Computational data analysis shows that key developments towards the periodic system occurred in the 1840s

The periodic system arose from knowledge about substances, which constitute the chemical space. Despite the importance of this interplay, little is known about how the expanding space affected the system. Here we show, by analysing the space between 1800 and 1869, how the periodic system evolved until its formulation. We found that after an unstable period culminating around 1826, the system began to converge to a backbone structure, unveiled in the 1860s, which was clearly evident in the 1840s. Hence, contrary to the belief that the ``ripe moment'' to formulate the system was in the 1860s, it was in the 1840s. The evolution of the system is marked by the rise of organic chemistry in the first quarter of the nineteenth-century, which prompted the recognition of relationships among main group elements and obscured some of transition metals, which explains why the formulators of the periodic system struggled accommodating them. We also introduced an algorithm to adjust the chemical space according to different sets of atomic weights, which allowed for estimating the resulting periodic systems of chemists using one or the other nineteenth-century atomic weights. These weights produce orderings of the elements very similar to that of 1869, while providing different similarity relationships among the elements, therefore producing different periodic systems. By analysing these systems, from Dalton up to Mendeleev, we found that Gmelin's atomic weights of 1843 produce systems remarkably similar to that of 1869, a similarity that was reinforced by the atomic weights on the years to come.

Detecting malware samples with similar image sets

This paper proposes a method for identifying and visualizing similarity relationships between malware samples based on their embedded graphical assets (such as desktop icons and button skins). We argue that analyzing such relationships has practical merit for a number of reasons. For example, we find that malware desktop icons are often used to trick users into running malware programs, so identifying groups of related malware samples based on these visual features can highlight themes in the social engineering tactics of today’s malware authors. Also, when malware samples share rare images, these image sharing relationships may indicate that the samples were generated or deployed by the same adversaries.To explore and evaluate this malware comparison method, the paper makes two contributions. First, we provide a scalable and intuitive method for computing similarity measurements between malware based on the visual similarity of their sets of images. Second, we give a visualization method that combines a force- directed graph layout with a set visualization technique so as to highlight visual similarity relationships in malware corpora. We evaluate the accuracy of our image set similarity comparison method against a hand curated malware relationship ground truth dataset, finding that our method performs well. We also evaluate our overall concept through a small qualitative study we conducted with three cyber security researchers. Feedback from the researchers confirmed our use cases and suggests that computer network defenders are interested in this capability.

Visual and Semantic Similarity Norms for a Photographic Image Stimulus Set Containing Recognizable Objects, Animals and Scenes

We collected visual and semantic similarity norms for a set of photographic images comprising 120 recognizable objects/animals and 120 indoor/outdoor scenes. Human observers rated the similarity of pairs of images within four categories of stimulus ‒ inanimate objects, animals, indoor scenes and outdoor scenes ‒ via Amazon's Mechanical Turk. We performed multi-dimensional scaling (MDS) on the collected similarity ratings to visualize the perceived similarity for each image category, for both visual and semantic ratings. The MDS solutions revealed the expected similarity relationships between images within each category, along with intuitively sensible differences between visual and semantic similarity relationships for each category. Stress tests performed on the MDS solutions indicated that the MDS analyses captured meaningful levels of variance in the similarity data. These stimuli, associated norms and naming data are made publicly available, and should provide a useful resource for researchers of vision, memory and conceptual knowledge wishing to run experiments using well-parameterized stimulus sets.

OTUs clustering should be avoided for defining oral microbiome

This in silico investigation aimed to: 1) evaluate a set of primer pairs with high coverage, including those most commonly used in the literature, to find the different oral species with 16S rRNA gene amplicon similarity/identity (ASI) values ≥97%; and 2) identify oral species that may be erroneously clustered in the same operational taxonomic unit (OTU) and ascertain whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain amplicon sequence variants (ASVs) from the complete genomes of 186 bacterial and 135 archaeal species. For each primer, ASVs without mismatches were aligned using BLASTN and their similarity values were obtained. Finally, we selected ASVs from different species with an ASI value ≥97% that were covered 100% by the query sequences. For each primer, the percentage of species-level coverage with no ASI≥97% (SC-NASI≥97%) was calculated. Based on the SC-NASI≥97% values, the best primer pairs were OP_F053-KP_R020 for bacteria (65.05%), KP_F018-KP_R002 for archaea (51.11%), and OP_F114-KP_R031 for bacteria and archaea together (52.02%). Eighty percent of the oral-bacteria and oral-archaea species shared an ASI≥97% with at least one other taxa, including Campylobacter, Rothia, Streptococcus, and Tannerella, which played conflicting roles in the oral microbiota. Moreover, around a quarter and a third of these two-by-two similarity relationships were between species from different bacteria and archaea genera, respectively. Furthermore, even taxa from distinct families, orders, and classes could be grouped in the same cluster. Consequently, irrespective of the primer pair used, OTUs constructed with a 97% similarity provide an inaccurate description of oral-bacterial and oral-archaeal species, greatly affecting microbial diversity parameters. As a result, clustering by OTUs impacts the credibility of the associations between some oral species and certain health and disease conditions. This limits significantly the comparability of the microbial diversity findings reported in oral microbiome literature.

Sympatric threatened Iberian leuciscids exhibit differences in Aeromonas diversity and skin lesions’ prevalence

Assessments regarding health aspects of Iberian leuciscids are limited. There is currently an information gap regarding effects of infectious diseases on these populations and their role as a possible conservation threat. Moreover, differences in susceptibility to particular agents, such as Aeromonas spp., by different species/populations is not clear. To understand potential differences in Aeromonas diversity and load, as well as in the prevalence and proportion of skin lesions, in fishes exposed to similar environmental conditions, an observational study was implemented. Using a set of 12 individuals belonging to two sympatric Iberian leuciscid species (Squalius pyrenaicus and Iberochondrostoma lusitanicum), the skin lesion score in each individual was analyzed. Furthermore, a bacterial collection of Aeromonas spp. isolated from each individual was created and isolates’ load was quantified by plate counting, identified at species level using a multiplex-PCR assay and virulence profiles established using classical phenotypic methods. The similarity relationships of the isolates were evaluated using a RAPD analysis. The skin lesion score was significantly higher in S. pyrenaicus, while the Aeromonas spp. load did not differ between species. When analyzing Aeromonas species diversity between fishes, different patterns were observed. A predominance of A. hydrophila was detected in S. pyrenaicus individuals, while I. lusitanicum individuals displayed a more diverse structure. Similarly, the virulence index of isolates from S. pyrenaicus was higher, mostly due to the isolated Aeromonas species. Genomic typing clustered the isolates mainly by fish species and skin lesion score. Specific Aeromonas clusters were associated with higher virulence indexes. Current results suggest potential differences in susceptibility to Aeromonas spp. at the fish species/individual level, and constitute important knowledge for proper wildlife management through the signalization of at-risk fish populations and hierarchization of conservation measures.

Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification

Recent works show that mean-teaching is an effective framework for unsupervised domain adaptive person re-identification. However, existing methods perform contrastive learning on selected samples between teacher and student networks, which is sensitive to noises in pseudo labels and neglects the relationship among most samples. Moreover, these methods are not effective in cooperation of different teacher networks. To handle these issues, this paper proposes a Graph Consistency based Mean-Teaching (GCMT) method with constructing the Graph Consistency Constraint (GCC) between teacher and student networks. Specifically, given unlabeled training images, we apply teacher networks to extract corresponding features and further construct a teacher graph for each teacher network to describe the similarity relationships among training images. To boost the representation learning, different teacher graphs are fused to provide the supervise signal for optimizing student networks. GCMT fuses similarity relationships predicted by different teacher networks as supervision and effectively optimizes student networks with more sample relationships involved. Experiments on three datasets, i.e., Market-1501, DukeMTMCreID, and MSMT17, show that proposed GCMT outperforms state-of-the-art methods by clear margin. Specially, GCMT even outperforms the previous method that uses a deeper backbone. Experimental results also show that GCMT can effectively boost the performance with multiple teacher and student networks. Our code is available at https://github.com/liu-xb/GCMT .

Enriched category as a model of qualia structure based on similarity judgements

Qualitative relationships between two instances of conscious experiences can be quantified through the perceived similarity. Previously, we proposed that by defining similarity relationships as arrows and conscious experiences as objects, we can define a category of qualia in the context of category theory. However, the example qualia categories we proposed were highly idealized and limited to cases where perceived similarity is binary: either present or absent without any gradation. Here, we introduce enriched category theory to address the graded levels of similarity that arises in many instances of qualia. Enriched categories generalize the concept of a relation between objects as a directed arrow (or morphism) in ordinary category theory to a more flexible notion, such as a measure of distance. As an alternative relation, here we propose a graded measure of perceived dissimilarity between the two objects. We claim that enriched categories accommodate various types of conscious experiences. An important extension of this claim is the application of the Yoneda lemma in enriched category; we can characterize a quale through a collection of relationships between the quale and the other qualia up to an (enriched) isomorphism.

Enriched category as a model of qualia structure based on similarity judgements

Qualitative relationships between two instances of conscious experiences can be quantified through the perceived similarity. Previously, we proposed that by defining similarity relationships as arrows and conscious experiences as objects, we can define a category of qualia in the context of category theory. However, the example qualia categories we proposed were highly idealized and limited to cases where perceived similarity is binary: either present or absent without any gradation. When similarity is graded, a situation can arise where A0 is similar to A1, A1 is similar to A2, and so on, yet A0 is not similar to An, which is called the Sorites paradox. Here, we introduce enriched category theory to address this situation. Enriched categories generalize the concept of a relation between objects as a directed arrow (or morphism) in ordinary category theory to a more flexible notion, such as a measure of distance. As an alternative relation, here we propose a graded measure of perceived dissimilarity between the two objects. These measures combine in a way that addresses the Sorites paradox; even if the dissimilarity between Ai and Ai+1 is small for i = 0 … n, hence perceived as similar, the dissimilarity between A0 and An can be large, hence perceived as different. In this way, we show how dissimilarity-enriched categories of qualia resolve the Sorites paradox. We claim that enriched categories accommodate various types of conscious experiences. An important extension of this claim is the application of the Yoneda lemma in enriched category; we can characterize a quale through a collection of relationships between the quale and the other qualia up to an (enriched) isomorphism.

Binary Interactome Models of Inner- Versus Outer-Complexome Organization

The interactome is often conceived of as a collection of hundreds of multimeric machines, collectively the "complexome". However, the large proportion of the proteome that exists outside of the complexome, (the "outer-complexome") is expected to account for most of the functional plasticity exhibited by cellular systems. To compare features of inner- versus outer-complexome organization, we generated an all-by-all yeast systematic binary interactome map, integrated it with previous binary maps, and compared the resulting binary interactome "atlas" with systematic co-complex association and functional similarity network maps. Direct binary protein-protein interactions in the inner-complexome tend to be readily detected in different assays and exhibit high levels of coherence with functional similarity relationships. In contrast, pairs of proteins connected by relatively transient, harder to detect binary interactions in the outer-complexome appear to exhibit higher levels of functional heterogeneity. Thus a small proportion of the interactome corresponds to a stable, functionally homogeneous inner-complexome, while a much greater proportion consists of mostly transient interactions between pairs of functionally heterogeneous proteins in the outer-complexome.