Flora Europaea Notulae Systematicae ad Floram Europaeam spectantes No. 20 Corrigenda et Addenda

Following the publication of the last of the series of Flora Europaea Notulae, No. 20 in the Botanical Journal of the Linnean Society , 76: 297–384 (1978), a number of additions or alterations have been drawn to our attention. These are published in continuation.     

Evolutionary Stability of Gene Regulatory Networks That Define the Temporal Identity of Neuroblasts

Molecular Biology - The ensemble of gap genes is one of the best studied and most conserved gene regulatory networks (GRNs). Gap genes, such as hunchback (hb), Krüppel (Kr), pou-domain (pdm;...     

Erratum to: The original online version of the following articles was revised: the issue date is not January 2020, but January 2021

Journal of Evolutionary Biochemistry and Physiology - A Correction to this paper has been published: https://doi.org/10.1134/S0022093021020216     

Generation of soft X-ray emission by Z-pinches powered from helical explosive magnetocumulative generators

Results are presented from experiments on the implosion of wire arrays powered from 100-and 200-mm-diameter helical explosive magnetocumulative generators with explosive opening switches. The experiments were performed at load currents of up to 4 MA, the current rise time being 0.3–0.4 μs. The maximum soft X-ray yield of ～ 100 kJ was achieved at a pinch plasma temperature of 55 eV. A two-dimensional MHD code was developed to simulate the process of liner implosion and the generation of X-ray emission. The results of computer simulations agree satisfactorily with the experimental data.     

The role of Bicoid cooperative binding in the patterning of sharp borders in Drosophila melanogaster

In Drosophila embryonic development, the Bicoid (Bcd) protein establishes positional information of downstream developmental genes like hunchback (hb), which has a strong anterior expression and a sharp on-off boundary in the mid-embryo. The role of Bcd cooperative binding in the positioning of the Hb pattern has been previously demonstrated. However, there are discrepancies in the reported results about the role of this mechanism in the sharp Hb border. Here, we determined the Hill coefficient (n(H)) required for Bcd to generate the sharp border of Hb in wild-type (WT) embryos. We found that an n(H) of approximately 6.3 (s.d. 1.4) and 10.8 (s.d. 4.0) is required to account for Hb sharpness at early and late cycle 14A, respectively. Additional mechanisms are possibly required because the high n(H) is likely unachievable for Bcd binding to the hb promoter. To test this idea, we determined the n(H) required to pattern the Hb profile of 15 embryos expressing an hb(14F) allele that is defective in self-activation and found n(H) to be 3.0 (s.d. 1.0). This result indicates that in WT embryos, the hb self-activation is important for Hb sharpness. Corroborating our results, we also found a progressive increase in the required value of n(H) spanning from 4.0 to 9.2 by determining this coefficient from averaged profiles of eight temporal classes at cycle 14A (T1 to T8). Our results indicate that there is a transition in the mechanisms responsible for the sharp Hb border during cycle 14A: in early stages of this cycle, Bcd cooperative binding is primarily responsible for Hb sharpness; in late cycle 14A, hb self-activation becomes the dominant mechanism.     

Use of inert gas jets to measure the forces required for mechanical gene transfection

Background

Abnormal blood glucose (BG) concentrations have been associated with increased morbidity and mortality in both critically ill adults and infants. Furthermore, hypoglycaemia and glycaemic variability have both been independently linked to mortality in these patients. Continuous Glucose Monitoring (CGM) devices have the potential to improve detection and diagnosis of these glycaemic abnormalities. However, sensor noise is a trade-off of the high measurement rate and must be managed effectively if CGMs are going to be used to monitor, diagnose and potentially help treat glycaemic abnormalities.

Aim

To develop a tool that will aid clinicians in identifying unusual CGM behaviour and highlight CGM data that potentially need to be interpreted with care.

Methods

CGM data and BG measurements from 50 infants at risk of hypoglycaemia were used. Unusual CGM measurements were classified using a stochastic model based on the kernel density method and historical CGM measurements from the cohort. CGM traces were colour coded with very unusual measurements coloured red, highlighting areas to be interpreted with care. A 5-fold validation of the model was Monte Carlo simulated 25 times to ensure an adequate model fit.

Results

The stochastic model was generated using ~67,000 CGM measurements, spread across the glycaemic range ~2-10?mmol/L. A 5-fold validation showed a good model fit: the model 80% confidence interval (CI) captured 83% of clinical CGM data, the model 90% CI captured 91% of clinical CGM data, and the model 99% CI captured 99% of clinical CGM data. Three patient examples show the stochastic classification method in use with 1) A stable, low variability patient which shows no unusual CGM measurements, 2) A patient with a very sudden, short hypoglycaemic event (classified as unusual), and, 3) A patient with very high, potentially un-physiological, glycaemic variability after day 3 of monitoring (classified as very unusual).

Conclusions

This study has produced a stochastic model and classification method capable of highlighting unusual CGM behaviour. This method has the potential to classify important glycaemic events (e.g. hypoglycaemia) as true clinical events or sensor noise, and to help identify possible sensor degradation. Colour coded CGM traces convey the information quickly and efficiently, while remaining computationally light enough to be used retrospectively or in real-time.     

Cortical potential imaging using L-curve and GCV method to choose the regularisation parameter

Background

The electroencephalography (EEG) is an attractive and a simple technique to measure the brain activity. It is attractive due its excellent temporal resolution and simple due to its non-invasiveness and sensor design. However, the spatial resolution of EEG is reduced due to the low conducting skull. In this paper, we compute the potential distribution over the closed surface covering the brain (cortex) from the EEG scalp potential. We compare two methods – L-curve and generalised cross validation (GCV) used to obtain the regularisation parameter and also investigate the feasibility in applying such techniques to N170 component of the visually evoked potential (VEP) data.

Methods

Using the image data set of the visible human man (VHM), a finite difference method (FDM) model of the head was constructed. The EEG dataset (256-channel) used was the N170 component of the VEP. A forward transfer matrix relating the cortical potential to the scalp potential was obtained. Using Tikhonov regularisation, the potential distribution over the cortex was obtained.

Results

The cortical potential distribution for three subjects was solved using both L-curve and GCV method. A total of 18 cortical potential distributions were obtained (3 subjects with three stimuli each – fearful face, neutral face, control objects).

Conclusions

The GCV method is a more robust method compared to L-curve to find the optimal regularisation parameter. Cortical potential imaging is a reliable method to obtain the potential distribution over cortex for VEP data.