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433 Treffer

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  1. Detailansicht für Treffer 1 öffnen
    Oxidized Phospholipids Promote NETosis and Arterial Thrombosis in LNK(SH2B3) Deficiency
    Dou, Huijuan ; Kotini, Andriana ; et al.
    In: Circulation, vol 144, iss 24, 2021
    academicJournal
    Wie komme ich dran?
  2. Detailansicht für Treffer 2 öffnen
    LNK suppresses interferon signaling in melanoma
    Ding, Ling-Wen ; Sun, Qiao-Yang ; et al.
    In: Nature Communications, vol 10, iss 1, 2019
    academicJournal
    Wie komme ich dran?
  3. Detailansicht für Treffer 3 öffnen
    LNK suppresses interferon signaling in melanoma
    Ding, L. W. ; Sun, Q. Y. ; et al.
    In: Scopus OA2019, 2019
    Online academicJournal
    Zugriff:
    Full Text Finder (Volltext)
    Wie komme ich dran?
  4. Detailansicht für Treffer 4 öffnen
    MOESM1 of Adaptor protein LNK promotes anaplastic thyroid carcinoma cell growth via 14-3-3 ε/γ binding ...
    Zhong, Zhao-Ming ; Chen, Xue ; et al.
    2020
    academicJournal
    Wie komme ich dran?
  5. Detailansicht für Treffer 5 öffnen
    MOESM1 of Adaptor protein LNK promotes anaplastic thyroid carcinoma cell growth via 14-3-3 ε/γ binding ...
    Zhong, Zhao-Ming ; Chen, Xue ; et al.
    2020
    academicJournal
    Wie komme ich dran?
  6. Detailansicht für Treffer 6 öffnen
    LNK (SH2B3): paradoxical effects in ovarian cancer
    Ding, L-W ; Sun, Q-Y ; et al.
    In: Oncogene, vol 34, iss 11, 2015
    academicJournal
    Wie komme ich dran?
  7. Detailansicht für Treffer 7 öffnen
    Neolitické objekty. Analýza formativních procesů na sídlišti LnK v Bylanech ; Neolithic pits. Formation processes analysis of LBK settlement in Bylany
    Pilař, Daniel ; Květina, Petr ; et al.
    2022
    Online Hochschulschrift
    Zugriff:
    Volltext
    Wie komme ich dran?
  8. Detailansicht für Treffer 8 öffnen
    The Genetics of Coronary Artery Disease: A Vascular Perspective
    Quaye, LNK ; Dalzell, CE ; et al.
    2023
    academicJournal
    Wie komme ich dran?
  9. Detailansicht für Treffer 9 öffnen
    Specific domains of LNK (SH2B3) bind and regulate TPOR stability and signalling
    Lee, Christine ; Brooks, Andrew ; et al.
    2019
    Online Konferenz
    Zugriff:
    Full Text Finder (Volltext)
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  10. Detailansicht für Treffer 10 öffnen
    Possible new LNK mutations in myeloproliferative neoplasms
    Ha, Jung-Sook ; Jeon, Dong-Seok ; et al.
    2011
    academicJournal
    Wie komme ich dran?
  11. Detailansicht für Treffer 11 öffnen
    SH2B3 (LNK) mutations from myeloproliferative neoplasms patients have mild loss of function against wild type JAK2 and JAK2 V617F
    Koren-Michowitz, M. ; Gery, S. ; et al.
    In: Scopus, 2013
    Online academicJournal
    Zugriff:
    Full Text Finder (Volltext)
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  12. Detailansicht für Treffer 12 öffnen
    Lnk
    In: Encyclopedia of Signaling Molecules ; page 2886-2886; (2018)
    Buch
    Wie komme ich dran?
  13. Detailansicht für Treffer 13 öffnen
    Targeted Recruitment of the Basal Transcriptional Machinery by LNK Clock Components Controls the Circadian Rhythms of Nascent RNAs in Arabidopsis
    Ma, Yuan ; Gil, Sergio ; et al.
    2018
    academicJournal
    Wie komme ich dran?
  14. Detailansicht für Treffer 14 öffnen
    Structural and functional characterisation of the thrombopoietin receptor and its negative regulator, LNK in myeloid malignancies
    Lee, Christine
    2020
    Online Hochschulschrift
    Zugriff:
    Volltext
    Wie komme ich dran?
  15. Detailansicht für Treffer 15 öffnen
    LNK Market Community Conversations
    PytlikZillig, Lisa M. ; USDA Value Added Producer Grant Steering Committee
    In: Lisa PytlikZillig Publications, 2018
    academicJournal
    Wie komme ich dran?
  16. Detailansicht für Treffer 16 öffnen
    LNK Market Community Conversations
    PytlikZillig, Lisa M. ; USDA Value Added Producer Grant Steering Committee
    In: Lisa PytlikZillig Publications, 2018
    academicJournal
    Wie komme ich dran?
  17. Detailansicht für Treffer 17 öffnen
    SUSTAINABLE ENHANCEMENT OF HEALTHCARE DELIVERY: A COMPREHENSIVE PARADIGM FOR OPTIMIZING ELECTIVE SURGERY RESOURCES
    Koushan, M ; Wood, LC ; et al.
    2023
    Online Konferenz
    Zugriff:
    Volltext
    Wie komme ich dran?
  18. Detailansicht für Treffer 18 öffnen
    Figure 4—figure supplement 1. DivS model localizes the suppressive components of LNK model and reproduces its simulated response. ; We simulated LNK models resembling the example neurons considered in Figure 4. (A–D) LNK simulation in response to a temporally modulated spot. (A) The LNK model components consist of a temporal filter k (left) and static nonlinearity f(∙) (middle), whose output u(t)=f[k∙s(t)] governs the transition rate between the resting (R) and active (A) states. The current output is proportional to active state occupation, and other constants govern the transition to inactive (I) state and back to resting state. The parameters for this LNK simulation were derived from an LNK fit to an example neuron (see Materials and methods). (B) A DivS model was fit to the LNK model simulated response, with components labeled as in Figure 2. The temporal filter of suppression (cyan) is delayed relative to the excitation (left) and only results in suppression for ON stimuli, as expected given its relationship to synaptic depression. (C) Model performance (R2) for the LN model and DivS model across all neurons demonstrates that the DivS model could reproduce LNK simulations with greater than 90% accuracy, across simulations of all LNK models of recorded neurons (n = 13). (D) Simulated response of the LNK model in (A) in response to a temporal modulated spot stimulus (top). 2nd row: The output of the LNK simulation (black) could be reproduced better by a DivS model (red) fit to the simulated data, as compared to the LN model (blue). 3rd row: The occupation of each internal state determined the current output in addition to the output of the LN component of the model. 4th row: The dynamics of the divisive suppression of the DivS model (cyan) roughly matched the occupation of the resting state of the LNK model (3rd row, green): the resting state occupancy (and availability for transition to the active state and resulting current output in the LNK model) was low at the same times there is suppression in the DivS model. (E–G) LNK simulation in response to the spot-annulus stimulus. (E) LNK components are labeled identically as in (A), but now the filter k consists of separate components for the spot (left, solid) and annulus (dashed) regions of the stimulus. The temporal filter and nonlinearity were derived from the example cell in Figure 4B, but the kinetics parameters of the temporally modulated stimulus (A) were used in place of those derived for the spot-annulus stimuli, because the latter parameters did not result in nonlinear effects. (F) A DivS model fitted to the LNK model simulated response, with components labeled as in Figure 2, resulting in the expected delayed ON suppression (as with the temporally modulated spot simulations in B). (G) Simulated response using the LNK model with the spot-annulus stimulus, again with the divisive suppression of the DivS model (4th row, cyan) capturing the occupancy of resting state of the LNK model (3rd row, green).
    2016
    unknown
    Wie komme ich dran?
  19. Detailansicht für Treffer 19 öffnen
    Figure 4—figure supplement 2. DivS model descriptions of extended LNK models. ; Here we consider additional model structures involving synaptic depression. The simulations here incorporate nonlinear rectified subunits, and were limited to two components corresponding to those independently modulated in the stimulus: spot and annulus. (A–D) First we considered an extended LNK model with independent stimulus processing of the spot and annulus stimuli, and a shared synaptic depression stage. (A) Model schematic, showing that the separate 'center' and 'surround' components (corresponding to spot and annulus stimuli) are each rectified before being combined, and fed into the LNK model for synaptic depression, using the same kinetic parameters considered for simulations in Figure 4—figure supplement 1. Simulated data were generated for a range of models of this form, where the weight for the ‘spot’ component wspot was fixed and the annulus component weight wannu was varied. (B) The DivS model components fit to an example simulated response of the extended LNK model (with wspot = wannu). As with simpler circuits (e.g., Figure 4—figure supplement 1), suppression was delayed relative to excitation. Note that the DivS model was limited to only a single rectified component to match the form used to describe experiments described in Figure 4. (C) The performance of the LN (red) and DivS (purple) models across simulations with different annulus component weights. The DivS model performance was significantly better than that of the LN model over a wide range of parameters (each point corresponds to the results of simulation with different choice of wannu), suggesting a large portion of the synaptic depression effect was captured by the DivS model. Note, however, that the DivS model has a more difficult time explaining this [simulated] data than the data from real ON-Alpha ganglion cells (i.e., Figure 4D). (D) For all simulations, the 'spatial profile' of suppression matched that of excitation, as measured by the 'center fraction', which was given by the norm of the center component of the filter divided by the norm of the full filter. [The center fraction is one for no surround component, and zero for no center component.] (E–H) We next considered an extended LNK model with both independent stimulus processing and independent kinetics. (E) Model schematic, showing the separate center and surround components each with independent synaptic depression — again with the same kinetic parameters previously considered. (F) The DivS model components fit to an example simulated response of the extended LNK model (with wspot = wannu). (G) Performance of DivS model and LN model on simulated LNK model response. (H) Tight correlation of the center fractions of excitation versus divisive suppression of the DivS model components (as in panel D). This and related simulations (i.e., with additional center-surround filtering prior to the rectification stage) involving synaptic depression never yielded a case where DivS excitation was largely from the center and suppression was largely from the surround, which was observed in the real ON-Alpha cell data (e.g., Figure 4E).
    2016
    unknown
    Wie komme ich dran?
  20. Detailansicht für Treffer 20 öffnen
    Figure 4. Probing the mechanism of divisive suppression with center-surround stimuli. ; (A) For the large spot stimulus, the Linear-Nonlinear-Kinetic (LNK) model nearly matches the performance of the DivS model, and outperforms the LN model. (B) To distinguish between different sources of divisive suppression, we presented a spot-annulus stimulus (left), where each region is independently modulated. Model filters can be extended to this stimulus using a separate temporal kernel for center and surround, shown for the LN and LNK model filters (right), which are very similar. (C) After the linear filter, the LNK model applies a nonlinearity (left), whose output drives the transition between resting and activated states (middle), which is further governed by kinetics parameters as shown. Critical kinetics parameters for LNK models differed between the large-spot and spot-annulus stimulus (right), with the spot-annulus model very quickly transitioning from Inactive back to Active states, minimizing the effects of synaptic depression. (D) The performance of the spatiotemporal LNK model is only slightly better than that of the LN model, and neither captures the details of the modulation in synaptic current, compared with the DivS model. (E) The spatiotemporal DivS model shown for an example neuron exhibits different spatial footprints for excitation and suppression, with excitation largely driven by the spot and suppression by the annulus. This divisive suppression cannot be explained exclusively by synaptic depression, which predicts overlapping sources of suppression and excitation (Figure 4—figure supplement 1 and 2). (F) The contribution of the center component in the DivS model for excitation (left) and suppression (right). Excitation was stronger in the center than in the surround (center contribution>0.5, p=0.016, n = 7) and suppression was weaker in the center (center contribution
    2016
    unknown
    Wie komme ich dran?
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