Newly identified YSO candidates towards the LDN 1188

We present an analysis of Young Stellar Object (YSO) candidates towards the LDN 1188 molecular cloud. The YSO candidates were selected from the WISE all-sky catalogue, based on a statistical method. We found 601 candidates in the region, and classified them as Class I, Flat and Class II YSOs. Groups were identified and described with the Minimal Spanning Tree (MST) method. Previously identified molecular cores show evidence of ongoing star formation at different stages throughout the cloud complex.Comment: 4 pages, 4 figures, 2 table

MolMarker: A Simple Tool for DNA Fingerprinting Studies and Polymorphic Information Content Calculation

Molecular markers and mapping are used to analyze an organism’s genes. They allow the selection of target genetic areas based on marker genotype (and not trait phenotype), facilitate the study of genetic variability and diversity, create linkage maps, and follow individuals or lines carrying certain genes. They may be used to select parental genotypes, remove linkage drag in back-crossing, and choose difficult-to-measure characteristics. Due to a lack of genetic variety in crops, the gene pools of wild crop relatives for future agricultural production have been examined. The invention of RFLP (Restriction Fragment Length Polymorphism) for linkage mapping allowed for the creation of other traditional approaches such as RAPD (Random Amplified Polymorphic DNA) and AFLP (Amplified Fragment Length Polymorphism). Accordingly, the need to describe the polymorphic information content (PIC) of the ideal marker has been raised. Marker selection reliability depends on the marker’s relationship to the genomic area of interest. Although informativeness must be estimated for genetic study design, there are no readily available tools. Earlier, PICcalc was developed to calculate heterozygosity (H) and PIC to simplify molecular investigations. These two values were corrected for dominant and co-dominant markers (binary and allelic data) to determine polymorphism quality. Due to the popularity of PICcalc web, we developed a downloadable version called MolMarker with extra functionality to reduce server maintenance

Accelerometer-Based Event Detector for Low-Power Applications

In this paper, an adaptive, autocovariance-based event detection algorithm is proposed, which can be used with micro-electro-mechanical systems (MEMS) accelerometer sensors to build inexpensive and power efficient event detectors. The algorithm works well with low signal-to-noise ratio input signals, and its computational complexity is very low, allowing its utilization on inexpensive low-end embedded sensor devices. The proposed algorithm decreases its energy consumption by lowering its duty cycle, as much as the event to be detected allows it. The performance of the algorithm is tested and compared to the conventional filter-based approach. The comparison was performed in an application where illegal entering of vehicles into restricted areas was detected

Accelerometer-Based Event Detector for Low-Power Applications

In this paper, an adaptive, autocovariance-based event detection algorithm is proposed, which can be used with micro-electro-mechanical systems (MEMS) accelerometer sensors to build inexpensive and power efficient event detectors. The algorithm works well with low signal-to-noise ratio input signals, and its computational complexity is very low, allowing its utilization on inexpensive low-end embedded sensor devices. The proposed algorithm decreases its energy consumption by lowering its duty cycle, as much as the event to be detected allows it. The performance of the algorithm is tested and compared to the conventional filter-based approach. The comparison was performed in an application where illegal entering of vehicles into restricted areas was detected

MolMarker: a simple tool for DNA fingerprinting studies and polymorphic information content calculation

Molecular markers and mapping are used to analyze an organism’s genes. They allow the selection of target genetic areas based on marker genotype (and not trait phenotype), facilitate the study of genetic variability and diversity, create linkage maps, and follow individuals or lines carrying certain genes. They may be used to select parental genotypes, remove linkage drag in back-crossing, and choose difficult-to-measure characteristics. Due to a lack of genetic variety in crops, the gene pools of wild crop relatives for future agricultural production have been examined. The invention of RFLP (Restriction Fragment Length Polymorphism) for linkage mapping allowed for the creation of other traditional approaches such as RAPD (Random Amplified Polymorphic DNA) and AFLP (Amplified Fragment Length Polymorphism). Accordingly, the need to describe the polymorphic information content (PIC) of the ideal marker has been raised. Marker selection reliability depends on the marker’s relationship to the genomic area of interest. Although informativeness must be estimated for genetic study design, there are no readily available tools. Earlier, PICcalc was developed to calculate heterozygosity (H) and PIC to simplify molecular investigations. These two values were corrected for dominant and co-dominant markers (binary and allelic data) to determine polymorphism quality. Due to the popularity of PICcalc web, we developed a downloadable version called MolMarker with extra functionality to reduce server maintenance