Improving Speed of Dilithium’s Signing Procedure

Dilithium is a round 2 candidate for digital signature schemes in NIST initiative for post-quantum cryptographic schemes. Since Dilithium is built upon the “Fiat Shamir with Aborts” framework, its signing procedure performs rejection sampling of its signatures to ensure they do not leak information about the secret key. Thus, the signing procedure is iterative in nature with a number of rejected iterations, which serve as unnecessary overheads hampering its overall performance. As a first contribution, we propose an optimization that reduces the computations in the rejected iterations through early-evaluation of the conditional checks. This allows to perform an early detection of the rejection condition and reject a given iteration as early as possible. We also incorporate a number of standard optimizations such as unrolling and inlining to further improve the speed of the signing procedure. We incorporate and evaluate our optimizations over the software implementation of Dilithium on both the Intel Core i5-4460 and ARM Cortex-M4 CPUs. As a second contribution, we identify opportunities to present a more refined evaluation of Dilithium’s signing procedure in several scenarios where pre-computations can be carried out. We also evaluate the performance of our optimizations and the memory requirements for the pre-computed intermediates in the considered scenarios. We could yield speed-ups in the range of 6% upto 35%, considering all the aforementioned scenarios, thus presenting the fastest software implementation of Dilithium till date

Gremlin 1 identifies a skeletal stem cell with bone, cartilage, and reticular stromal potential

The stem cells that maintain and repair the postnatal skeleton remain undefined. One model suggests that perisinusoidal mesenchymal stem cells (MSCs) give rise to osteoblasts, chondrocytes, marrow stromal cells, and adipocytes, although the existence of these cells has not been proven through fate-mapping experiments. We demonstrate here that expression of the bone morphogenetic protein (BMP) antagonist gremlin 1 defines a population of osteochondroreticular (OCR) stem cells in the bone marrow. OCR stem cells self-renew and generate osteoblasts, chondrocytes, and reticular marrow stromal cells, but not adipocytes. OCR stem cells are concentrated within the metaphysis of long bones not in the perisinusoidal space and are needed for bone development, bone remodeling, and fracture repair. Grem1 expression also identifies intestinal reticular stem cells (iRSCs) that are cells of origin for the periepithelial intestinal mesenchymal sheath. Grem1 expression identifies distinct connective tissue stem cells in both the bone (OCR stem cells) and the intestine (iRSCs)

A Tale of Two Shares: Why Two-Share Threshold Implementation Seems Worthwhile-and Why it is Not

In this work, we explore the possibilities for practical Threshold Implementation (TI) with only two shares in order for a smaller design that needs less randomness but is still first-order leakage resistant. We present the first two-share Threshold Implementations of two lightweight block ciphers---Simon and Present. The implementation results show that two-share TI gains in compactness while loses in throughput compared with three-share schemes. Moreover, the leakage analyses show that two-share TI retains perfect first-order resistance but is shadowed by a strong second-order leakage, making it less worthwhile

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10−8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10−5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15–0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1–0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈−0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction

Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.</p

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Genome-wide association study identifies 74 loci associated with educational attainment

Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals1. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1,2 of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases

Food waste treatment option selection through spherical fuzzy AHP

In line with the increase in consciousness on sustainability in today's global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions

A polygenic score for educational attainment partially predicts voter turnout

Twin and adoption studies have shown that individual differences in political participation can be explained, in part, by genetic variation. However, these research designs cannot identify which genes are related to voting or the pathways through which they exert influence, and their conclusions rely on possibly restrictive assumptions. In this study, we use three different US samples and a Swedish sample to test whether genes that have been identified as associated with educational attainment, one of the strongest correlates of political participation, predict self-reported and validated voter turnout. We find that a polygenic score capturing individuals' genetic propensity to acquire education is significantly related to turnout. The strongest associations we observe are in second-ordermidterm elections in the United States and European Parliament elections in Sweden, which tend to be viewed as less important by voters, parties, and the media and thus present a more information-poor electoral environment for citizens to navigate. A within-family analysis suggests that individuals' education-linked genes directly affect their voting behavior, but, for second-order elections, it also reveals evidence of genetic nurture. Finally, a mediation analysis suggests that educational attainment and cognitive ability combine to account for between 41% and 63% of the relationship between the genetic propensity to acquire education and voter turnout

Peritonsillar infiltration with levobupivacaine for posttonsillectomy pain relief: does concentration have any effect? A double-blind randomized controlled clinical study

OBJECTIVE: Post-tonsillectomy pain is believed to be mediated by noxious stimulation of C-fiber afferents located in the peritonsillary space, and local anesthetic infiltration to this area may decrease pain by blocking the sensory pathways and thus preventing the nociceptive impulses. We aimed to compare the effects of different concentrations of preincisional peritonsillar levobupivacaine (0.25% and 0.5%) infiltration on postoperative pain and bleeding in a placebo-controlled design