Search

Text-to-3D modelling has seen exciting progress by combining generative text-to-image models with image-to-3D methods like Neural Radiance Fields. DreamFusion recently achieved high-quality results but requires a lengthy, per-prompt optimization to create 3D objects. To address this, we amortize optimization over text prompts by training on many prompts simultaneously with a unified model, instead of separately. With this, we share computation across a prompt set, training in less time than per-prompt optimization. Our framework - Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to generalize to unseen setups and smooth interpolations between text for novel assets and simple animations., Comment: 22 pages, 20 figures

Objective: The aim of this systematic review and meta-analysis was to obtain an estimate of the prevalence of occult uterine malignancies of any subtype found at surgery for uterine fibroids. Metho...

Supplemental Material for LGBTQ+ Loss and Grief in a Cis-Heteronormative Pandemic: A Qualitative Evidence Synthesis of the COVID-19 Literature by James J. Lucas, Stéphane L. Bouchoucha, Rojan Afrouz, Kirk Reed, and Sharon L. Brennan-Olsen in Qualitative Health Research

Calibration is a fundamental property of a good predictive model: it requires that the model predicts correctly in proportion to its confidence. Modern neural networks, however, provide no strong guarantees on their calibration -- and can be either poorly calibrated or well-calibrated depending on the setting. It is currently unclear which factors contribute to good calibration (architecture, data augmentation, overparameterization, etc), though various claims exist in the literature. We propose a systematic way to study the calibration error: by decomposing it into (1) calibration error on the train set, and (2) the calibration generalization gap. This mirrors the fundamental decomposition of generalization. We then investigate each of these terms, and give empirical evidence that (1) DNNs are typically always calibrated on their train set, and (2) the calibration generalization gap is upper-bounded by the standard generalization gap. Taken together, this implies that models with small generalization gap (|Test Error - Train Error|) are well-calibrated. This perspective unifies many results in the literature, and suggests that interventions which reduce the generalization gap (such as adding data, using heavy augmentation, or smaller model size) also improve calibration. We thus hope our initial study lays the groundwork for a more systematic and comprehensive understanding of the relation between calibration, generalization, and optimization., Comment: Appeared at ICML 2022 Workshop on Distribution-Free Uncertainty Quantification

Modern computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection. These modules frequently have low expected error overall but high error on atypical groups of data due to biases inherent in the training process. In building autonomous vehicles (AV), this problem is an especially important challenge because their perception modules are crucial to the overall system performance. After identifying failures in AV, a human team will comb through the associated data to group perception failures that share common causes. More data from these groups is then collected and annotated before retraining the model to fix the issue. In other words, error groups are found and addressed in hindsight. Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes. To keep our interventions on the data manifold, we utilize masked language models. We verify that the prioritized groups found via intervention are challenging for the object detector and show that retraining with data collected from these groups helps inordinately compared to adding more IID data. We also plan to release software to run interventions in simulated scenes, which we hope will benefit the causality community., Comment: In submission at JMLR; 0xe5110eA3B5014cd9a585Dc76c74Ee509F504Be14

Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance in applications such as autonomous driving or medical imaging where collecting data is expensive and time-consuming. Overestimating or underestimating data requirements incurs substantial costs that could be avoided with an adequate budget. Prior work on neural scaling laws suggest that the power-law function can fit the validation performance curve and extrapolate it to larger data set sizes. We find that this does not immediately translate to the more difficult downstream task of estimating the required data set size to meet a target performance. In this work, we consider a broad class of computer vision tasks and systematically investigate a family of functions that generalize the power-law function to allow for better estimation of data requirements. Finally, we show that incorporating a tuned correction factor and collecting over multiple rounds significantly improves the performance of the data estimators. Using our guidelines, practitioners can accurately estimate data requirements of machine learning systems to gain savings in both development time and data acquisition costs., Comment: Accepted to CVPR 2022

This thesis explores the construction and architectural qualities of historic bank barns found in Chester county Pennsylvania. Analyzing the timber joints and adapting their characteristics for modern programmatic needs. It aims to extend the role of the heavy timber frame and reintroduce the methods of construction found in these historic handcrafted structures. Using the programmatic lens of a public library to isolate the distinct qualities of a barn including heavy and light, cellular and open, and the bent. The site of this Library is Kennett Square Pennsylvania, a rapidly expanding town in Chester county. While the town has made efforts to preserve the historic structures that surround it, the new construction has turned its back to these historic methods. Located in the center of downtown, this proposal would replace the existing inadequate public library. The program of a library offers the opportunity to educate the public and revitalize interest in timber structures. Master of Architecture This project analyzes the design of Pennsylvania bank barns and applies those architectural themes and details to the program of a Library. The bank barn is a significant agricultural structure that is uniquely linked to Pennsylvania's architectural history. Many of the building techniques and details of these barns haven't been utilized since advances in building technologies made them obsolete. Although, the structures that have been preserved continue to peak the interest of the public and the qualities continue to be emulated in residential projects. This thesis brings together the design of a bank barn and the program of a library located in Kennett Square Pennsylvania. The project experiments with extracting layouts and details from one type of structure and applying them to a completely different type of building. Experimenting with the qualities found in different building typologies could offer an opportunity to reinvent structures whose designs have remained stagnant.

Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decrease in the training objective. This Monotonic Linear Interpolation (MLI) property, first observed by Goodfellow et al. (2014) persists in spite of the non-convex objectives and highly non-linear training dynamics of neural networks. Extending this work, we evaluate several hypotheses for this property that, to our knowledge, have not yet been explored. Using tools from differential geometry, we draw connections between the interpolated paths in function space and the monotonicity of the network - providing sufficient conditions for the MLI property under mean squared error. While the MLI property holds under various settings (e.g. network architectures and learning problems), we show in practice that networks violating the MLI property can be produced systematically, by encouraging the weights to move far from initialization. The MLI property raises important questions about the loss landscape geometry of neural networks and highlights the need to further study their global properties., Comment: 15 pages in main paper, 4 pages of references, 24 pages in appendix. 29 figures in total

Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems' modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstrapping the global spatial representation. We further extract the geometric features from the segmented hemorrhage volume and fuse them with clinical features such as CT angiography (CTA) spot, Glasgow Coma Scale (GCS), and age to predict the ICH stability. Several state-of-the-art machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), gradient boosting, and random forests are applied to train stability estimation and to compare the performances. To align clinical intuition with model learning, we determine the shapely values (SHAP) and explain the most significant features for the ICH risk scoring system. A total of 79 patients are included, of which 20 are found in critical condition. Our proposed single pipeline model achieves a segmentation accuracy of 86.3%, stability prediction accuracy of 78.3%, and precision of 82.9%; the mean square error of exact expansion rate regression is observed to be 0.46. The SHAP analysis reveals that CTA spot sign, age, solidity, location, and length of the first axis of the ICH volume are the most critical characteristics that help define the stability of the stroke lesion. We also show that integrating significant geometric features with clinical features can improve the ICH progression scoring by predicting long-term outcomes. Graphical abstract Overview of our proposed method comprising of spatial attention and feature extraction mechanisms. The architecture is trained on the input CT images, and the first step output is the predicted segmentation of the hemorrhagic region. The output is fed into a geometric feature extractor and is fused with clinical features to estimate ICH stability using a multilayer perceptron (MLP).

Objective To conduct a systematic review and meta-analysis to estimate the prevalence of occult uterine malignancy of any subtype specifically at the time of hysterectomy for pelvic organ prolapse (POP). Methods We primarily used MEDLINE to access existing literature. The search terms used were "occult malignancy" or "occult uterine pathology" paired with "morcellation" or "hysterectomy." Our cutoff date for articles was March 25, 2019. We further narrowed articles down based on whether they included data on occult malignancy at time of hysterectomy specifically for POP. Results Our search yielded a total of 233 journal articles, of which 53 met the criteria for a full-text review. Eight studies provided specific data on occult uterine malignancy among women undergoing surgery for POP and formed the basis for this meta-analysis. Among the 8 studies examined, the total number of patients combined was 35,880, and there were 144 total occult uterine malignancies. There were 7 case series and 1 population-based study included. The pooled prevalence of occult uterine malignancy at the time of hysterectomy for POP was 0.22% (95% confidence interval, 0.11%-0.35%]. There was very little heterogeneity observed across the 8 studies (I2 = 12.06%, Q χ2[7] = 7.96, P = 0.336). Conclusions The pooled prevalence of occult uterine malignancy, of any subtype, at the time of surgery for POP specifically is 0.22% based on meta-analysis of existing studies. Surgeons can use this statistic as part of the preoperative counseling of patients with POP before hysterectomy.

BACKGROUND Outcomes after peripheral nerve injuries are poor despite current nerve repair techniques. Currently, there is no conclusive evidence that mammalian axons are capable of spontaneous fusion after transection. Notably, certain invertebrate species are able to auto-fuse after transection. Although mammalian axonal auto-fusion has not been observed experimentally, no mammalian study to date has demonstrated regenerating axolemmal membranes contacting intact distal segment axolemmal membranes to determine whether mammalian peripheral nerve axons have the intrinsic mechanisms necessary to auto-fuse after transection. OBJECTIVE This study aims to assess fusion competence between regenerating axons and intact distal segment axons by enhancing axon regeneration, delaying Wallerian degeneration, limiting the immune response, and preventing myelin obstruction. METHODS This study will use a rat sciatic nerve model to evaluate the effects of a novel peripheral nerve repair protocol on behavioral, electrophysiologic, and morphologic parameters. This protocol consists of a variety of preoperative, intraoperative, and postoperative interventions. Fusion will be assessed with electrophysiological conduction of action potentials across the repaired transection site. Axon-axon contact will be assessed with transmission electron microscopy. Behavioral recovery will be analyzed with the sciatic functional index. A total of 36 rats will be used for this study. The experimental group will use 24 rats and the negative control group will use 12 rats. For both the experimental and negative control groups, there will be both a behavior group and another group that will undergo electrophysiological and morphological analysis. The primary end point will be the presence or absence of action potentials across the lesion site. Secondary end points will include behavioral recovery with the sciatic functional index and morphological analysis of axon-axon contact between regenerating axons and intact distal segment axons. RESULTS The author is in the process of grant funding and institutional review board approval as of March 2020. The final follow-up will be completed by December 2021. CONCLUSIONS In this study, the efficacy of the proposed novel peripheral nerve repair protocol will be evaluated using behavioral and electrophysiologic parameters. The author believes this study will provide information regarding whether spontaneous axon fusion is possible in mammals under the proper conditions. This information could potentially be translated to clinical trials if successful to improve outcomes after peripheral nerve injury. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/18706

Semantic concepts are frequently defined by combinations of underlying attributes. As mappings from attributes to classes are often simple, attribute-based representations facilitate novel concept learning with zero or few examples. A significant limitation of existing attribute-based learning paradigms, such as zero-shot learning, is that the attributes are assumed to be known and fixed. In this work we study the rapid learning of attributes that were not previously labeled. Compared to standard few-shot learning of semantic classes, in which novel classes may be defined by attributes that were relevant at training time, learning new attributes imposes a stiffer challenge. We found that supervised learning with training attributes does not generalize well to new test attributes, whereas self-supervised pre-training brings significant improvement. We further experimented with random splits of the attribute space and found that predictability of test attributes provides an informative estimate of a model's generalization ability., Technical report, 25 pages

Lipschitz constraints under L2 norm on deep neural networks are useful for provable adversarial robustness bounds, stable training, and Wasserstein distance estimation. While heuristic approaches such as the gradient penalty have seen much practical success, it is challenging to achieve similar practical performance while provably enforcing a Lipschitz constraint. In principle, one can design Lipschitz constrained architectures using the composition property of Lipschitz functions, but Anil et al. recently identified a key obstacle to this approach: gradient norm attenuation. They showed how to circumvent this problem in the case of fully connected networks by designing each layer to be gradient norm preserving. We extend their approach to train scalable, expressive, provably Lipschitz convolutional networks. In particular, we present the Block Convolution Orthogonal Parameterization (BCOP), an expressive parameterization of orthogonal convolution operations. We show that even though the space of orthogonal convolutions is disconnected, the largest connected component of BCOP with 2n channels can represent arbitrary BCOP convolutions over n channels. Our BCOP parameterization allows us to train large convolutional networks with provable Lipschitz bounds. Empirically, we find that it is competitive with existing approaches to provable adversarial robustness and Wasserstein distance estimation., Comment: 9 main pages, 31 pages total, 3 figures. Accepted at 33rd Conference on Neural Information Processing Systems (NeurIPS 2019)

The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms. Recent attempts to improve SGD can be broadly categorized into two approaches: (1) adaptive learning rate schemes, such as AdaGrad and Adam, and (2) accelerated schemes, such as heavy-ball and Nesterov momentum. In this paper, we propose a new optimization algorithm, Lookahead, that is orthogonal to these previous approaches and iteratively updates two sets of weights. Intuitively, the algorithm chooses a search direction by looking ahead at the sequence of fast weights generated by another optimizer. We show that Lookahead improves the learning stability and lowers the variance of its inner optimizer with negligible computation and memory cost. We empirically demonstrate Lookahead can significantly improve the performance of SGD and Adam, even with their default hyperparameter settings on ImageNet, CIFAR-10/100, neural machine translation, and Penn Treebank., Comment: Accepted to Neural Information Processing Systems 2019. Code available at: https://github.com/michaelrzhang/lookahead

Hematoma expansion (HE) occurs in approximately one-third of patients with intracerebral hemorrhage (ICH) and is known to be a strong predictor of neurological deterioration as well as poor functional outcome. This study aims to externally validate three risk prediction models of HE (PREDICT, 9-point, and BRAIN scores) in an Asian population.A prospective cohort of 123 spontaneous ICH patients admitted to a tertiary hospital (certified stroke center) in Singapore was recruited. Logistic recalibrations were performed to obtain updated calibration slopes and intercepts for all models. The discrimination (c-statistic), calibration (Hosmer-Lemeshow test, le Cessie-van Houwelingen-Copas-Hosmer test, Akaike information criterion), overall performance (Brier score, ROverall, the recalibrated PREDICT performed best among the three models in our study cohort based on the novel matrix comprising of Akaike information criterion and c-statistic. The PREDICT model had the highest RTo our knowledge, this is the first study to validate HE scores, namely PREDICT, 9-Point and BRAIN, in a multi-ethnic Asian ICH patient population. The PREDICT score was the best performing model in our study cohort, based on the performance metrics employed in this study. Our findings also showed support for CT angiography spot sign as a predictor of outcome after ICH. Although the models assessed are sufficient for risk stratification, the discrimination and calibration are at best moderate and could be improved.

Study Objective To conduct a systematic review to estimate the prevalence of occult uterine malignancy, of any subtype, among women undergoing surgery for pelvic organ prolapse. Design Systematic review Setting N/A Patients or Participants Women undergoing surgery for pelvic organ prolapse. Interventions Hysterectomy Measurements and Main Results The PRISMA guidelines were followed in this systematic review. The search terms used were “occult malignancy” or “occult uterine pathology” paired with “morcellation” or “hysterectomy.” March 25, 2019 was the last date that articles were searched. We did not restrict articles based on language or publication date. Inclusion criteria included any peer-reviewed journal articles reporting occult uterine malignancy rates at the time of surgery for benign conditions, regardless of whether morcellation was used or not. We excluded articles that were reported exclusively on women with pre-operatively diagnosed or suspected uterine malignancies. Our search yielded a total of 233 journal articles, of which 53 met the criteria for a full-text review and 27 were included in the final systematic review. Of these 27 studies, 9 studies provided specific data on occult uterine malignancy among women undergoing surgery for pelvic organ prolapse. Among the 9 studies examined, the total number of patients combined was 35,880 and there were 144 total occult uterine malignancies. The overall crude prevalence of occult uterine malignancy was 0.40% (95% CI 0.30 – 0.47%). Among the 9 studies, the occult uterine malignancy rate ranged from a low 0% to a high of 3.17%. The largest of these 9 studies incorporated 31,567 patients and the occult uterine malignancy rate was 0.40% in that study. Conclusion This is the first attempt to synthesize data on occult uterine malignancies among women specifically undergoing surgery for pelvic organ prolapse. Based on this systematic review incorporating over 35,000 patients, the crude prevalence of occult uterine malignancy among this population is approximately 0.40%.

Study Objective To conduct an updated systematic review to estimate the prevalence of occult uterine malignancy, of any subtype, among women undergoing surgery for benign gynecologic conditions. Design Systematic review. Setting N/A Patients or Participants Women undergoing surgery for presumed benign gynecologic conditions. Interventions Hysterectomy or Myomectomy. Measurements and Main Results The PRISMA guidelines were followed in this systematic review. The search terms used were “occult malignancy” or “occult uterine pathology” paired with “morcellation” or “hysterectomy.” March 25, 2019 was the last date that articles were searched. We did not restrict articles based on language or publication date. Inclusion criteria included any peer-reviewed journal articles reporting occult uterine malignancy rates at the time of surgery for benign conditions, regardless of whether morcellation was used or not. We excluded articles that were reported exclusively on women with pre-operatively diagnosed or suspected uterine malignancies. Our search yielded a total of 233 journal articles, of which 53 met the criteria for a full-text review and 27 were included in the final systematic review. There were 339,420 patients across 9 countries that were included in these 27 studies. As a comparison, the previous systematic review done by the Agency for Healthcare Research and Quality (AHRQ) only included a total of 136,195 patients. There were a total 2,479 occult uterine malignancies (all subtypes combined) among these 339,420 patients. The crude unadjusted occult uterine malignancy rate was 0.73% (95% CI 0.70 – 0.76%). When we examined a subset of the included studies that reported specific data on women undergoing morcellation, there were 82 occult malignancies among a total of 25,422 patients (Crude proportion = 0.32% [95% CI 0.25 – 0.39%]). Conclusion Based on this systematic review, incorporating over 300,000 patients, the overall prevalence of occult uterine malignancy at the time of surgery for benign gynecologic indications is approximately 0.73%.

Study Objective Previous systematic reviews have focused specifically on the prevalence of leiomyosarcoma at the time of surgery for uterine fibroids. Recognizing that the other subtypes of uterine cancer are more prevalent, we wanted to conduct a systematic review to estimate the prevalence of occult uterine malignancy, of any subtype, among women undergoing surgery for uterine fibroids. Design Systematic Review. Setting N/A Patients or Participants Women undergoing surgery for uterine fibroids. Interventions Hysterectomy and Myomectomy. Measurements and Main Results The PRISMA guidelines were followed in this systematic review. The search terms used were “occult malignancy” or “occult uterine pathology” paired with “morcellation” or “hysterectomy.” March 25, 2019 was the last date that articles were searched. We did not restrict articles based on language or publication date. Inclusion criteria included any peer-reviewed journal articles reporting occult uterine malignancy rates at the time of surgery for benign conditions, regardless of whether morcellation was used or not. We excluded articles that were reported exclusively on women with pre-operatively diagnosed or suspected uterine malignancies. Our search yielded a total of 233 journal articles, of which 53 met the criteria for a full-text review and 27 were included in the final systematic review. Of these 27 studies, 8 specifically provided data on occult uterine malignancy among women undergoing surgery for uterine fibroids. These 8 studies incorporated 105,890 patients, across 5 countries, with presumed fibroids. A total of 578 occult uterine malignancies were found. The overall crude prevalence of occult uterine malignancy was 0.55% (95% CI 0.50 – 0.59%). Conclusion This is the first systematic review to estimate the prevalence of occult uterine malignancies (all subtypes combined) at the time of surgery specifically for uterine fibroids. Based on our systematic review, the crude prevalence of occult uterine malignancy across this population is 0.55%.

Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed along low curvature directions. Its performance depends crucially on a damping coefficient $\beta$. Large $\beta$ values can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different $\beta$ parameters. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive $\beta$ values such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and analyze rates of convergence for quadratic objectives. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence., Comment: 11 primary pages, 11 supplementary pages, 12 figures total

Bayesian neural networks (BNNs) allow us to reason about uncertainty in a principled way. Stochastic Gradient Langevin Dynamics (SGLD) enables efficient BNN learning by drawing samples from the BNN posterior using mini-batches. However, SGLD and its extensions require storage of many copies of the model parameters, a potentially prohibitive cost, especially for large neural networks. We propose a framework, Adversarial Posterior Distillation, to distill the SGLD samples using a Generative Adversarial Network (GAN). At test-time, samples are generated by the GAN. We show that this distillation framework incurs no loss in performance on recent BNN applications including anomaly detection, active learning, and defense against adversarial attacks. By construction, our framework not only distills the Bayesian predictive distribution, but the posterior itself. This allows one to compute quantities such as the approximate model variance, which is useful in downstream tasks. To our knowledge, these are the first results applying MCMC-based BNNs to the aforementioned downstream applications., Comment: accepted at ICML 2018

Purpose: Surgical retained foreign objects (RFOs) have significant morbidity and mortality. They are associated with approximately $1.5 × 109 annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. The authors address the RFO problem with two complementary technologies: a three-dimensional (3D) gossypiboma micro tag, the μTag that improves the visibility of RFOs on radiographs, and a computer aided detection (CAD) system that detects the μTag. It is desirable for the CAD system to operate in a high specificity mode in the operating room (OR) and function as a first reader for the surgeon. This allows for fast point of care results and seamless workflow integration. The CAD system can also operate in a high sensitivity mode as a second reader for the radiologist to ensure the highest possible detection accuracy. Methods: The 3D geometry of the μTag produces a similar two dimensional (2D) depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. The authors created a data set of 1800 cadaver images with the 3D μTag and other common man-made surgical objects positioned randomly. A total of 1061 cadaver images contained a single μTag and the remaining 739 were without μTag. A radiologist marked the location of the μTag using an in-house developed graphical user interface. The data set was partitioned into three independent subsets: a training set, a validation set, and a test set, consisting of 540, 560, and 700 images, respectively. A CAD system with modules that included preprocessing μTag enhancement, labeling, segmentation, feature analysis, classification, and detection was developed. The CAD system was developed using the training and the validation sets. Results: On the training set, the CAD achieved 81.5% sensitivity with 0.014 false positives (FPs) per image in a high specificity mode for the surgeons in the OR and 96.1% sensitivity with 0.81 FPs per image in a high sensitivity mode for the radiologists. On the independent test set, the CAD achieved 79.5% sensitivity with 0.003 FPs per image in a high specificity mode for the surgeons and 90.2% sensitivity with 0.23 FPs per image in a high sensitivity mode for the radiologists. Conclusions: To the best of the authors’ knowledge, this is the first time a 3D μTag is used to produce a recognizable, substantially similar 2D projection on radiographs regardless of orientation in space. It is the first time a CAD system is used to search for man-made objects over anatomic background. The CAD system for the μTags achieved reasonable performance in both the high specificity and the high sensitivity modes.

Free to read It is becoming increasing clear that microRNAs contribute to the regulation of many biological processes, including wound healing. After injury, keratinocytes need to undergo what is known as an epithelial-to-mesenchymal transition (EMT) to initiate re-epithelialisation. During this process, keratinocytes reduce their attachment to the underlying matrix, extend membrane protrusions, become motile and migrate over the wound bed, affecting wound closure. MicroRNAs that regulate EMT are aberrantly upregulated in keratinocytes at the edge of non-healing wounds and potentially play a role in the chronicity of these wounds. In vitro and in vivo, downregulation of these microRNAs promotes EMT and migration, facilitating re-epithelialisation in wound models. This review will focus on the role of microRNAs that regulate or have potential to regulate EMT and re-epithelialisation during wound healing

This Joint Applied Project addresses what can be done within the Naval Facilities Engineering Command Northwest community to better align its goals among competing interests from various stakeholders, while balancing the operational and regulatory constraints that often conflict with stakeholder goals and objectives. As a cross-functional organization, competing interests among the various business lines, support lines, and other stakeholders often result in a disconnect between how the command defines success compared to how other stakeholders define success. Methodology includes a literature review, employee and customer surveys and a Strength, Weaknesses, Opportunities, Threats analysis. It was found that the extent of fit among performance goals aligned with higher Naval and national goals was normal when considering the organization’s strategic issue identification process and dissemination. However, the extent of goal accomplishment appeared to be lacking in some areas when bearing in mind customer perceptions. Recommendations include employee training centered on goal alignment, which is vital to highlight the importance of the organization’s goals. To better align the commands goals with departmental goals, setting and continuously communicating goals and goal achievement to every department is needed. Organizational goals would benefit if given the same emphasis the command places on the goal of safety. http://archive.org/details/therigorsofligni1094545868 Civilian, Department of the Navy Approved for public release; distribution is unlimited.

Researchers have utilised normative, heuristic, and process approaches to understand people’s decision-making but with less focus on how satisfying these decisions are. There is a gap in the literature regarding the contribution of appraisals, stress, coping, and metacognitive awareness and acceptance to decision satisfaction. The conflict approach to decision-making, cognitive theories of stress and coping, and recent developments in cognitive-behavioural therapies may further our understanding of decision satisfaction. A Cognitive-Affective Model of Decision-Making Stress and Satisfaction was developed from the literature and tested in a three-phase longitudinal study. In Phase One (n = 182 adults; Mage = 40.48 years; SD = 12.04), a modified model focusing on a decision related to participants’ occupation or study was supported. It explained 61% of the variance in participants’ decision satisfaction. In Phase Two (n = 84; Mage = 39.43 years, SD = 11.94), the model was found to be invariant across a family/relationship decision. In Phase Three (n = 54; Mage = 39.54 years; SD = 12.29), model replication was not feasible although mean differences in the model’s manifest factors across occupation/study, family/relationship, and physical/mental health decision types were small or not significant, suggesting that the model is potentially invariant across all three decision types. The Cognitive-Affective Model of Decision-Making Stress and Satisfaction has both theoretical and practical implications for clinicians and researchers alike. These implications are discussed together with the thesis’ limitations and avenues for future research.

Surgical Retained Foreign Objects (RFOs) cause significant morbidity and mortality. They are associated with $1.5 billion annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. We address the RFO problem with two complementary technologies: a three dimensional (3D) Gossypiboma Micro Tag (μTa) that improves the visibility of RFOs on radiographs, and a Computer Aided Detection (CAD) system that detects the μTag. The 3D geometry of the μTag produces a similar 2D depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. We create a database of cadaveric radiographs with the μTag and other common man-made objects positioned randomly. We develop the CAD modules that include preprocessing, μTag enhancement, labeling, segmentation, feature analysis, classification and detection. The CAD can operate in a high specificity mode for the surgeon to allow for seamless workflow integration and function as a first reader. The CAD can also operate in a high sensitivity mode for the radiologist to ensure accurate detection. On a data set of 346 cadaveric radiographs, the CAD system performed at a high specificity (85.5% sensitivity, 0.02 FPs/image) for the OR and a high sensitivity (96% sensitivity, 0.73 FPs/image) for the radiologists.

Boundary Layer Ingestion (BLI) is currently being researched as a potential method to improve efficiency and decrease emissions for the next generation of commercial aircraft. While re-energizing the boundary layer formed over the fuselage of an aircraft has many system level benefits, ingesting the low velocity boundary layer flow through a serpentine inlet into a turbofan engine adversely affects the performance of the engine. The available literature has only yielded studies of the effects of this specific type of inlet distortion on engine performance in the form of numerical simulations. This work seeks to provide an experimental analysis of the effects of BLI-type distortion on a turbofan engine's performance. A modified JT15D-1 turbofan engine was investigated in this study. Inlet flow distortion was created by a layered wire mesh distortion screen designed to create a total pressure distortion profile at the aerodynamic interface plane (AIP) similar to NASA's Inlet A boundary layer ingesting inlet flow profile. Results of this investigation showed a 15.5% decrease in stream thrust and a 14% increase in TSFC in the presence of BLI-type distortion. Flow measurements at the AIP and the bypass nozzle exit plane provided information about the losses throughout the fan flow path. The presence of the distortion screen resulted in a 24% increase in mass-averaged entropy production along the entire fan flow path compared to the non-distorted test. A mass-averaged fan flow path efficiency was also calculated assuming an isentropic process as ideal. The non-distorted fan flow path efficiency was computed to be 60%, while the distorted fan flow path efficiency was computed to be 50.5%, a reduction in efficiency of 9.5%. The entropy generation between ambient conditions and the AIP was compared to the entropy production along the entire fan flow path. It was found that the majority of entropy generation occurred between the AIP and bypass nozzle exit. Based on flow measurements at the bypass nozzle exit plane, it was concluded that inlet flow distortion should be located away from the tip region of the fan in order to minimize losses in a very lossy region. It was also determined that the fan and bypass duct process the different regions of the total pressure distortion in different ways. In some regions the entropy production decreased for the distorted test compared to the clean test, while in other regions the entropy production increased for the distorted test compared to the clean test. Finally, it was found that small improvements in total pressure and total temperature variation at the bypass nozzle exit plane will greatly improve the fan flow path efficiency and entropy generation, thereby decreasing performance losses. Master of Science

A Hammett study was carried out on the tributyltin hydride reduction of substituted benzyl bromides. Electron-withdrawing groups facilitate the reaction, whereas electron-releasing groups have little or no impact on the rate. This unusual effect creates a discontinuity in the Hammett plot. Electron-withdrawing groups stabilize both the free radical and polar character of the transition state. Electron-releasing groups, however, stabilize the free radical character but destabilize the polar character of the transition state.

This thesis presents the methods utilized to model a steel deck truss bridge over the New River in Hillsville, Virginia. These methods were evaluated by comparing analytical results with data recorded from 14 members during live load testing. The research presented herein is part of a larger endeavor to understand the structural behavior and collapse mechanism of the erstwhile I-35W bridge in Minneapolis, MN. Objectives accomplished toward this end include investigation of lacing effects on built up member strain detection, live load testing of a steel truss bridge, and evaluating modeling techniques in comparison to recorded data. Before any live load testing could be performed, it was necessary to confirm an acceptable strain gage layout for measuring member strains. The effect of riveted lacing in built-up members was investigated by constructing a two-thirds mockup of a typical bridge member. The mockup was then instrumented with strain gages and subjected to known strains in order to determine the most effective strain gage arrangement. Testing analysis concluded that for a built up member consisting of laced channels, one strain gage installed on the middle of the extreme fiber of each channel's flanges was sufficient. Thus, laced members on the bridge were mounted with four strain gages each. Data from live loads were obtained by loading two trucks to 25 tons each. Trucks were positioned at eight locations on the bridge in four different relative truck positions. Data were recorded continuously and reduced to member forces for model validation comparisons. Deflections at selected truss nodes were also recorded for model validation purposes. The model validation process began by developing four simple truss models, each reflecting different expected restraint conditions, in the hopes of bracketing data from recorded results. Models were refined to frames, and then frames including floor beams and stringers for greater accuracy. The final, most accurate model was selected and used for a failure analysis. This model showed where the minimum amount of load could be applied in order to learn about the bridge's failure behavior, for a test to be conducted at a later time. Master of Science

A holographic material based on a photopolymerizable nematic acrylate was improved by the addition of the trithiocarbonate, EtO2CCH2SC(S)SCH2CO2Et, increasing uniformity and reproducibility in the formation of electrically switchable holographic gratings. Diffraction efficiencies as high as 34%, the thin-grating theoretical maximum, were achieved at large (10-14 microm) grating spacings. Improved diffraction efficiency was seen over a range of grating spacings (0.35-10 microm). The exposure window for grating formation was increased by an order of magnitude, resulting in much improved reproducibility.