Role of the Discriminator Sequence in the Supercoiling Sensitivity of Bacterial Promoters

In this study, we highlight the role of the discriminator as a global sensor of supercoiling variations and propose the first quantitative regulatory model of this principle, based on the specific step of promoter opening during transcription initiation. It defines the predictive rule by which SC quantitatively modulates the expression rate of bacterial promoters, depending on the G/C content of their discriminator and independently from promoter-specific regulatory proteins.

Individual prediction of the reliability of high power transistors for electronic devices of medical purposes

When assembling electronic complexes for medical purposes, it is important to install highly reliable semiconductor devices in electronic equipment. Experimental studies and the example of high-power bipolar transistors in this work show how you can select copies of an increased level of reliability for their subsequent installation in critical electronic devices. To select highly reliable samples, individual forecasting was used according to informative parameters measured for a particular sample at the initial moment in time. Experimental studies (training experiment) included measuring at the initial moment of time for each sample of transistors of electrical parameters, which may contain information on reliability, and then conducting accelerated tests of transistors for reliability for a time corresponding to normal operating conditions specified in the technical documentation. The training experiment is performed once and used to obtain a predictive rule, which is applied to other similar samples that did not participate in the training experiment. To obtain a predictive rule, the method of majority logic was used. Prediction is performed in the form of assigning a specific sample to the class of highly reliable samples for a given future operating time. To perform prediction, the values of the informative parameters are measured at the initial moment of time for a particular sample of interest, they are converted into binary numbers (zero or one) using the threshold values found from the results of the training experiment, and the decision on the correspondence of the sample to the class of highly reliable transistors is made by a set of binary numbers. To classify a sample as a highly reliable one, it is sufficient that the number of ones exceeds the number of zeros in the resulting set of binary numbers.

Proposing a new strategy of combining machine learning with controlling cross-transmission of multi-drug resistant organisms of ICU

Background Multi-drug resistant organisms (MDROs) has become a global threat to public health. MDROs are normally transmitted from patients to patients via the hands of healthcare workers (HCWs). The key management of MDROs is control dissemination as sooner as possible. Method: We established a predictive rule based simply on experiences, and according to the result of this predictive rule we take a series of precautions of a general intensive care unit (ICU) from January 1, 2018, to December 31, 2019, only in one ward experimentally. In this study, we aim to assess the efficiency of the routine care practice which include pre-discrimination by the predictive rule and sequent precautions by doing difference comparisons. Results After comparing two wards in the hospital expenses and length of ICU stay, there are no statistical differences. Precautions contribute to the association between room number and status of MDROs infection/ colonization(p = 0.033), and infection/colonization rate of MDROs is different statistically between two wards(p = 0.006). Conclusion the routine care practice had controlled the cross-transmission of MDROs in some extent. Future, studies can engage in updating the predictive model based on big data and referred to experts’ experiences and adopt more efficient precautions for strengthening the transmission efficiency.

Can Placental Thickness predict Fetal Weight?

Background The placenta is the principal influence on fetal birth weight, and it is thought that abnormalities of placental growth may precede abnormalities in fetal growth. Because the placenta may be the first organ to manifest changes of disease in pregnancy, placental features may have a role in screening for pregnancy complications. Aim of the Work The aim this study is to assess the accuracy of placental thickness (estimated by ultrasonography) in predicting fetal weight. Patients and Methods This prospective observational study was conducted at Ain Shams University Maternity Hospital November, 15th, 2016 to November, 1st, 2017. 100 Normal antenatal pregnant women at 24-28 weeks of pregnancy who attended antenatal clinic at the department of obstetrics and gynecology, Ain Shams University Maternity Hospital were recruited, after fulfilling the inclusion and exclusion criteria. Results The predictive rule could estimate actual birth weight with an accuracy of ± 0.450 kg (SEest = 0.450 kg). In visits 1 and 2; ± 0.448 kg (SEest = 0.448 kg) in visit 3. Conclusion The actual birth weight was regressed on the placental thickness using simple linear regression to obtain a predictive rule. There is very weak correlation between the actual neonatal weight and placental thickness. The current study deduced a new formula for correction of EFW using placental thickness which has a promising role to offer in the prediction of birth weight.