8 research outputs found
ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΠΏΠΎ ΠΎΠ±ΡΠ°Π·ΡΠ°ΠΌ ΠΊΡΠΎΠ²ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ CpG-Π΄ΠΈΠ½ΡΠΊΠ»Π΅ΠΎΡΠΈΠ΄ΠΎΠ²
Based on the bioinformatic and statistical analysis of the GEO-projects to determine the genome-wide profile of human DNA methylation, a list of 27 CpG dinucleotides with a high predictive potential was formed to create models for prediction of the human age from blood samples. The methylation level was determined for 245 samples of individuals from the Republic of Belarus. The correlation coefficients R were calculated, and the mathematical models for determining the age of an individual were constructed. The average accuracy value of the age prediction from blood samples using 12 CpG-dinucleotides was 3.4 years (for men β 3.3, for women β 3.5). The results obtained will be used as a basis for development of calculators for predicting the age of an individual based on the biological traces for forensic experts.ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ Π±ΠΈΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° GEO-ΠΏΡΠΎΠ΅ΠΊΡΠΎΠ² ΠΏΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠΈΠ»Ρ ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΠΠ ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ ΠΏΠ΅ΡΠ΅ΡΠ΅Π½Ρ ΠΈΠ· 27 CpG-Π΄ΠΈΠ½ΡΠΊΠ»Π΅ΠΎΡΠΈΠ΄ΠΎΠ² Ρ Π²ΡΡΠΎΠΊΠΈΠΌ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΠΎΠΌ Π΄Π»Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΠΏΠΎ ΠΎΠ±ΡΠ°Π·ΡΠ°ΠΌ ΠΊΡΠΎΠ²ΠΈ. ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ ΡΡΠΎΠ²Π΅Π½Ρ ΠΌΠ΅ΡΠΈΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π»Ρ 245 ΠΎΠ±ΡΠ°Π·ΡΠΎΠ² ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΠΎΠ² ΠΈΠ· Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠΈ ΠΠ΅Π»Π°ΡΡΡΡ, ΡΠ°ΡΡΡΠΈΡΠ°Π½Ρ ΠΊΠΎΡΡΡΠΈΡΠΈΠ΅Π½ΡΡ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ R ΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ°. Π‘ΡΠ΅Π΄Π½Π΅Π΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΠΏΠΎ ΠΎΠ±ΡΠ°Π·ΡΠ°ΠΌ ΠΊΡΠΎΠ²ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ 12 CpG-Π΄ΠΈΠ½ΡΠΊΠ»Π΅ΠΎΡΠΈΠ΄ΠΎΠ² ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΎ 3,4 Π³ΠΎΠ΄Π° (Π΄Π»Ρ ΠΌΡΠΆΡΠΈΠ½ β 3,3, Π΄Π»Ρ ΠΆΠ΅Π½ΡΠΈΠ½ β 3,5). ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π±ΡΠ΄ΡΡ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Ρ Π² ΠΎΡΠ½ΠΎΠ²Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΊΠ°Π»ΡΠΊΡΠ»ΡΡΠΎΡΠΎΠ² ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ° ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄Π° ΠΏΠΎ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΠ»Π΅Π΄Π°ΠΌ Π΄Π»Ρ ΡΠΊΡΠΏΠ΅ΡΡΠΎΠ²-ΠΊΡΠΈΠΌΠΈΠ½Π°Π»ΠΈΡΡΠΎΠ²
A line-balancing strategy for designing flexible assembly systems
We present a rough-cut analysis tool that quickly determines a few potential cost-effective designs at the initial design stage of flexible assembly systems (FASs) prior to a detailed analysis such as simulation. It uses quantitative methods for selecting and configuring the components of an FAS suitable for medium to high volumes of several similar products. The system is organized as a series of assembly stations linked with an automated material-handling system moving parts in a unidirectional flow. Each station consists of a single machine or of identical parallel machines. The methods exploit the ability of flexible hardware to switch almost instantaneously from product to product. Our approach is particularly suitable where the product mix is expected to be stable, since we combine the hardware-configuration phase with the task-allocation phase.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45513/1/10696_2004_Article_BF00167513.pd