Can Developmental AIS Provides Immunity to a Multi-cellular Robotics System?

The major challenge to multi-cellular robotics system is how to ensure the system is homeostatically stable. This position paper pro- poses a developmental artificial immune system (dev-AIS) framework that tries to provide and maintain homeostasis to the multi-cellular robotics system. If immunity is defined as the ability to maintain home- ostasis; the dev-AIS framework will be designed based on the under- standing and the abstraction of how different organisms attain for this property through evolution and developmental process. Early form of In- nate Immunity evolve from the predator-and-anti prey relationship of the single-celled organism. Progress in evolution drove the evolution of im- munity from this simple relationship to the development of the immune system in multi-cellular organisms

On Sparse Modern Hopfield Model

We introduce the sparse modern Hopfield model as a sparse extension of the modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a memory-retrieval dynamics whose one-step approximation corresponds to the sparse attention mechanism. Theoretically, our key contribution is a principled derivation of a closed-form sparse Hopfield energy using the convex conjugate of the sparse entropic regularizer. Building upon this, we derive the sparse memory retrieval dynamics from the sparse energy function and show its one-step approximation is equivalent to the sparse-structured attention. Importantly, we provide a sparsity-dependent memory retrieval error bound which is provably tighter than its dense analog. The conditions for the benefits of sparsity to arise are therefore identified and discussed. In addition, we show that the sparse modern Hopfield model maintains the robust theoretical properties of its dense counterpart, including rapid fixed point convergence and exponential memory capacity. Empirically, we use both synthetic and real-world datasets to demonstrate that the sparse Hopfield model outperforms its dense counterpart in many situations.Comment: 37 pages, accepted to NeurIPS 202

The development of meibomian glands in mice

PurposeThe purpose of this study was to characterize the natural history of meibomian gland morphogenesis in the mouse.MethodsEmbryonic (E) and post natal (P) C57Bl/6 mouse pups were obtained at E18.5, P0, P1, P3, P5, P8, P15, and P60. Eyelids were fixed and processed for en bloc staining with Phalloidin/DAPI to identify gland morphogenesis, or frozen for immunohistochemistry staining with Oil red O (ORO) to identify lipid and antibodies specific against peroxisome proliferator-activated receptor gamma (PPARγ) to identify meibocyte differentiation. Samples were then evaluated using a Zeiss 510 Meta laser scanning confocal microscope or Nikon epi-fluorescent microscope. Tissues from adult mice (2 month-old) were also collected for western blotting.ResultsMeibomian gland morphogenesis was first detected at E18.5 with the formation of an epithelial placode within the fused eyelid margin. Invagination of the epithelium into the eyelid was detected at P0. From P1 to P3 there was continued extension of the epithelium into the eyelid. ORO and PPARγ staining was first detected at P3, localized to the central core of the epithelial cord thus forming the presumptive ductal lumen. Ductal branching was first detected at P5 associated with acinar differentiation identified by ORO and PPARγ staining. Adult meibomian glands were observed by P15. Western blotting of meibomian gland proteins identified a 50 kDa and a 72 kDa band that stained with antibodies specific to PPARγ.ConclusionsWe have demonstrated that meibomian gland development bears distinct similarities to hair development with the formation of an epithelial placode and expression of PPARγ co-incident with lipid synthesis and meibocyte differentiation

Effect of Nitrogen Addition on Selection of Germination Trait in an Alpine Meadow on the Tibet Plateau

Seed germination requirements may determine the kinds of habitat in which plants can survive. We tested the hypothesis that nitrogen (N) addition can change seed germination trait-environmental filter interactions and ultimately redistribute seed germination traits in alpine meadows. We determined the role of N addition on germination trait selection in an alpine meadow after N addition by combining a 3-year N addition experiment in an alpine meadow and laboratory germination experiments. At the species level, germination percentage, germination rate (speed) and breadth of temperature niche for germination (BTN) were positively related to survival of a species in the fertilized community. In addition, community-weighted means of germination percentage, germination rate, germination response to alternating temperature and BTN increased. However, germination response to wet-cold storage (cold stratification) and functional richness of germination traits was lower in alpine meadows with high-nitrogen addition than in those with no, low and medium N addition. Thus, N addition had a significant influence on environmental filter-germination trait interactions and generated a different set of germination traits in the alpine meadow. Further, the effect of N addition on germination trait selection by environmental filters was amount-dependent. Low and medium levels of N addition had less effect on redistribution of germination traits than the high level

Carbon Dioxide and Water Electrolysis Using New Alkaline Stable Anion Membranes

The recent development and market introduction of a new type of alkaline stable imidazole-based anion exchange membrane and related ionomers by Dioxide Materials is enabling the advancement of new and improved electrochemical processes which can operate at commercially viable operating voltages, current efficiencies, and current densities. These processes include the electrochemical conversion of CO2 to formic acid (HCOOH), CO2 to carbon monoxide (CO), and alkaline water electrolysis, generating hydrogen at high current densities at low voltages without the need for any precious metal electrocatalysts. The first process is the direct electrochemical generation of pure formic acid in a three-compartment cell configuration using the alkaline stable anion exchange membrane and a cation exchange membrane. The cell operates at a current density of 140 mA/cm2 at a cell voltage of 3.5 V. The power consumption for production of formic acid (FA) is about 4.3–4.7 kWh/kg of FA. The second process is the electrochemical conversion of CO2 to CO, a key focus product in the generation of renewable fuels and chemicals. The CO2 cell consists of a two-compartment design utilizing the alkaline stable anion exchange membrane to separate the anode and cathode compartments. A nanoparticle IrO2 catalyst on a GDE structure is used as the anode and a GDE utilizing a nanoparticle Ag/imidazolium-based ionomer catalyst combination is used as a cathode. The CO2 cell has been operated at current densities of 200 to 600 mA/cm2 at voltages of 3.0 to 3.2 respectively with CO2 to CO conversion selectivities of 95–99%. The third process is an alkaline water electrolysis cell process, where the alkaline stable anion exchange membrane allows stable cell operation in 1 M KOH electrolyte solutions at current densities of 1 A/cm2 at about 1.90 V. The cell has demonstrated operation for thousands of hours, showing a voltage increase in time of only 5 μV/h. The alkaline electrolysis technology does not require any precious metal catalysts as compared to polymer electrolyte membrane (PEM) design water electrolyzers. In this paper, we discuss the detailed technical aspects of these three technologies utilizing this unique anion exchange membrane

Seed Germination Responses to Seasonal Temperature and Drought Stress Are Species‐Specific but Not Related to Seed Size in a Desert Steppe: Implications for Effect of Climate Change on Community Structure

Investigating how seed germination of multiple species in an ecosystem responds to environmental conditions is crucial for understanding the mechanisms for community structure and biodiversity maintenance. However, knowledge of seed germination response of species to environmental conditions is still scarce at the community level. We hypothesized that responses of seed germination to environmental conditions differ among species at the community level, and that germination response is not correlated with seed size. To test this hypothesis, we determined the response of seed germination of 20 common species in the Siziwang Desert Steppe, China, to seasonal temperature regimes (representing April, May, June, and July) and drought stress (0, −0.003, −0.027, −0.155, and −0.87 MPa). Seed germination percentage increased with increasing temperature regime, but Allium ramosum, Allium tenuissimum, Artemisia annua, Artemisia mongolica, Artemisia scoparia, Artemisia sieversiana, Bassia dasyphylla, Kochia prastrata, and Neopallasia pectinata germinated to \u3e60% in the lowest temperature regime (April). Germination decreased with increasing water stress, but Allium ramosum, Artemisia annua, Artemisia scoparia, Bassia dasyphylla, Heteropappus altaicus, Kochia prastrata, Neopallasia pectinata, and Potentilla tanacetifolia germinated to near 60% at −0.87 MPa. Among these eight species, germination of six was tolerant to both temperature and water stress. Mean germination percentage in the four temperature regimes and the five water potentials was not significantly correlated with seed mass or seed area, which were highly correlated. Our results suggest that the species‐specific germination responses to environmental conditions are important in structuring the desert steppe community and have implications for predicting community structure under climate change. Thus, the predicted warmer and dryer climate will favor germination of drought‐tolerant species, resulting in altered proportions of germinants of different species and subsequently change in community composition of the desert steppe

A second polymorph of β-arteether

The crystal structure of the title compound, C17H28O5, reported here is a polymorph of the structure first reported by El-Feraly, Al-Yahya, Orabi, McPhail & McPhail [J. Nat. Prod. (1992). 55, 878–883]. It is a derivative of the anti­malaria compound artemisinin and consists primarily of three substituted ring systems fused together. A cyclo­hexane ring (distorted chair conformation) fused to a tetra­hydro­pyran group (distorted chair) is adjacent to an oxacyclo­heptane unit containing an endo-peroxide bridge, giving the mol­ecule its particular three-dimensional arrangement. The crystal packing is stabilized by inter­molecular C—H⋯O inter­actions between an O atom from the endo-peroxide bridge and H atoms from both the cyclo­hexane and seven-membered oxacyclo­heptane fused rings, as well as between an O atom and H atom from adjacent tetra­hydro­pyran rings. The two polymorphs have the same space group and similar cell parameters for the a and b axes, but significantly different values for the c axis

Generation, annotation, analysis and database integration of 16,500 white spruce EST clusters

BACKGROUND: The sequencing and analysis of ESTs is for now the only practical approach for large-scale gene discovery and annotation in conifers because their very large genomes are unlikely to be sequenced in the near future. Our objective was to produce extensive collections of ESTs and cDNA clones to support manufacture of cDNA microarrays and gene discovery in white spruce (Picea glauca [Moench] Voss). RESULTS: We produced 16 cDNA libraries from different tissues and a variety of treatments, and partially sequenced 50,000 cDNA clones. High quality 3' and 5' reads were assembled into 16,578 consensus sequences, 45% of which represented full length inserts. Consensus sequences derived from 5' and 3' reads of the same cDNA clone were linked to define 14,471 transcripts. A large proportion (84%) of the spruce sequences matched a pine sequence, but only 68% of the spruce transcripts had homologs in Arabidopsis or rice. Nearly all the sequences that matched the Populus trichocarpa genome (the only sequenced tree genome) also matched rice or Arabidopsis genomes. We used several sequence similarity search approaches for assignment of putative functions, including blast searches against general and specialized databases (transcription factors, cell wall related proteins), Gene Ontology term assignation and Hidden Markov Model searches against PFAM protein families and domains. In total, 70% of the spruce transcripts displayed matches to proteins of known or unknown function in the Uniref100 database (blastx e-value < 1e-10). We identified multigenic families that appeared larger in spruce than in the Arabidopsis or rice genomes. Detailed analysis of translationally controlled tumour proteins and S-adenosylmethionine synthetase families confirmed a twofold size difference. Sequences and annotations were organized in a dedicated database, SpruceDB. Several search tools were developed to mine the data either based on their occurrence in the cDNA libraries or on functional annotations. CONCLUSION: This report illustrates specific approaches for large-scale gene discovery and annotation in an organism that is very distantly related to any of the fully sequenced genomes. The ArboreaSet sequences and cDNA clones represent a valuable resource for investigations ranging from plant comparative genomics to applied conifer genetics

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments