Study of the combined effects of PTH treatment and mechanical loading in postmenopausal osteoporosis using a new mechanistic PK-PD model

Biomechanics and Modeling in Mechanobiology - One of only a few approved and available anabolic treatments for severe osteoporosis is daily injections of PTH (1-34). This drug has a specific dual...     

Supply versus use designs of environmental extensions in input–output analysis: Conceptual and empirical implications for the case of energy

Input–output analysis is one of the central methodological pillars of industrial ecology. However, the literature that discusses different structures of environmental extensions (EEs), that is, the scope of physical flows and their attribution to sectors in the monetary input–output table (MIOT), remains fragmented. This article investigates the conceptual and empirical implications of applying two different but frequently used designs of EEs, using the case of energy accounting, where one represents energy supply while the other energy use in the economy. We derive both extensions from an official energy supply–use dataset and apply them to the same single‐region input–output (SRIO) model of Austria, thereby isolating the effect that stems from the decision for the extension design. We also crosscheck the SRIO results with energy footprints from the global multi‐regional input–output (GMRIO) dataset EXIOBASE. Our results show that the ranking of footprints of final demand categories (e.g., household and export) is sensitive to the extension design and that product‐level results can vary by several orders of magnitude. The GMRIO‐based comparison further reveals that for a few countries the supply‐extension result can be twice the size of the use‐extension footprint (e.g., Australia and Norway). We propose a graph approach to provide a generalized framework to disclosing the design of EEs. We discuss the conceptual differences between the two extension designs by applying analogies to hybrid life‐cycle assessment and conclude that our findings are relevant for monitoring of energy efficiency and emission reduction targets and corporate footprint accounting.     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

Dimethyl Disulfide Produced by the Naturally Associated Bacterium Bacillus sp B55 Promotes Nicotiana attenuata Growth by Enhancing Sulfur Nutrition

Bacillus sp B55, a bacterium naturally associated with Nicotiana attenuata roots, promotes growth and survival of wild-type and, particularly, ethylene (ET)–insensitive 35S-ethylene response1 (etr1) N. attenuata plants, which heterologously express the mutant Arabidopsis thaliana receptor ETR1-1. We found that the volatile organic compound (VOC) blend emitted by B55 promotes seedling growth, which is dominated by the S-containing compound dimethyl disulfide (DMDS). DMDS was depleted from the headspace during cocultivation with seedlings in bipartite Petri dishes, and ³⁵S was assimilated from the bacterial VOC bouquet and incorporated into plant proteins. In wild-type and 35S-etr1 seedlings grown under different sulfate (SO₄⁻²) supply conditions, exposure to synthetic DMDS led to genotype-dependent plant growth promotion effects. For the wild type, only S-starved seedlings benefited from DMDS exposure. By contrast, growth of 35S-etr1 seedlings, which we demonstrate to have an unregulated S metabolism, increased at all SO₄⁻² supply rates. Exposure to B55 VOCs and DMDS rescued many of the growth phenotypes exhibited by ET-insensitive plants, including the lack of root hairs, poor lateral root growth, and low chlorophyll content. DMDS supplementation significantly reduced the expression of S assimilation genes, as well as Met biosynthesis and recycling. We conclude that DMDS by B55 production is a plant growth promotion mechanism that likely enhances the availability of reduced S, which is particularly beneficial for wild-type plants growing in S-deficient soils and for 35S-etr1 plants due to their impaired S uptake/assimilation/metabolism.     

An automated immunoassay for early specificity profiling of antibodies

Antibody-based therapeutics are of great value for the treatment of human diseases. In addition to functional activity, affinity or physico-chemical properties, antibody specificity is considered to be one of the most crucial attributes for safety and efficacy. Consequently, appropriate studies are required before entering clinical trials.

High content protein arrays are widely applied to assess antibody specificity, but this commercial solution can only be applied to final therapeutic antibody candidates because such arrays are expensive and their throughput is limited. A flexible, high-throughput and economical assay that allows specificity testing of IgG or Fab molecules during early discovery is described here. The 384-well microtiter plate assay contains a comprehensive panel of 32 test proteins and uses electrochemiluminescence as readout.

The Protein Panel Profiling (3P) was used to analyze marketed therapeutic antibodies that all showed highly specific binding profiles. Subsequently, 3P was applied to antibody candidates from early discovery and the results compared well with those obtained with a commercially available high content protein chip. Our results suggest that 3P can be applied as an additional filter for lead selection, allowing the identification of favorable antibody candidates in early discovery and thereby increasing the speed and possibility of success in drug development.