Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks

Objective: To test automated in vivo estimation of active and passive skeletal muscle states using ultrasonic imaging. Background: Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal muscle states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction nor generalisation of independently varying, active and passive states. We use deep learning to investigate the generalizable content of 2D US muscle images. Method: US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle were recorded from 32 healthy participants (7 female, ages: 27.5, 19-65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, driftfree, components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous, independent variation of passive (joint angle) and active (electromyography) inputs. Results: For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography, and joint moment were estimated to accuracy 55±8%, 57±11%, and 46±9% respectively. Significance: With 2D US imaging, deep neural networks can encode in generalizable form, the activitylength-tension state relationship of these muscles. Observation only, low power, 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalised muscle assessment in pain, injury, neurological conditions, neuropathies, myopathies and ageing

Improved functional overview of protein complexes using inferred epistatic relationships

<p>Abstract</p> <p>Background</p> <p>Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.</p> <p>Results</p> <p>We introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.</p> <p>Conclusion</p> <p>Overall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.</p

Preparation and Crystal Structure of a Platinum(II) Complex of [CH2N(CH2COOH)CH2CONH2]2, the Hydrolysis Product of an Anti-Tumour Bis(3,5-Dioxopiperazin-1-YL)Alkane

The synthesis and crystal and molecular structures of the platinum(II) complex Pt(HL)Cl where H2L is the diacid diamide –[CH2N(CH2COOH)CH2CONH2]2, a hydrolytic metabolite of an antitumour active bis(3,5-dioxopiperazin-1-yl)alkane are reported. The complex is square planar and contains HL– as a tridentate 2N (amino), O (carboxylate) donor. The metal to ligand bond distances are Pt-Cl 2.287(1) Å, Pt-O 2.002 (1) Å, Pt-Ntrans Cl 2.014(1) Å and Pt-Ntrans O 2.073 Å. There is extensive hydrogen bonding, each molecule of Pt(HL)Cl being intermolecularly hydrogen bonded to ten others giving a 3-dimensional network. There is also one intramolecular H-bond

Selective nitrogen adsorption via backbonding in a metal-organic framework with exposed vanadium sites.

Industrial processes prominently feature π-acidic gases, and an adsorbent capable of selectively interacting with these molecules could enable important chemical separations1-4. Biological systems use accessible, reducing metal centres to bind and activate weakly π-acidic species, such as N2, through backbonding interactions5-7, and incorporating analogous moieties into a porous material should give rise to a similar adsorption mechanism for these gaseous substrates8. Here, we report a metal-organic framework featuring exposed vanadium(II) centres capable of back-donating electron density to weak π acids to successfully target π acidity for separation applications. This adsorption mechanism, together with a high concentration of available adsorption sites, results in record N2 capacities and selectivities for the removal of N2 from mixtures with CH4, while further enabling olefin/paraffin separations at elevated temperatures. Ultimately, incorporating such π-basic metal centres into porous materials offers a handle for capturing and activating key molecular species within next-generation adsorbents

Sleep spectral power correlates of prospective memory maintenance

Prospective memory involves setting an intention to act that is maintained over time and executed when appropriate. Slow wave sleep (SWS) has been implicated in maintaining prospective memories, although which SWS oscillations most benefit this memory type remains unclear. Here, we investigated SWS spectral power correlates of prospective memory. Healthy young adult participants completed three ongoing tasks in the morning or evening. They were then given the prospective memory instruction to remember to press "Q" when viewing the words "horse" or "table" when repeating the ongoing task after a 12-h delay including overnight, polysomnographically recorded sleep or continued daytime wakefulness. Spectral power analysis was performed on recorded sleep EEG. Two additional groups were tested in the morning or evening only, serving as time-of-day controls. Participants who slept demonstrated superior prospective memory compared with those who remained awake, an effect not attributable to time-of-day of testing. Contrary to prior work, prospective memory was negatively associated with SWS. Furthermore, significant increases in spectral power in the delta-theta frequency range (1.56 Hz-6.84 Hz) during SWS was observed in participants who failed to execute the prospective memory instructions. Although sleep benefits prospective memory maintenance, this benefit may be compromised if SWS is enriched with delta-theta activity

The angiotensin II type I receptor contributes to impaired cerebral blood flow autoregulation caused by placental ischemia in pregnant rats

BACKGROUND: Placental ischemia and hypertension, characteristic features of preeclampsia, are associated with impaired cerebral blood flow (CBF) autoregulation and cerebral edema. However, the factors that contribute to these cerebral abnormalities are not clear. Several lines of evidence suggest that angiotensin II can impact cerebrovascular function; however, the role of the renin angiotensin system in cerebrovascular function during placental ischemia has not been examined. We tested whether the angiotensin type 1 (AT1) receptor contributes to impaired CBF autoregulation in pregnant rats with placental ischemia caused by surgically reducing uterine perfusion pressure. METHODS: Placental ischemic or sham operated rats were treated with vehicle or losartan from gestational day (GD) 14 to 19 in the drinking water. On GD 19, we assessed CBF autoregulation in anesthetized rats using laser Doppler flowmetry. RESULTS: Placental ischemic rats had impaired CBF autoregulation that was attenuated by treatment with losartan. In addition, we examined whether an agonistic autoantibody to the AT1 receptor (AT1-AA), reported to be present in preeclamptic women, contributes to impaired CBF autoregulation. Purified rat AT1-AA or vehicle was infused into pregnant rats from GD 12 to 19 via mini-osmotic pumps after which CBF autoregulation was assessed. AT1-AA infusion impaired CBF autoregulation but did not affect brain water content. CONCLUSIONS: These results suggest that the impaired CBF autoregulation associated with placental ischemia is due, at least in part, to activation of the AT1 receptor and that the RAS may interact with other placental factors to promote cerebrovascular changes common to preeclampsia

The influence of encoding strategy on associative memory consolidation across wake and sleep

Sleep benefits memory consolidation. However, factors present at initial encoding may moderate this effect. Here, we examined the role that encoding strategy plays in subsequent memory consolidation during sleep. Eighty-nine participants encoded pairs of words using two different strategies. Each participant encoded half of the word pairs using an integrative visualization technique, where the two items were imagined in an integrated scene. The other half were encoded nonintegratively, with each word pair item visualized separately. Memory was tested before and after a period of nocturnal sleep ( N = 47) or daytime wake ( N = 42) via cued recall tests. Immediate memory performance was significantly better for word pairs encoded using the integrative strategy compared with the nonintegrative strategy ( P < 0.001). When looking at the change in recall across the delay, there was significantly less forgetting of integrated word pairs across a night of sleep compared with a day spent awake ( P < 0.001), with no significant difference in the nonintegrated pairs ( P = 0.19). This finding was driven by more forgetting of integrated compared with not-integrated pairs across the wake delay ( P < 0.001), whereas forgetting was equivalent across the sleep delay ( P = 0.26). Together, these results show that the strategy engaged in during encoding impacts both the immediate retention of memories and their subsequent consolidation across sleep and wake intervals