The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks.

In laboratory object recognition tasks based on undistorted photographs, both adult humans and deep neural networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1.3M images) perform poorly on distorted images. However, the last 2 years have seen impressive gains in DNN distortion robustness, predominantly achieved through ever-increasing large-scale datasets-orders of magnitude larger than ImageNet. Although this simple brute-force approach is very effective in achieving human-level robustness in DNNs, it raises the question of whether human robustness, too, is simply due to extensive experience with (distorted) visual input during childhood and beyond. Here we investigate this question by comparing the core object recognition performance of 146 children (aged 4-15 years) against adults and against DNNs. We find, first, that already 4- to 6-year-olds show remarkable robustness to image distortions and outperform DNNs trained on ImageNet. Second, we estimated the number of images children had been exposed to during their lifetime. Compared with various DNNs, children's high robustness requires relatively little data. Third, when recognizing objects, children-like adults but unlike DNNs-rely heavily on shape but not on texture cues. Together our results suggest that the remarkable robustness to distortions emerges early in the developmental trajectory of human object recognition and is unlikely the result of a mere accumulation of experience with distorted visual input. Even though current DNNs match human performance regarding robustness, they seem to rely on different and more data-hungry strategies to do so

How open lists undermine the electoral support of cohesive parties

How does ballot structure affect party choice? We argue that open lists undermine the electoral support of cohesive parties, to the benefit of internally divided parties. We conduct a survey-embedded experiment in the aftermath of the European migrant crisis, presenting German voters with real parties but fictitious politicians. A crossover design varies ballot type and exposure to candidate positions on immigration. We find that the internally divided Christian Democrats gain votes at the expense of the cohesive Alternative for Germany when open lists are used and candidate positions are known. For individuals who are equally attracted to both parties, switching is most likely if their immigration preferences lie near the midpoint between the two parties. Overall, our analysis establishes conditions under which ballot structure can affect the electoral performance of parties in general, and that of the populist right in particular

OCT-4 expression in follicular and luteal phase endometrium: a pilot study

<p>Abstract</p> <p>Background</p> <p>The stem cell marker Octamer-4 (OCT-4) is expressed in human endometrium. Menstrual cycle-dependency of OCT-4 expression has not been investigated to date.</p> <p>Methods</p> <p>In a prospective, single center cohort study of 98 women undergoing hysteroscopy during the follicular (n = 49) and the luteal (n = 40) phases of the menstrual cycle, we obtained endometrial samples. Specimens were investigated for OCT-4 expression on the mRNA and protein levels using reverse transcriptase polymerase chain reaction (RT-PCR) and immunohistochemistry. Expression of OCT-4 was correlated to menstrual cycle phase.</p> <p>Results</p> <p>Of 89 women sampled, 49 were in the follicular phase and 40 were in the luteal phase. OCT-4 mRNA was detected in all samples. Increased OCT-4 mRNA levels in the follicular and luteal phases was found in 35/49 (71%) and 27/40 (68%) of women, respectively (p = 0.9). Increased expression of OCT-4 protein was identified in 56/89 (63%) samples. Increased expression of OCT-4 protein in the follicular and luteal phases was found in 33/49 (67%) and 23/40 (58%) of women, respectively (p = 0.5).</p> <p>Conclusions</p> <p>On the mRNA and protein levels, OCT-4 is not differentially expressed during the menstrual cycle. Endometrial OCT-4 is not involved in or modulated by hormone-induced cyclical changes of the endometrium.</p

Critical behavior of the (2+1)-dimensional Thirring model

We investigate chiral symmetry breaking in the (2+1)-dimensional Thirring model as a function of the coupling as well as the Dirac flavor number Nf with the aid of the functional renormalization group. For small enough flavor number Nf < Nfc, the model exhibits a chiral quantum phase transition for sufficiently large coupling. We compute the critical exponents of this second order transition as well as the fermionic and bosonic mass spectrum inside the broken phase within a next-to-leading order derivative expansion. We also determine the quantum critical behavior of the many-flavor transition which arises due to a competition between vector and chiral-scalar channel and which is of second order as well. Due to the problem of competing channels, our results rely crucially on the RG technique of dynamical bosonization. For the critical flavor number, we find Nfc ~ 5.1 with an estimated systematic error of approximately one flavor.Comment: 28 pages, 14 figure

Analysis of Cd44-Containing Lipid Rafts: Recruitment of Annexin II and Stabilization by the Actin Cytoskeleton

CD44, the major cell surface receptor for hyaluronic acid (HA), was shown to localize to detergent-resistant cholesterol-rich microdomains, called lipid rafts, in fibroblasts and blood cells. Here, we have investigated the molecular environment of CD44 within the plane of the basolateral membrane of polarized mammary epithelial cells. We show that CD44 partitions into lipid rafts that contain annexin II at their cytoplasmic face. Both CD44 and annexin II were released from these lipid rafts by sequestration of plasma membrane cholesterol. Partition of annexin II and CD44 to the same type of lipid rafts was demonstrated by cross-linking experiments in living cells. First, when CD44 was clustered at the cell surface by anti-CD44 antibodies, annexin II was recruited into the cytoplasmic leaflet of CD44 clusters. Second, the formation of intracellular, submembranous annexin II–p11 aggregates caused by expression of a trans-dominant mutant of annexin II resulted in coclustering of CD44. Moreover, a frequent redirection of actin bundles to these clusters was observed. These basolateral CD44/annexin II–lipid raft complexes were stabilized by addition of GTPγS or phalloidin in a semipermeabilized and cholesterol-depleted cell system. The low lateral mobility of CD44 in the plasma membrane, as assessed with fluorescent recovery after photobleaching (FRAP), was dependent on the presence of plasma membrane cholesterol and an intact actin cytoskeleton. Disruption of the actin cytoskeleton dramatically increased the fraction of CD44 which could be recovered from the light detergent-insoluble membrane fraction. Taken together, our data indicate that in mammary epithelial cells the vast majority of CD44 interacts with annexin II in lipid rafts in a cholesterol-dependent manner. These CD44-containing lipid microdomains interact with the underlying actin cytoskeleton

Machine Learning for Outcome Prediction in First-Line Surgery of Prolactinomas.

Background First-line surgery for prolactinomas has gained increasing acceptance, but the indication still remains controversial. Thus, accurate prediction of unfavorable outcomes after upfront surgery in prolactinoma patients is critical for the triage of therapy and for interdisciplinary decision-making. Objective To evaluate whether contemporary machine learning (ML) methods can facilitate this crucial prediction task in a large cohort of prolactinoma patients with first-line surgery, we investigated the performance of various classes of supervised classification algorithms. The primary endpoint was ML-applied risk prediction of long-term dopamine agonist (DA) dependency. The secondary outcome was the prediction of the early and long-term control of hyperprolactinemia. Methods By jointly examining two independent performance metrics - the area under the receiver operating characteristic (AUROC) and the Matthews correlation coefficient (MCC) - in combination with a stacked super learner, we present a novel perspective on how to assess and compare the discrimination capacity of a set of binary classifiers. Results We demonstrate that for upfront surgery in prolactinoma patients there are not a one-algorithm-fits-all solution in outcome prediction: different algorithms perform best for different time points and different outcomes parameters. In addition, ML classifiers outperform logistic regression in both performance metrics in our cohort when predicting the primary outcome at long-term follow-up and secondary outcome at early follow-up, thus provide an added benefit in risk prediction modeling. In such a setting, the stacking framework of combining the predictions of individual base learners in a so-called super learner offers great potential: the super learner exhibits very good prediction skill for the primary outcome (AUROC: mean 0.9, 95% CI: 0.92 - 1.00; MCC: 0.85, 95% CI: 0.60 - 1.00). In contrast, predicting control of hyperprolactinemia is challenging, in particular in terms of early follow-up (AUROC: 0.69, 95% CI: 0.50 - 0.83) vs. long-term follow-up (AUROC: 0.80, 95% CI: 0.58 - 0.97). It is of clinical importance that baseline prolactin levels are by far the most important outcome predictor at early follow-up, whereas remissions at 30 days dominate the ML prediction skill for DA-dependency over the long-term. Conclusions This study highlights the performance benefits of combining a diverse set of classification algorithms to predict the outcome of first-line surgery in prolactinoma patients. We demonstrate the added benefit of considering two performance metrics jointly to assess the discrimination capacity of a diverse set of classifiers

Artificial Intelligence and Interstitial Lung Disease: Diagnosis and Prognosis.

Interstitial lung disease (ILD) is now diagnosed by an ILD-board consisting of radiologists, pulmonologists, and pathologists. They discuss the combination of computed tomography (CT) images, pulmonary function tests, demographic information, and histology and then agree on one of the 200 ILD diagnoses. Recent approaches employ computer-aided diagnostic tools to improve detection of disease, monitoring, and accurate prognostication. Methods based on artificial intelligence (AI) may be used in computational medicine, especially in image-based specialties such as radiology. This review summarises and highlights the strengths and weaknesses of the latest and most significant published methods that could lead to a holistic system for ILD diagnosis. We explore current AI methods and the data use to predict the prognosis and progression of ILDs. It is then essential to highlight the data that holds the most information related to risk factors for progression, e.g., CT scans and pulmonary function tests. This review aims to identify potential gaps, highlight areas that require further research, and identify the methods that could be combined to yield more promising results in future studies

Influence of organic diet on the amount of conjugated linoleic acids in breast milk of lactating women in the Netherlands

The aim of the present study was to find out whether the incorporation of organic dairy and meat products in the maternal diet affects the contents of the conjugated linoleic acid isomers (CLA) and trans-vaccenic acid (TVA) in human breast milk. To this purpose, milk samples from 312 breastfeeding mothers participating in the KOALA Birth Cohort Study have been analysed. The participants had documented varying lifestyles in relation to the use of conventional or organic products. Breast milk samples were collected 1month postpartum and analysed for fatty acid composition. The content of rumenic acid (the main CLA) increased in a statistically significant way while going from a conventional diet (no organic dairy/meat products, 0·25 weight % (wt%), n 186) to a moderately organic diet (50-90% organic dairy/meat, 0·29wt%, n 33, P=0·02) and to a strict organic diet (>90% organic dairy/meat, 0·34wt%, n 37, P≤0·001). The levels of TVA were augmented among the participants with a moderately organic diet (0·54wt%) and those with a strict organic diet (0·59wt%, P≤0·001), in comparison with the conventional group (0·48wt%). After adjusting for covariables (recruitment group, maternal age, maternal education, use of supplements and season), statistical significance was retained in the group of the strict organic dairy users (P<0·001 for rumenic acid). Hence, the levels of CLA and TVA in human milk can be modulated if breastfeeding mothers replace conventional dairy and/or meat products by organic ones. A potential contribution of CLA and TVA to health improvement is briefly discusse

Network of topological nodal planes, multifold degeneracies, and Weyl points in CoSi

We report the identification of symmetry-enforced nodal planes (NPs) in CoSi providing the missing topological charges in an entire network of band-crossings comprising in addition multifold degeneracies and Weyl points, such that the fermion doubling theorem is satisfied. In our study we have combined measurements of Shubnikov-de Haas (SdH) oscillations in CoSi with material-specific calculations of the electronic structure and Berry curvature, as well as a general analysis of the band topology of space group (SG) 198. The observation of two nearly dispersionless SdH frequency branches provides unambiguous evidence of four Fermi surface sheets at the R point that reflect the symmetry-enforced orthogonality of the underlying wave functions at the intersections with the NPs. Hence, irrespective of the spin-orbit coupling strength, SG198 features always six- and fourfold degenerate crossings at R and $\Gamma$ that are intimately connected to the topological charges distributed across the network

Rab GDP dissociation inhibitor as a general regulator for the membrane association of rab proteins

Rab proteins comprise a family of small GTPases that serve a regulatory role in membrane traffic. These proteins are in part cytosolic and in part associated with the membranes of specific exocytic and endocytic organelles. Smg p25A/rab3A GDI, a cytosolic protein which inhibits the dissociation of GDP from smg p25A/rab3A, Sec4p, and rab11, has also been found to prevent association of rab3A with the membrane. In this study, we have used Madin-Darby canine kidney cells permeabilized with the bacterial toxin streptolysin O to test the general activity of rab3A GDI in modulating the membrane association of various small GTP-binding proteins. Rab3A GDP dissociation inhibitor (GDI) removed from the membrane all rab proteins we have tested and inhibited the membrane binding of in vitro translated rab proteins. However, rab3A GDI had a limited effect on the membrane association of a mutant rab5 protein which contained a farnesylated cysteine motif. Finally, we found that, although rab3A GDI resides primarily in the cytosol, it is also associated with compartments of the exocytic and endocytic pathways. Since rab3A GDI can modulate the membrane association of various rab proteins, we propose to rename it rab GDI