Although germ cell formation has been relatively well understood in worms and insects, how germ cell-specific developmental programs are initiated is not clear. In Caenorhabditis elegans, translational activation of maternal nos-2 mRNA is the earliest known molecular event specific to the germline founder cell P4. Cis-elements in nos-2 3′UTR have been shown to mediate translational control, however the trans-acting proteins are not known. Here, we provide evidence that four maternal RNA-binding proteins, namely OMA-1, OMA-2, MEX-3 and SPN-4, bind nos-2 3′UTR to suppress its translation, and POS-1, another maternal RNA-binding protein, relieves this suppression in P4. The POS-1: SPN-4 ratio in P4 increases significantly over its precursor, P3; and POS-1 competes with SPN-4 for binding to nos-2 RNA in vitro. We propose temporal changes in the relative concentrations of POS-1 and SPN-4, through their effect on the translational status of maternal mRNAs such as nos-2, initiate germ cell-specific developmental programs in C. elegans.

No abstract

No abstract

No abstract

αKlotho is primarily known to express as a transmembrane protein. Proteolytic cleavage results in shedding of the extracellular domain which enters systemic circulation. A truncated form of αKlotho resulting from alternative splicing of the αKLOTHO transcript exists and is believed to be secreted, thereby also entering systemic circulation. Existing ELISA methods fail to distinguish between the two circulating isoforms resulting in inconsistencies in assessing circulating αKlotho levels. We have exploited a unique 15aa peptide sequence present in the alternatively spliced secreted isoform to generate an antibody and show that it is able to specifically detect only the secreted Klotho isoform in human plasma. This finding will facilitate in distinguishing the levels of different circulating Klotho isoforms in health and disease and enhance their potential to serve as a biomarker for CKD and other conditions.

The swarm robotics inspired from nature is a combination of swarm intelligence and robotics, which shows a great potential in several aspects. Swarm robotics is a relatively new and rapidly developing field which draws inspiration from swarm intelligence. It is an interesting alternative to classical approaches to robotics because of some properties of problem solving present in social insects, which is flexible, robust, decentralized and self-organized. Fire detection and extinguishment are the hazardous job that invariably put the life of a fire fighter in danger. By putting a robot to perform this task in a fire-prone area, it can aid to avoid annoying incidents or the loss of lives. This paper describes the development of Swarm Intelligence Fire Fighting Robot using (SIFFR) that is equipped with the basic fighting equipment that can round through the hazardous site via a guiding track with the aim of early detection for fire. When the fire source is being identified, the temperature will be promptly extinguished using the fire extinguishing system that is mounted on receiving robots platform. To detect for fire source, the input from temperature sensors were finely-tuned in relation to the surrounding area, external interference and the mobility of the SIFFR prior the deployment of the platform. This paper presents the design, implementation and experimental demonstrations of the SIFFR. The contents are organized as follows. Section 2 introduces the concept Savita Jadhav et al .

The proportion of individuals with depression has rapidly increased along with the growth of the global population. Depression has been the currently most prevalent mental health disorder. An effective depression recognition system is especially crucial for the early detection of potential depression risk. A depression-related dataset is also critical while evaluating the system for depression or potential depression risk detection. Due to the sensitive nature of clinical data, the availability and scale of such datasets are scarce. Depression is classified as a mood disorder. It may be described as feelings of sadness, anger, or loss that interfere with a person’s everyday activities. People experience depression in different ways. In certain cases, depression may lead to fatal cases. To avoid all of these, depression must be detected at the earliest and victim must be treated with appropriate remedies. The objective of the project is to analyze the emotion of a user using real-time video. This is achieved using Convolutional Neural Networks [CNN]. The final decision result comes from the combination of the two models. Finally, we evaluate all proposed deep models on our built dataset. The experimental results demonstrate that (1) our proposed method can identify patients/users with potential depression risk; (2) the recognition performance of combined 2D and 3D features model outperforms using either 2D or 3D features model only; (3) the performance of depression recognition is higher in the positive and negative emotional stimulus. Meanwhile, we compare the performance with other methods on the same dataset.

No abstract