Nowhere to Run; Nowhere to Hide: The Reality of Being a Law Library Director in Times of Great Opportunity and Significant Challenges

This is an edited version of remarks presented at \u27Nowhere to Run, Nowhere to Hide\u27: The Reality of Being a Law Library Director in Times of Great Opportunity and Significant Challenges, January 5, 2015, at the Association of American Law Schools Annual Meeting, Washington, D.C

Automated detection and tracking of marine mammals : a novel sonar tool for monitoring effects of marine industry

Funding: The work was funded under the Scottish Government Demonstration Strategy (Project no. USA/010/14)and as part of the Department of Energy and Climate Change’s Offshore Energy Strategic Environmental Assessment programme, with additional resources from the Natural Environment Research Council (grant numbers: NE/R014639/1 and SMRU1001).1. Many marine industries may pose acute risks to marine wildlife. For example, tidal turbines have the potential to injure or kill marine mammals through collisions with turbine blades. However, the quantification of collision risk is currently limited by a lack of suitable technologies to collect long‐term data on marine mammal behaviour around tidal turbines. 2. Sonar provides a potential means of tracking marine mammals around tidal turbines. However, its effectiveness for long‐term data collection is hindered by the large data volumes and the need for manual validation of detections. Therefore, the aim here was to develop and test automated classification algorithms for marine mammals in sonar data. 3. Data on the movements of harbour seals were collected in a tidally energetic environment using a high‐frequency multibeam sonar on a custom designed seabed‐mounted platform. The study area was monitored by observers to provide visual validation of seals and other targets detected by the sonar. 4. Sixty‐five confirmed seals and 96 other targets were detected by the sonar. Movement and shape parameters associated with each target were extracted and used to develop a series of classification algorithms. Kernel support vector machines were used to classify targets (seal vs. nonseal) and cross‐validation analyses were carried out to quantify classifier efficiency. 5. The best‐fit kernel support vector machine correctly classified all the confirmed seals but misclassified a small percentage of non‐seal targets (~8%) as seals. Shape and non‐spectral movement parameters were considered to be the most important in achieving successful classification. 6. Results indicate that sonar is an effective method for detecting and tracking seals in tidal environments, and the automated classification approach developed here provides a key tool that could be applied to collecting long‐term behavioural data around anthropogenic activities such as tidal turbines.PostprintPeer reviewe

Predicting the Spectrum of UGC 2885, Rubin’s Galaxy with Machine Learning

Wu & Peek predict SDSS-quality spectra based on Pan-STARRS broadband grizy images using machine learning (ML). In this article, we test their prediction for a unique object, UGC 2885 ( Rubin\u27s galaxy ), the largest and most massive, isolated disk galaxy in the local universe (D \u3c 100 Mpc). After obtaining the ML predicted spectrum, we compare it to all existing spectroscopic information that is comparable to an SDSS spectrum of the central region: two archival spectra, one extracted from the VIRUS-P observations of this galaxy, and a new, targeted MMT/Binospec observation. Agreement is qualitatively good, though the ML prediction prefers line ratios slightly more toward those of an active galactic nucleus (AGN), compared to archival and VIRUS-P observed values. The MMT/Binospec nuclear spectrum unequivocally shows strong emission lines except Hβ, the ratios of which are consistent with AGN activity. The ML approach to galaxy spectra may be a viable way to identify AGN supplementing NIR colors. How such a massive disk galaxy (M* = 1011 M⊙), which uncharacteristically shows no sign of interaction or mergers, manages to fuel its central AGN remains to be investigated

CRLX101, a Nanoparticle–Drug Conjugate Containing Camptothecin, Improves Rectal Cancer Chemoradiotherapy by Inhibiting DNA Repair and HIF1α

Novel agents are needed to improve chemoradiotherapy for locally advanced rectal cancer. In this study, we assessed the ability of CRLX101, an investigational nanoparticle-drug conjugate containing the payload camptothecin (CPT), to improve therapeutic responses as compared to standard chemotherapy. CRLX101 was evaluated as a radiosensitizer in colorectal cancer cell lines and murine xenograft models. CRLX101 was as potent as CPT in vitro in its ability to radiosensitize cancer cells. Evaluations in vivo demonstrated that the addition of CRLX101 to standard chemoradiotherapy significantly increased therapeutic efficacy by inhibiting DNA repair and HIF-1α pathway activation in tumor cells. Notably, CRLX101 was more effective than oxaliplatin at enhancing the efficacy of chemoradiotherapy, with CRLX101 and 5-fluorouracil (5-FU) producing the highest therapeutic efficacy. Gastrointestinal toxicity was also significantly lower for CRLX101 compared to CPT when combined with radiotherapy. Our results offer a preclinical proof of concept for CRLX101 as a modality to improve the outcome of neoadjuvant chemoradiotherapy for rectal cancer treatment, in support of ongoing clinical evaluation of this agent (LCC1315 {"type":"clinical-trial","attrs":{"text":"NCT02010567","term_id":"NCT02010567"}}NCT02010567)

IMI – Interventions myopia institute:Interventions for controlling myopia onset and progression report

Myopia has been predicted to affect approximately 50% of the world’s population based on trending myopia prevalence figures. Critical to minimizing the associated adverse visual consequences of complicating ocular pathologies are interventions to prevent or delay the onset of myopia, slow its progression, and to address the problem of mechanical instability of highly myopic eyes. Although treatment approaches are growing in number, evidence of treatment efficacy is variable. This article reviews research behind such interventions under four categories: optical, pharmacological, environmental (behavioral), and surgical. In summarizing the evidence of efficacy, results from randomized controlled trials have been given most weight, although such data are very limited for some treatments. The overall conclusion of this review is that there are multiple avenues for intervention worthy of exploration in all categories, although in the case of optical, pharmacological, and behavioral interventions for preventing or slowing progression of myopia, treatment efficacy at an individual level appears quite variable, with no one treatment being 100% effective in all patients. Further research is critical to understanding the factors underlying such variability and underlying mechanisms, to guide recommendations for combined treatments. There is also room for research into novel treatment options

Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative

Knee osteoarthritis is one of the most common musculoskeletal diseases and is usually diagnosed with medical imaging techniques. Conventionally, case identification using plain radiography is practiced. However, we acknowledge that knee osteoarthritis is a 3D complexity; hence, magnetic resonance imaging will be the ideal modality to reveal the hidden osteoarthritis features from a three-dimensional view. In this work, the feasibility of well-known convolutional neural network (CNN) structures (ResNet, DenseNet, VGG, and AlexNet) to distinguish knees with and without osteoarthritis (OA) is investigated. Using 3D convolutional layers, we demonstrated the potential of 3D convolutional neural networks of 13 different architectures in knee osteoarthritis diagnosis. We used transfer learning by transforming 2D pre-trained weights into 3D as initial weights for the training of the 3D models. The performance of the models was compared and evaluated based on the performance metrics [balanced accuracy, precision, F1 score, and area under receiver operating characteristic (AUC) curve]. This study suggested that transfer learning indeed enhanced the performance of the models, especially for ResNet and DenseNet models. Transfer learning-based models presented promising results, with ResNet34 achieving the best overall accuracy of 0.875 and an F1 score of 0.871. The results also showed that shallow networks yielded better performance than deeper neural networks, demonstrated by ResNet18, DenseNet121, and VGG11 with AUC values of 0.945, 0.914, and 0.928, respectively. This encourages the application of clinical diagnostic aid for knee osteoarthritis using 3DCNN even in limited hardware conditions