the effectiveness of our approach on both full-label and missing-label data.
Purpose: Arterial Spin Labeling (ASL) methods allow for the non-invasive acquisition of cerebral blood flow (1). The technique relies on the repeated acquisition of the same data to achieve ...sufficient signal. Thereby, the method is generally not time-resolved. Methods: In this study the acquisition order of the slices is changed during each repetition of the scan instead of having the same order for each scan. The order we propose equals a permutation (i. e. each slice is on each position but only acquired once per repetition). Following this, each slice is read out equally often at each time-point. The experiments have been conducted on a Philips 3T Achieva MRI scanner. Results: The initial results of the study appear promising to acquire time-resolved ASL perfusion data. The image quality however needs to be increased to be comparable to the standard acquisition. Conclusion: By re-ordering the Slices during acquisition it is possible to obtain time-resolved ASL perfusion data with freely chosen time intervals. In future studies the influence of physiological parameters like heartbeat should be further investigated as the temporal resolution can be as low as 15ms.
Hintergrund: Die maschinenassistierte Diagnose von neurodegenerativen Krankheiten anhand von Perfusionsdaten hat bereits vielversprechende Erfolge produziert. Eine wichtige offene Fragestellung ist ...hierbei, welche spezifischen Merkmale hauptsachlich fur die Klassifikation verwendet werden. Eine statistische Principal Component Analyse (PcA) erlaubt Einblicke in die struktur von komplexen Datensatzen. Methodik: 40 Kontrollen und 120 neurodegenerativ Erkrankte wurden mit einer Arterial spin Labeling sequenz gemessen. Die resultierenden Perfusionsdaten wurden mit gangigen Methoden (Collij, 2016) postprozessiert und anschliessend erst separat und dann gesammelt visualisiert und analysiert. Resultate: Zwei Principal Components reichen aus, um >85% der Varianz zu erklaren. Eine Reduktion der Daten offenbart einen monotonen Zusammenhang zwischen dem Alter und der Perfusion, wie er bezugnehmend auf (Zhang N. M., 2016) zu erwarten ist. In der neurodegenerativen Gruppe zeigt sich dies mit Werten > 2 standardabweichungen uber dem Mittelwert speziell parietal bzw. prafrontal. Die Principal Components der Kontrollgruppe schliessen teilweise andere Hirnareale mit ein und weisen eine insgesamt niedrigere Varianz auf. Diskussion: PcA produziert handhabbare reprasentationen von grossen Datensatzen, die Aussagen uber die vorhandene Variation innerhalb und zwischen den subjektgruppen erlauben. Trotzdem reichen die Pcs nicht aus, um verlasslich anhand ihrer Position diagnostizieren zu konnen. Hierfur werden weitere Postprozessierungsschritte benotigt.
PDF
