A REVIEW ON PATHOGENESIS OF DISEASE WITH SPECIAL REFERENCE TO ANUBANDHA AND ANANUBANDHA VAADA

Introduction: In Ayurveda a detailed explanation of causative factors, pathogenesis, signs and symptoms and prognosis of different Nija Rogas are available. Even then the subjectivity of a disease in its origin, clinical presentation and curative facets with respect to individuals are found to be different. This can be cleared by the knowledge of Anubandha – Anubandha Vaada. Materials and Methods: The present work is a literature review on the theory of Anubandha -Ananubandha and its importance in Nija Roga manifes-tation. Results and Discussion: The strength of disease may be assessed through the Anubandha and Ananubandha of Nidana, Dosha and Dooshya. A well treatment plan with either Brimhana or Langhana should be assigned through this principle. Similarly, an appropriate lifestyle can be followed which may essentially help to cure a disease or prevent a disease. Conclusion: By proper knowledge Nidana Panchaka and Shat-Kriyakala, a Vaidhya is able to break the factor sequence in a disease development. Understand-ing in depth the different facets of Anubandha - Ananubandha Vaada helps in preventing, recognizing and curing disease.

Research on Influencing Factor Selection of Pilot’s Intention

The physiological, psychological, and physical characteristics of the pilot will have an impact on flight safety, mainly in the pilot’s intention. In another word, this means the pilot’s psychological experience of flight status under the influence of various factors and the preference for decision-making or behavioral value that is displayed. The pilot’s intention is to reflect the cognitive state that the pilot showed during the maneuvering of the aircraft. The exploration of intention is very important for the study of automatic pilot and flight control active safety system. Also, it is an important concept often involved in the study of human factors in flight, especially the microbehavior of pilots. Pilot’s intention is taken as the study object in this paper; physiological-psychological-physical parameters are obtained through analyzing their influencing factors from the simulating flight experiments designed. The random forest analysis method is used to rank the main influencing factors affecting the pilot’s intention, and the factor sequence is formed. The results provide a good foundation for further research on the pilot’s intention identification.

Computer simulation of weld toe stress concentration factor sequence for fatigue analysis

Purpose The purpose of this paper is to complete fatigue analysis of welded joints considering both the crack initiation sites and crack coalescence, and to generate virtual welded specimens for computer simulation of fatigue life on a specimen-by-specimen basis; knowledge regarding the weld toe stress concentration factor (SCF) sequence is essential. In this study, attempts were made to analyze the sequence and to find a simple method to generate the sequence using computers. Design/methodology/approach Laser scanning technique was used to acquire the real three-dimensional weld toe geometry of welded specimens. The scanned geometry was digitally sectioned, and three-dimensional finite element (FE) models of the scanned specimens were constructed and the weld toe SCF sequence was calculated. The numbers in the sequence were analyzed using a simple autoregression model and the statistical properties of the sequence were acquired. Findings The autoregression analysis showed the value of a weld toe SCF is linearly related to its neighboring factor with a high correlation. When a factor value at a toe location is known, the neighboring factor can be simulated by a simple linear equation with a random residual. The weld toe factor sequence can thus be formed by repeatedly using the linear equation with a residual. The generated sequence exhibits close statistical properties to those of the real sequence obtained from FE results. Practical implications When the weld toe SCF sequence is known, it is possible to foresee potential crack locations and the subsequent crack coalescence. The results of the current study will be the foundation for the future work on fatigue analysis of welded joints considering the effects of crack initiation site and crack coalescence. Originality/value The weld toe SCF sequence was rarely discussed previously because of a lack of the available data. The current study is the first work to investigate the statistical properties of the sequence and found that a simple autoregression equation can be used to perform the analysis. This study is also the first work that successfully generates a weld toe SCF sequence, which can be used to simulate virtual welded specimens.

Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

MotivationIdentifying transcription factor binding sites is the first step in pinpointing non-coding mutations that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is the most common method for identifying binding sites, but performing it on patient samples is hampered by the amount of available biological material and the cost of the experiment. Existing methods for computational prediction of regulatory elements primarily predict binding in genomic regions with sequence similarity to known transcription factor sequence preferences. This has limited efficacy since most binding sites do not resemble known transcription factor sequence motifs, and many transcription factors are not even sequence-specific.ResultsWe developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new cell types using an artificial neural network that integrates ChIP-seq results from other cell types and chromatin accessibility data in the new cell type. Virtual ChIP-seq also uses learned associations between gene expression and transcription factor binding at specific genomic regions. This approach outperforms methods that predict TF binding solely based on sequence preference, pre-dicting binding for 36 transcription factors (Matthews correlation coefficient > 0.3).AvailabilityThe datasets we used for training and validation are available at https://virchip.hoffmanlab.org. We have deposited in Zenodo the current version of our software (http://doi.org/10.5281/zenodo.1066928), datasets (http://doi.org/10.5281/zenodo.823297), predictions for 36 transcription factors on Roadmap Epigenomics cell types (http://doi.org/10.5281/zenodo.1455759), and predictions in Cistrome as well as ENCODE-DREAM in vivo TF Binding Site Prediction Challenge (http://doi.org/10.5281/zenodo.1209308).

AVERAGE WEIGHTED RECEIVING TIME OF WEIGHTED TETRAHEDRON KOCH NETWORKS

We introduce weighted tetrahedron Koch networks with infinite weight factors, which are generalization of finite ones. The term of weighted time is firstly defined in this literature. The mean weighted first-passing time (MWFPT) and the average weighted receiving time (AWRT) are defined by weighted time accordingly. We study the AWRT with weight-dependent walk. Results show that the AWRT for a nontrivial weight factor sequence grows sublinearly with the network order. To investigate the reason of sublinearity, the average receiving time (ART) for four cases are discussed.