Rigidity of spherical codes

A packing of spherical caps on the surface of a sphere (that is, a spherical code) is called rigid or jammed if it is isolated within the space of packings. In other words, aside from applying a global isometry, the packing cannot be deformed. In this paper, we systematically study the rigidity of spherical codes, particularly kissing configurations. One surprise is that the kissing configuration of the Coxeter-Todd lattice is not jammed, despite being locally jammed (each individual cap is held in place if its neighbors are fixed); in this respect, the Coxeter-Todd lattice is analogous to the face-centered cubic lattice in three dimensions. By contrast, we find that many other packings have jammed kissing configurations, including the Barnes-Wall lattice and all of the best kissing configurations known in four through twelve dimensions. Jamming seems to become much less common for large kissing configurations in higher dimensions, and in particular it fails for the best kissing configurations known in 25 through 31 dimensions. Motivated by this phenomenon, we find new kissing configurations in these dimensions, which improve on the records set in 1982 by the laminated lattices.Comment: 39 pages, 8 figure

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Genetic learning particle swarm optimization

Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO

High Efficient Multisites Genome Editing in Allotetraploid Cotton (Gossypium Hirsutum) Using CRISPR/Cas9 System

Gossypium hirsutum is an allotetraploid with a complex genome. Most genes have multiple copies that belong to At and Dt subgenomes. Sequence similarity is also very high between gene homologues. To efficiently achieve site/gene‐specific mutation is quite needed. Due to its high efficiency and robustness, the CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 system has exerted broad site‐specific genome editing from prokaryotes to eukaryotes. In this study, we utilized a CRISPR/Cas9 system to generate two sgRNAs in a single vector to conduct multiple sites genome editing in allotetraploid cotton. An exogenously transformed gene Discosoma red fluorescent protein2(DsRed2) and an endogenous gene GhCLA1 were chosen as targets. The DsRed2‐edited plants in T0 generation reverted its traits to wild type, with vanished red fluorescence the whole plants. Besides, the mutated phenotype and genotype were inherited to their T1 progenies. For the endogenous gene GhCLA1, 75% of regenerated plants exhibited albino phenotype with obvious nucleotides and DNA fragments deletion. The efficiency of gene editing at each target site is 66.7–100%. The mutation genotype was checked for both genes with Sanger sequencing. Barcode‐based high‐throughput sequencing, which could be highly efficient for genotyping to a population of mutants, was conducted in GhCLA1‐edited T0 plants and it matched well with Sanger sequencing results. No off‐target editing was detected at the potential off‐target sites. These results prove that the CRISPR/Cas9 system is highly efficient and reliable for allotetraploid cotton genome editing

Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, $10^{12}$ probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.Comment: Pre-print under review, submitted to Proceedings of Machine Learning Researc

Fluorescent probing for RNA molecules by an unnatural base-pair system

Fluorescent labeling of nucleic acids is widely used in basic research and medical applications. We describe the efficient site-specific incorporation of a fluorescent base analog, 2-amino-6-(2-thienyl)purine (s), into RNA by transcription mediated by an unnatural base pair between s and pyrrole-2-carbaldehyde (Pa). The ribonucleoside 5′-triphosphate of s was site-specifically incorporated into RNA, by T7 RNA polymerase, opposite Pa in DNA templates. The fluorescent intensity of s in RNA molecules changes according to the structural environment. The site-specific s labeling of RNA hairpins and tRNA molecules provided characteristic fluorescent profiles, depending on the labeling sites, temperature and Mg2+ concentration. The Pa-containing DNA templates can be amplified by PCR using 7-(2-thienyl)imidazo[4,5-b]pyridine (Ds), another pairing partner of Pa. This site-specific fluorescent probing by the unnatural pair system including the s-Pa and Ds-Pa pairs provides a powerful tool for studying the dynamics of the local structural features of 3D RNA molecules and their intra- and intermolecular interactions

High throughput methods applied in biomaterial development and discovery

The high throughput discovery of new materials can be achieved by rapidly screening many different materials synthesised by a combinatorial approach to identify the optimal material that fulfils a particular biomedical application. Here we review the literature in this area and conclude that for polymers, this process is best achieved in a microarray format, which enable thousands of cell-material interactions to be monitored on a single chip. Polymer microarrays can be formed by printing pre-synthesised polymers or by printing monomers onto the chip where on-slide polymerisation is initiated. The surface properties of the material can be analysed and correlated to the biological performance using high throughput surface analysis, including time-of-flight secondary ion mass spectrometry (ToF-SIMS), X-ray photoelectron spectroscopy (XPS) and water contact angle (WCA) measurements. This approach enables the surface properties responsible for the success of a material to be understood, which in turn provides the foundations of future material design. The high throughput discovery of materials using polymer microarrays has been explored for many cell-based applications including the isolation of specific cells from heterogeneous populations, the attachment and differentiation of stem cells and the controlled transfection of cells. Further development of polymerisation techniques and high throughput biological assays amenable to the polymer microarray format will broaden the combinatorial space and biological phenomenon that polymer microarrays can explore, and increase their efficacy. This will, in turn, result in the discovery of optimised polymeric materials for many biomaterial applications

Eutrophication and acidification: Do they induce changes in the dissolvedorganic matter dynamics in the coastal Mediterranean Sea?

Original research paperTwo mesocosms experiments were conducted in winter 2010 and summer 2011 to examine how increased pCO2and/or nutrient concentrations potentially perturbate dissolved organic matter dynamics in natural microbialassemblages. Thefluorescence signals of protein- and humic-like compounds were used as a proxy for labileand non-labile material, respectively, while the evolution of bacterial populations, chlorophylla(Chla) anddissolved organic carbon (DOC) concentrations were used as a proxy for biological activity. For both seasons,the presence of elevated pCO2did not cause any significant change in the DOC dynamics (p-valueb0.05). Theconditions that showed the greatest changes in prokaryote abundances and Chlacontent were those amendedwith nutrients, regardless of the change in pH. The temporal evolution offluorophores and optical indices re-vealed that the degree of humification of the organic molecules and their molecular weight changed significantlyin the nutrient-amended treatment. The generation of protein-like compounds was paired to increases in theprokaryote abundance, being higher in the nutrient-amended tanks than in the control. Different patterns inthe magnitude and direction of the generation of humic-like molecules suggested that these changes dependedon initial microbial populations and the availability of extra nutrient inputs. Based on our results, it is expected that in the future projected coastal scenarios the eutrophication processes will favor the transformations of labile and recalcitrant carbon regardless of changes in pCO2.MINECO, European Union, Generalitat de Catalunya, CSICVersión del editor3,25