Lewis acid-driven self-assembly of diiridium macrocyclic catalysts imparts substrate selectivity and glutathione tolerance.

Molecular inorganic catalysts (MICs) tend to have solvent-exposed metal centers that lack substrate specificity and are easily inhibited by biological nucleophiles. Unfortunately, these limitations exclude many MICs from being considered for in vivo applications. To overcome this challenge, a strategy to spatially confine MICs using Lewis acid-driven self-assembly is presented. It was shown that in the presence of external cations (e.g., Li+, Na+, K+, or Cs+) or phosphate buffered saline, diiridium macrocycles spontaneously formed supramolecular iridium-cation species, which were characterized by X-ray crystallography and dynamic light scattering. These nanoassemblies selectively reduced sterically unhindered C[double bond, length as m-dash]O groups via transfer hydrogenation and tolerated up to 1 mM of glutathione. In contrast, when non-coordinating tetraalkylammonium cations were used, the diiridium catalysts were unable to form higher-ordered structures and discriminate between different aldehyde substrates. This work suggests that in situ coordination self-assembly could be a versatile approach to enable or enhance the integration of MICs with biological hosts.

Disseminated primary herpes simplex infection imitating preterm prelabor rupture of membranes - a case report.

Background: We describe a case of primary herpes simplex virus (HSV) infection imitating preterm prelabor rupture of membranes (PPROM) and review the intricacies of establishing the diagnosis. Case presentation: At 18 weeks of gestation, a patient was referred for suspected PPROM following leakage of fluid and a positive nitrazine test. The patient had a swollen inguinal lymph node, intermittent fevers, transaminitis, labial lesions, and cervical ulceration with vaginal discharge. Amniotic fluid volume was normal. An HSV PCR test was positive. Intravenous acyclovir followed by oral valacyclovir resulted in resolution of symptoms. Conclusion: Discharge from HSV cervicitis can present as nitrazine-positive pooling, imitating PPROM. A high index of suspicion is warranted, especially when the amniotic fluid volume is normal and arborization is not seen on microscopic exam.

Proteins rather than mRNAs regulate nucleation and persistence of Oskar germ granules in Drosophila.

RNA granules are membraneless condensates that provide functional compartmentalization within cells. The mechanisms by which RNA granules form are under intense investigation. Here, we characterize the role of mRNAs and proteins in the formation of germ granules in Drosophila. Super-resolution microscopy reveals that the number, size, and distribution of germ granules is precisely controlled. Surprisingly, germ granule mRNAs are not required for the nucleation or the persistence of germ granules but instead control their size and composition. Using an RNAi screen, we determine that RNA regulators, helicases, and mitochondrial proteins regulate germ granule number and size, while the proteins of the endoplasmic reticulum, nuclear pore complex, and cytoskeleton control their distribution. Therefore, the protein-driven formation of Drosophila germ granules is mechanistically distinct from the RNA-dependent condensation observed for other RNA granules such as stress granules and P-bodies.

Accurate 3-DoF Camera Geo-Localization via Ground-to-Satellite Image Matching.

We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground camera can be located at any point within the image. To further consolidate our prior research finding, we present a novel geometry-aware geo-localization method. Our new method is able to achieve the fine-grained location of a query image, up to pixel size precision of the satellite image, once its coarse location and orientation have been determined. Moreover, we propose a new geometry-aware image retrieval pipeline to improve the coarse localization accuracy. Apart from a polar transform in our conference work, this new pipeline also maps satellite image pixels to the ground-level plane in the ground-view via a geometry-constrained projective transform to emphasize informative regions, such as road structures, for cross-view geo-localization. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our newly proposed framework. We also significantly improve the performance of coarse localization results compared to the state-of-the-art in terms of location recalls.

Moral psychology from the lab to the wild: Relief registries as a paradigm for studying real-world altruism.

Experimental psychology's recent shift toward low-effort, high-volume methods (e.g., self-reports, online studies) and away from the more effortful study of naturalistic behavior raises concerns about the ecological validity of findings from these fields, concerns that have become particularly apparent in the field of moral psychology. To help address these concerns, we introduce a method allowing researchers to investigate an important, widespread form of altruistic behavior-charitable donations-in a manner balancing competing concerns about internal validity, ecological validity, and ease of implementation: relief registries, which leverage existing online gift registry platforms to allow research subjects to choose among highly needed donation items to ship directly to charitable organizations. Here, we demonstrate the use of relief registries in two experiments exploring the ecological validity of the finding from our own research that people are more willing to help others after having imagined themselves doing so. In this way, we sought to provide a blueprint for researchers seeking to enhance the ecological validity of their own research in a narrow sense (i.e., by using the relief registry method we introduce) and in broader terms by adapting methods that take advantage of modern technology to directly impact others' lives outside the lab.

Geometry-Guided Street-View Panorama Synthesis From Satellite Imagery.

This paper presents a new approach for synthesizing a novel street-view panorama given a satellite image, as if captured from the geographical location at the center of the satellite image. Existing works approach this as an image generation problem, adopting generative adversarial networks to implicitly learn the cross-view transformations, but ignore the geometric constraints. In this paper, we make the geometric correspondences between the satellite and street-view images explicit so as to facilitate the transfer of information between domains. Specifically, we observe that when a 3D point is visible in both views, and the height of the point relative to the camera is known, there is a deterministic mapping between the projected points in the images. Motivated by this, we develop a novel satellite to street-view projection (S2SP) module which learns the height map and projects the satellite image to the ground-level viewpoint, explicitly connecting corresponding pixels. With these projected satellite images as input, we next employ a generator to synthesize realistic street-view panoramas that are geometrically consistent with the satellite images. Our S2SP module is differentiable and the whole framework is trained in an end-to-end manner. Extensive experimental results on two cross-view benchmark datasets demonstrate that our method generates more accurate and consistent images than existing approaches.

Deep Declarative Networks.

We explore a class of end-to-end learnable models wherein data processing nodes (or network layers) are defined in terms of desired behavior rather than an explicit forward function. Specifically, the forward function is implicitly defined as the solution to a mathematical optimization problem. Consistent with nomenclature in the programming languages community, we name these models deep declarative networks. Importantly, it can be shown that the class of deep declarative networks subsumes current deep learning models. Moreover, invoking the implicit function theorem, we show how gradients can be back-propagated through many declaratively defined data processing nodes thereby enabling end-to-end learning. We discuss how these declarative processing nodes can be implemented in the popular PyTorch deep learning software library allowing declarative and imperative nodes to co-exist within the same network. We also provide numerous insights and illustrative examples of declarative nodes and demonstrate their application for image and point cloud classification tasks.

Biased Benevolence: The Perceived Morality of Effective Altruism Across Social Distance.

Is altruism always morally good, or is the morality of altruism fundamentally shaped by the social opportunity costs that often accompany helping decisions? Across four studies, we reveal that in cases of realistic tradeoffs in social distance for gains in welfare where helping socially distant others necessitates not helping socially closer others with the same resources, helping is deemed as less morally acceptable. Making helping decisions at a cost to socially closer others also negatively affects judgments of relationship quality (Study 2) and in turn, decreases cooperative behavior with the helper (Study 3). Ruling out an alternative explanation of physical distance accounting for the effects in Studies 1 to 3, social distance continued to impact moral acceptability when physical distance across social targets was matched (Study 4). These findings reveal that attempts to decrease biases in helping may have previously unconsidered consequences for moral judgments, relationships, and cooperation.

Globally-Optimal Inlier Set Maximisation for Camera Pose and Correspondence Estimation.

Estimating the 6-DoF pose of a camera from a single image relative to a 3D point-set is an important task for many computer vision applications. Perspective-n-point solvers are routinely used for camera pose estimation, but are contingent on the provision of good quality 2D-3D correspondences. However, finding cross-modality correspondences between 2D image points and a 3D point-set is non-trivial, particularly when only geometric information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers and many local optima are common for this problem, we instead propose a robust and globally-optimal inlier set maximisation approach that jointly estimates the optimal camera pose and correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds on the number of inliers and local optimisation is integrated to accelerate convergence. The algorithm outperforms existing approaches on challenging synthetic and real datasets, reliably finding the global optimum, with a GPU implementation greatly reducing runtime.

Must the show go on? The (in)ability of counterevidence to change attitudes toward crime control theater policies.

OBJECTIVES: Crime control theater refers to intuitively appealing laws that appear to address crime while lacking any evidence that they actually do so (e.g., sex offender registration and residence restriction laws, which do not reduce recidivism). Despite their ineffectiveness, public support for such laws tends to be high. HYPOTHESES: We predicted that making people aware of these laws' failure to reduce crime would lower support for them. METHOD: Participants (recruited from Amazon Mechanical Turk; Study 1: N = 298, mean age = 35.60, 47.7% self-identified as women and 75.8% as White; Study 2: N = 147, mean age = 35.03, 40.1% self-identified as women and 85.0% as White; Study 3: N = 552, mean age = 35.86, 42.9% self-identified as women and 76.4% as White) read about sex offender registration and residence restriction policies and rated their support for these laws, confidence in their opinions about them, and perceptions of their efficacy before and after reading counterevidence highlighting these laws' failure to reduce sex crimes. RESULTS: Although exposure to counterevidence somewhat lowered support (average within-subjects d = -0.69), general attitudes remained positive even at the postcounterevidence phase (average d = 0.46 against the scale midpoint). This pattern held when manipulating the criminal population being targeted (sex offenders vs. white-collar offenders; Study 1), when tailoring counterevidence to people's self-stated justifications for supporting these laws (Studies 2-3), and despite favorable ratings of the counterevidence's strength and credibility. CONCLUSION: Support for crime control theater policies persists despite explicit knowledge that they do not reduce crime, highlighting the need for alternative methods of dissuading people from their support for these ineffective laws. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

High dose-rate tandem and ovoid brachytherapy in cervical cancer: dosimetric predictors of adverse events.

BACKGROUND: Brachytherapy (BT) is a vital component of the curative treatment of locally advanced cervical cancer. The American Brachytherapy Society has published guidelines for high dose rate (HDR) BT with recommended dose limits. However, recent reports suggest lower doses may be needed to avoid toxicity. The purpose of this study is to investigate incidence and predictive factors influencing gastrointestinal (GI) and genitourinary (GU) toxicity following HDR intracavitary brachytherapy for locally advanced cervical cancer. METHODS: We retrospectively evaluated a cohort of patients with locally advanced cervical cancer who received CT-based HDR BT. Cumulative doses were calculated using the linear-quadratic model. Statistical analyses were used to investigate clinical and dosimetric predictors of GI and GU toxicity following HDR brachytherapy according to CTCAE v4.0 grading criteria. RESULTS: Fifty-six women with FIGO IB1 - IVA cervical cancer were included. The overall rate of any GU adverse event (Grade 1+) was 23.3% (n = 13) and severe adverse events (Grade 3+) was 7.1% (n = 4). Of those, the bladder equivalent dose in 2- Gray (Gy) fractions (EQD2) D2cc was ≥80 for three of the four patients. The overall rate of any GI adverse event was 26.8% (n = 15) and the rate of severe adverse events was 14.3% (n = 8). Of those, six of the eight patients had a rectal EQD2 D2cc ≥ 65 Gy and seven patients had a sigmoid D2cc ≥ 65 Gy. Amongst clinically meaningful factors for development of adverse events (i.e. diabetes, smoking status, ovoid size, and treatment duration), there were no statistically significant prognostic factors identified. CONCLUSIONS: Severe adverse events are observed even with adherence to current ABS guidelines. In the era of recent multi-institutional study results, our data also supports more stringent dosimetric goals. We suggest cumulative D2cc dose limits of: less than 80 Gy for the bladder and less than 65 Gy for the rectum and sigmoid.

Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration.

The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the L2 error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space SE(3). By exploiting the special structure of SE(3) geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.