Sex differences in the effects of juvenile and adult diet on age‐dependent reproductive effort

Sexual selection should cause sex differences in patterns of resource allocation. When current and future reproductive effort trade off, variation in resource acquisition might further cause sex differences in age‐dependent investment, or in sensitivity to changes in resource availability over time. However, the nature and prevalence of sex differences in age‐dependent investment remain unclear. We manipulated resource acquisition at juvenile and adult stages in decorated crickets, Gryllodes sigillatus, and assessed effects on sex‐specific allocation to age‐dependent reproductive effort (calling in males, fecundity in females) and longevity. We predicted that the resource and time demands of egg production would result in relatively consistent female strategies across treatments, whereas male investment should depend sharply on diet. Contrary to expectations, female age‐dependent reproductive effort diverged substantially across treatments, with resource‐limited females showing much lower and later investment in reproduction; the highest fecundity was associated with intermediate lifespans. In contrast, long‐lived males always signalled more than short‐lived males, and male age‐dependent reproductive effort did not depend on diet. We found consistently positive covariance between male reproductive effort and lifespan, whereas diet altered this covariance in females, revealing sex differences in the benefits of allocation to longevity. Our results support sex‐specific selection on allocation patterns, but also suggest a simpler alternative: males may use social feedback to make allocation decisions and preferentially store resources as energetic reserves in its absence. Increased calling effort with age therefore could be caused by gradual resource accumulation, heightened mortality risk over time, and a lack of feedback from available mates.     

High definition bronchoscopy: a randomized exploratory study of diagnostic value compared to standard white light bronchoscopy and autofluorescence bronchoscopy

Background

Videobronchoscopy is an essential diagnostic procedure for evaluation of the central airways and pivotal for the diagnosis and staging of lung cancer. Technological improvements have resulted in high definition (HD) images with advanced real time image enhancement techniques (i-scan).

Objectives

In this study we aimed to explore the sensitivity of HD+ i-scan bronchoscopy for detection of epithelial changes like vascular abnormalities and suspicious preinvasive lesions, and tumors.

Methods

In patients scheduled for a therapeutic or diagnostic procedure under general anesthesia videos of the bronchial tree were made using 5 videobronchoscopy modes in random order: normal white light videobronchoscopy (WLB), HD-bronchoscopy (HD), HD bronchoscopy with surface enhancement technique (i-scan1), HD with surface- and tone enhancement technique (i-scan2) and dual mode autofluorescence videobronchoscopy (AFB). The videos were scored in random order by two independent and blinded expert bronchoscopists.

Results

In 29 patients all videos were available for analysis. Vascular abnormalities were scored most frequently in HD + i-scan2 bronchoscopy (1.33 ± 0.29 abnormal or suspicious sites per patient) as compared to 0.12 ± 0.05 site for AFB (P = 0.003). Sites suspicious for preinvasive lesions were most frequently reported using AFB (0.74 ± 0.12 sites per patient) as compared to 0.17 ± 0.06 for both WLB and HD bronchoscopy (P = 0.003). Tumors were detected equally by all modalities. The preferred modality was HD bronchoscopy with i-scan (tone- plus surface and surface enhancement in respectively 38% and 35% of cases P = 0.006).

Conclusions

This study shows that high definition bronchoscopy with image enhancement technique may result in better detection of subtle vascular abnormalities in the airways. Since these abnormalities may be related to preneoplastic lesions and tumors this is of clinical relevance. Further investigations using this technique relating imaging to histology are warranted.     

The BioLexicon: a large-scale terminological resource for biomedical text mining

Background

Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

Results

This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.

Conclusions

The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.     

Performance and prospects of Rag genes for management of soybean aphid

The soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is an invasive insect pest of soybean [Glycine max (L.) Merr. (Fabaceae)] in North America, and it has led to extensive insecticide use in northern soybean‐growing regions there. Host plant resistance is one potential alternative strategy for managing soybean aphid. Several Rag genes that show antibiosis and antixenosis to soybean aphid have been recently identified in soybean, and field‐testing and commercial release of resistant soybean lines have followed. In this article, we review results of field tests with soybean lines containing Rag genes in North America, then present results from a coordinated regional test across several field sites in the north‐central USA, and finally discuss prospects for use of Rag genes to manage soybean aphids. Field tests conducted independently at multiple sites showed that soybean aphid populations peaked in late summer on lines with Rag1 or Rag2 and reached economically injurious levels on susceptible lines, whereas lines with a pyramid of Rag1 + Rag2 held soybean aphid populations below economic levels. In the regional test, aphid populations were generally suppressed by lines containing one of the Rag genes. Aphids reached putative economic levels on Rag1 lines for some site years, but yield loss was moderated, indicating that Rag1 may confer tolerance to soybean aphid in addition to antibiosis and antixenosis. Moreover, no yield penalty has been found for lines with Rag1, Rag2, or pyramids. Results suggest that use of aphid‐resistant soybean lines with Rag genes may be viable for managing soybean aphids. However, virulent biotypes of soybean aphid were identified before release of aphid‐resistant soybean, and thus a strategy for optimal deployment of aphid‐resistant soybean is needed to ensure sustainability of this technology.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

Adult activity and oviposition of corn rootworms,Diabrotica spp. (Coleoptera: Chrysomelidae), in Miscanthus,corn and switchgrass

The ability of the biomass crop Miscanthus (Miscanthus × giganteus Greef and Deuter ex Hodkinson and Renvoize) to support larval development for both United States and European populations of the western corn rootworm, Diabrotica virgifera virgifera LeConte, suggests an avenue for interactions with corn (Zea mays L.). To provide context to survival of D. v. virgifera on Miscanthus, adult activity and oviposition of Diabrotica spp. were monitored in central Illinois in 2010–2011 in Miscanthus, corn and switchgrass (Panicum virgatum L.). For D. v. virgifera, vial traps within corn plots captured 3–10 times as many adults as in Miscanthus or switchgrass, while soil samples showed females laid approximately 10 times as many eggs in corn as in the perennial grasses. Adult southern corn rootworms, Diabrotica undecimpunctata howardi Barber, were the most abundant species in 2010 and clearly preferred switchgrass as an adult habitat, with vial traps in switchgrass capturing 5–10 times as many D. u. howardi as those in corn or Miscanthus. Based on the small production areas for Miscanthus and switchgrass (and low use of both by D. v. virgifera), it seems likely that there are no current impacts of these perennial grasses on pest status of Diabrotica spp. in corn or other crops. However, adaptations by Diabrotica spp. to pest management practices suggest they could be a source for interactions between biomass and food or feed crops. Early‐season soil samples did not recover eggs of D. u. howardi, but their use of switchgrass as an adult habitat suggests additional research in areas where switchgrass may be grown near peanuts, alfalfa or other hosts may be needed. Also, investigation of other candidate bioenergy crops known to support Diabrotica spp. larval development is needed to better understand the possible effects of a changing agricultural landscape on corn rootworms.     

Quirks of dye nomenclature. 3. Trypan blue

Trypan blue is colorant from the 19^th century that has an association with Africa as a chemotherapeutic agent against protozoan (Trypanosomal) infections, which cause sleeping sickness. The dye still is used for staining biopsies, living cells and organisms, and it also has been used as a colorant for textiles.     

Genetic diversity and parasite prevalence in two species of bumblebee

Many bumblebee species have been suffering from significant declines across their ranges in the Northern Hemisphere over the last few decades. The remaining populations of the rare species are now often isolated due to habitat fragmentation and have reduced levels of genetic diversity. The persistence of these populations may be threatened by inbreeding depression, which may result in a higher susceptibility to parasites. Here we investigate the relationship between genetic diversity and prevalence of the parasitic mite Locustacarus buchneri in bumblebees, using the previously-studied system of Bombus muscorum and Bombus jonellus in the Western Isles of Scotland. We recorded L. buchneri prevalence in 17 populations of B. muscorum and 13 populations of B. jonellus and related the results to levels of heterozygosity. For B. muscorum, we found that prevalence of the mite was higher in populations with lower genetic diversity but there was no such relationship in the more genetically diverse B. jonellus. In contrast to population-level measures of genetic diversity, the heterozygosity of individual bees was not correlated with infection status. We suggest population-level genetic homogeneity may facilitate parasite transmission and elevate prevalence, with potential consequences for population persistence.