Detecting Determinacy in Prolog Programs: 22nd International Conference, ICLP 2006, Seattle, WA, USA, August 17-20, 2006. Proceedings

In program development it is useful to know that a call to a Prolog program will not inadvertently leave a choice-point on the stack. Determinacy inference has been proposed for solving this problem yet the analysis was found to be wanting in that it could not infer determinacy conditions for programs that contained cuts or applied certain tests to select a clause. This paper shows how to remedy these serious deficiencies. It also addresses the problem of identifying those predicates which can be rewritten in a more deterministic fashion. To this end, a radically new form of determinacy inference is introduced, which is founded on ideas in ccp, that is capable of reasoning about the way bindings imposed by a rightmost goal can make a leftmost goal deterministic

Insulin-like growth factor-I (IGF-I) and thioredoxin are differentially expressed along the reproductive tract of the ewe during the oestrous cycle and after ovariectomy

Insulin-like growth factor-I (IGF-I) and thioredoxin are regulated by gonadal steroids in the female reproductive tract of many species. Oestradiol regulates IGF-I and thioredoxin mRNA levels in the reproductive tract of prepubertal lambs. The physiological status (different endocrine environment) may affect the sensitivity of the reproductive tract to oestradiol and progesterone. We studied the effects of different endocrine milieus (late-follicular and luteal phases of the oestrous cycle, and ovariectomy before or after puberty) on the expression of IGF-I, thioredoxin, oestrogen receptor α (ERα) and progesterone receptor (PR) in sheep. The mRNA levels were determined by a solution hybridisation technique. In the uterus the levels of ERα, PR and thioredoxin mRNA were higher in the late-follicular phase group than in the other three groups, and IGF-I mRNA was high during both the late-follicular and the luteal phases. In the cervix only PR mRNA was significantly higher in the ewes in the late-follicular phase than in the other groups. In the oviducts the levels of thioredoxin and ERα mRNA were highest in the ovariectomised adult ewes, and thioredoxin mRNA was higher than the levels found in the ewes in the late-follicular phase. The IGF-I mRNA levels in the oviduct did not differ between any of the groups. The transcripts of IGF-I, thioredoxin, ERα and PR, varied according to the physiological status and also along the female reproductive tract, suggesting that the regulation of the mRNA levels of these factors by the steroid environment is tissue specific. Koncentrationen av insulin-like growth factor-I (IGF-I) och thioredoxin regleras hos många arter i honors reproduktionsorgan av könssteroider. Sålunda reglerar östradiol IGF-I och thioredoxin mRNA i reproduktionsorganen hos prepubertala lamm. Djurets fysiologiska status (dvs den endokrina miljön) kan påverka känsligheten hos reproduktionsorganen för östradiol och progesteron. Vi studerade effekterna av olika endokrina miljöer (sen follikelfas och lutealfas i östruscykeln, samt ovariektomi före och efter puberteten) på uttrycket av IGF-I, thioredoxin, östrogenreceptor α (ERα) och progesteronreceptorn (PR) hos får. Lösningshybridisering användes för att bestämma mRNA nivåerna. I livmodern var mRNA koncentrationen för ERα, PR och thioredoxin högre i sen follikelfas än i de andra tre grupperna och IGF-I mRNA nivån var hög både under sen follikelfas och i lutealfas. PR mRNA i cervix var signifikant högre hos tackorna under sen follikelfas än i de andra grupperna. I äggledarna var mRNA nivåerna av thioredoxin och ERα högst i de djur som ovariektomerats som vuxna, och thioredoxin mRNA var högre än hos tackorna under sen follikelfas. Det förelåg ingen skillnad vad gäller IGF-I mRNA nivåerna i äggledaren mellan någon av grupperna. IGF-I, thioredoxin, ERα och PR mRNA nivåerna varierade beroende på fysiologisk status och morfologisk lokalisation i reproduktionsorganen. Detta tyder på att steroidhormonernas reglering av dessa faktorers mRNA uttryck också är vävnadsspecifik

Pollinators, pests and yield-Multiple trade-offs from insecticide use in a mass-flowering crop

Multiple trade-offs likely occur between pesticide use, pollinators and yield (via crop flowers) in pollinator-dependent, mass-flowering crops (MFCs), causing potential conflict between conservation and agronomic goals. To date, no studies have looked at both outcomes within the same system, meaning win-win solutions for pollinators and yield can only be inferred. Here, we outline a new framework to explore these trade-offs, using red clover (Trifolium pratense) grown for seed production as an example. Specifically, we address how the insecticide thiacloprid affects densities of seed-eating weevils (Protapion spp.), pollination rates, yield, floral resources and colony dynamics of the key pollinator, Bombus terrestris. Thiacloprid did not affect the amount of nectar provided by, or pollinator visitation to, red clover flowers but did reduce weevil density, correlating to increased yield and gross profit. In addition, colonies of B. terrestris significantly increased their weight and reproductive output in landscapes with (compared with without) red clover, regardless of insecticide use. Synthesis and applications. We propose a holistic conceptual framework to explore trade-offs between pollinators, pesticides and yield that we believe to be essential for achieving conservation and agronomic goals. This framework applies to all insecticide-treated mass-flowering crops (MFCs) and can be adapted to include other ecological processes. Trialling the framework in our study system, we found that our focal insecticide, thiacloprid, improved red clover seed yield with no detected effects on its key pollinator, B. terrestris, and that the presence of red clover in the landscape can benefit pollinator populations

Myogenin Regulates Exercise Capacity and Skeletal Muscle Metabolism in the Adult Mouse

Although skeletal muscle metabolism is a well-studied physiological process, little is known about how it is regulated at the transcriptional level. The myogenic transcription factor myogenin is required for skeletal muscle development during embryonic and fetal life, but myogenin's role in adult skeletal muscle is unclear. We sought to determine myogenin's function in adult muscle metabolism. A Myog conditional allele and Cre-ER transgene were used to delete Myog in adult mice. Mice were analyzed for exercise capacity by involuntary treadmill running. To assess oxidative and glycolytic metabolism, we performed indirect calorimetry, monitored blood glucose and lactate levels, and performed histochemical analyses on muscle fibers. Surprisingly, we found that Myog-deleted mice performed significantly better than controls in high- and low-intensity treadmill running. This enhanced exercise capacity was due to more efficient oxidative metabolism during low- and high-intensity exercise and more efficient glycolytic metabolism during high-intensity exercise. Furthermore, Myog-deleted mice had an enhanced response to long-term voluntary exercise training on running wheels. We identified several candidate genes whose expression was altered in exercise-stressed muscle of mice lacking myogenin. The results suggest that myogenin plays a critical role as a high-level transcriptional regulator to control the energy balance between aerobic and anaerobic metabolism in adult skeletal muscle

Safe and complete contig assembly via omnitigs

Contig assembly is the first stage that most assemblers solve when reconstructing a genome from a set of reads. Its output consists of contigs -- a set of strings that are promised to appear in any genome that could have generated the reads. From the introduction of contigs 20 years ago, assemblers have tried to obtain longer and longer contigs, but the following question was never solved: given a genome graph $G$ (e.g. a de Bruijn, or a string graph), what are all the strings that can be safely reported from $G$ as contigs? In this paper we finally answer this question, and also give a polynomial time algorithm to find them. Our experiments show that these strings, which we call omnitigs, are 66% to 82% longer on average than the popular unitigs, and 29% of dbSNP locations have more neighbors in omnitigs than in unitigs.Comment: Full version of the paper in the proceedings of RECOMB 201

Fluid flow at the interface between elastic solids with randomly rough surfaces

I study fluid flow at the interface between elastic solids with randomly rough surfaces. I use the contact mechanics model of Persson to take into account the elastic interaction between the solid walls and the Bruggeman effective medium theory to account for the influence of the disorder on the fluid flow. I calculate the flow tensor which determines the pressure flow factor and, e.g., the leak-rate of static seals. I show how the perturbation treatment of Tripp can be extended to arbitrary order in the ratio between the root-mean-square roughness amplitude and the average interfacial surface separation. I introduce a matrix D(Zeta), determined by the surface roughness power spectrum, which can be used to describe the anisotropy of the surface at any magnification Zeta. I present results for the asymmetry factor Gamma(Zeta) (generalized Peklenik number) for grinded steel and sandblasted PMMA surfaces.Comment: 16 pages, 14 figure

A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines

Background: The outstanding performance of an elite athlete might be associated with changes in their blood metabolic profile. The aims of this study were to compare the blood metabolic profiles between moderate- and high-power and endurance elite athletes and to identify the potential metabolic pathways underlying these differences. Methods: Metabolic profiling of serum samples from 191 elite athletes from different sports disciplines (121 high- and 70 moderate-endurance athletes, including 44 high- and 144 moderate-power athletes), who participated in national or international sports events and tested negative for doping abuse at anti-doping laboratories, was performed using non-targeted metabolomics-based mass spectroscopy combined with ultrahigh-performance liquid chromatography. Multivariate analysis was conducted using orthogonal partial least squares discriminant analysis. Differences in metabolic levels between high- and moderate-power and endurance sports were assessed by univariate linear models. Results: Out of 743 analyzed metabolites, gamma-glutamyl amino acids were significantly reduced in both high-power and high-endurance athletes compared to moderate counterparts, indicating active glutathione cycle. High-endurance athletes exhibited significant increases in the levels of several sex hormone steroids involved in testosterone and progesterone synthesis, but decreases in diacylglycerols and ecosanoids. High-power athletes had increased levels of phospholipids and xanthine metabolites compared to moderate-power counterparts. Conclusions: This pilot data provides evidence that high-power and high-endurance athletes exhibit a distinct metabolic profile that reflects steroid biosynthesis, fatty acid metabolism, oxidative stress, and energy-related metabolites. Replication studies are warranted to confirm differences in the metabolic profiles associated with athletes’ elite performance in independent data sets, aiming ultimately for deeper understanding of the underlying biochemical processes that could be utilized as biomarkers with potential therapeutic implications

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.publishedVersio

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering

Specializing Interpreters using Offline Partial Deduction

We present the latest version of the Logen partial evaluation system for logic programs. In particular we present new binding-types, and show how they can be used to effectively specialise a wide variety of interpreters.We show how to achieve Jones-optimality in a systematic way for several interpreters. Finally, we present and specialise a non-trivial interpreter for a small functional programming language. Experimental results are also presented, highlighting that the Logen system can be a good basis for generating compilers for high-level languages