The Challenges facing midwifery educators in sustaining a future education workforce

Background national and international trends have identified concerns over the ability of health and social care workforces in meeting the needs of service users. Attention has increasingly been drawn to problems of recruiting and retaining professionals within higher education; however data in relation to the midwifery profession is scant. Aim to examine the perceptions and experiences of midwifery educators, in south-west England, about the challenges facing them sustaining the education workforce of the future. Design a mixed methodology approach was adopted involving heads of midwifery education and midwife educators. Methodology midwifery participants were recruited from three higher education institutions in south west England. Data collection comprised of self-administered questionnaires plus individual qualitative interviews with heads of midwifery education (n=3), and tape recorded focus groups with midwife academics (n=19). Numerical data were analysed using descriptive statistics. Textual data were analysed for themes that represented the experiences and perspectives of participants. Ethics approval was granted by one University Ethics committee. Findings demographic data suggests that within south-west England, there is a clear ageing population and few in possession of a doctorate within midwifery. The six identified sub-themes represented in the data describe challenges and tensions that midwifery academics experienced in their efforts to attract new recruits and retain those in post in a highly changing educational environment which demands more from a contracting workforce. Conclusion and implications for practice there remain some serious challenges facing midwifery educators in sustaining the future education workforce, which if unresolved may jeopardise standards of education and quality of care women receive. Active succession planning and more radical approaches that embrace flexible careers will enable educational workforce to be sustained and by a clinically credible and scholarly orientated midwifery workforce

Nuclear cGMP-Dependent Kinase Regulates Gene Expression via Activity-Dependent Recruitment of a Conserved Histone Deacetylase Complex

Elevation of the second messenger cGMP by nitric oxide (NO) activates the cGMP-dependent protein kinase PKG, which is key in regulating cardiovascular, intestinal, and neuronal functions in mammals. The NO-cGMP-PKG signaling pathway is also a major therapeutic target for cardiovascular and male reproductive diseases. Despite widespread effects of PKG activation, few molecular targets of PKG are known. We study how EGL-4, the Caenorhabditis elegans PKG ortholog, modulates foraging behavior and egg-laying and seeks the downstream effectors of EGL-4 activity. Using a combination of unbiased forward genetic screen and proteomic analysis, we have identified a conserved SAEG-1/SAEG-2/HDA-2 histone deacetylase complex that is specifically recruited by activated nuclear EGL-4. Gene expression profiling by microarrays revealed >40 genes that are sensitive to EGL-4 activity in a SAEG-1–dependent manner. We present evidence that EGL-4 controls egg laying via one of these genes, Y45F10C.2, which encodes a novel protein that is expressed exclusively in the uterine epithelium. Our results indicate that, in addition to cytoplasmic functions, active EGL-4/PKG acts in the nucleus via a conserved Class I histone deacetylase complex to regulate gene expression pertinent to behavioral and physiological responses to cGMP. We also identify transcriptional targets of EGL-4 that carry out discrete components of the physiological response

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

An ecological future for weed science to sustain crop production and the environment. A review

Sustainable strategies for managing weeds are critical to meeting agriculture's potential to feed the world's population while conserving the ecosystems and biodiversity on which we depend. The dominant paradigm of weed management in developed countries is currently founded on the two principal tools of herbicides and tillage to remove weeds. However, evidence of negative environmental impacts from both tools is growing, and herbicide resistance is increasingly prevalent. These challenges emerge from a lack of attention to how weeds interact with and are regulated by the agroecosystem as a whole. Novel technological tools proposed for weed control, such as new herbicides, gene editing, and seed destructors, do not address these systemic challenges and thus are unlikely to provide truly sustainable solutions. Combining multiple tools and techniques in an Integrated Weed Management strategy is a step forward, but many integrated strategies still remain overly reliant on too few tools. In contrast, advances in weed ecology are revealing a wealth of options to manage weedsat the agroecosystem levelthat, rather than aiming to eradicate weeds, act to regulate populations to limit their negative impacts while conserving diversity. Here, we review the current state of knowledge in weed ecology and identify how this can be translated into practical weed management. The major points are the following: (1) the diversity and type of crops, management actions and limiting resources can be manipulated to limit weed competitiveness while promoting weed diversity; (2) in contrast to technological tools, ecological approaches to weed management tend to be synergistic with other agroecosystem functions; and (3) there are many existing practices compatible with this approach that could be integrated into current systems, alongside new options to explore. Overall, this review demonstrates that integrating systems-level ecological thinking into agronomic decision-making offers the best route to achieving sustainable weed management

Two perceptually motivated strategies for shape classification

In this paper, we propose two new, perceptually motivated strategies to better measure the similarity of 2D shape instances that are in the form of closed contours. The first strategy handles shapes that can be decomposed into a base structure and a set of inward or outward pointing “strand” structures, where a strand structure represents a very thin, elongated shape part attached to the base structure. The similarity of two such shape contours can be better described by measuring the similarity of their base structures and strand structures in different ways. The second strategy handles shapes that exhibit good bilateral symmetry. In many cases, such shapes are invariant to a certain level of scaling transformation along their symmetry axis. In our experiments, we show that these two strategies can be integrated into available shape matching methods to improve the performance of shape classification on several widelyused shape data sets. Shape matching through nonrigid shape deformation is a typical approach to measure shape similarity [6, 14, 10, 21, 13, 16]. In general, this approach measures the amount of energy required to deform one shape contour into another based on some physical or mathematical model. The model is then optimized using methods such as dynamic programming to obtain a set of corresponded points on the two shape contours that minimize the deformation cost of this model. However, this approach is often very sensitive to strong, local shape variations that human vision may handle very well. For example, the two shape contours shown in Figs. 1(a) and (b) are similar in general, but their outward parts, represented by the dashed curves, are quite different from each other. A large deformation cost may be required to match these two shape contours. 1

Correction: "Congenital hyperinsulinism as the presenting feature of Kabuki syndrome: clinical and molecular characterization of 10 affected individuals".

The author Diva D. De Leon was incorrectly listed as instead of Diva D. De Leó-Critchlow in the original version of this paper