Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Microbial carbonate production in a starved basin: The crenistria Limestone of the upper Viséan German Kulm facies

The crenistria Limestone is a set of three autochthonous massive limestone beds occurring with great lithological persistence in the Kulm Facies (cd III , upper Viséan) of the eastern Rheinisches Schiefergebirge. Microfacies analysis reveals mainly minipeloidal fabrics and homogeneous micrite. Uncrushed, sediment-filled conchs of goniatites represent loci of sheltered preservation of primary carbonate textures. Calcified radiolarians are abundant, forming between 20 and 80% of total rock volume. The alleged algal genus Rectangulina is common in the crenistria Limestone. It is reinterpreted to represent the faeces of goniatites. For the first time the presence of in situ preserved sponges is reported. They can be recognized as delicate networks of microsparitic needles embedded in peloidal fabrics. Hexactinellids with primary spicule arrangements can be found embedded in homogeneous micrite. The carbonate forming the limestone beds was produced microbially during decomposition of soft tissue of the radiolarians and sponges. During the cd III , anoxia in the bottom waters of the Kulm Basin persisted for long periods due to stable density stratification of the water column under humid climatic conditions. Oxic conditions in the bottom waters during formation of the limestone are indicated by bioturbation, the presence of sponges and the high Mn-contents of the carbonate. The latter derived from reduction of Mn-oxides during microbial carbonate formation.     

Effect of submergence on translocation,starch content and amylolytic activity in deep-water rice

Submergence induces rapid internodal elongation in deep-water rice (Oryza sativa L. cv. Habiganj Aman II). We investigated the metabolic activities which help to support such fast growth. Three days of submergence in water under continuous light led to the mobilization of 65% of the starch from those regions of rice internodes which had been formed prior to submergence. Disappearance of starch was accompanied by a 70-fold enhancement of amylolytic activity. Similar increases in amylolytic activity were detected in response to ethylene and gibberellic acid. Submergence also caused a 26-fold increase in the translocation of newly synthesized photosynthetic assimilates from the leaves to the internodes and younger regions of the culms. These physiological processes are likely to provide the metabolic energy required for internodal elongation in response to submergence.Abbreviation GA₃ gibberellic acid     

Hydrography and characteristics of the phytoplankton in shelf and oceanic waters off southeastern Brazil during winter (July/August 1982) and summer (February/March 1984)

The physical and chemical environment, and the phytoplankton primary production of southeastern Brazil were studied in relation to the general oceanographic structure during two research cruises (winter and summer). In each cruise, a total of 91 stations were occupied. Data were collected on the spatial distribution of nutrients, phytoplankton biomass and photosynthetic capacity over the coastal, shelf and oceanic areas off São Paulo, Paraná and Santa Catarina States.During wintertime, the mixing processes between tropical warm waters of the Brazil Current and subantarctic waters of the Malvinas Current formed strong environmental gradients. The drainings of Rio de La Plata and Lagoa dos Patos are transported northwards by coastal currents, enriching the shelf waters off Santa Catarina State with inorganic nutrients and consequently increasing the chlorophyll a to the highest concentrations (> 3.5 mg m ^–3) measured during the two cruises. In slope waters chlorophyll values were always low (0.05–0.45 mg m ^–3). The chlorophyll within the euphotic layer varied from 8.8–36.7 and 1.2–18.5 mg m^–2 during winter and summer, respectively.The surface photosynthetic rates during winter and summer cruises ranged respectively from 0.21–9.17 and 0.66–19.60 mgC/mgChl.a/h. The mean rates were higher in nearshore waters and decreased seaward.The thermal structure of the water column affected the vertical distribution of chlorophyll a and photosynthesis within the euphotic zone; During unstratified periods (winter) they were uniformly distributed but the occurrence of subsurface peaks of chlorophyll and strong photosynthetic inhibition of low light adapted cells in deeper layers are associated to the seasonal thermocline. Occasionally, upwelling of deep waters from shelf break enriched the deeper euphotic layers in offshore areas. Intensive upwelling was observed off Paranagua Bay (Parana State) and the mechanisms of its formation are discussed.     

Changes in Prokaryote and Eukaryote Assemblages Along a Gradient of Hydrocarbon Contamination in Groundwater

Groundwater biota are particularly sensitive to environmental perturbations such as groundwater contamination. The diversity of prokaryotic and eukaryotic biota has been examined along a gradient of chlorinated hydrocarbon (CHC) contamination in the Botany Sands, an urban coastal sand-bed aquifer (Sydney, Australia). Molecular techniques were used to analyze the richness and composition of prokaryote and eukaryote assemblages using 16S and 18S rDNA, respectively. Taxon richness did not change significantly along the gradient for either prokaryotes or eukaryotes; however, significant shifts in assemblage composition were evident for both groups. Assemblage changes were most strongly correlated with concentrations of the major CHC, cis-1,2-dichloroethene, but the concentrations of a number of the contaminants were also correlated, making it difficult to infer if effects were due to any particular contaminant. The presence of cis-1,2-dichloroethene and other secondary ethenes suggests in situ breakdown of the primary CHCs via natural attenuation. The current focus of management of the Botany aquifer is to stop the contaminant plume reaching the adjoining estuary. This approach is clearly justified given the changes evident in the microbial assemblages in the groundwater, which are a likely consequence of the contamination.     

Homeoviscous theory under pressure: II. The molecular order of membranes from deep-sea fish

The molecular order of brain and liver membranes isolated from deep sea and continental shelf fish species have been estimated and compared using the fluorescence polarization technique in order to determine whether life in a high pressure habitat is associated with an adjustment of membrane order. Fish were trawled at depths between 200 m and 4000 m, liver and brain membranes were fractionated, and fluorescence polarization was measured at 4°C and ambient pressure. Polarization of the brain myelin fraction provided a statistically significant regression with depth of capture ($\text{P<0.001}$) with a slope of ?0.004 km^?1. This change in polarization with depth was sufficient to offset approximately half of the pressure-induced increase in polarization and thus represents the first structural evidence of homeoviscous adaptation to pressure. Polarization of the brain synaptic and liver mitochondrial fraction was not significantly related to depth. This may be due, at least in part, to a high individual variability of polarization compared to laboratory-acclimated freshwater fish.     

1D-CADCapsNet: One dimensional deep capsule networks for coronary artery disease detection using ECG signals

PurposeCardiovascular disease (CVD) is a leading cause of death globally. Electrocardiogram (ECG), which records the electrical activity of the heart, has been used for the diagnosis of CVD. The automated and robust detection of CVD from ECG signals plays a significant role for early and accurate clinical diagnosis. The purpose of this study is to provide automated detection of coronary artery disease (CAD) from ECG signals using capsule networks (CapsNet).MethodsDeep learning-based approaches have become increasingly popular in computer aided diagnosis systems. Capsule networks are one of the new promising approaches in the field of deep learning. In this study, we used 1D version of CapsNet for the automated detection of coronary artery disease (CAD) on two second (95,300) and five second-long (38,120) ECG segments. These segments are obtained from 40 normal and 7 CAD subjects. In the experimental studies, 5-fold cross validation technique is employed to evaluate performance of the model.ResultsThe proposed model, which is named as 1D-CADCapsNet, yielded a promising 5-fold diagnosis accuracy of 99.44% and 98.62% for two- and five-second ECG signal groups, respectively. We have obtained the highest performance results using 2 s ECG segment than the state-of-art studies reported in the literature.Conclusions1D-CADCapsNet model automatically learns the pertinent representations from raw ECG data without using any hand-crafted technique and can be used as a fast and accurate diagnostic tool to help cardiologists.     

Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images

PurposeDeep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management of medical imaging data, because of ethical complications and the limited number of imaging studies available at a single facility.This study aimed to find a simple and low-cost method to increase the accuracy of deep learning semantic segmentation for radiation therapy of prostate cancer.MethodsIn total, 556 cases with non-contrast CT images for prostate cancer radiation therapy were examined using a two-dimensional U-Net. Initially, all slices were used for the input data. Then, we removed slices of the cranial portions, which were beyond the margins of the bladder and rectum. Finally, the ground truth labels for the bladder and rectum were added as channels to the input for the prostate training dataset.ResultsThe highest mean dice similarity coefficients (DSCs) for each organ in the test dataset of 56 cases were 0.85 ± 0.05, 0.94 ± 0.04 and 0.85 ± 0.07 for the prostate, bladder and rectum, respectively. Removal of the cranial slices from the original images significantly increased the DSC of the rectum from 0.83 ± 0.09 to 0.85 ± 0.07 (p < 0.05). Adding bladder and rectum information to prostate training without removing the slices significantly increased the DSC of the prostate from 0.79 ± 0.05 to 0.85 ± 0.05 (p < 0.05).ConclusionsThese cost-free approaches may be useful for new applications, which may include updated models and datasets. They may be applicable to other organs at risk (OARs) and clinical targets such as elective nodal irradiation.