Miracle-dz/UniFAR-FTUs huggingface dataset:
Way-finding in displaced clock-shifted bees proves bees use a cognitive map

Significance The question of the computational capacities of the brains of widely separated genera of animals is of interest to behavioral biologists, comparative psychologists, computational neuroscientists, philosophers of mind, and—we believe—much of the scientific community. Half a century ago, the claim that any nonhuman animal had a cognitive map was deeply controversial. If true, it greatly favored a computational theory of mind, as opposed to an antirepresentational behaviorist theory. Now that it is well established by behavioral and neurobiological evidence that rodents have a metric cognitive map, the question of whether insects do is a frontier question, the answer to which has broad implications in several disciplines. Mammals navigate by means of a metric cognitive map. Insects, most notably bees and ants, are also impressive navigators. The question whether they, too, have a metric cognitive map is important to cognitive science and neuroscience. Experimentally captured and displaced bees often depart from the release site in the compass direction they were bent on before their capture, even though this no longer heads them toward their goal. When they discover their error, however, the bees set off more or less directly toward their goal. This ability to orient toward a goal from an arbitrary point in the familiar environment is evidence that they have an integrated metric map of the experienced environment. We report a test of an alternative hypothesis, which is that all the bees have in memory is a collection of snapshots that enable them to recognize different landmarks and, associated with each such snapshot, a sun-compass–referenced home vector derived from dead reckoning done before and after previous visits to the landmark. We show that a large shift in the sun-compass rapidly induced by general anesthesia does not alter the accuracy or speed of the homeward-oriented flight made after the bees discover the error in their initial postrelease flight. This result rules out the sun-referenced home-vector hypothesis, further strengthening the now extensive evidence for a metric cognitive map in bees.
Consumer-Resource Dynamics: Quantity, Quality, and Allocation

Background The dominant paradigm for modeling the complexities of interacting populations and food webs is a system of coupled ordinary differential equations in which the state of each species, population, or functional trophic group is represented by an aggregated numbers-density or biomass-density variable. Here, using the metaphysiological approach to model consumer-resource interactions, we formulate a two-state paradigm that represents each population or group in a food web in terms of both its quantity and quality. Methodology and Principal Findings The formulation includes an allocation function controlling the relative proportion of extracted resources to increasing quantity versus elevating quality. Since lower quality individuals senesce more rapidly than higher quality individuals, an optimal allocation proportion exists and we derive an expression for how this proportion depends on population parameters that determine the senescence rate, the per-capita mortality rate, and the effects of these rates on the dynamics of the quality variable. We demonstrate that oscillations do not arise in our model from quantity-quality interactions alone, but require consumer-resource interactions across trophic levels that can be stabilized through judicious resource allocation strategies. Analysis and simulations provide compelling arguments for the necessity of populations to evolve quality-related dynamics in the form of maternal effects, storage or other appropriate structures. They also indicate that resource allocation switching between investments in abundance versus quality provide a powerful mechanism for promoting the stability of consumer-resource interactions in seasonally forcing environments. Conclusions/Significance Our simulations show that physiological inefficiencies associated with this switching can be favored by selection due to the diminished exposure of inefficient consumers to strong oscillations associated with the well-known paradox of enrichment. Also our results demonstrate how allocation switching can explain observed growth patterns in experimental microbial cultures and discuss how our formulation can address questions that cannot be answered using the quantity-only paradigms that currently predominate.
Microexons: discovery, regulation, and function

The importance of RNA splicing in numerous cellular processes is well established. However, an underappreciated aspect is the ability of the spliceosome to recognize a set of very small (3–30 nucleotide, 1–10 amino acid) exons named microexons. Despite their small size, microexons and their regulation through alternative splicing have now been shown to play critical roles in protein and system function. Here we review the discovery of microexons over time and the mechanisms by which their splicing is regulated, including recent progress made through deep RNA sequencing. We also discuss the functional role of microexons in biology and disease. WIREs RNA 2017, 8:e1418. doi: 10.1002/wrna.1418
Cost-effectiveness of Injectable Preexposure Prophylaxis for HIV Prevention in South Africa.

BACKGROUND Long-acting injectable antiretrovirals such as rilpivirine (RPV) could promote adherence to preexposure prophylaxis (PrEP) for human immunodeficiency virus (HIV) prevention. However, the cost-effectiveness of injectable PrEP is unclear. METHODS We constructed a dynamic model of the heterosexual HIV epidemic in KwaZulu-Natal, South Africa, and analyzed scenarios of RPV PrEP scale-up for combination HIV prevention in comparison with a reference scenario without PrEP. We estimated new HIV infections, life-years and costs, and incremental cost-effectiveness ratios (ICERs), over 10-year and lifetime horizons, assuming a societal perspective. RESULTS Compared with no PrEP, unprioritized scale-up of RVP PrEP covering 2.5%-15% of adults prevented up to 9% of new infections over 10 years. HIV prevention doubled (17%) when the same coverage was prioritized to 20- to 29-year-old women, costing $10 880-$19 213 per infection prevented. Prioritization of PrEP to 80% of individuals at highest behavioral risk achieved comparable prevention (4%-8%) at <1% overall coverage, costing $298-$1242 per infection prevented. Over lifetime, PrEP scale-up among 20- to 29-year-old women was very cost-effective (<$1600 per life-year gained), dominating unprioritized PrEP, while risk prioritization was cost-saving. PrEP's 10-year impact decreased by almost 50% with increases in ICERs (up to 4.2-fold) in conservative base-case analysis. Sensitivity analysis identified PrEP's costs, efficacy, and reliability of delivery as the principal drivers of uncertainty in PrEP's cost-effectiveness, and PrEP remained cost-effective under the assumption of universal access to second-line antiretroviral therapy. CONCLUSIONS Compared with no PrEP, prioritized scale-up of RPV PrEP in KwaZulu-Natal could be very cost-effective or cost-saving, but suboptimal PrEP would erode benefits and increase costs.
A Variable Region within the Genome of Streptococcus pneumoniae Contributes to Strain-Strain Variation in Virulence

The bacterial factors responsible for the variation in invasive potential between different clones and serotypes of Streptococcus pneumoniae are largely unknown. Therefore, the isolation of rare serotype 1 carriage strains in Indigenous Australian communities provided a unique opportunity to compare the genomes of non-invasive and invasive isolates of the same serotype in order to identify such factors. The human virulence status of non-invasive, intermediately virulent and highly virulent serotype 1 isolates was reflected in mice and showed that whilst both human non-invasive and highly virulent isolates were able to colonize the murine nasopharynx equally, only the human highly virulent isolates were able to invade and survive in the murine lungs and blood. Genomic sequencing comparisons between these isolates identified 8 regions >1 kb in size that were specific to only the highly virulent isolates, and included a version of the pneumococcal pathogenicity island 1 variable region (PPI-1v), phage-associated adherence factors, transporters and metabolic enzymes. In particular, a phage-associated endolysin, a putative iron/lead permease and an operon within PPI-1v exhibited niche-specific changes in expression that suggest important roles for these genes in the lungs and blood. Moreover, in vivo competition between pneumococci carrying PPI-1v derivatives representing the two identified versions of the region showed that the version of PPI-1v in the highly virulent isolates was more competitive than the version from the less virulent isolates in the nasopharyngeal tissue, blood and lungs. This study is the first to perform genomic comparisons between serotype 1 isolates with distinct virulence profiles that correlate between mice and humans, and has highlighted the important role that hypervariable genomic loci, such as PPI-1v, play in pneumococcal disease. The findings of this study have important implications for understanding the processes that drive progression from colonization to invasive disease and will help direct the development of novel therapeutic strategies.
Solving Fourier ptychographic imaging problems via neural network modeling and TensorFlow

Fourier ptychography is a recently developed imaging approach for large field-of-view and high-resolution microscopy. Here we model the Fourier ptychographic forward imaging process using a convolutional neural network (CNN) and recover the complex object information in a network training process. In this approach, the input of the network is the point spread function in the spatial domain or the coherent transfer function in the Fourier domain. The object is treated as 2D learnable weights of a convolutional or a multiplication layer. The output of the network is modeled as the loss function we aim to minimize. The batch size of the network corresponds to the number of captured low-resolution images in one forward/backward pass. We use a popular open-source machine learning library, TensorFlow, for setting up the network and conducting the optimization process. We analyze the performance of different learning rates, different solvers, and different batch sizes. It is shown that a large batch size with the Adam optimizer achieves the best performance in general. To accelerate the phase retrieval process, we also discuss a strategy to implement Fourier-magnitude projection using a multiplication neural network model. Since convolution and multiplication are the two most-common operations in imaging modeling, the reported approach may provide a new perspective to examine many coherent and incoherent systems. As a demonstration, we discuss the extensions of the reported networks for modeling single-pixel imaging and structured illumination microscopy (SIM). 4-frame resolution doubling is demonstrated using a neural network for SIM. The link between imaging systems and neural network modeling may enable the use of machine-learning hardware such as neural engine and tensor processing unit for accelerating the image reconstruction process. We have made our implementation code open-source for researchers.
Soft-Robust Actor-Critic Policy-Gradient

Robust Reinforcement Learning aims to derive optimal behavior that accounts for model uncertainty in dynamical systems. However, previous studies have shown that by considering the worst case scenario, robust policies can be overly conservative. Our soft-robust framework is an attempt to overcome this issue. In this paper, we present a novel Soft-Robust Actor-Critic algorithm (SR-AC). It learns an optimal policy with respect to a distribution over an uncertainty set and stays robust to model uncertainty but avoids the conservativeness of robust strategies. We show the convergence of SR-AC and test the efficiency of our approach on different domains by comparing it against regular learning methods and their robust formulations.
Porcine Cytochrome P 450 and Metabolism

The pig and especially the minipig are becoming inc reasingly used as a test animal both in pharmacological and toxicological testing of new compounds. The minipig is used because of its size, it is easy to handle and less test substrate is required. When using an animal species for testing it is of importance to k n w if the test animal’s posses the same abilities to metabolize drugs as humans. Some of th e P450 enzymes have been characterized in the pig regarding substrate specif icity, inhibition and regulation. The porcine enzymes CYP1A, CYP2A and CYP3A all metaboli ze the same test substrates as the human enzymes, whereas the enzymes CYP2B, CYP2D , and CYP2E in pig on the other hand seem to be different from the human enzy mes concerning metabolism of the well know test substrates. Some of the porcine enzy mes have been sequenced i.e. CYP1A, CYP2A, CYP2B, CYP2D, CYP2E and CYP3A and not surprisingly the porcine CYPs that metabolize the human test substrates are about 75% identical in cDNA sequences. What is needed is inhibitory antibodies against eac h of the porcine enzymes, in order to test whether a test compound is metabolized by one or the other enzyme. Until now chemical inhibitors have been used, but they are ra ly 100% specific. Anti-human inhibitory antibodies have also been used, but they ma not recognize the porcine enzyme and therefore will not inhibit the reaction. Antibo dies for immunoblotting would also make it possible to estimate how much of the total P450 the individual enzymes comprise. From what is known about the porcine P450 , it can be concluded that the pig seems to be a good test species if CYP1A, CYP2A or CYP3A are involved in the metabolism of the test compound, depending on the c ontribution of other enzymes in competing pathways.
A Randomized, Double-Blind, Placebo-Controlled Trial to Determine the Effectiveness and Safety of a Thermogenic Supplement in Addition to an Energy-Restricted Diet in Apparently Healthy Females

ABSTRACT The increasing interest in weight loss has seen a concurrent rise in the supplemental use of thermogenics to aid weight loss efforts. To date, the effectiveness and safety of supplemental proprietary blend thermogenics, in conjunction with high-protein energy-restricted diets have not been thoroughly evaluated. The purpose of this study was to investigate the efficacy of a low-calorie, high-protein diet with and without the concomitant use of a thermogenic supplement on body weight and body composition in apparently healthy females. Subjects were divided into three groups, Bizzy Diet+FitMiss Burn (BURN, N = 12), Bizzy Diet+Placebo (PLA, N = 13), and Control (CON, N = 14), and underwent two testing sessions separated by approximately 3 weeks. Resting blood pressure (BP), resting heart rate (RHR), clinical safety markers, body weight (BW), and body composition were assessed during each testing session. Repeated measures analysis of variance (ANOVA) revealed a significant effect for time relative to BW, total body fat mass (FM), leg FM, and trunk FM. Post hoc analysis revealed that the BURN and PLA groups experienced significant decreases in both BW and total body FM compared to CON (p <.05). There were no significant interactions for BP, RHR, or clinical safety markers over the course of the study. The Bizzy Diet, both with and without the addition of FitMiss Burn thermogenic, appears to be safe for short-term use and may lead to greater improvement in body composition and BW in an apparently healthy female population.
Wasserstein Distance Guided Representation Learning for Domain Adaptation

Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.
On Signature-Based Gröbner Bases Over Euclidean Rings

In this paper we present first steps in using signature-based Gröbner basis algorithms like Faugère's F5 or GVW for computation over Euclidean rings. We present problems appearing when having to deal with coefficients and zero divisors and give practical solution techniques. A hybrid algorithm is presented trying to combine the advantages of signature-based and non-signature-based Gröbner basis computation. For some examples speedups are achieved due to faster finding good reducers with the hybrid technique.
Visual Perceptual Difficulties and Under-Achievement at School in a Large Community-Based Sample of Children

Introduction Difficulties with visual perception (VP) are often described in children with neurological or developmental problems. However, there are few data regarding the range of visual perceptual abilities in populations of normal children, or on the impact of these abilities on children's day-to-day functioning. Methods Data were obtained for 4512 participants in an ongoing birth cohort study (Avon Longitudinal Study of Parents and Children; ALSPAC). The children's mothers responded to questions designed to elicit indications of visual perceptual difficulties or immaturity, when their children were aged 13 years. We examined associations with standardised school test results in reading and in mathematics at age 13–14 years (SATS-KS3), accounting for potential confounders including IQ. Results Three underlying factors explained half the variance in the VP question responses. These correlated best with questions on interpreting cluttered scenes; guidance of movement and face recognition. The adjusted parameter estimates (95% CI) for the cluttered-scenes factor (0.05; 0.02 to 0.08; p<0.001) suggested positive associations with the reading test results whilst that for the guidance-of-movement factor (0.03; 0.00 to 0.06; p = 0.026) suggested positive association with the mathematics results. The raw scores were associated with both test results. Discussion VP abilities were widely distributed in this sample of 13-year old children. Lower levels of VP function were associated with under-achievement in reading and in mathematics. Simple interventions can help children with VP difficulties, so research is needed into practicable, cost-effective strategies for identification and assessment, so that support can be targeted appropriately.
Singular FIOs in SAR Imaging, II: Transmitter and Receiver at Different Speeds

In this article, we consider two bistatic cases arising in synthetic aperture radar imaging: when the transmitter and receiver are both moving with different speeds along a single line parallel to the ground in the same direction or in the opposite directions. In both cases, we classify the forward operator $\mathcal{F}$ as a Fourier integral operator with fold/blowdown singularities. Next we analyze the normal operator $\mathcal{F}^*\mathcal{F}$ in both cases (where $\mathcal{F}^{*}$ is the $L^{2}$-adjoint of $\mathcal{F}$). When the transmitter and receiver move in the same direction, we prove that $\mathcal{F}^*\mathcal{F}$ belongs to a class of operators associated to two cleanly intersecting Lagrangians, $I^{p,l} (\Delta, C_1)$. When they move in opposite directions, $\mathcal{F}^*\mathcal{F}$ is a sum of such operators. In both cases artifacts appear, and we show that they are, in general, as strong as the bona fide part of the image. Moreover, we demonstrate that as soon as the source and receiver .
Novel Rank-Based Approaches for Discovery and Replication in Genome-Wide Association Studies

In recent years, genome-wide association studies (GWAS) have uncovered a large number of susceptibility variants. Nevertheless, GWAS findings provide only tentative evidence of association, and replication studies are required to establish their validity. Due to this uncertainty, researchers often focus on top-ranking SNPs, instead of considering strict significance thresholds to guide replication efforts. The number of SNPs for replication is often determined ad hoc. We show how the rank-based approach can be used for sample size allocation in GWAS as well as for deciding on a number of SNPs for replication. The basis of this approach is the “ranking probability”: chances that at least j true associations will rank among top u SNPs, when SNPs are sorted by P-value. By employing simple but accurate approximations for ranking probabilities, we accommodate linkage disequilibrium (LD) and evaluate consequences of ignoring LD. Further, we relate ranking probabilities to the proportion of false discoveries among top u SNPs. A study-specific proportion can be estimated from P-values, and its expected value can be predicted for study design applications.
Automatic Pixel-Level Land-use Prediction Using Deep Convolutional Neural Networks

Land-use classification provides information on the types of human activity in an area. Its accurate mapping facilitates the study of the environmental impact that different planning decisions may have to the community. This is especially important in rapidly-developing cities with under-resourced local government, where centrally-organised mapping is difficult to maintain up to date. We show how deep convolutional neural networks can be combined with unnamed-aerial-vehicle (UAV) imagery to provide land-use pixel-level predictions. With data from Dar es Salaam we obtain a Jaccard Index of 78%, and show how in many cases predictions find clear annotation errors, outperforming human annotators.
An Algorithmic Regularity Lemma for Lp Regular Sparse Matrices

We prove an algorithmic regularity lemma for $L_p$ regular matrices $(1 < p \leq \infty),$ a class of sparse $\{0,1\}$ matrices which obey a natural pseudorandomness condition. This extends a result of Coja-Oghlan, Cooper and Frieze who treated the case of $L_{\infty}$ regular matrices. We also present applications of this result for tensors and MAX-CSP instances.