Dynamics of indices of metabolism in muscle tissue, liver and gonads of Abramis brama in different periods of the annual cycle

The aim of this research was to study the dynamics of metabolism in the body of bream Abramis brama (L.) at different periods of the annual cycle: pre-spawning, spawning, post-spawning and feeding. The object of the study was 55 male and 53 female bream aged 9–13 years, to reduce the effect of age on the studied parameters. Individuals were measured, weighed, gender was determined, gonad maturity stage, age; the condition factor and organ indices were calculated. The content of moisture, dry matter, lipids, proteins, ash and nitrogen-free extracts (NFE) in the muscles, liver and gonads of male and female bream have been analyzed. The lowest coefficients of condition factor were found in bream during the autumn feeding season, the highest in males during spawning, in females before and after spawning. In the muscle tissue of male and female bream the content of metabolic products in the feeding period and before spawning differed slightly. In the spawning period, the muscles of males exceeded the muscle tissue of females in lipid content by more than two times. In the annual cycle of the bream, the ovaries contain more protein than the testes. The bream of different sex revealed differences in the intensity of metabolic processes during the maturation of gonads: in males, energy costs are more pronounced after wintering before spawning, while in females, during spawning. The main energy reserve in the form of lipids in the annual cycle changes more significantly in the body of male bream than in females. Ovarian maturation requires more metabolite reserves than maturation of the testes, however, males have increased expenditure associated with reproductive behaviour during spawning. Therefore, the total energy contribution to reproduction may be the same for female and for male bream. Bream’s liver and muscle tissue can serve as a source of energy during food shortages and during spawning. In the annual cycle, the condition factor of bream increases due to an increase in the moisture content in the muscles

A Case of Suspected Lisdexamfetamine (Vyvanse) Drug-Induced Liver Injury

Amphetamines are a well-established cause of liver injury though the typical presentation is that of illicit drug abuse rather than liver injury occurring in a prescribed setting.1 Lisdexamfetamine (Vyvanse) is one of the most commonly prescribed stimulant medications used for the treatment of Attention Deficit Hyperactive Disorder (ADHD). The authors were only able to find a single case of lisdexamfetamine-related drug-induced liver injury (DILI) occurring in the pediatric population in their review of the literature. Here we present a case of suspected lisdexamfetamine DILI in an adult patient

Biochemical and hematological composition of blood of cattle fed with Chlorella

We researched the influence of the additive Chlorella cultivated in a closed bioreactor under the influence of an electrostatistic field, on the biochemical and hematological parameters of blood in different age groups of cattle. The experiment was conducted on two groups of three month old heifers and two groups of lactating cows in the 7th month of lactation, with 15 individuals in each group; all animals in the groups were given the basic diet. During the 120-day experiment, the experimental group of heifers was fed on a suspension of Chlorella cultivated with the technology using an electrostatic field, in the following amounts: 1–30th day – 1.51 g, 31–60th day – 1.82 g, 61–90th day – 2.01 g, 91–120th day – 2.28 g of dry substance per individual daily; the group of lactating cows during 60 days was given the Chlorella additive to the amount of 6 g of dry substance per individual per day. Over the following 30 days the heifers and lactating cows were kept under observation to determine the “aftereffects” of the Chlorella suspension fodder additive. On the first day of the experiment and after every 30 days, blood was drawn from the animals’ jugular veins for determination of the biochemical and hematological parameters of the blood During the experiment, the experimental groups of heifers and lactating cows were observed to have a strengthened antioxidant system due to increase in the activity of superoxide dismutase and catalase, and also to have a strengthened hematopoietic function and intensified metabolic and redox processes. Due to the stimulating effect of the suspension of Chlorella cultivated using the intense technology involving using an electrostatic field on the erythro- and leukopoiesis of different age groups of the cattle, we determined a strengthening of the cellular element of the animals’ immune system. During the following 30 days, the heifers and lactating cows of the experimental groups were observed to exhibit “aftereffects” of the Chlorella suspension additive, namely increases in the values of biochemical, hematological and morphological indicators of the blood in comparison with these indicators for the animals in the control groups. As a result, we concluded that feeding different age groups of cattle with suspension of Chlorella cultivated in the conditions of a closed bioreactor under the influence of an electrostatic field, in the dosage of 155 ml per kg of dry substance of the animals’ diet, contributes to the strengthening of non-specific defence of their organism, forming a potential for increasing the productive indicators and maintenance of the livestock

Acute Systemic Infection-Associated Russell Body Gastroesophagitis: A Case Report and Literature Review

Russell body esophagitis/gastritis (RBG) is a rare gastrointestinal inflammatory condition characterized by accumulation of plasma cells containing dense eosinophilic cytoplasmic inclusions, i.e., Russell bodies. Herein, we report a case of RBG in a patient with a systemic inflammation background. A 61-year-old female presented with oral infection. Upper gastrointestinal endoscopy revealed patchy salmon-colored esophageal mucosa proximally to the gastroesophageal junction, suggestive of “Barrett’s esophagus”. Histologic examination of the biopsy tissue from the lower esophagus showed diffuse lymphoplasmacytic infiltration with abundant admixed enlarged plasma cells (Mott cells) containing bright eosinophilic, round, dense, homogenous inclusions (Russell bodies) in cytoplasm. Immunohistochemical study demonstrated membranous staining of CD138 in the Mott cells, while immunoglobulin light chain in situ hybridization revealed positivity of only kappa light chain, indicating kappa light chain restriction and clonality. A proton-pump inhibitor therapy was initiated, but the patient passed away due to generalized infection. Our case suggests that Russell body esophagitis/gastritis (RBG) can be a gastrointestinal presentation associated with acute systemic infection

Učinak dodatka proteina iz otpada kože na hematološke i biokemijske pokazatelje metabolizma i mliječnost visokoproduktivnih krava

This study was aimed to establish the effect of a fodder protein supplement from minced subcutaneous epithelium of farm animals on hematological, biochemical parameters of metabolism and milk productivity of high-yielding cows during the early lactation. For this, three groups of cows, six animals each, were formed according to the principle of analogous pairs. The control group received the main diet, the second and third experimental groups were daily fed with 300 g and 500 g of protein supplement for 60 days in addition to the main diet, respectively. The effect of prolongation of the supplement was studied over the next 30 days. Hematological and biochemical blood parameters, qualitative and quantitative composition of milk were studied in all animals. The animals of the experimental groups showed an increase in metabolic and redox processes during the experiment. An increase in the metabolism of nutrients in the liver has been found. Due to the sufficient intake of protein in the organism of animals, there was an increase in milk productivity and milk quality indicators.Cilj je ovog istraživanja utvrditi učinak dodatka proteina iz mljevenog potkožnog epitela domaćih životinja, na hematološke i biokemijske parametre metabolizma te proizvodnost visokoproduktivnih krava tijekom rane laktacije. Za to su prema principu analognih parova formirane tri skupine krava, svaka po šest životinja. Kontrolna skupina hranjena je osnovnim obrokom, a druga i treća eksperimentalna skupina svakodnevno su uz osnovnu hranidbu hranjene s 300 g i 500 g proteinskog dodatka tijekom 60 dana. Učinak dodatka proučavan je tijekom sljedećih 30 dana. U svih životinja proučavani su hematološki i biokemijski parametri krvi, te kvalitativni i kvantitativni sastav mlijeka. Životinje pokusnih skupina pokazale su porast metaboličkih i redoks procesa tijekom pokusa. Utvrđen je porast metabolizma hranjivih sastojaka u jetri. Zbog dovoljnog unosa proteina u organizam životinja eksperimentalnih skupina došlo je do povećanja pokazatelja mliječne proizvodnosti i pokazatelja kvalitete mlijeka

Diverse M-Best Solutions by Dynamic Programming

Many computer vision pipelines involve dynamic programming primitives such as finding a shortest path or the minimum energy solution in a tree-shaped probabilistic graphical model. In such cases, extracting not merely the best, but the set of M-best solutions is useful to generate a rich collection of candidate proposals that can be used in downstream processing. In this work, we show how M-best solutions of tree-shaped graphical models can be obtained by dynamic programming on a special graph with M layers. The proposed multi-layer concept is optimal for searching M-best solutions, and so flexible that it can also approximate M-best diverse solutions. We illustrate the usefulness with applications to object detection, panorama stitching and centerline extraction

Methods for advancing combinatorial optimization over graphical models

Graphical models are a well-known convenient tool to describe complex interactions between variables. A graphical model defines a function over many variables that factors over an underlying graph structure. One of the popular tasks over graphical models is that of combinatorial optimization. Although many algorithms have been developed with this task in mind, the vast majority are designed to find an optimal solution, minimum or maximum, of an objective function. In many applications, however, it is desirable to obtain not just a single optimal solution, but a set of the first m best solutions for some integer m. The main part of this dissertation focuses on this problem, which we call the m-best optimization task. We show that the m-best task can be expressed within the unifying framework of semirings, making known inference algorithms defined, and their correctness and completeness for the m-best task immediately implied. We subsequently describe elim-m-opt, a new bucket elimination algorithm for solving the m-best task, provide algorithms for its defining combination and marginalization operators and analyze its worst-case performance. An extension of the algorithm to the mini-bucket framework provides bounds for each of the m best solutions. Subsequently, we extend existing search algorithms to the m-best task. We present a new algorithm m-A* and prove that all A*’s desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since best-first algorithms require extensive memory, we also extend the memory-efficient depth-first branch and bound to the m-best task. We adapt both algorithms to optimization tasks over graphical models (e.g., Weighted Constraint Satisfaction Problems and Most Probable Explanation in Bayesian networks), and provide complexity analysis and an empirical evaluation. Our experiments with 5 variants of best-first search and depth-first branch and bound search confirm that the best-first approach is largely superior when memory is available, but branch and bound is more robust. We also demonstrate that both styles of search benefit greatly from the heuristic evaluation function with increased accuracy. Unlike the leading previously developed m-best schemes that utilize LP-relaxation techniques, e.g., algorithms by Fromer and Globerson (2009) and Batra (2012), our algorithms always guarantee solution optimality. We will show that, when the number of required solutions is small, our m-best search schemes are quite competitive with these related algorithms in terms of runtime, while for a larger number of required solutions our methods are by far superior. The second part of this thesis focuses on finding approximate solutions to optimization problems. Unfortunately solving exactly optimization problems over complex models, that represent intricate dependencies occurring in real life domains, can often be infeasible within practical time and space limits. Many approximation schemes exist, but most of them do not come with any solution sub-optimality guarantees. We apply the ideas of weighted heuristic search, popular in path-finding, to graphical models, yielding new search algorithms that not only provide sub-optimality bounds, but also utilize extra available time and space to improve the accuracy of the solution in an anytime manner and, if resources are available, eventually terminate with an optimal solution. We report on a significant empirical evaluation, demonstrating the usefulness of weighted best-first search as approximation anytime schemes (that have sub-optimality bounds) and compare against one of the best depth-first branch and bound solvers to date. We also investigate the impact of different heuristic functions on the behavior of the algorithms. Additionally, we explore several algorithms taking advantage of two common approaches for bounding MPE queries in graphical models: mini-bucket elimination and message-passing updates for linear programming relaxations. Each method offers a useful perspective for the other; our hybrid approaches attempt to balance the advantage of each. We demonstrate the power of our hybrid algorithms through extensive empirical evaluation. Most notably, a branch and bound search guided by the heuristic functions calculated by our new scheme won the first place in the 2011 Pascal2 inference challenge

Methods for advancing combinatorial optimization over graphical models

Graphical models are a well-known convenient tool to describe complex interactions between variables. A graphical model defines a function over many variables that factors over an underlying graph structure. One of the popular tasks over graphical models is that of combinatorial optimization. Although many algorithms have been developed with this task in mind, the vast majority are designed to find an optimal solution, minimum or maximum, of an objective function. In many applications, however, it is desirable to obtain not just a single optimal solution, but a set of the first m best solutions for some integer m. The main part of this dissertation focuses on this problem, which we call the m-best optimization task. We show that the m-best task can be expressed within the unifying framework of semirings, making known inference algorithms defined, and their correctness and completeness for the m-best task immediately implied. We subsequently describe elim-m-opt, a new bucket elimination algorithm for solving the m-best task, provide algorithms for its defining combination and marginalization operators and analyze its worst-case performance. An extension of the algorithm to the mini-bucket framework provides bounds for each of the m best solutions. Subsequently, we extend existing search algorithms to the m-best task. We present a new algorithm m-A* and prove that all A*’s desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since best-first algorithms require extensive memory, we also extend the memory-efficient depth-first branch and bound to the m-best task. We adapt both algorithms to optimization tasks over graphical models (e.g., Weighted Constraint Satisfaction Problems and Most Probable Explanation in Bayesian networks), and provide complexity analysis and an empirical evaluation. Our experiments with 5 variants of best-first search and depth-first branch and bound search confirm that the best-first approach is largely superior when memory is available, but branch and bound is more robust. We also demonstrate that both styles of search benefit greatly from the heuristic evaluation function with increased accuracy. Unlike the leading previously developed m-best schemes that utilize LP-relaxation techniques, e.g., algorithms by Fromer and Globerson (2009) and Batra (2012), our algorithms always guarantee solution optimality. We will show that, when the number of required solutions is small, our m-best search schemes are quite competitive with these related algorithms in terms of runtime, while for a larger number of required solutions our methods are by far superior. The second part of this thesis focuses on finding approximate solutions to optimization problems. Unfortunately solving exactly optimization problems over complex models, that represent intricate dependencies occurring in real life domains, can often be infeasible within practical time and space limits. Many approximation schemes exist, but most of them do not come with any solution sub-optimality guarantees. We apply the ideas of weighted heuristic search, popular in path-finding, to graphical models, yielding new search algorithms that not only provide sub-optimality bounds, but also utilize extra available time and space to improve the accuracy of the solution in an anytime manner and, if resources are available, eventually terminate with an optimal solution. We report on a significant empirical evaluation, demonstrating the usefulness of weighted best-first search as approximation anytime schemes (that have sub-optimality bounds) and compare against one of the best depth-first branch and bound solvers to date. We also investigate the impact of different heuristic functions on the behavior of the algorithms. Additionally, we explore several algorithms taking advantage of two common approaches for bounding MPE queries in graphical models: mini-bucket elimination and message-passing updates for linear programming relaxations. Each method offers a useful perspective for the other; our hybrid approaches attempt to balance the advantage of each. We demonstrate the power of our hybrid algorithms through extensive empirical evaluation. Most notably, a branch and bound search guided by the heuristic functions calculated by our new scheme won the first place in the 2011 Pascal2 inference challenge

Heuristic Search for m Best Solutions with Applications to Graphical Models

Abstract. The paper focuses on finding the m best solutions to a combinatorial optimization problems using Best-First or Branch-and-Bound search. We are interested in graphical model optimization tasks (e.g., Weighted CSP), which can be formulated as finding the m-best solutionpaths in a weighted search graph. Specifically, we present m-A*, extending the well-known A * to the m-best problem, and prove that all A*’s properties are maintained, including soundness and completeness of m-A*, dominance withrespect toimprovedheuristics andmost significantly optimal efficiency compared with any other search algorithm that use the same heuristic function. We also present and analyse m-B&B, an extension of a Depth First Branch and Bound algorithm to the task of finding the m best solutions. Finally, for graphical models, a hybrid of A * and a variable-elimination scheme yields an algorithm which has the best complexity bound compared with earlier known m-best algorithms.