Deep4D: A Compact Generative Representation for Volumetric Video

This paper introduces Deep4D a compact generative representation of shape and appearance from captured 4D volumetric video sequences of people. 4D volumetric video achieves highly realistic reproduction, replay and free-viewpoint rendering of actor performance from multiple view video acquisition systems. A deep generative network is trained on 4D video sequences of an actor performing multiple motions to learn a generative model of the dynamic shape and appearance. We demonstrate the proposed generative model can provide a compact encoded representation capable of high-quality synthesis of 4D volumetric video with two orders of magnitude compression. A variational encoder-decoder network is employed to learn an encoded latent space that maps from 3D skeletal pose to 4D shape and appearance. This enables high-quality 4D volumetric video synthesis to be driven by skeletal motion, including skeletal motion capture data. This encoded latent space supports the representation of multiple sequences with dynamic interpolation to transition between motions. Therefore we introduce Deep4D motion graphs, a direct application of the proposed generative representation. Deep4D motion graphs allow real-tiome interactive character animation whilst preserving the plausible realism of movement and appearance from the captured volumetric video. Deep4D motion graphs implicitly combine multiple captured motions from a unified representation for character animation from volumetric video, allowing novel character movements to be generated with dynamic shape and appearance detail.

Coordinated modulation of multiple processes through phase variation of a c-di-GMP phosphodiesterase in Clostridioides difficile

The opportunistic nosocomial pathogen Clostridioides difficile exhibits phenotypic heterogeneity through phase variation, a stochastic, reversible process that modulates expression. In C. difficile, multiple sequences in the genome undergo inversion through site-specific recombination. Two such loci lie upstream of pdcB and pdcC, which encode phosphodiesterases (PDEs) that degrade the signaling molecule c-di-GMP. Numerous phenotypes are influenced by c-di-GMP in C. difficile including cell and colony morphology, motility, colonization, and virulence. In this study, we aimed to assess whether PdcB phase varies, identify the mechanism of regulation, and determine the effects on intracellular c-di-GMP levels and regulated phenotypes. We found that expression of pdcB is heterogeneous and the orientation of the invertible sequence, or pdcB switch, determines expression. The pdcB switch contains a promoter that when properly oriented promotes pdcB expression. Expression is augmented by an additional promoter upstream of the pdcB switch. Mutation of nucleotides at the site of recombination resulted in phase-locked strains with significant differences in pdcB expression. Characterization of these mutants showed that the pdcB locked-ON mutant has reduced intracellular c-di-GMP compared to the locked-OFF mutant, consistent with increased and decreased PdcB activity, respectively. These alterations in c-di-GMP had concomitant effects on multiple known c-di-GMP regulated processes. These results indicate that phase variation of PdcB allows C. difficile to coordinately diversify multiple phenotypes in the population to enhance survival.

Numerical phenetic and phylogenetic relationships in silico among brown seaweeds (Phaeophyceae) from Gunungkidul, Yogyakarta, Indonesia

Ningrum AM, Chasani AR. 2021. Numerical phenetic and phylogenetic relationships in silico among brown seaweeds (Phaeophyceae) from Gunungkidul, Yogyakarta, Indonesia. Biodiversitas 22: 3057-3064. Human activities such as industrial and tourism development on coastal areas in Indonesia are dreaded to affect seaweed diversity, including brown seaweeds (Phaeophyceae). Though study on brown seaweeds diversity has been done quite a lot, there is no record of analysis of phenetic and phylogenetic relationships among brown seaweeds. Hence, this study aims to determine the phenetic and phylogenetic relationships in silico among brown seaweeds and define characters that play a role in the clustering of brown seaweeds from Gunungkidul, Yogyakarta, Indonesia. Exploration was done using purposive sampling method. Numerical phenetic analysis was generated using MVSP 3.1. Further clustering method was implemented to identify phenetic relationships The PCA method was used to reveal morphological, anatomical, and biochemical characters that determine the clustering pattern. The phylogenetic relationships in silico analysis were conducted using rbcL genes from NCBI GenBank database. All multiple sequences were aligned using ClustalW and phylogram reconstruction was performed using Neighbor-Joining (NJ) method in MEGA 7.0. Our study showed that both the analyses, i.e., numerical phenetic and phylogenetic relationships in silico resulted in two main clusters although the species composition of the clusters was slightly different. The PCA analysis indicated that the morphological characters i.e. blade shape, phylloid shape, thalli height, length of the main axis, and the water bladder shape play an important role in the clustering of brown seaweeds species.

Structuring time: The hippocampus constructs sequence memories that generalize temporal relations across experiences

The hippocampal-entorhinal region supports memory for episodic details, such as temporal relations of sequential events, and mnemonic constructions combining experiences for inferential reasoning. However, it is unclear whether hippocampal event representations reflect temporal relations derived from mnemonic constructions, event order, or elapsing time, and whether they generalize temporal relations across similar sequences. Here, participants mnemonically constructed times of events from multiple sequences using infrequent cues and their experience of passing time. After learning, event representations in the anterior hippocampus reflected sequence relations based on constructed times. These event representations generalized across sequences, revealing distinct representational formats for events from the same or different sequences. Structural knowledge about time patterns, abstracted from different sequences, biased the construction of specific event times. These findings demonstrate that the hippocampus reconciles representations of specific relations with the generalization across different episodes, consistent with memory-based constructions combining episodic details and general knowledge to simulate scenarios.

Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that order information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This associative cluster-dependent chain (ACDC) model modularly stores order and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous Thunderstruck song and then flexibly play it in a bossa nova rhythm without further training.

Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons

Sequential behaviour is often compositional and organised across multiple time scales: a set of individual elements developing on short time scales (motifs) are combined to form longer functional sequences (syntax). Such organisation leads to a natural hierarchy that can be used advantageously for learning, since the motifs and the syntax can be acquired independently. Despite mounting experimental evidence for hierarchical structures in neuroscience, models for temporal learning based on neuronal networks have mostly focused on serial methods. Here, we introduce a network model of spiking neurons with a hierarchical organisation aimed at sequence learning on multiple time scales. Using biophysically motivated neuron dynamics and local plasticity rules, the model can learn motifs and syntax independently. Furthermore, the model can relearn sequences efficiently and store multiple sequences. Compared to serial learning, the hierarchical model displays faster learning, more flexible relearning, increased capacity, and higher robustness to perturbations. The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.

First Record of Colletotrichum gloeosporioides Causing Anthracnose of Banana in Pakistan

Banana (Musa spp.) is one of the most widely grown and consumed fruits in Pakistan and all around the world due to their distinct aroma and taste. In 2018, anthracnose symptoms were observed on banana fruit harvested from different plantations of Sindh- a major banana producing Province of Pakistan. Approximately, 25% of banana fruit collected from different plantations were infected. The symptoms consisted of small brown to reddish-brown spots on the fruit surface and then became sunken lesions as the disease progressed. To identify the pathogen, infected tissues (5 mm in diameter) from the margin of the lesions were surface sterilized by dipping in 1% sodium hypochlorite (NaOCl) for 2 min, 70% ethanol for 30 s, and then rinsed twice with sterile distilled water, plated onto potato dextrose agar (PDA), and incubated at 27°C for 5 days with 12 h light and darkness cycle. Colonies with a similar pattern were consistently isolated and all colonies were sub-cultured using the single-spore method. Colonies first appeared with white colored mycelium and later turned to dark gray. Conidia produced in acervuli were cylindric, hyaline, straight, and aseptate, with both ends rounded. Conidia measured 14.0 ± 0.5 × 3.4 ± 0.6 μm. Conidiomata were dark brown and spherical. On the basis of morphological characterization, the pathogen was identified as Colletotrichum gloeosporioides (Penz.) Penz. & Sacc. (Weir et al. 2012). Two independent isolates (PDL2031 and PDL2032) were used for further genetic analysis. The internal transcribed spacer (ITS) region and chitin synthase 1 (CHS-1) gene were amplified from genomic DNA using primer pairs of ITS1/ITS4 and CHS-79F/CHS-345R, respectively (White et al. 1990; Damm et al. 2012). The GenBank accession numbers (MW493198, MW504711 for ITS and MW530421, MW530422 for CHS-1) of the sequences exhibited 99% to 100% identity to multiple sequences of C. gloeosporioides. To conduct a pathogenicity test, 10 healthy fruits were selected and surface sterilized with 70% ethanol followed by a wash of sterilized water. The fruits were stabbed with a sterile needle and a drop of 20 µl of spore suspension (106 spores/ml) was placed on each wound independently. Meanwhile 10 fruits inoculated with sterile water were treated as controls. The fruits were incubated at 27°C with 90% relative humidity for 10 days. Inoculated fruits exhibited symptoms similar to the original infection. No visible lesions appeared on control fruit. C. gloeosporioides was successfully reisolated from the inoculated fruit, confirming Koch’s postulates. Anthracnose of banana is known to be caused by C. musae, C. gloeosporioides, C. siamense, C. tropicale, C. chrysophilum, C. theobromicola, and C. scovillei (Kumar et al. 2017; Peres et al. 2001; Vieira et al. 2017; Zakaria et al. 2009; Zhou et al. 2017). To our knowledge, this is first report of anthracnose of banana caused by C. gloeosporioides in Pakistan. The new disease primarily reduces the quality and yield of Banana. Effective measures should be taken to manage this disease.