Nearly irreducibility of polynomials and the Newton diagrams

Let f be a polynomial in two complex variables. We say that f is nearly irreducible if any two nonconstant polynomial factors of f have a common zero in C2. In the paper we give a criterion of nearly irreducibility for a given polynomial f in terms of its Newton diagram.

Artin L-functions of almost monomial Galois groups

If {K/\mathbb{Q}} is a finite Galois extension with an almost monomial Galois group and if {s_{0}\in\mathbb{C}\setminus\{1\}} is not a common zero for any two Artin L-functions associated to distinct complex irreducible characters of the Galois group, then all Artin L-functions of {K/\mathbb{Q}} are holomorphic at {s_{0}}. We present examples and basic properties of almost monomial groups.

Naught all zeros in sequence count data are the same

Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply common zero-handling models to gene-expression and microbiome datasets and show models disagree on average by 46% in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as “zero-inflation” was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.