Conformational activation of ADAMTS13

A disintegrin and metalloprotease with thrombospondin motifs 13 (ADAMTS13) is a metalloprotease that regulates von Willebrand factor (VWF) function. ADAMTS13-mediated proteolysis is determined by conformational changes in VWF, but also may depend on its own conformational activation. Kinetic analysis of WT ADAMTS13 revealed ∼2.5-fold reduced activity compared with ADAMTS13 lacking its C-terminal tail (MDTCS) or its CUB1-2 domains (WTΔCUB1-2), suggesting that the CUB domains naturally limit ADAMTS13 function. Consistent with this suggestion, WT ADAMTS13 activity was enhanced ∼2.5-fold by preincubation with either an anti-CUB mAb (20E9) or VWF D4CK (the natural binding partner for the CUB domains). Furthermore, the isolated CUB1-2 domains not only bound MDTCS, but also inhibited activity by up to 2.5-fold. Interestingly, a gain-of-function (GoF) ADAMTS13 spacer domain variant (R568K/F592Y/R660K/Y661F/Y665F) was ∼2.5-fold more active than WT ADAMTS13, but could not be further activated by 20E9 mAb or VWF D4CK and was unable to bind or to be inhibited by the CUB1-2 domains, suggesting that the inhibitory effects of the CUB domains involve an interaction with the spacer domain that is disrupted in GoF ADAMTS13. Electron microscopy demonstrated a “closed” conformation of WT ADAMTS13 and suggested a more “open” conformation for GoF ADAMTS13. The cryptic spacer domain epitope revealed by conformational unfolding also represents the core antigenic target for autoantibodies in thrombotic thrombocytopenic purpura. We propose that ADAMTS13 circulates in a closed conformation, which is maintained by a CUB–spacer domain binding interaction. ADAMTS13 becomes conformationally activated on demand through interaction of its C-terminal CUB domains with VWF, making it susceptible to immune recognition

The embeddedness of organizational performance: multiple membership multiple classification models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical mer- its of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and par- tially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteris- tics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covari- ates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures

A comparison of photometric redshift techniques for large radio surveys

Future radio surveys will generate catalogs of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources (most radio surveys have a median redshift greater than 1) and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multiwavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all-sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best in the presence of high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately, using specific templates and priors. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine-learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts

Using smartphone survey and GPS data to inform smoking cessation intervention delivery: Case study

Background: Interest in quitting smoking is common among young adults who smoke, but it can prove challenging. Although evidence-based smoking cessation interventions exist and are effective, a lack of access to these interventions specifically designed for young adults remains a major barrier for this population to successfully quit smoking. Therefore, researchers have begun to develop modern, smartphone-based interventions to deliver smoking cessation messages at the appropriate place and time for an individual. A promising approach is the delivery of interventions using geofences—spatial buffers around high-risk locations for smoking that trigger intervention messages when an individual’s phone enters the perimeter. Despite growth in personalized and ubiquitous smoking cessation interventions, few studies have incorporated spatial methods to optimize intervention delivery using place and time information. Objective: This study demonstrates an exploratory method of generating person-specific geofences around high-risk areas for smoking by presenting 4 case studies using a combination of self-reported smartphone-based surveys and passively tracked location data. The study also examines which geofence construction method could inform a subsequent study design that will automate the process of deploying coping messages when young adults enter geofence boundaries. Methods: Data came from an ecological momentary assessment study with young adult smokers conducted from 2016 to 2017 in the San Francisco Bay area. Participants reported smoking and nonsmoking events through a smartphone app for 30 days, and GPS data was recorded by the app. We sampled 4 cases along ecological momentary assessment compliance quartiles and constructed person-specific geofences around locations with self-reported smoking events for each 3-hour time interval using zones with normalized mean kernel density estimates exceeding 0.7. We assessed the percentage of smoking events captured within geofences constructed for 3 types of zones (census blocks, 500 ft2 fishnet grids, and 1000 ft2 fishnet grids). Descriptive comparisons were made across the 4 cases to better understand the strengths and limitations of each geofence construction method. Results: The number of reported past 30-day smoking events ranged from 12 to 177 for the 4 cases. Each 3-hour geofence for 3 of the 4 cases captured over 50% of smoking events. The 1000 ft2 fishnet grid captured the highest percentage of smoking events compared to census blocks across the 4 cases. Across 3-hour periods except for 3:00 AM-5:59 AM for 1 case, geofences contained an average of 36.4%-100% of smoking events. Findings showed that fishnet grid geofences may capture more smoking events compared to census blocks. Conclusions: Our findings suggest that this geofence construction method can identify high-risk smoking situations by time and place and has potential for generating individually tailored geofences for smoking cessation intervention delivery. In a subsequent smartphone-based smoking cessation intervention study, we plan to use fishnet grid geofences to inform the delivery of intervention messages

Phase I Trial of First-in-Class ATR Inhibitor M6620 (VX-970) as Monotherapy or in Combination With Carboplatin in Patients With Advanced Solid Tumors.

Purpose Preclinical studies demonstrated that ATR inhibition can exploit synthetic lethality (eg, in cancer cells with impaired compensatory DNA damage responses through ATM loss) as monotherapy and combined with DNA-damaging drugs such as carboplatin.Patients and methods This phase I trial assessed the ATR inhibitor M6620 (VX-970) as monotherapy (once or twice weekly) and combined with carboplatin (carboplatin on day 1 and M6620 on days 2 and 9 in 21-day cycles). Primary objectives were safety, tolerability, and maximum tolerated dose; secondary objectives included pharmacokinetics and antitumor activity; exploratory objectives included pharmacodynamics in timed paired tumor biopsies.Results Forty patients were enrolled; 17 received M6620 monotherapy, which was safe and well tolerated. The recommended phase II dose (RP2D) for once- or twice-weekly administration was 240 mg/m2. A patient with metastatic colorectal cancer harboring molecular aberrations, including ATM loss and an ARID1A mutation, achieved RECISTv1.1 complete response and maintained this response, with a progression-free survival of 29 months at last assessment. Twenty-three patients received M6620 with carboplatin, with mechanism-based hematologic toxicities at higher doses, requiring dose delays and reductions. The RP2D for combination therapy was M6620 90 mg/m2 with carboplatin AUC5. A patient with advanced germline BRCA1 ovarian cancer achieved RECISTv1.1 partial response and Gynecologic Cancer Intergroup CA125 response despite being platinum refractory and PARP inhibitor resistant. An additional 15 patients had RECISTv1.1 stable disease as best response. Pharmacokinetics were dose proportional and exceeded preclinical efficacious levels. Pharmacodynamic studies demonstrated substantial inhibition of phosphorylation of CHK1, the downstream ATR substrate.Conclusion To our knowledge, this report is the first of an ATR inhibitor as monotherapy and combined with carboplatin. M6620 was well tolerated, with target engagement and preliminary antitumor responses observed

MeerKAT uncovers the physics of an odd radio circle

Odd radio circles (ORCs) are recently-discovered faint diffuse circles of radio emission, of unknown cause, surrounding galaxies at moderate redshift (z ∼0.2-0.6). Here, we present detailed new MeerKAT radio images at 1284 MHz of the first ORC, originally discovered with the Australian Square Kilometre Array Pathfinder, with higher resolution (6 arcsec) and sensitivity (∼2.4 μJy/beam). In addition to the new images, which reveal a complex internal structure consisting of multiple arcs, we also present polarization and spectral index maps. Based on these new data, we consider potential mechanisms that may generate the ORCs