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A method to define the labels of the state diagram of a linear finite-state code is presented and investigated. This method is particularly suitable for simple hardware implementation since it simplifies the encoder structure. The method can also be applied to the labeling of a state diagram that is not completely connected to obtain a linear finite state code with larger free distance.
Method for labeling state diagrams of linear finite-state codes developed. Simplifies implementation of encoder hardware. Used to label state diagram not completely connected to obtain linear finite-state code that has larger free distance.
The effective parallel symbolic computation of operators under composition is discussed. Examples include differential operators under composition and vector fields under the Lie bracket. Data structures consisting of formal linear combinations of rooted labeled trees are discussed. A multiplication on rooted labeled trees is defined, thereby making the set of these data structures into an associative algebra. An algebra homomorphism is defined from the original algebra of operators into this algebra of trees. An algebra homomorphism from the algebra of trees into the algebra of differential operators is then described. The cancellation which occurs when noncommuting operators are expressed in terms of commuting ones occurs naturally when the operators are represented using this data structure. This leads to an algorithm which, for operators which are derivations, speeds up the computation exponentially in the degree of the operator. It is shown that the algebra of trees leads naturally to a parallel version of the algorithm.
The objective of the Energy Expenditure in Space Flight (ESS) experiment is to demonstrate and evaluate the doubly labeled water method of measuring the energy expended by crew members during approximately 7 days in microgravity. The doubly labeled water technique determines carbon dioxide production which is then used to calculate energy expenditure. The method relies on the equilibrium between oxygen in respiratory carbon dioxide and oxygen in body water. Because of this equilibrium, the kinetic of water turnover and respiration are interdependent. Under normal conditions, man contains small but significant amounts of deuterium and oxygen 18. Deuterium is eliminated from the body as water while oxygen 18 is eliminated as water and carbon dioxide. The difference in the turnover rates in the two isotopes is proportional to the carbon dioxide production. Deliberately enriching the total body water with both of these isotopes allows the isotope turnovers to be accurately measured in urine, plasma, or saliva samples. The samples are taken to the laboratory for analysis using an ion-ratio spectrometer.
Photoaffinity labeling in heart tissue of rats flown on Cosmos 2044 was used to measure the regulatory (R) subunits of adenosine monophosphate-dependent protein kinase. A significant decrease of RII subunits in the particulate cell fraction extract (S2; P less than 0.05 in all cases) was observed when extracts of tissue samples from vivarium controls were compared with those from flight animals. Photoaffinity labeling of the soluble fraction (S1) was observed to be unaffected by spaceflight or any of the simulation conditions. Proteins of the S2 fraction constitute a minor (less than 10 percent) component of the total, whereas the S1 fraction contained most of the cell proteins. Changes in a relatively minor aspect of adenosine monophosphate-mediated reactions are considered to be representative of a metabolic effect.
The membrane ATPase from Halobacterium saccharovorum was purified as described by Hochstein et al. (1987) and was incubated with C-14 labeled N-ethylmaleimide (NEM), with and without adenine nucleotides, to determine the effect of nucleotides on the enzyme labeling. It was found that NEM incorporates into the 87,000-Da subunit (subunit I) of the enzyme and that the conditions for enzyme modification are similar to those which result in the inhibition of the enzyme activity. The presence of ATP, ADP, and AMP was found to reduce both the inhibitor incorporation and enzyme inhibition. It was shown that the reaction involves a modification of thiol groups.
Solid NMR of C-13 isotope-labeled samples of PMR-15 was used to follow the cross-linking reaction of the nadic end cap. Some samples were labeled on one of the carbon atoms of the nadic end cap, and others on the methylene carbon atom of the methylenedianiline portion of the polymer. NMR spectra were run on these samples both before and after cross-linking. In this way, direct evidence of the major products of cross-linking under normal cure conditions is provided. The majority (approximately 85%) of the cross-linking derives from olefin polymerization through the double bond of the end cap. Approximately 15% of the products could come from a pathway involving a retro-Diels-Alder reaction. However, all of the products could be explained by a biradical intermediate without a retro-Diels-Alder reaction. Evidence is also presented that the methylene moiety in the methylenedianiline part of the polymer chain also participates in the cross-linking, albeit to a small extent, by a radical transfer reaction. Different cure conditions (higher temperatures, longer times) could change the relative distribution of the products.
There is a strong need for faster, cheaper, and simpler methods for nucleic acid analysis in today s clinical tests. Nanotechnologies can potentially provide solutions to these requirements by integrating nanomaterials with biofunctionalities. Dramatic improvement in the sensitivity and multiplexing can be achieved through the high-degree miniaturization. Here, we present our study in the development of an ultrasensitive label-free electronic chip for DNA/RNA analysis based on carbon nanotube nanoelectrode arrays. A reliable nanoelectrode array based on vertically aligned multi-walled carbon nanotubes (MWNTs) embedded in a SiO2 matrix is fabricated using a bottom-up approach. Characteristic nanoelectrode behavior is observed with a low-density MWNT nanoelectrode array in measuring both the bulk and surface immobilized redox species. The open-end of MWNTs are found to present similar properties as graphite edge-plane electrodes, with a wide potential window, flexible chemical functionalities, and good biocompatibility. A BRCA1 related oligonucleotide probe with 18 bases is covalently functionalized at the open ends of the MWNTs and specifically hybridized with an oligonucleotide target as well as a PCR amplicon. The guanine bases in the target molecules are employed as the signal moieties for the electrochemical measurements. Ru(bpy)3(2+) mediator is used to further amplify the guanine oxidation signal. This technique has been employed for direct electrochemical detection of label-free PCR amplicon through specific hybridization with the BRCAl probe. The detection limit is estimated to be less than approximately 1000 DNA molecules, approaching the limit of the sensitivity by laser-based fluorescence techniques in DNA microarray. This system provides a general electronic platform for rapid molecular diagnostics in applications requiring ultrahigh sensitivity, high-degree of miniaturization, simple sample preparation, and low- cost operation.
The doubly labeled water method for measuring total energy expenditure is subject to error from natural variations in the background 2H and 18O in body water. There is disagreement as to whether the variations in background abundances of the two stable isotopes covary and what relative doses of 2H and 18O minimize the impact of variation on the precision of the method. We have performed two studies to investigate the amount and covariance of the background variations. These were a study of urine collected weekly from eight subjects who remained in the Madison, WI locale for 6 wk and frequent urine samples from 14 subjects during round-trip travel to a locale > or = 500 miles from Madison, WI. Background variation in excess of analytical error was detected in six of the eight nontravelers, and covariance was demonstrated in four subjects. Background variation was detected in all 14 travelers, and covariance was demonstrated in 11 subjects. The median slopes of the regression lines of delta2H vs. delta18O were 6 and 7, respectively. Modeling indicated that 2H and 18O doses yielding a 6:1 ratio of final enrichments should minimize this error introduced to the doubly labeled water method.
The aim of the study was to validate a nonradioactive method for relative blood flow measurements in severely injured lungs that avoids labor-intensive tissue processing. The use of fluorescent-labeled microspheres was compared with the standard radiolabeled-microsphere method. In seven sheep, lung injury was established by using oleic acid. Five pairs of radio- and fluorescent-labeled microspheres were injected before and after established lung injury. Across all animals, 175 pieces were selected randomly. The radioactivity of each piece was determined by using a scintillation counter. The fluorescent dye was extracted from each piece with a solvent without digestion or filtering. The fluorescence was determined with an automated fluorescent spectrophotometer. Perfusion was calculated for each piece from both the radioactivity and fluorescence and volume normalized. Correlations between flow determined by the two methods were in the range from 0.987 +/- 0.007 (SD) to 0.991 +/- 0.002 (SD) after 9 days of soaking. Thus the fluorescent microsphere technique is a valuable tool for investigating regional perfusion in severely injured lungs and can replace radioactivity.
An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.
Highly fluorescent, stable and biocompatible microspheres are obtained by copolymerizing an acrylic monomer containing a covalent bonding group such as hydroxyl, amine or carboxyl, for example, hydroxyethylmethacrylate, with an addition polymerizable fluorescent comonomer such as dansyl allyl amine. A lectin or antibody is bound to the covalent site to provide cell specificity. When the microspheres are added to a cell suspension the marked microspheres will specifically label a cell membrane by binding to a specific receptor site thereon. The labeled membrane can then be detected by fluorescence of the fluorescent monomer.
An automatic identification system and method are provided which employ a machine readable multiple layer label. The label has a plurality of machine readable marking layers stacked one upon another. Each of the marking layers encodes an identification symbol detectable using one or more sensing technologies. The various marking layers may comprise the same marking material or each marking layer may comprise a different medium having characteristics detectable by a different sensing technology. These sensing technologies include x-ray, radar, capacitance, thermal, magnetic and ultrasonic. A complete symbol may be encoded within each marking layer or a symbol may be segmented into fragments which are then divided within a single marking layer or encoded across multiple marking layers.
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain segmentation methods rely on supervised learning which is very data hungry and difficult to train where only a small number of labeled samples are available. Moreover, the semantic classes are defined differently for different applications (e.g., rover traversal vs. geological) and as a result the network has to be trained from scratch each time, which is an inefficient use of resources. This research proposes a semi-supervised learning framework for Mars terrain segmentation where a deep segmentation network trained in an unsupervised manner on unlabeled images is transferred to the task of terrain segmentation trained on few labeled images. The network incorporates a backbone module which is trained using a contrastive loss function and an output atrous convolution module which is trained using a pixel-wise cross-entropy loss function. Evaluation results using the metric of segmentation accuracy show that the proposed method with contrastive pre-training outperforms plain supervised learning by 2%-10%. Moreover, the proposed model is able to achieve a segmentation accuracy of 91.1% using only 161 training images (1% of the original dataset) compared to 81.9% with plain supervised learning.
Methods of labelling one or more subcellular components (e.g., an organelle and/or subcellular region) in vivo are provided. Methods of labelling a protein in vivo are provided. Methods of determining a nucleic acid sequence in situ are also provided.
Big Data plays a central role in the remarkable results achieved by Machine Learning (ML) and especially Deep Learning (DL) in the recent years. However, the difficulty in obtaining a reasonable amount of labeled samples limits ML/DL application in various domains, including industrial equipment and system monitoring. In this paper the need for methods that turn manual labeling into a scalable process is highlighted. A real world problem is analyzed for which weak supervision methods, successfully employed in other domains, did not produce acceptable results. An alternative approach based on clustering ensembles is described and tested, achieving good performance.
In transportation research, applications of travel behavior data collection are context-specific and require different types of qualitative inputs. These inputs can be viewed as spanning a spectrum of user burden and data quality, from simple trip labels to complex time-use surveys. However, each currently active smartphone-based travel diary platform appears to only support one type of qualitative input, and the effort required for customization is unclear. In this paper, we characterize the spectrum by defining four canonical use cases: (i) trip labels, (ii) trip questionnaire, (iii) counterfactual trips, and (iv) time-use surveys. We then outline a mechanism for supporting configurable user inputs on the same underlying smartphone-based sensing mechanism and demonstrate that it can support all the use cases without any code changes. We further demonstrate that the flexible data model that underpins this mechanism can enable real-time monitoring and analysis. Finally, we evaluate per-user data collection and engagement metrics for large-scale deployments of three canonical use cases, spanning 10 programs, 435 users, and 251,041 trips, and a maximum duration of 800 days. Future efforts may support additional use cases through an expanded configuration and provide greater insight into user engagement. We hope that these insights enable the research community to look at qualitative inputs through a new lens and experiment with novel use cases to fill in the spectrum.
A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying collision or detector response. To help mitigate this problem of domain shift, we propose RINO (Renormalization Group Invariance with No Labels), a self-supervised learning approach that can instead pretrain models directly on collision data, learning embeddings invariant to renormalization group flow scales. In this work, we pretrain a transformer-based model on jets originating from quantum chromodynamic (QCD) interactions from the JetClass dataset, emulating real QCD-dominated experimental data, and then finetune on the JetNet dataset -- emulating simulations -- for the task of identifying jets originating from top quark decays. RINO demonstrates improved generalization from the JetNet training data to JetClass data compared to supervised training on JetNet from scratch, demonstrating the potential for RINO pretraining on real collision data followed by fine-tuning on small, high-quality MC datasets, to improve the robustness of ML models in HEP.