SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group

Many important geometric estimation problems naturally take the form of synchronization over the special Euclidean group: estimate the values of a set of unknown group elements [Formula: see text] given noisy measurements of a subset of their pairwise relative transforms [Formula: see text]. Examples of this class include the foundational problems of pose-graph simultaneous localization and mapping (SLAM) (in robotics), camera motion estimation (in computer vision), and sensor network localization (in distributed sensing), among others. This inference problem is typically formulated as a non-convex maximum-likelihood estimation that is computationally hard to solve in general. Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of the special Euclidean synchronization problem in a non-adversarial noise regime. The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation (MLE) whose minimizer provides an exact maximum-likelihood estimate so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate. We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem defined on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently. Finally, we combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync. Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering certifiably globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics and computer vision applications, and does so significantly faster than the Gauss–Newton-based approach that forms the basis of current state-of-the-art techniques.

Mapping and determining the center of mass of a rotating object using a moving observer

For certain applications, such as on-orbit inspection of orbital debris, defunct satellites, and natural objects, it is necessary to obtain a map of a rotating object from a moving observer, as well to estimate the object’s center of mass. This paper addresses these tasks using an observer that measures its own orientation, angular rate, and acceleration, and is equipped with a dense 3D visual sensor, such as a stereo camera or a light detection and ranging (LiDAR) sensor. The observer’s trajectory is estimated independently of the target object’s rotational motion. Pose-graph mapping is performed using visual odometry to estimate the observer’s trajectory in an arbitrary target-fixed frame. In addition to applying pose constraint factors between successive frames, loop closure is performed between temporally non-adjacent frames. A kinematic constraint on the target-fixed frame, resulting from the rigidity of the target object, is exploited to create a novel rotation kinematic factor. This factor connects a trajectory estimation factor graph with the mapping pose graph, and facilitates estimation of the target’s center of mass. Map creation is performed by transforming detected feature points into the target-fixed frame, centered at the estimated center of mass. Analysis of the algorithm’s computational performance reveals that its computational cost is negligible compared with that of the requisite image processing.

The Manhattan Frame Model-Manhattan World Inference in the Space of Surface Normals

Objects and structures within man-made environments typically exhibit a high degree of organization in the form of orthogonal and parallel planes. Traditional approaches utilize these regularities via the restrictive, and rather local, Manhattan World (MW) assumption which posits that every plane is perpendicular to one of the axes of a single coordinate system. The aforementioned regularities are especially evident in the surface normal distribution of a scene where they manifest as orthogonally-coupled clusters. This motivates the introduction of the Manhattan-Frame (MF) model which captures the notion of an MW in the surface normals space, the unit sphere, and two probabilistic MF models over this space. First, for a single MF we propose novel real-time MAP inference algorithms, evaluate their performance and their use in drift-free rotation estimation. Second, to capture the complexity of real-world scenes at a global scale, we extend the MF model to a probabilistic mixture of Manhattan Frames (MMF). For MMF inference we propose a simple MAP inference algorithm and an adaptive Markov-Chain Monte-Carlo sampling algorithm with Metropolis-Hastings split/merge moves that let us infer the unknown number of mixture components. We demonstrate the versatility of the MMF model and inference algorithm across several scales of man-made environments.

Probabilistic cooperative mobile robot area coverage and its application to autonomous seabed mapping

There are many applications that require mobile robots to autonomously cover an entire area with a sensor or end effector. The vast majority of the literature on this subject is focused on addressing path planning for area coverage under the assumption that the robot’s pose is known or that error is bounded. In this work, we remove this assumption and develop a completely probabilistic representation of coverage. We show that coverage is guaranteed as long as the robot pose estimates are consistent, a much milder assumption than zero or bounded error. After formally connecting robot sensor uncertainty with area coverage, we propose an adaptive sliding window filter pose estimator that provides a close approximation to the full maximum a posteriori estimate with a computation cost that is bounded over time. Subsequently, an adaptive planning strategy is presented that automatically exploits conditions of low vehicle uncertainty to more efficiently cover an area. We further extend this approach to the multi-robot case where robots can communicate through a (possibly faulty and low-bandwidth) channel and make relative measurements of one another. In this case, area coverage is achieved more quickly since the uncertainty over the robots’ trajectories is reduced. We apply the framework to the scenario of mapping an area of seabed with an autonomous underwater vehicle. Experimental results support the claim that our method achieves guaranteed complete coverage notwithstanding poor navigational sensors and that resulting path lengths required to cover the entire area are shortest using the proposed cooperative and adaptive approach.

Mapping Partially Observable Features from Multiple Uncertain Vantage Points

In this paper we present a technique for mapping partially observable features from multiple uncertain vantage points. The problem of concurrent mapping and localization (CML) is stated as follows. Starting from an initial known position, a mobile robot travels through a sequence of positions, obtaining a set of sensor measurements at each position. The goal is to process the sensor data to produce an estimate of the trajectory of the robot while concurrently building a map of the environment. In this paper, we describe a generalized framework for CML that incorporates temporal as well as spatial correlations. The representation is expanded to incorporate past vehicle positions in the state vector. Estimates of the correlations between current and previous vehicle states are explicitly maintained. This enables the consistent initialization of map features using data from multiple time steps. Updates to the map and the vehicle trajectory can also be performed in batches of data acquired from multiple vantage points. The method is illustrated with sonar data from a testing tank and via experiments with a B21 land mobile robot, demonstrating the ability to perform CML with sparse and ambiguous data.

Physical activity induced protection against breast cancer risk associated with delayed parity

Epidemiological evidence indicates that physical activity between menarche and first pregnancy is associated with a lower risk of breast cancer among women with at least 20years between these reproductive events. The mechanism by which physical activity during this interval confers protection is unknown. This study used a novel animal model to assess potentially protective effects of physical activity on tumor development in delayed parity. Thirty-six female Sprague Dawley rats received an i.p. injection of 50mg/kg N-methyl-N-nitrosourea (MNU) at 5weeks of age. Estrogen and progesterone pellets were implanted subcutaneously 1week (early parity, EP, n=8) or 4weeks (delayed parity, DP, n=11) following MNU injection. An additional group of DP rats were progressively exercise trained (Ex+DP, n=9) on a treadmill following MNU injection for 7weeks (up to 20m/min at 15% incline for 30min). We observed the greatest tumor latency and smallest tumor burden in Ex+DP animals. Ductal hyperplasia and inflammation of non-tumor bearing mammary glands were only found in DP, and we detected a significant increase in collagen for DP and Ex+DP compared to EP. Exercise induced differential gene expression of cyclin-dependent kinase-inhibitor 1C (Cdkn1c) and urokinase-plasminogen activator (Plau) in mammary tissue of Ex+DP animals compared to DP alone. While there are delayed parity-induced changes in mammary gland collagen and gene expression levels, Ex+DP animals had longer tumor latency, smaller tumor burden, and glandular tissue resistant to ductal hyperplasia. Exercise may induce protection through beneficial regulation of gene expression profiles.

Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters

This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Real-world results are reported for a vision-based, six degree of freedom SLAM implementation using data from a recent survey of the wreck of the RMS Titanic.

Adaptive Mobile Robot Navigation and Mapping

The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman filter to incorporate vehicle localization and environmental mapping. The authors describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. The authors introduce a metric for adaptive sensing that is defined in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action that yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for (1) adaptive control of motion and (2) adaptive control of motion and scanning. The vehicle tends to explore selectively different objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.

Robust Mapping and Localization in Indoor Environments Using Sonar Data

In this paper we describe a new technique for the creation of feature-based stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison.

Dynamic Map Building for an Autonomous Mobile Robot

This article presents an algorithm for autonomous map building and maintenance for a mobile robot. We believe that mobile robot navigation can be treated as a problem of tracking ge ometric features that occur naturally in the environment. We represent each feature in the map by a location estimate (the feature state vector) and two distinct measures of uncertainty: a covariance matrix to represent uncertainty in feature loca tion, and a credibility measure to represent our belief in the validity of the feature. During each position update cycle, pre dicted measurements are generated for each geometric feature in the map and compared with actual sensor observations. Suc cessful matches cause a feature's credibility to be increased. Unpredicted observations are used to initialize new geometric features, while unobserved predictions result in a geometric feature's credibility being decreased. We describe experimental results obtained with the algorithm that demonstrate successful map building using real sonar data.

Exactly Sparse Extended Information Filters for Feature-based SLAM

Recent research concerning the Gaussian canonical form for Simultaneous Localization and Mapping (SLAM) has given rise to a handful of algorithms that attempt to solve the SLAM scalability problem for arbitrarily large environments. One such estimator that has received due attention is the Sparse Extended Information Filter (SEIF) proposed by Thrun et al., which is reported to be nearly constant time, irrespective of the size of the map. The key to the SEIF's scalability is to prune weak links in what is a dense information (inverse covariance) matrix to achieve a sparse approximation that allows for efficient, scalable SLAM. We demonstrate that the SEIF sparsification strategy yields error estimates that are overconfident when expressed in the global reference frame, while empirical results show that relative map consistency is maintained.

In this paper, we propose an alternative scalable estimator based on an information form that maintains sparsity while preserving consistency. The paper describes a method for controlling the population of the information matrix, whereby we track a modified version of the SLAM posterior, essentially by ignoring a small fraction of temporal measurements. In this manner, the Exactly Sparse Extended Information Filter (ESEIF) performs inference over a model that is conservative relative to the standard Gaussian distribution. We compare our algorithm to the SEIF and standard EKF both in simulation as well as on two nonlinear datasets. The results convincingly show that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the EKF.

Real-time large-scale dense RGB-D SLAM with volumetric fusion

We present a new simultaneous localization and mapping (SLAM) system capable of producing high-quality globally consistent surface reconstructions over hundreds of meters in real time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every-frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an ‘as-rigid-as-possible’ space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.

Concurrent filtering and smoothing: A parallel architecture for real-time navigation and full smoothing

We present a parallelized navigation architecture that is capable of running in real-time and incorporating long-term loop closure constraints while producing the optimal Bayesian solution. This architecture splits the inference problem into a low-latency update that incorporates new measurements using just the most recent states (filter), and a high-latency update that is capable of closing long loops and smooths using all past states (smoother). This architecture employs the probabilistic graphical models of factor graphs, which allows the low-latency inference and high-latency inference to be viewed as sub-operations of a single optimization performed within a single graphical model. A specific factorization of the full joint density is employed that allows the different inference operations to be performed asynchronously while still recovering the optimal solution produced by a full batch optimization. Due to the real-time, asynchronous nature of this algorithm, updates to the state estimates from the high-latency smoother will naturally be delayed until the smoother calculations have completed. This architecture has been tested within a simulated aerial environment and on real data collected from an autonomous ground vehicle. In all cases, the concurrent architecture is shown to recover the full batch solution, even while updated state estimates are produced in real-time.

Cisplatin induces neuronal activation and increases central AMPA and NMDA receptor subunit gene expression in mice

Although rats and mice do not vomit, these species are widely studied as models of energy balance and sickness behavior. Previous work has shown that rats exhibit similar neuroanatomical activation of brain and visceral afferent pathways following cisplatin chemotherapy compared to vomiting species. However, the neural response to cisplatin in mice is understudied. Here, food intake, body weight, and central c-Fos immunofluorescence were analyzed in the hindbrains of male C57BL/6 mice following IP saline or cisplatin (5mg/kg, and 20mg/kg doses). As glutamate receptor signaling is classically linked to inhibitory feeding pathways in the rodent, gene expression of selected α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-methyl-d-aspartic acid (NMDA) receptor subunits were assessed in the dorsal vagal complex (DVC), parabrachial nucleus (PBN), amygdala, and bed nucleus of the stria terminalis (BNST). Our results show dose-dependent reductions in food intake and body weight following cisplatin treatment, as well as increases in cisplatin-induced c-Fos in the PBN and throughout the DVC. Quantitative PCR analysis shows cisplatin-induced increases in NMDA receptor subunit expression, particularly NR2B, in the DVC, PBN, BNST, and amygdala. In addition, upregulation of AMPA receptor subunits (GluA1 and/or GluA2) were observed in all regions examined except the amygdala. Taken together, these results suggest similar neural pathways mediating cisplatin effects in mice compared to other well-studied species, which are likely mediated by central upregulation of AMPA and NMDA receptors.

The MIT Stata Center dataset

This paper presents a large scale dataset of vision (stereo and RGB-D), laser and proprioceptive data collected over an extended duration by a Willow Garage PR2 robot in the 10 story MIT Stata Center. As of September 2012 the dataset comprises over 2.3 TB, 38 h and 42 km (the length of a marathon). The dataset is of particular interest to robotics and computer vision researchers interested in long-term autonomy. It is expected to be useful in a variety of research areas—robotic mapping (long-term, visual, RGB-D or laser), change detection in indoor environments, human pattern analysis, long-term path planning. For ease of use the original ROS ‘bag’ log files are provided and also a derivative version combining human readable data and imagery in standard formats. Of particular importance, this dataset also includes ground-truth position estimates of the robot at every instance (to typical accuracy of 2 cm) using as-built floor-plans—which were carefully extracted using our software tools. The provision of ground-truth for such a large dataset enables more meaningful comparison between algorithms than has previously been possible.

Advanced perception, navigation and planning for autonomous in-water ship hull inspection

Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closed-loop control relative to features such as weld-lines and biofouling. In the complex area, we implemented new large-scale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent operations on naval ships.

iSAM2: Incremental smoothing and mapping using the Bayes tree

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.

Cooperative AUV Navigation using a Single Maneuvering Surface Craft

In this paper we describe the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is to the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is to provide a quantitative performance comparison of three estimators: particle filtering (PF), non-linear least-squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.

The effect of steady-state increases in systemic arterial pressure on the duration of left ventricular ejection time

The effect of steady-state increases in systemic arterial pressure on the duration of left ventricular ejection time was studied in 11 normal male subjects. Methoxamine, a pressor amine of predominantly vasoconstrictor activity but lacking significant inotropic effect, was administered intravenously resulting in an average increase in mean arterial pressure of 27 mm Hg. Heart rate was held constant by high right atrial pacing, and there was no significant change in cardiac output. During methoxamine infusion, when stroke volume, heart rate, and inotropic state were held constant, left ventricular ejection time increased as mean arterial pressure increased. There was a highly significant correlation between the increase in mean systolic blood pressure and the prolongation of left ventricular ejection time (r = 0.870). In one subject, an increase in mean systolic pressure of 75 mm Hg prolonged left ventricular ejection time 55 msec, producing paradoxical splitting of the second heart sound. The prolongation of left ventricular ejection time during infusion was not blocked by the prior intravenous administration of atropine sulfate or propranolol hydrochloride, thus ruling out both vagal inhibition of the left ventricle and reflex withdrawal of sympathetic tone as its cause. In three subjects, left ventricular end diastolic pressure was measured and found to be significantly increased. This finding suggests that the normal left ventricle maintains a constant stroke volume in the presence of an increased pressure load by the Frank Starling mechanism. This study concludes that arterial pressure must be included as a prime determinant of left ventricular ejection time along with stroke volume, heart rate, and inotropic state in intact man.

Effects of age of cattle, turning technology and compost environment on disappearance of bone from mortality compost

As residual bones in mortality compost negatively impact subsequent tillage, two studies were performed. For the first study, windrows of mature cattle or calves were placed on a base of barley straw and covered with beef manure. Windrows were divided into two sections and turned at 3-month intervals. Approximately 5000 kg of finished compost per windrow was passed through a 6mm trommel screen, with bones collected and weighed. Bone weight was 0.66% of mature cattle compost and 0.38% of calf compost on a dry matter basis, but did not differ after adjustment for weights of compost ingredients. In a subsequent study, four windrows were constructed containing mortalities, straw and beef manure (STATC) or straw, manure and slaughter waste (STATW). Also, straw, beef manure and slaughter waste was added to an 850 L rolling drum composter (DRUMW). Fresh bovine long-bones from calves were collected, weighed and embedded in the compost. Bones were retrieved and weighed when windrows were turned, or with DRUMW, after 8 weeks. Temperatures achieved followed the order STATW>STATC>DRUMW (p<0.05). Rate of bone disappearance followed a pattern identical to temperature, with the weight of bones in STATW declining by 53.7% during 7 weeks of composting. For STATC, temperatures were uniform over three composting periods, but bone disappearance was improved (p<0.05) when compost dry matter was lower (46%), as compared to 58%. Using a ratio of five parts manure to one part mortalities, results of this study demonstrated that residual bone was <1% of cured cattle compost and may be reduced by maintaining a high compost temperature and moisture content.

Cooperative Localization for Autonomous Underwater Vehicles

This paper describes an algorithm for distributed acoustic navigation for Autonomous Underwater Vehicles (AUVs). Whereas typical AUV navigation systems utilize pre-calibrated arrays of static transponders, our work seeks to create a fully mobile network of AUVs that perform acoustic ranging and data exchange with one another to achieve cooperative positioning for extended duration missions over large areas. The algorithm enumerates possible solutions for the AUV trajectory based on dead-reckoning and range-only measurements provided by acoustic modems that are mounted on each vehicle, and chooses the trajectory via minimization of a cost function based on these constraints. The resulting algorithm is computationally efficient, meets the strict bandwidth requirements of available AUV modems, and has potential to scale well to networks of large numbers of vehicles. The method has undergone extensive experimentation, and results from three different scenarios are reported in this paper, each of which utilizes MIT SCOUT Autonomous Surface Craft (ASC) as convenient platforms for testing. In the first experiment, we utilize three ASCs, each equipped with a Woods Hole acoustic modem, as surrogates for AUVs. In this scenario, two ASCs serve as Communication/Navigation Aids (CNAs) for a third ASC that computes its position based exclusively on GPS positions of the CNAs and acoustic range measurements between platforms. In the second scenario, an undersea glider is used in conjunction with two ASCs serving as CNAs. Finally, in the third experiment, a Bluefin12 AUV serves as the target vehicle. All three experiments demonstrate the successful operation of the technique with real ocean data.

Influence of carbon and buffer amendment on ammonia volatilization in composting

Laboratory-scale experiments were carried out to test a mathematical model of the nitrogen dynamics in a composting process. The main ingredients of composting materials were wheat straw and dairy manure. The influence of (a) two carbon amendments, i.e. molasses and office paper, and (b) two chemicals forming buffer solutions on ammonia volatilization were investigated. Nitrogen losses amounted to 12-25% of initial nitrogen, in which ammonia volatilization accounted for 60-99%. Addition of molasses, a readily available form of carbon, reduced cumulative ammonia emissions substantially, but office paper, i.e. cellulose, had only a small influence. The addition of buffering chemicals did not significantly reduce ammonia volatilization.

Effect of peptide concentration and temperature on leuprolide stability in dimethyl sulfoxide

The effects of temperature and concentration on leuprolide degradation in dimethyl sulfoxide (DMSO) were explored. Leuprolide degradation products were analyzed by reverse phase high-performance liquid chromatography (RP-HPLC), size exclusion chromatography (SEC) and structurally characterized by mass spectrometry. Leuprolide solution stability in DMSO was characterized at 50, 100, 200, 400 mg/ml at 37-80 degrees C for 2 months to 3 years. Leuprolide degradation products were identified by mass spectrometry and could generally be attributed to isomerization, hydrolysis, oxidation, or aggregation. The hydrolytic degradation products consisted primarily of backbone cleavage C-terminal to Trp(3), Ser(4), Tyr(5), Leu(6) and Leu(7), and oxidation of Trp(3) and beta-elimination of Ser(4) were identified. Leuprolide degradation at 50 degrees C, 65 degrees C and 80 degrees C proceeded in an exponential fashion (E(a)=22. 6+/-1.2 kcal/mol); however, leuprolide degradation plateau'd after approximately 6 months at 37 degrees C. Upon closer examination, degradation product peak areas were seen to vary with temperature. For example, aggregation products did not increase with time at 37 degrees C, but aggregation peak intensities increased sharply with time at 80 degrees C. Increasing the temperature also increased the proportion of leuprolide degrading via isomerization/hydrolytic pathways, and decreased the proportion degrading via oxidation. These variations suggested that solvent dielectric, free H(+) in an aprotic solvent, oxygen solubility, impurities and residual moisture may play a role. Leuprolide solubilized in DMSO yields adequate stabililty for a 1 year implantable osmotic delivery system, where use of a dry aprotic solvent results in conditions similar to solid state stability.

Characterization and comparison of leuprolide degradation profiles in water and dimethyl sulfoxide

The effect of solvent on the rate of leuprolide degradation and on the structure of the degradation products was explored. Leuprolide solutions (370 mg/mL) were prepared in water and dimethyl sulfoxide (DMSO) for delivery in DUROS osmotic implants. Both solvent systems demonstrated better than 90% stability after 1 year at 37 degrees C, where the DMSO formulation afforded better stability than the aqueous formulation and was used in subsequent clinical trials. The rate of leuprolide degradation in DMSO was also observed to accelerate with increasing moisture content, indicating that the aprotic solvent minimized chemical degradation. Interestingly, leuprolide degradation products varied with formulation vehicle. The proportions of leuprolide degradation products observed to form in water and DMSO at 37 degrees C were hydrolysis > aggregation > isomerization > oxidation and aggregation > oxidation > hydrolysis > isomerization, respectively. Specifically, more N-terminal hydrolysis and acetylation were observed under aqueous conditions, and increased Trp oxidation and Ser beta-elimination were seen under non-aqueous conditions. Furthermore, the major chemical degradation pathway changed with temperature in the DMSO formulation (decreasing oxidation with increasing temperature), but not in the aqueous formulation.

Surgical intervention in acute myocardial infarction: an historical perspective

Acute myocardial infarction is an evolving event that lends itself well to surgical intervention. An historical review of surgery of acute myocardial infarction, with specific emphasis on the Spokane data, shows that this can be done safely and efficiently with myocardial salvage. Those people who were operated on within 6 hours of the onset of symptoms of acute myocardial infarction had a clear reduction in hospital mortality incidence and a better long-term result. The conclusion of our review is that emergency coronary artery bypass grafting for acute evolving myocardial infarction should be considered as a therapeutic option in every patient. All other modalities of therapy should be compared with the results of acute bypass surgery.

Adult cardiology and the expanding supply of physicians

The number of cardiologists can be projected with considerable accuracy into the next century. The total cardiology pool of physicians will increase until the year 2015 at which time those entering and leaving the pool will come into equilibrium. At that time the ratio of active cardiologists to the population will have greatly increased. This nation's future need for cardiologists is difficult to assess with any degree of precision. Therefore, this is the time for updating practice profile studies. Such studies today could be formulated in a manner to provide more detailed information on the cardiologist's daily activities. In addition, a data base developed through methodology such as the consensus formation approach must be developed and updated on a periodic basis. Through such analyses it will be possible to quantitate the future needs of cardiovascular manpower.