Locality of Not-So-Weak Coloring

Many graph problems are locally checkable: a solution is globally feasible if it looks valid in all constant-radius neighborhoods. This idea is formalized in the concept of locally checkable labelings (LCLs), introduced by Naor and Stockmeyer (1995). Recently, Chang et al. (2016) showed that in bounded-degree graphs, every LCL problem belongs to one of the following classes: - "Easy": solvable in $O(\log^* n)$ rounds with both deterministic and randomized distributed algorithms. - "Hard": requires at least $\Omega(\log n)$ rounds with deterministic and $\Omega(\log \log n)$ rounds with randomized distributed algorithms. Hence for any parameterized LCL problem, when we move from local problems towards global problems, there is some point at which complexity suddenly jumps from easy to hard. For example, for vertex coloring in $d$-regular graphs it is now known that this jump is at precisely $d$ colors: coloring with $d+1$ colors is easy, while coloring with $d$ colors is hard. However, it is currently poorly understood where this jump takes place when one looks at defective colorings. To study this question, we define $k$-partial $c$-coloring as follows: nodes are labeled with numbers between $1$ and $c$, and every node is incident to at least $k$ properly colored edges. It is known that $1$-partial $2$-coloring (a.k.a. weak $2$-coloring) is easy for any $d \ge 1$. As our main result, we show that $k$-partial $2$-coloring becomes hard as soon as $k \ge 2$, no matter how large a $d$ we have. We also show that this is fundamentally different from $k$-partial $3$-coloring: no matter which $k \ge 3$ we choose, the problem is always hard for $d = k$ but it becomes easy when $d \gg k$. The same was known previously for partial $c$-coloring with $c \ge 4$, but the case of $c < 4$ was open

BiDAl: Big Data Analyzer for Cluster Traces

Modern data centers that provide Internet-scale services are stadium-size structures housing tens of thousands of heterogeneous devices (server clusters, networking equipment, power and cooling infrastructures) that must operate continuously and reliably. As part of their operation, these devices produce large amounts of data in the form of event and error logs that are essential not only for identifying problems but also for improving data center efficiency and management. These activities employ data analytics and often exploit hidden statistical patterns and correlations among different factors present in the data. Uncovering these patterns and correlations is challenging due to the sheer volume of data to be analyzed. This paper presents BiDAl, a prototype “log-data analysis framework” that incorporates various Big Data technologies to simplify the analysis of data traces from large clusters. BiDAl is written in Java with a modular and extensible architecture so that different storage backends (currently, HDFS and SQLite are supported), as well as different analysis languages (current implementation supports SQL, R and Hadoop MapReduce) can be easily selected as appropriate. We present the design of BiDAl and describe our experience using it to analyze several public traces of Google data clusters for building a simulation model capable of reproducing observed behavior

A Guide to Directing Group Removal: 8‐Aminoquinoline

The use of directing groups allows high levels of selectivity to be achieved in transition metal‐catalyzed transformations. Efficient removal of these auxiliaries after successful functionalization, however, can be very challenging. This review provides a critical overview of strategies used for removal of Daugulis’ 8‐aminoquinoline (2005–2020), one of the most widely used N,N‐bidentate directing groups. The limitations of these strategies are discussed and alternative approaches are suggested for challenging substrates. Our aim is to provide a comprehensive end‐users’ guide for chemists in academia and industry who want to harness the synthetic power of directing groups—and be able to remove them from their final products

The new low-toxic histone deacetylase inhibitor S-(2) induces apoptosis in various acute myeloid leukemia cells

Histone deacetylase inhibitors (HDACi) induce tumour cell cycle arrest and/or apoptosis, and some of them are currently used in cancer therapy. Recently, we described a series of powerful HDACi characterized by a 1,4-benzodiazepine (BDZ) ring hybridized with a linear alkyl chain bearing a hydroxamate function as Zn(++)-chelating group. Here, we explored the anti-leukaemic properties of three novel hybrids, namely the chiral compounds (S)-2 and (R)-2, and their non-chiral analogue 4, which were first comparatively tested in promyelocytic NB4 cells. (S)-2 and partially 4– but not (R)-2 – caused G0/G1 cell-cycle arrest by up-regulating cyclin G2 and p21 expression and down-regulating cyclin D2 expression, and also apoptosis as assessed by cell morphology and cytofluorimetric assay, histone H2AX phosphorylation and PARP cleavage. Notably, these events were partly prevented by an anti-oxidant. Moreover, novel HDACi prompted p53 and α-tubulin acetylation and, consistently, inhibited HDAC1 and 6 activity. The rank order of potency was (S)-2 > 4 > (R)-2, reflecting that of other biological assays and addressing (S)-2 as the most effective compound capable of triggering apoptosis in various acute myeloid leukaemia (AML) cell lines and blasts from patients with different AML subtypes. Importantly, (S)-2 was safe in mice (up to 150 mg/kg/week) as determined by liver, spleen, kidney and bone marrow histopathology; and displayed negligible affinity for peripheral/central BDZ-receptors. Overall, the BDZ-hydroxamate (S)-2 showed to be a low-toxic HDACi with powerful anti-proliferative and pro-apototic activities towards different cultured and primary AML cells, and therefore of clinical interest to support conventional anti-leukaemic therapy

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution

Faba Bean Cultivation – Revealing Novel Managing Practices for More Sustainable and Competitive European Cropping Systems

Faba beans are highly nutritious because of their high protein content: they are a good source of mineral nutrients, vitamins, and numerous bioactive compounds. Equally important is the contribution of faba bean in maintaining the sustainability of agricultural systems, as it is highly efficient in the symbiotic fixation of atmospheric nitrogen. This article provides an overview of factors influencing faba bean yield and quality, and addresses the main biotic and abiotic constraints. It also reviews the factors relating to the availability of genetic material and the agronomic features of faba bean production that contribute to high yield and the improvement of European cropping systems. Emphasis is to the importance of using new high-yielding cultivars that are characterized by a high protein content, low antinutritional compound content, and resistance to biotic and abiotic stresses. New cultivars should combine several of these characteristics if an increased and more stable production of faba bean in specific agroecological zones is to be achieved. Considering that climate change is also gradually affecting many European regions, it is imperative to breed elite cultivars that feature a higher abiotic–biotic stress resistance and nutritional value than currently used cultivars. Improved agronomical practices for faba bean crops, such as crop establishment and plant density, fertilization and irrigation regime, weed, pest and disease management, harvesting time, and harvesting practices are also addressed, since they play a crucial role in both the production and quality of faba bean

The impact of sex on gene expression across human tissues

Many complex human phenotypes exhibit sex-differentiated characteristics. However, the molecular mechanisms underlying these differences remain largely unknown. We generated a catalog of sex differences in gene expression and in the genetic regulation of gene expression across 44 human tissue sources surveyed by the Genotype-Tissue Expression project (GTEx, v8 release). We demonstrate that sex influences gene expression levels and cellular composition of tissue samples across the human body. A total of 37% of all genes exhibit sex-biased expression in at least one tissue. We identify cis expression quantitative trait loci (eQTLs) with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation of gene expression in a single sex. These findings provide an extensive characterization of sex differences in the human transcriptome and its genetic regulation

The impact of sex on gene expression across human tissues

Many complex human phenotypes exhibit sex-differentiated characteristics, however the underlying molecular mechanisms of these differences remain largely unknown. Here, we present an extensive catalog of both sex differences in gene expression and its genetic regulation across 44 human tissue sources surveyed by GTEx (v8 release). We demonstrate that sex strongly influences gene expression levels and cellular composition of tissue samples across the human body. The effect of sex on gene expression is widespread, with a total of 37% of all genes exhibiting sex-biased expression in at least one tissue. This suggests that many if not most biological processes, and thus complex traits and diseases, are impacted by sex effects on the transciptome. We expand the identification of cis-eQTLs with sex-differentiated effects and characterize their cellular origin. By integrating sex-biased eQTLs with genome-wide association study data, we identify 58 gene-trait associations that are driven by genetic regulation in a single sex, including novel associations not detected with sex-agnostic approaches. Altogether we provide the most comprehensive characterization of sex differences in the human transcriptome and its regulation to date.Peer ReviewedPreprin

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology

Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes