Applications of deep convolutional neural networks to predict length,circumference, and weight from mostly dewatered images of fish

Simple biometric data of fish aid fishery management tasks such as monitoring the structure of fish populations and regulating recreational harvest. While these data are foundational to fishery research and management, the collection of length and weight data through physical handling of the fish is challenging as it is time consuming for personnel and can be stressful for the fish. Recent advances in imaging technology and machine learning now offer alternatives for capturing biometric data. To investigate the potential of deep convolutional neural networks to predict biometric data, several regressors were trained and evaluated on data stemming from the FishL? Recognition System and manual measurements of length, girth, and weight. The dataset consisted of 694 fish from 22 different species common to Laurentian Great Lakes. Even with such a diverse dataset and variety of presentations by the fish, the regressors proved to be robust and achieved competitive mean percent errors in the range of 5.5 to 7.6% for length and girth on an evaluation dataset. Potential applications of this work could increase the efficiency and accuracy of routine survey work by fishery professionals and provide a means for longer‐term automated collection of fish biometric data.     

The cortical control of movement revisited

Recently, we found that electrical stimulation of motor cortex caused monkeys to make coordinated, complex movements. These evoked movements were arranged across the cortex in a map of spatial locations to which the hand moved. We suggest that some of the subdivisions previously described within primary motor and premotor cortex may represent different types of actions that monkeys tend to make in different regions of space. According to this view, primary and premotor cortex may fit together into a larger map of manual space.     

Investigating plant-plant interference by metabolic fingerprinting

New analytical developments in post-genomic technologies are being introduced to the field of plant ecology. FT-IR fingerprinting coupled with chemometrics via cluster analysis is proposed as a tool for correlating global metabolic changes with abiotic or biotic perturbation and/or interactions. The current study concentrates on detecting chemical responses by inter-species competition between a monocotyledon Brachypodium distachyion and a dicotyledon Arabidopsis thaliana. Growth analysis of 42 days old plants showed differences in both species under competition. Clear changes in the FT-IR metabolic fingerprints of B. distachyion in competition with A. thaliana were observed, whilst there were no apparent chemical differences in the A. thaliana plant tissues. This study demonstrates the power of this approach in detecting changes in the global metabolic profiles of plants in response to biotic interactions, and we believe FT-IR is appropriate for rapid screening (10 s per sample) prior to targeted metabolite analyses.     

EST-based gene discovery in pig: virtual expression patterns and comparative mapping to human

A molecular understanding of porcine reproduction is of biological interest and economic importance. Our Midwest Consortium has produced cDNA libraries containing the majority of genes expressed in major female reproductive tissues, and we have deposited into public databases 21,499 expressed sequence tag (EST) gene sequences from the 3 end of clones from these libraries. These sequences represent 10,574 different genes, based on sequence comparison among these data, and comparison with existing porcine ESTs and genes indicate as many as 4652 of these EST clusters are novel. In silico analysis identified sequences that are expressed in specific pig tissues or organs and confirmed the broad expression in pig for many genes ubiquitously expressed in human tissues. Furthermore, we have developed computer software to identify sequence similarity of these pig genes with their human counterparts, and to extract the mapping information of these human homologues from genome databases. We demonstrate the utility of this software for comparative mapping by localizing 61 genes on the porcine physical map for Chromosomes (Chrs) 5, 10, and 14. The following Accession numbers were assigned to our deposited sequences: BF701840 – BF704551, BF708383, BF708386 – BF713604, BG322266 – BG322271, BI398567 – BI405235, BQ597354 – BQ605166.     

Growth and nitrogen utilization in seedlings of mountain birch (Betula pubescens ssp. tortuosa ) as affected by ultraviolet radiation (UV-A and UV-B) under laboratory and outdoor conditions

 Growth patterns and nitrogen economy were studied in pot-grown seedlings of mountain birch subjected to different ultraviolet radiation under both laboratory and outdoor conditions at Abisko in northern Sweden. In the laboratory, nutrient supply, temperature, humidity, ultraviolet radiation-A (UV-A, 320–400 nm) and B (UV-B, 280–320 nm) were controlled, while photosynthetically active radiation (PAR, 400–700 nm) and photoperiod varied naturally. Under outdoor conditions nutrient supply was controlled, and the irradiation treatments were ambient and above-ambient UV-B using additional fluorescent lamps. Mountain birch nitrogen economy was affected by increased ultraviolet radiation, as reflected by a changed relationship between plant growth and plant nitrogen both in the laboratory and outdoors. In the laboratory enhanced UV-A decreased leaf area per unit plant biomass (leaf area ratio) but increased biomass productivity, both per unit leaf area (leaf area productivity) and per unit leaf nitrogen (leaf nitrogen productivity). Low levels of UV-B affected growth patterns and nitrogen economy in a similar way to enhanced UV-A. High levels of UV-B clearly decreased relative growth rate and nitrogen productivity, as leaf area ratio, leaf area productivity and leaf nitrogen productivity were all decreased. Under outdoor conditions above-ambient levels of UV-B did not alter growth or biomass allocation traits of the seedlings, whilst nitrogen productivity was increased. Mountain birch seedlings originating from different mother trees varied significantly in their responses to different ultraviolet radiation. Received: 10 April 1997 / Accepted: 19 September 1997     

Kinetic Characterization of 100 Glycoside Hydrolase Mutants Enables the Discovery of Structural Features Correlated with Kinetic Constants

The use of computational modeling algorithms to guide the design of novel enzyme catalysts is a rapidly growing field. Force-field based methods have now been used to engineer both enzyme specificity and activity. However, the proportion of designed mutants with the intended function is often less than ten percent. One potential reason for this is that current force-field based approaches are trained on indirect measures of function rather than direct correlation to experimentally-determined functional effects of mutations. We hypothesize that this is partially due to the lack of data sets for which a large panel of enzyme variants has been produced, purified, and kinetically characterized. Here we report the k_cat and K_M values of 100 purified mutants of a glycoside hydrolase enzyme. We demonstrate the utility of this data set by using machine learning to train a new algorithm that enables prediction of each kinetic parameter based on readily-modeled structural features. The generated dataset and analyses carried out in this study not only provide insight into how this enzyme functions, they also provide a clear path forward for the improvement of computational enzyme redesign algorithms.     

Metalloproteinases and their inhibitors in angiogenesis

Angiogenesis, the formation of new blood vessels from the pre-existing vasculature, is an integral part of physiological processes such as embryonic development, the female reproductive cycle and wound healing. Angiogenesis is also central to a variety of pathologies including cancer, where it is recognised as being crucial for the growth of solid tumours. Matrix metalloproteinases (MMPs) are a family of soluble and membrane-anchored proteolytic enzymes that can degrade components of the extracellular matrix (ECM) as well as a growing number of modulators of cell function. Several of the MMPs, most notably MMP-2 and -9 and membrane-type-1 MMP (MT1-MMP), have been linked to angiogenesis. Potential roles for these proteases during the angiogenic process include degradation of the basement membrane and perivascular ECM components, liberation of angiogenic factors, production of endogenous angiogenic inhibitors, and the unmasking of cryptic biologically relevant sites in ECM components. This review brings together what is currently known about the functions of the MMPs and the closely related adamalysin metalloproteinase (ADAM) family in angiogenesis, and discusses how this information might be useful in manipulation of the angiogenic process, with a view to controlling aberrant neovascularisation.