ChemSpectra: a web-based spectra editor for analytical data

ChemSpectra, a web-based software to visualize and analyze spectroscopic data, integrating solutions for infrared spectroscopy (IR), mass spectrometry (MS), and one-dimensional $^{1}$H and $^{13}$C NMR (proton and carbon nuclear magnetic resonance) spectroscopy, is described. ChemSpectra serves as web-based tool for the analysis of the most often used types of one-dimensional spectroscopic data in synthetic (organic) chemistry research. It was developed to support in particular processes for the use of open file formats which enable the work according to the FAIR data principles. The software can deal with the open file formats JCAMP-DX (IR, MS, NMR) and mzML (MS) proposing these data file types to gain interoperable data. ChemSpectra can be extended to read also other formats as exemplified by selected proprietary mass spectrometry data files of type RAW and NMR spectra files of type FID. The JavaScript-based editor can be integrated with other software, as demonstrated by integration into the Chemotion electronic lab notebook (ELN) and Chemotion repository, demonstrating the implementation into a digital work environment that offers additional functionality and sustainable research data management options. ChemSpectra supports different functions for working with spectroscopic data such as zoom functions, peak picking and automatic peak detection according to a default or manually defined threshold. NMR specific functions include the definition of a reference signal, the integration of signals, coupling constant calculation and multiplicity assignment. Embedded into a web application such as an ELN or a repository, the editor can also be used to generate an association of spectra to a sample and a file management. The file management supports the storage of the original spectra along with the last edited version and an automatically generated image of the spectra in png format. To maximize the benefit of the spectra editor for e.g. ELN users, an automated procedure for the transfer of the detected or manually chosen signals to the ELN was implemented. ChemSpectra is released under the AGPL license to encourage its re-use and further developments by the community

Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

Comparison of logistic regression, SVM and random forest performance in the plasma training data set. Table S2. Pathway significance and relative log fold changes in our metabolomics data and TCGA breast cancer RNA-Seq data. Table S3. Detected metabolites and their differential test results among the two models. a All-stage diagnosis model. b Early-stage diagnosis model. Table S4. Single-variate logistic analysis of metabolites or pathways selected as features in the metabolite-based or pathway-based early-stage diagnosis model. Table S5. Comparison of pathway features in the full-size (101 input pathways) and half-size (51 input pathways) pathway-based early-stage diagnosis models. (DOCX 34 kb

Segmenting Lecture Videos by Topic: From Manual to Automated Methods

More and more universities and corporations are starting to provide videotaped lectures online for knowledge sharing and learning. Segmenting lecture videos into short clips by topic can extract the hidden information structure of the videos and facilitate information searching and learning. Manual segmentation has high accuracy rates but is very labor intensive. In order to develop a high performance automated segmentation method for lecture videos, we conducted a case study to learn the segmentation process of humans and the effective segmentation features used in the process. Based on the findings from the case study, we designed an automated segmentation approach with two phases: initial segmentation and segmentation refinement. The approach combines segmentation features from three information sources of video (speech text transcript, audio and video) and makes use of various knowledge sources such as world knowledge and domain knowledge. Our preliminary results show that the proposed two-phase approach is promising

Gamma-Ray Bursts in Circumstellar Shells: A Possible Explanation for Flares

It is now generally accepted that long-duration gamma ray bursts (GRBs) are due to the collapse of massive rotating stars. The precise collapse process itself, however, is not yet fully understood. Strong winds, outbursts, and intense ionizing UV radiation from single stars or strongly interacting binaries are expected to destroy the molecular cloud cores that give birth to them and create highly complex circumburst environments for the explosion. Such environments might imprint features on GRB light curves that uniquely identify the nature of the progenitor and its collapse. We have performed numerical simulations of realistic environments for a variety of long-duration GRB progenitors with ZEUS-MP, and have developed an analytical method for calculating GRB light curves in these profiles. Though a full, three-dimensional, relativistic magnetohydrodynamical computational model is required to precisely describe the light curve from a GRB in complex environments, our method can provide a qualitative understanding of these phenomena. We find that, in the context of the standard afterglow model, massive shells around GRBs produce strong signatures in their light curves, and that this can distinguish them from those occurring in uniform media or steady winds. These features can constrain the mass of the shell and the properties of the wind before and after the ejection. Moreover, the interaction of the GRB with the circumburst shell is seen to produce features that are consistent with observed X-ray flares that are often attributed to delayed energy injection by the central engine. Our algorithm for computing light curves is also applicable to GRBs in a variety of environments such as those in high-redshift cosmological halos or protogalaxies, both of which will soon be targets of future surveys such as JANUS or Lobster.Comment: 12 pages, 5 figures, Accepted by Ap

HYR1-Mediated Detoxification of Reactive Oxygen Species Is Required for Full Virulence in the Rice Blast Fungus

During plant-pathogen interactions, the plant may mount several types of defense responses to either block the pathogen completely or ameliorate the amount of disease. Such responses include release of reactive oxygen species (ROS) to attack the pathogen, as well as formation of cell wall appositions (CWAs) to physically block pathogen penetration. A successful pathogen will likely have its own ROS detoxification mechanisms to cope with this inhospitable environment. Here, we report one such candidate mechanism in the rice blast fungus, Magnaporthe oryzae, governed by a gene we refer to as MoHYR1. This gene (MGG_07460) encodes a glutathione peroxidase (GSHPx) domain, and its homologue in yeast was reported to specifically detoxify phospholipid peroxides. To characterize this gene in M. oryzae, we generated a deletion mutantΔhyr1 which showed growth inhibition with increased amounts of hydrogen peroxide (H2O2). Moreover, we observed that the fungal mutants had a decreased ability to tolerate ROS generated by a susceptible plant, including ROS found associated with CWAs. Ultimately, this resulted in significantly smaller lesion sizes on both barley and rice. In order to determine how this gene interacts with other (ROS) scavenging-related genes in M. oryzae, we compared expression levels of ten genes in mutant versus wild type with and without H2O2. Our results indicated that the HYR1 gene was important for allowing the fungus to tolerate H2O2 in vitro and in planta and that this ability was directly related to fungal virulence

Transcutaneous medial fixation sutures for free flap inset after robot-assisted nipple-sparing mastectomy

The application of minimal invasive mastectomy has allowed surgeons to perform nipplesparing mastectomy via a shorter, inconspicuous incision under clear vision and with more precise hemostasis. However, it poses new challenges in microsurgical breast reconstruction, such as vascular anastomosis and flap insetting, which are considerably more difficult to perform through the shorter incision on the lateral breast border. We propose an innovative technique of transcutaneous medial fixation sutures to help in flap insetting and creating and maintaining the medial breast border. The sutures are placed after mastectomy and before flap transfer. Three 4-0 nylon suture loops are placed transcutaneously and into the pocket at the markings of the preferred lower medial border of the reconstructed breast. After microvascular anastomosis and temporary shaping of the flap on top of the mastectomy skin, the three corresponding points for the sutures are identified. The three nylon loops are then sutured to the dermis of the corresponding medial point of the flap. The flap is placed into the pocket by a simultaneous gentle pull on the three sutures and a combined lateral push. The stitches are then tied and buried after completion of flap inset

Animating Synthetic Dyadic Conversations With Variations Based on Context and Agent Attributes

Conversations between two people are ubiquitous in many inhabited contexts. The kinds of conversations that occur depend on several factors, including the time, the location of the participating agents, the spatial relationship between the agents, and the type of conversation in which they are engaged. The statistical distribution of dyadic conversations among a population of agents will therefore depend on these factors. In addition, the conversation types, flow, and duration will depend on agent attributes such as interpersonal relationships, emotional state, personal priorities, and socio-cultural proxemics. We present a framework for distributing conversations among virtual embodied agents in a real-time simulation. To avoid generating actual language dialogues, we express variations in the conversational flow by using behavior trees implementing a set of conversation archetypes. The flow of these behavior trees depends in part on the agents’ attributes and progresses based on parametrically estimated transitional probabilities. With the participating agents’ state, a ‘smart event’ model steers the interchange to different possible outcomes as it executes. Example behavior trees are developed for two conversation archetypes: buyer–seller negotiations and simple asking–answering; the model can be readily extended to others. Because the conversation archetype is known to participating agents, they can animate their gestures appropriate to their conversational state. The resulting animated conversations demonstrate reasonable variety and variability within the environmental context. Copyright © 2012 John Wiley & Sons, Ltd

Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference

The warming of the Arctic, also known as Arctic amplification, is led by several atmospheric and oceanic drivers. However, the details of its underlying thermodynamic causes are still unknown. Inferring the causal effects of atmospheric processes on sea ice melt using fixed treatment effect strategies leads to unrealistic counterfactual estimations. Such models are also prone to bias due to time-varying confoundedness. Further, the complex non-linearity in Earth science data makes it infeasible to perform causal inference using existing marginal structural techniques. In order to tackle these challenges, we propose TCINet - time-series causal inference model to infer causation under continuous treatment using recurrent neural networks and a novel probabilistic balancing technique. Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science