Holistic engineering design : a combined synchronous and asynchronous approach

To aid the creation and through-life support of large, complex engineering products, organizations are placing a greater emphasis on constructing complete and accurate records of design activities. Current documentary approaches are not sufficient to capture activities and decisions in their entirety and can lead to organizations revisiting and in some cases reworking design decisions in order to understand previous design episodes. Design activities are undertaken in a variety of modes; many of which are dichotomous, and thus each require separate documentary mechanisms to capture information in an efficient manner. It is possible to identify the modes of learning and transaction to describe whether an activity is aimed at increasing a level of understanding or whether it involves manipulating information to achieve a tangible task. The dichotomy of interest in this paper is that of synchronous and asynchronous working, where engineers may work alternately as part of a group or as individuals and where different forms of record are necessary to adequately capture the processes and rationale employed in each mode. This paper introduces complimentary approaches to achieving richer representations of design activities performed synchronously and asynchronously, and through the undertaking of a design based case study, highlights the benefit of each approach. The resulting records serve to provide a more complete depiction of activities undertaken, and provide positive direction for future co-development of the approaches

The development of a set of principles for the through-life management of engineering information

Belgium Herbarium image of Meise Botanic Garden

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Emotional experience in patients with clinically isolated syndrome and early multiple sclerosis

Background and purpose: Evidence suggests that there are changes in the processing of emotional information (EP) in people with multiple sclerosis (MS). It is unclear which functional domains of EP are affected, whether these changes are secondary to other MS-related neuropsychological or psychiatric symptoms and if EP changes are present in early MS. The aim of the study was to investigate EP in patients with early MS (clinically isolated syndrome and early relapsing/remitting MS) and healthy controls (HCs). Methods: A total of 29 patients without neuropsychological or psychiatric deficits and 29 matched HCs were presented with pictures from the International Affective Picture System with negative, positive or neutral content. Participants rated the induced emotion regarding valence and arousal using nine-level Likert scales. A speeded recognition test assessed memory for the emotional stimuli and for the emotional modulation of response time. A subgroup of participants was tested during a magnetic resonance imaging (MRI) session. Results: Patients in the MRI subgroup rated the experience induced by pictures with positive or negative emotional content significantly more weakly than HCs. Further, these patients were significantly less aroused when watching the pictures from the International Affective Picture System. There were no effects in the non-MRI subgroup or effects on emotional memory or response times. Conclusions: Emotional processing changes may be present in early MS in the form of flattened emotional experience on both the valence and arousal dimensions. These changes do not appear to be secondary to neuropsychological or psychiatric deficits. The fact that emotional flattening was only found in the MRI setting suggests that EP changes may be unmasked within stressful environments and points to the potential yet underestimated impact of the MRI setting on behavioral outcomes

Feasibility and accuracy of digital breast tomosynthesis–guided vacuum-assisted breast biopsy for noncalcified mammographic targets

PURPOSEWe aimed to determine the feasibility and accuracy of digital breast tomosynthesis–guided vacuum-assisted breast biopsy (DBT-VAB) for noncalcified lesions without a sonographic correlate and to assess the concordance of imaging and pathology findings.METHODSA retrospective review of our institutional biopsy database between December 11, 2015, and August 31, 2016, identified 72 consecutive women with 73 noncalcified lesions on digital breast tomosynthesis who underwent attempted DBT-VAB. Relevant imaging was reviewed in consensus by 3 fellowship-trained breast radiologists for imaging features and biopsy parameters. Medical records were reviewed for histopathology and imaging follow-up.RESULTSThe target lesion was successfully sampled by DBT-VAB in 99% (72 of 73) of cases. The median time to complete DBT-VAB was 16 minutes. No major complications were reported. Findings included 3 focal asymmetries (4%), 7 asymmetries (10%), 21 masses (29%), and 41 architectural distortions (ADs) (57%). Final histopathology was malignant in 24% (17 of 72), actionable high-risk in 4% (3 of 72), and benign in 72% (52 of 72). VAB pathology was concordant in 86% (62 of 72): 21% malignant, 6% high risk, and 60% benign. VAB pathology was discordant in 14% (10 of 72). One malignancy and 4 complex sclerosing lesions were missed after DBT-VAB of AD, which was confirmed on surgical excision. Therefore, the misdiagnosis rate for DBT-VAB was 7% (5 of 72).CONCLUSIONDBT-VAB is a quick and feasible biopsy method for targeting noncalcified mammographic lesions without a sonographic correlate. The 24% malignancy rate reaffirms that biopsy is necessary for suspicious mammographic lesions occult on ultrasound. Although DBT-VAB shows high accuracy for noncalcified lesions, meticulous radiology-pathology correlation is required in the interpretation of DBT-VAB results, with surgical excision of discordant cases

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge

Feet and Leg Traits are Moderately to Lowly Heritable in Red Angus Cattle

Objective: The goals of this study were to identify feet and leg indicator traits to be used in beef breed genetic evaluations and develop a scoring method that can be easily adopted by cattle producers. Description: Data were analyzed on 1,885 Red Angus cattle, and after editing, 1,720 records were used for analysis. Feet and leg phenotypes were obtained from August 2015 through September 2017 for 14 traits shown in the following table. Trained livestock evaluators collected measurements using an electronic tablet with offline data storage capabilities. Heritability estimates for all 14 traits were calculated from two different measurements of scale, the original 1-100 scale (1 and 100 are extreme, 50 is desirable), and scores truncated to a 1-9 scale (1 and 9 are extreme, 5 is desirable). Genetic parameters were estimated using maximum log likelihood procedures. The Bottom Line: Feet and leg traits are moderately to lowly heritable; however, producers can still select on traits for improved soundness. Scoring on a simpler, less granular measurement of scale (1-9) is appropriate to be used in further research

7 Tesla MRI of Balo's concentric sclerosis versus multiple sclerosis lesions

Background: Baló’s concentric sclerosis (BCS) is a rare condition characterized by concentrically layered white matter lesions. While its pathogenesis is unknown, hypoxia-induced tissue injury and chemotactic stimuli have been proposed as potential causes of BCS lesion formation. BCS has been suggested to be a variant of multiple sclerosis (MS). Here, we aimed to elucidate similarities and differences between BCS and MS by describing lesion morphology and localization in high-resolution 7 Tesla (7 T) magnetic resonance imaging (MRI) scans. Methods: Ten patients with Baló-type lesions underwent 7 T MRI, and 10 relapsing remitting MS patients served as controls. The 7 T MR imaging protocol included 3D T1-weighted (T1w) magnetization-prepared rapid gradient echo, 2D high spatial resolution T2*-weighted (T2*w) fast low-angle shot and susceptibility-weighted imaging. Results: Intralesional veins were visible in the center of all but one Baló-type lesion. Four Baló-type lesions displayed inhomogeneous intralesional T2*w signal intensities, which are suggestive of microhemorrhages or small ectatic venules. Eight of 10 BCS patients presented with 97 additional lesions, 36 of which (37%) had a central vein. Lesions involving the cortical gray matter and the U-fibers were not detected in BCS patients. Conclusion: Our findings support the hypothesis that BCS and MS share common pathogenetic mechanisms but patients present with different lesion phenotypes

Inhomogeneous Phases in a Double-Exchange Magnet with Long Range Coulomb Interactions

We consider a model with competing double-exchange (ferromagnetic) and super-exchange (anti-ferromagnetic) interactions in the regime where phase separation takes place. The presence of a long range Coulomb interaction frustrates a macroscopic phase separation, and favors microscopically inhomogeneous configurations. We use the variational Hartree-Fock approach, in conjunction with Monte-Carlo simulations to study the geometry of such configurations in a two-dimensional system. We find that an array of diamond shaped ferromagnetic droplets is the preferred configuration at low electronic densities, while alternating ferromagnetic and anti-ferromagnetic diagonal stripes emerge at higher densities. These findings are expected to be relevant for thin films of colossal magneto-resistive manganates.Comment: 15 pages, 9 figures. Journal Ref. added, errors correcte