Modeling vegetation greenness and its climate sensitivity with deep‐learning technology

Climate sensitivity of vegetation has long been explored using statistical or process‐based models. However, great uncertainties still remain due to the methodologies’ deficiency in capturing the complex interactions between climate and vegetation. Here, we developed global gridded climate–vegetation models based on long short‐term memory (LSTM) network, which is a powerful deep‐learning algorithm for long‐time series modeling, to achieve accurate vegetation monitoring and investigate the complex relationship between climate and vegetation. We selected the normalized difference vegetation index (NDVI) that represents vegetation greenness as model outputs. The climate data (monthly temperature and precipitation) were used as inputs. We trained the networks with data from 1982 to 2003, and the data from 2004 to 2015 were used to validate the models. Error analysis and sensitivity analysis were performed to assess the model errors and investigate the sensitivity of global vegetation to climate change. Results show that models based on deep learning are very effective in simulating and predicting the vegetation greenness dynamics. For models training, the root mean square error (RMSE) is <0.01. Model validation also assure the accuracy of our models. Furthermore, sensitivity analysis of models revealed a spatial pattern of global vegetation to climate, which provides us a new way to investigate the climate sensitivity of vegetation. Our study suggests that it is a good way to integrate deep‐learning method to monitor the vegetation change under global change. In the future, we can explore more complex climatic and ecological systems with deep learning and coupling with certain physical process to better understand the nature.     

Which factors influence the ability of a computational model to predict the in vivo deformation behaviour of skeletal muscle?

Deep tissue injury (DTI) is a severe form of pressure ulcer where tissue damage starts in deep tissues underneath intact skin. Tissue deformation may play an important role in the aetiology, which can be investigated using an experimental–numerical approach. Recently, an animal-specific finite element model has been developed to simulate experiments in which muscle tissue was compressed with an indenter. In this study, the material behaviour and boundary conditions were adapted to improve the agreement between model and experiment and to investigate the influence of these adaptations on the predicted strain distribution. The use of a highly nonlinear material law and including friction between the indenter and the muscle both improved the quality of the model and considerably influenced the estimated strain distribution. With the improved model, the required sample size to detect significant differences between loading conditions can be diminished, which is clearly relevant in experiments involving animals.     

How does muscle stiffness affect the internal deformations within the soft tissue layers of the buttocks under constant loading?

Mechanical loading of soft tissues covering bony prominences can cause skeletal muscle damage, ultimately resulting in a severe pressure ulcer termed deep tissue injury (DTI). Deformation plays an important role in the aetiology of DTI. Therefore, it is essential to minimise internal muscle deformations in subjects at risk of DTI. As an example, spinal cord-injured (SCI) individuals exhibit structural changes leading to a decrease in muscle thickness and stiffness, which subsequently increase the tissue deformations. In the present study, an animal-specific finite element model, where the geometry and boundary conditions were derived from magnetic resonance images, was developed. It was used to investigate the internal deformations in the muscle, fat and skin layers of the porcine buttocks during loading. The model indicated the presence of large deformations in both the muscle and the fat layers, with maximum shear strains up to 0.65 in muscle tissue and 0.63 in fat. Furthermore, a sensitivity analysis showed that the tissue deformations depend considerably on the relative stiffness values of the different tissues. For example, a change in muscle stiffness had a large effect on the muscle deformations. A 50% decrease in stiffness caused an increase in maximum shear strain from 0.65 to 0.99, whereas a 50% increase in stiffness resulted in a decrease in maximum shear strain from 0.65 to 0.49. These results indicate the importance of restoring tissue properties after SCI, with the use of, for example, electrical stimulation, to prevent the development of DTI.     

Synthesis and Evaluation of Pyrrole Polyamide- 2′-Deoxyguanosine 5′-Phosphate Hybrid

Pyrrole polyamide-2′-deoxyguanosine 5′-phosphate hybrid (Hybrid 4) was synthesized and evaluated in terms of the inhibition of mouse mammary carcinoma FM3A cell growth. Hybrid 4 was found to exhibit dose-dependent inhibition of cell growth.     

Fish biodiversity of Saint Peter and Saint Paulʼs Archipelago,Mid-Atlantic Ridge,Brazil: new records and a species database

Saint Peter and Saint Paul's Archipelago (SPSPA), one of the smallest and most isolated island groups in the world, is situated on the Mid-Atlantic Ridge, between Brazil and the African continent. SPSPA has low species richness and high endemism; nonetheless, the diversity of fishes from deep habitats (>30 m depth) had not been previously studied in detail. Several expeditions conducted between 2009 and 2018 explored the shallow and deep reefs of SPSPA using scuba, closed-circuit rebreathers, manned submersibles, baited remote underwater stereo-videos (stereo-BRUV) and fishing between 0 and 1050 m depth. These expeditions yielded 41 new records of fishes for SPSPA: 9 in open waters, 9 in shallow waters (0–30 m), 8 in mesophotic ecosystems (30–150 m) and 15 in deeper reefs (>150 m). Combined with literature records of adult pelagic, shallow and deep-reef species, as well as larvae, the database of the fish biodiversity for SPSPA currently comprises 225 species (169 recorded as adult fishes and 79 as larvae, with 23 species found in both stages). Most of them (112) are pelagic, 86 are reef-associated species and 27 are deep-water specialists. Species accumulation curves show that the number of fish species has not yet reached an asymptote. Whereas the number of species recorded in SPSPA is similar to that in other oceanic islands in the Atlantic Ocean, the proportion of shorefishes is relatively lower, and the endemism level is the third highest in the Atlantic. Twenty-nine species are listed as threatened with extinction. Observations confirm the paucity of top predators on shallow rocky reefs of the island, despite the presence of several pelagic shark species around SPSPA. Because all of the endemic species are reef associated, it is argued that the new marine-protected areas created by the Brazilian government do not ensure the protection and recovery of SPSPA's biodiversity because they allow exploitation of the most vulnerable species around the archipelago itself. This study suggests a ban on reef fish exploitation inside an area delimited by the 1000 m isobath around the islands (where all known endemics are concentrated) as the main conservation strategy to be included in the SPSPA management plan being prepared by the Brazilian government.     

Machine learning techniques for protein function prediction

Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of experimental limitations), reliable prediction of protein function through computational means has become crucial. This paper reviews the machine learning techniques used in the literature, following their evolution from simple algorithms such as logistic regression to more advanced methods like support vector machines and modern deep neural networks. Hyperparameter optimization methods adopted to boost prediction performance are presented. In parallel, the metamorphosis in the features used by these algorithms from classical physicochemical properties and amino acid composition, up to text-derived features from biomedical literature and learned feature representations using autoencoders, together with feature selection and dimensionality reduction techniques, are also reviewed. The success stories in the application of these techniques to both general and specific protein function prediction are discussed.     

Circular Dichroism Studies of the Structure of DNA Complex with Berberine

Abstract

The binding of the benzodioxolo-benzoquinolizine alkaloid, berberine chloride to natural and synthetic DNAs has been studied by intrinsic and extrinsic circular dichroic measurements. Binding of berberine causes changes in the circular dichroism spectrum of DNA as shown by the increase of molar ellipticity of the 270nm band, but with very little change of the 240nm band. The molar ellipticity at the saturation depends strongly on the base composition of DNA and also on salt concentration, but always larger for the AT rich DNA than the GC rich DNA The features in the circular dichroic spectral changes of berberine-synthetic DNA complexes were similar to that of native DNA but depends on the sequence of base pairs.

On binding to DNA and polynucleotides, the alkaloid becomes optically active. The extrinsic circular dichroism developed in the visible absorption region (300–500nm) for the berberine-DNA complexes shows two broad spectral bands in the regions 425–440nm and 340–360nm with the maximum varying depending on base composition and sequence of DNA While the 425nm band shows less variation on the binding ratio, the 360nm band is remarkably dependent on the DNA/alkaloid ratio. The generation of the alkaloid associated extrinsic circular dichroic bands is not dependent on the base composition or sequence of base pairs, but the nature and magnitude of the bands are very much dependent on these two factors and also on the salt concentration. The interpretation of the results with respect to the modes of the alkaloid binding to DNA are presented.     

Observations on the development and maintenance of the deep sea barnacle,Octolasmis aymonini geryonophila (Pilsbry)

Thirty-two pedunculate barnacles, O. a. geryonophila, were maintained in culture for a period of 2 yr in the laboratory. These barnacles were obtained from the pleopods and mouth parts of the giant marine isopod, Bathynomus giganteus, which had been collected, at a depth of 200 fathoms, in the Gulf of Mexico.

The carina, scutum, mandible and maxilla of adult barnacles were typical of deep water species. However, the tergum and labrum were intermediate between those of shallow and deep water species.

Adults 3.1–4.1 mm in length were cultured in sea water (15–19°C), and fed on benthic copepods such as Tisbe furcata and Laophonte sp. Three broods of nauplii from 8 barnacles were obtained in 2 yr. Larvae were reared on plankton collected from Coney Island waters in which nauplii reached Stage IV in 10–14 days at 16°C. Isochrysis galbana and Thalassiosira pseudonana individually or in combination maintained nauplii to Stage IV, but with very high mortality. The lack of spines on the carapace edge of the nauplii distinguishes this deep water species from the shallow water form, O. mulleri.     

Redox Buffer Capacity in Water-Rock-Microbe Interaction Systems in Subsurface Environments

An incubation experiment was conducted to estimate redox buffer capacity of “water-rock-microbe” interaction systems in sedimentary rocks. The water chemistry, microbial growth and community structure were analyzed during the incubations. The dissolved oxygen (DO) concentrations and oxidation-reduction potential (ORP) values decreased notably in the presence of active microorganisms, whereas abiotic reactions did not lead to reducing conditions during incubation. The change in microbial community structure suggests that nitrate-reducing and sulfate-reducing bacteria played an important role in reduction of water by using lignite-derived organic matter. These results show that the microbial role is extremely important for the redox buffering capacity in sedimentary rock environments.