Stance of Accounting Instructors to Forensic Accountancy Profession: Example of Turkey

Considerable firm scandals that have been experienced in recent years, have reduced the confidence to financial statements and have caused amendments to be made on existing regulations. This situation brought attention to accountants and auditors that expose the financial conditions of the firms. The scandals have been experienced, revive the importance of accounting auditing and the increasing responsibility of the auditors. Inadequacy of accountants and auditors that have been educated according to the existing curriculum, conduce a new profession to arise. Being its original name “Forensic Accounting”, this profession, that exhibits rapid progress in delevoped countries with United States of America in the lead, is known as “Forensic Accountancy-Adli Muhasebecilik” in our country. Forensic Accountancy profession; with its services as litigation support consultancy, expert testimony and fraud auditing (investigative accountancy) fills a large gap in this field. This study is aimed at measuring the awareness of accounting instructors against this profession that has not been applied in our country yet. For this purpose, the survey being developed is going to be forwarded to accounting instructors in an attempt to determine the level of awarenesses. This study deals with the scope of forensic accountancy profession, the specifications of forensic accountants, belonging to forensic accountancy profession and the education of forensic accountants, thereby this study attempts to explain the level of awareness and to comment on survey results

Dual-stressor selection alters eco-evolutionary dynamics in experimental communities

Recognizing when and how rapid evolution drives ecological change is fundamental for our understanding of almost all ecological and evolutionary processes such as community assembly, genetic diversification and the stability of communities and ecosystems. Generally, rapid evolutionary change is driven through selection on genetic variation and is affected by evolutionary constraints, such as tradeoffs and pleiotropic effects, all contributing to the overall rate of evolutionary change. Each of these processes can be influenced by the presence of multiple environmental stressors reducing a population's reproductive output. Potential consequences of multistressor selection for the occurrence and strength of the link from rapid evolution to ecological change are unclear. However, understanding these is necessary for predicting when rapid evolution might drive ecological change. Here we investigate how the presence of two stressors affects this link using experimental evolution with the bacterium Pseudomonas fluorescens and its predator Tetrahymena thermophila. We show that the combination of predation and sublethal antibiotic concentrations delays the evolution of anti-predator defence and antibiotic resistance compared with the presence of only one of the two stressors. Rapid defence evolution drives stabilization of the predator-prey dynamics but this link between evolution and ecology is weaker in the two-stressor environment, where defence evolution is slower, leading to less stable population dynamics. Tracking the molecular evolution of whole populations over time shows further that mutations in different genes are favoured under multistressor selection. Overall, we show that selection by multiple stressors can significantly alter eco-evolutionary dynamics and their predictability.Peer reviewe

smyRNA: A Novel Ab Initio ncRNA Gene Finder

Background: Non-coding RNAs (ncRNAs) have important functional roles in the cell: for example, they regulate gene expression by means of establishing stable joint structures with target mRNAs via complementary sequence motifs. Sequence motifs are also important determinants of the structure of ncRNAs. Although ncRNAs are abundant, discovering novel ncRNAs on genome sequences has proven to be a hard task; in particular past attempts for ab initio ncRNA search mostly failed with the exception of tools that can identify micro RNAs. Methodology/Principal Findings: We present a very general ab initio ncRNA gene finder that exploits differential distributions of sequence motifs between ncRNAs and background genome sequences. Conclusions/Significance: Our method, once trained on a set of ncRNAs from a given species, can be applied to a genome sequences of other organisms to find not only ncRNAs homologous to those in the training set but also others that potentially belong to novel (and perhaps unknown) ncRNA families. Availability

Insights into hominid evolution from the gorilla genome sequence.

Gorillas are humans' closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human-chimpanzee and human-chimpanzee-gorilla speciation events at approximately 6 and 10 million years ago. In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution

Computational studies on structure and functionality of biomolecular compounds

This thesis is on applying standard combinatorial optimization methods, dynamic programming and linear programming, to help solve two important problems in computational molecular biology: (1) predicting the secondary structure of RNA molecules and (2) predicting the functionality of small biological compounds. After 25 years of effort, the RNA secondary structure prediction has proven to be very elusive. Much of the available algorithms are based on total free energy minimization. Yet, despite the numerous attempts to perfect this thermodynamic approach, the end results are far from being practical. We demonstrate that delocalizing the thermodynamic cost of forming an RNA substructure through energy density notion can significantly improve available secondary structure prediction methods. Because the notion of energy density is non-linear, the standard dynamic programming approach had to be updated. This updated algorithm can capture the secondary structure of many non-coding RNAs which have been difficult to approximate with alternative methods. One key application of RNA structure prediction is in understanding how two or more RNAs interact (e.g. an mRNA and a regulatory RNA). In this thesis we formulate the RNA-RNA interaction prediction problem as a combinatorial optimization problem and show how to solve it again via dynamic programming. Because the complexity of the algorithm to solve the most involved formulation of the problem is very high, we also describe heuristic shortcuts, which, in practice, are highly accurate. The second set of problems we tackle are related to small chemical molecules, which have key cellular functions. In particular we focus on structural similarity search among small chemical molecules, a standard approach used for in-silico drug discovery. It is possible to use structural similarity to deduce the bioactivities of new compounds provided that the notion of similarity reflects the bioactivity in question and we have efficient data structures to perform structural similarity search. This thesis shows how to computationally design the ``optimal\u27\u27 weighted Minkowski distance wL_p for maximizing the discrimination between active and inactive compounds with respect to a bioactivity. It also demonstrates how to construct an iterative pruning based data structure for performing ``nearest neighbor\u27\u27 search under the weighted L_p distance computed

Novel Approaches for Small Biomolecule Classification and Structural Similarity Search ABSTRACT

Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds with respect to the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds. In this paper we summarize the recent applications of knearest-neighbor search method for small molecule classification. The k-nn classification of small molecules is based on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance Lp. Here we describe how to computationally design the optimal weighted Minkowski distance wLp for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. k-nn classification requires fast similarity search for predicting bioactivity of a new molecule. We then focus on construction of pruning based k-nn search data structures for any wLp distance that minimizes similarity search time. The accuracy achieved by k-nn classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, k-nn classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, k-nn classifier is capable of quantification of the level of bioactivity rather than returning a binary decision and can bring more insight to the nature of the activity via eliminating unrelated descriptors of the compounds with respect to the activity in question. 1

Fluid Replacement in Treatment of Hypovolemia and Shock: Crystalloids and Colloids

Shock is a pathologic state with high mortality rate and characterized by a reduction of systemic tissue perfusion and decresead oxygen delivery. Absolute or relative hypovolemia is a common pathology of most shock types. Correction of hypovolemia might reverse the disturbance and increase the tissue perfusion. Fluid resuscitation with crystalloid and colloid solutions can carry the risk of increasing morbidity and mortality if not used properly. Although crystalloid and colloid solutions are considered to have equal efficacy and safety profile, recent studies showed that this assumption may not be correct. Early and effective management of hypovolemia is the cornerstone of shock resuscitation. Initial management of patients with septic shock and hypovolemia should be done with 30ml/kg of crystalloids. Proper fluid replacement and resuscitation algoritms might increase the survival rate. [Archives Medical Review Journal 2013; 22(3.000): 347-361

Hemodynamic Support in Sepsis

Sepsis is called systemic inflammatory response syndrome due to infection. When added to organs failure and perfusion abnormality is defined in severe sepsis, Hypotension that do not respond to fluid therapy is as defined septic shock. Fluid resuscitation is a most important parts of the treatment in patients with septic shock. Ongoing hypotension that despite of the adequate fluid therapy, vasopressor support initiation is required. Sepsis and septic shock, hemodynamic support is often understood as the hemodynamic support. The different approaches to the development of methods to track and objective comes up. Patients with severe sepsis and septic shock should be follow in the intensive care unit and rapid fluid replacement and effectual hemodynamic support should be provided

Unleashing the Potential of Morphing Wings: A Novel Cost Effective Morphing Method for UAV Surfaces, Rear Spar Articulated Wing Camber

The implementation of morphing wing applications in aircraft design has sparked significant interest as it enables the dimensional properties of the aircraft to be modified during flight. By allowing manipulation of the 2D and 3D parameters on the aircraft’s wings, tail surfaces, or fuselage, a variety of possibilities have arisen. Two primary schools of thought have emerged in the field of morphing wing applications: the mechanisms school and the smart surfaces approach that uses shape-memory materials and smart actuators. Among the research in this field, the Fishbone Active Camber (FishBAC) approach has emerged as a promising avenue for controlling the deflection of the wing’s trailing edge. This study revisits previous research on morphing wings and the FishBAC concept, evaluates the current state of the field, and presents an original design process flow that includes the design of a unique and innovative UAV called the Stingray within the scope of the study. A novel morphing concept developed for the Stingray UAV, Rear Spar Articulated Wing Camber (RSAWC), employs a fishbone-like morphing wing rib design with rear spar articulation in a cost-effective manner. The design process and flight tests of the RSAWC are presented and directly compared with a conventional wing. Results are evaluated based on performance, weight, cost, and complexity. Semi-empirical data from the flight testing of the concept resulted in approximately a 19% flight endurance increment. The study also presents future directions of research on the RSAWC concept to guide the researchers