Characterization of the Active Site and Insight into the Binding Mode of the Anti-angiogenesis Agent Fumagillin to the Manganese(II)-Loaded Methionyl Aminopeptidase from \u3cem\u3eEscherichia coli\u3c/em\u3e

EPR spectra were recorded for methionine aminopeptidase from Escherichia coli (EcMetAP-I) samples (~2.5 mM) to which one and two equivalents of Mn(II) were added (the latter is referred to as [MnMn(EcMetAP-I)]). The spectra for each sample were indistinguishable except that the spectrum of [MnMn(EcMetAP-I)] was twice as intense. The EPR spectrum of [MnMn(EcMetAP-I)] exhibited the characteristic six-line g≈2 EPR signal of mononuclear Mn(II) with A av(55Mn)=9.3 mT (93 G) and exhibited Curie-law temperature dependence. This signal is typical of Mn(II) in a ligand sphere comprising oxygen and/or nitrogen atoms. Other features in the spectrum were observed only as the temperature was raised from that of liquid helium. The temperature dependences of these features are consistent with their assignment to excited state transitions in the S=1, 2 ... 5 non-Kramer’s doublets, due to two antiferromagnetically coupled Mn(II) ions with an S=0 ground state. This assignment is supported by the observation of a characteristic 4.5 mT hyperfine pattern, and by the presence of signals in the parallel mode consistent with a non-Kramers’ spin ladder. Upon the addition of the anti-angiogenesis agent fumagillin to [MnMn(EcMetAP-I)], very small changes were observed in the EPR spectrum. MALDI-TOF mass spectrometry indicated that fumagillin was, however, covalently coordinated to EcMetAP-I. Therefore, the inhibitory action of this anti-angiogenesis agent on EcMetAP-I appears to involve covalent binding to a polypeptide component at or near the active site rather than direct binding to the metal ions

Challenges and solutions for Latin named entity recognition

Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English. Data sparsity in Latin presents a number of challenges for traditional Named Entity Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90% F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201

Assessing the Vegetation Condition Impacts of the 2011 Drought across the U.S. Southern Great Plains Using the Vegetation Drought Response Index (VegDRI)

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of recordbreaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing

Pediatric Cardiac Tumors: A 45-year, Single Institution Review

Background: Cardiac tumors in children are rare. Of the cases reported in the literature, nearly all are benign and managed conservatively. Methods: This is a retrospective, observational study of pediatric patients <18 years who presented for surgical evaluation of a cardiac tumor, between 1969 and 2014 at a tertiary care children’s hospital. Presentation, pathology, management, and outcomes were evaluated. Results: Over the last 45 years, 64 patients were evaluated for surgical resection of a cardiac tumor. Rhabdomyoma was the most common neoplasm (58%), and 17% of the tumors had malignant pathologies. While 42% of benign cardiac neoplasms required surgical intervention for significant hemodynamic concerns, 73% of malignant neoplasms underwent radical excision, if possible, followed by adjuvant chemotherapy. Despite a 37% mortality in patients with malignant pathology, an aggressive surgical approach can yield long-term survival in some patients. There were no deaths among patients with benign tumors and 17% had postoperative complications mostly related to mitral regurgitation. Conclusion: Cardiac tumors in children are rare but can be managed aggressively with good outcomes. Benign tumors have an excellent survival with most complications related to tumor location. Malignant tumors have a high mortality rate, but surgery and adjuvant chemotherapy allow for prolonged survival in selected patients

The Vegetation Outlook (VegOut): A New Method for Predicting Vegetation Seasonal Greenness

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of “historical patterns” (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate–vegetation interactions and ocean–climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation’s response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regional level vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing–based approaches that monitor “current” vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions