The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

The bubbled slab, a type of reinforced concrete (RC) slab with plastic voids, is an innovative design that employs a biaxial distribution of voiding formers within the slab to reduce the slab’s self-weight while preserving a load-carrying capacity that is approximately comparable to that of solid slabs. This paper presents a new approach for figuring out the effective critical shear perimeter of voided slabs using the reduced-volume concept of concrete. This approach aims to reduce the coefficient of variation of the current design standards, namely the ACI 318-19 and Eurocode 2, for assessing the slabs’ resistance to punching shear. Our experimental program investigated the impact of voiding former patterns and the location of

In this work, we construct projectively distinct (k,3)-arcs in the projective plane PG(2,9) by applying a geometrical method. The cubic curves have been been constructed by using the general equation of the cubic. We found that there are complete (13,3)-arcs, complete (15,3)-arcs and we found that the only (16,3)-arcs lead to maximum completeness

A total of 20 raw milk samples were used as the fouling agent for evaluating the bacteriological effectiveness of cleaning and sanitizing of domestic milking equipment by using ozonated water at 0.5 ppm comparing to the warm water at 55! for 5 minutes respectively. The mean values of total aerobic bacteria, Coliform and E.coli that present on the plastic and stainless-steel containers after using the raw milk as fouling agent were 3.4×10-6 , 6.7x10-5 and 5.8×10-3 cfu/cm2 respectively , after cleaning the stainless steel containers by the ozonated water the mean values of total aerobic bacterial counts, Coliforms and E.coli bacteria were reduced to 1.2×10-6, 4.7×10-5 and 3.3×10-3 CFU/cm2 respectively. while after cleaning by the warm wa

Background: The ideal force-delivery system must: provide optimal tooth moving forces that elicit the desired effects, be comfortable and hygienic for the patient, require minimal operator manipulation and patient cooperation and provide rapid tooth movement with minimal mobility during orthodontic therapy, the elastomeric chains have the greatest potential to fulfill these requirements. Materials and Methods: This in vitro study was designed to determine the effect of three different mechanisms for canine retraction : (6-3 , 6-5-3 and chain loop ) on the load relaxation behavior of three types of elastomeric chains : (maximum clear , maximum silver and extreme silver) from the same company (Ortho Technology company) with two different bran

This research discussed, the process of comparison between the regression model of partial least squares and tree regression, where these models included two types of statistical methods represented by the first type "parameter statistics" of the partial least squares, which is adopted when the number of variables is greater than the number of observations and also when the number of observations larger than the number of variables, the second type is the "nonparametric statistic" represented by tree regression, which is the division of data in a hierarchical way. The regression models for the two models were estimated, and then the comparison between them, where the comparison between these methods was according to a Mean Square

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo