Public bus transport demand elasticities in India

A number of static and dynamic specifications of a log linear demand function for public transport are estimated using aggregate panel data for 22 Indian states over the period 1990 to 2001. Demand has been defined as total passenger kilometers to capture actual market transactions, while the regressors include public transit fare, per capita income, service quality, and other demographic and social variables. In all cases, transit demand is significant and inelastic to the fare. Service quality is the most significant policy variable. Finally, social and demographic variables highlight the complex nature of public bus transit demand in India.Demand Elasticities, Dynamic Panel Data, Bus Transport, India

Gram-negative bacterial LPS induced poor uterine receptivity and implantation failure in mouse: alterations in IL-1beta expression in the preimplantation embryo and uterine horns.

Genito-urinary tract or systemic infections of the gram-negative bacteria in pregnant women, causes abortions, preterm labor, and several other perinatal complications. LPS is the most potent antigenic component of the gram-negative bacterial cell wall and is known to modulate the expression of various proinflammatory cytokines. Here we investigate the role of the soluble form of IL-1 i.e., IL-1beta in the 'minimum dose' of LPS induced pregnancy loss in mice. Uterine cross-sections on each day of the preimplantation period of pregnancy were examined histopathologically for finding out LPS induced changes in the uterine preparation for embryo implantation. The expression of IL-1beta in the various stages of the preimplantation period of pregnancy was studied by RT-PCR in the embryos and the uterine horns of the LPS treated and normal pregnant mice. We found that LPS significantly alters the proliferation of the glandular epithelium, luminal epithelium and stroma during the preimplantation period. We also found large infiltration of macrophages into the uterine horns of the LPS treated animals. The level and pattern of IL-1beta expression in the preimplantation embryos and uterine horns were also altered in LPS treated animals. These observations indicate that LPS can alter the uterine preparation for blastocyst implantation, which could be due to the change in the IL-1beta expression in the uterine horns. However, a change in the expression pattern of IL-1beta in the preimplantation embryos underlines the significance of this molecule in LPS induced pregnancy loss or implantation failure in mouse

Effect of Bacterial Endotoxins on Superovulated Mouse Embryos In Vivo: Is CSF-1 Involved in Endotoxin-Induced Pregnancy Loss?

Mammalian embryonic development is regulated by several cytokines and growth factors from embryonic or maternal origins. Since CSF-1 plays important role in embryonic development and implantation, we investigated its role in gram-negative bacterial LPS-induced implantation failure. The effect of LPS on normal (nonsuperovulated) and superovulated in vivo-produced embryos was assessed by signs of morphological degeneration. A significantly similar number of morphologically degenerated embryos recovered from both nonsuperovulated and superovulated LPS treated animals on day 2.5 of pregnancy onwards were morphologically and developmentally abnormal as compared to their respective controls (P < .001. Normal CSF-1 expression level and pattern were also altered through the preimplantation period in the mouse embryos and uterine horns after LPS treatment. This deviation from the normal pattern and level of CSF-1 expression in the preimplantation embryos and uterine tissues suggest a role for CSF-1 in LPS-induced implantation failure

Person recognition based on deep gait: a survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future

Distributed Bioinformatics Computing System for DNA Sequence Analysis

This paper provides an effective design of computing technique of a distributed bioinformatics computing system for analysis of DNA sequences using OPTSDNA algorithm. This system could be used for disease detection, criminal forensic analysis, gene prediction, genetic system and protein analysis. Different types of distributed algorithms for the search and identification for DNA segments and repeat pattern in a given DNA sequence are developed. The search algorithm was developed to compute the number of DNA sequence which contains the same consecutive types of DNA segments. A distributed subsequence identifications algorithm was designed and implemented to detect the segment containing DNA sequences. Sequential and distributed implementation of these algorithms was executed with different length of search segments patterns and genetic sequences. OPTSDNA algorithm is used for storing various sizes of DNA sequence into database. DNA sequences of different lengths were tested by using this algorithm. These input DNA sequences varied in size from very small to very large. The performance of search technique distributed system is compared with sequential approach

Segmenting the License Plate Region Using a Color Model

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Y-27632 enhances differentiation of blastocyst like cystic human embryoid bodies to endocrinologically active trophoblast cells on a biomimetic platform

Trophoblast differentiation and formation of the placenta are important events linked to post-implantation embryonic development. Models mimicking the biology of trophoblast differentiation in a post-implantation maternal microenvironment are needed for understanding disorders like placental-ischemia or for applications in drug-screening, and would help in overcoming the ethical impasse on using human embryos for such research. Here we attempt to create such a model by using embryoid bodies (EBs) and a biomimetic platform composed of a bilayer of fibronectin and gelatin on top of low-melting agarose. Using this model we test the hypothesis that cystic-EBs (day 30) that resemble blastocysts morphologically, are better sources as compared to noncytic EBs (day 10), for functional trophoblast differentiation; and that the Rho kinases inhibitor Y27632 can enhance this differentiation. Non/cytic EBs with/out Y27632 were grown on this platform for 28 days, and screened from secretion and expression of trophoblast and other lineage markers using ECLIA, RT-PCR, and Immunofluorescence. All EBs attached on this surface and rapidly proliferated into hCG and progesterone (P2) secreting functional trophoblast cells. However, the cells derived from cytic-EBs and cytic-EBs+ Y27632 showed the maximum secretion of these hormones and expressed IGF2, supporting our hypothesis. Also Y27632 reduced extraembryonic endoderm and trophoblast lineage differentiation from early noncystic-EBs, whereas, it specifically enhanced the induction of trophoblast and multinucleated syncitiotrophoblast differentiation from late cystic-EBs. In vivo trophoblast differentiation can be replicated in fibronectin based biomaterials, using cytic-EBs and by maneuvering the Rho-ROCK pathways. Response of EBs to a compound may vary temporally, and determination of their right stage is crucial for applications in directed-differentiation or drug-screening

Attention-based human age estimation from face images to enhance public security

Age estimation from facial images has gained significant attention due to its practical applications such as public security. However, one of the major challenges faced in this field is the limited availability of comprehensive training data. Moreover, due to the gradual nature of aging, similar-aged faces tend to share similarities despite their race, gender, or location. Recent studies on age estimation utilize convolutional neural networks (CNN), treating every facial region equally and disregarding potentially informative patches that contain age-specific details. Therefore, an attention module can be used to focus extra attention on important patches in the image. In this study, tests are conducted on different attention modules, namely CBAM, SENet, and Self-attention, implemented with a convolutional neural network. The focus is on developing a lightweight model that requires a low number of parameters. A merged dataset and other cutting-edge datasets are used to test the proposed model’s performance. In addition, transfer learning is used alongside the scratch CNN model to achieve optimal performance more efficiently. Experimental results on different aging face databases show the remarkable advantages of the proposed attention-based CNN model over the conventional CNN model by attaining the lowest mean absolute error and the lowest number of parameters with a better cumulative score