An effective Chebotarev density theorem for families of number fields, with an application to ℓ\ell-torsion in class groups

We prove a new effective Chebotarev density theorem for Galois extensions $L/\mathbb{Q}$ that allows one to count small primes (even as small as an arbitrarily small power of the discriminant of $L$); this theorem holds for the Galois closures of "almost all" number fields that lie in an appropriate family of field extensions. Previously, applying Chebotarev in such small ranges required assuming the Generalized Riemann Hypothesis. The error term in this new Chebotarev density theorem also avoids the effect of an exceptional zero of the Dedekind zeta function of $L$, without assuming GRH. We give many different "appropriate families," including families of arbitrarily large degree. To do this, we first prove a new effective Chebotarev density theorem that requires a zero-free region of the Dedekind zeta function. Then we prove that almost all number fields in our families yield such a zero-free region. The innovation that allows us to achieve this is a delicate new method for controlling zeroes of certain families of non-cuspidal $L$-functions. This builds on, and greatly generalizes the applicability of, work of Kowalski and Michel on the average density of zeroes of a family of cuspidal $L$-functions. A surprising feature of this new method, which we expect will have independent interest, is that we control the number of zeroes in the family of $L$-functions by bounding the number of certain associated fields with fixed discriminant. As an application of the new Chebotarev density theorem, we prove the first nontrivial upper bounds for $\ell$-torsion in class groups, for all integers $\ell \geq 1$, applicable to infinite families of fields of arbitrarily large degree.Comment: 52 pages. This shorter version aligns with the published paper. Note that portions of Section 8 of the longer v1 have been developed as a separate paper with identifier arXiv:1902.0200

Advances in precision medicine: tailoring individualised therapies

The traditional bench-to-bedside pipeline involves using model systems and patient samples to provide insights into pathways deregulated in cancer. This discovery reveals new biomarkers and therapeutic targets, ultimately stratifying patients and informing cohort-based treatment options. Precision medicine (molecular profiling of individual tumors combined with established clinical-pathological parameters) reveals, in real-time, individual patient's diagnostic and prognostic risk profile, informing tailored and tumor-specific treatment plans. Here we discuss advances in precision medicine presented at the Irish Association for Cancer Research Annual Meeting, highlighting examples where personalized medicine approaches have led to precision discovery in individual tumors, informing customized treatment programs

Big data-led cancer research, applications and insights

Insights distilled from integratingmultiple big-data or "omic" datasets have revealed functional hierarchies of molecular networks driving tumorigenesis and modifiers of treatment response. Identifying these novel key regulatory and dysregulated elements is now informing personalized medicine. Crucially, although there are many advantages to this approach, there are several key considerations to address. Here, we examine how this big data-led approach is impacting many diverse areas of cancer research, through review of the key presentations given at the Irish Association for Cancer Research Meeting and importantly how the results may be applied to positively affect patient outcomes

High Precision Attitude Reference System /HPARS/ Final report

Test facilities for attitude control using analog and computerized simulation

Multiplex Screening for Interacting Compounds in Paediatric Acute Myeloid Leukaemia

Paediatric acute myeloid leukaemia (AML) is a heterogeneous disease characterised by the malignant transformation of myeloid precursor cells with impaired differentiation. Standard therapy for paediatric AML has remained largely unchanged for over four decades and, combined with inadequate understanding of the biology of paediatric AML, has limited the progress of targeted therapies in this cohort. In recent years, the search for novel targets for the treatment of paediatric AML has accelerated in parallel with advanced genomic technologies which explore the mutational and transcriptional landscape of this disease. Exploiting the large combinatorial space of existing drugs provides an untapped resource for the identification of potential combination therapies for the treatment of paediatric AML. We have previously designed a multiplex screening strategy known as Multiplex Screening for Interacting Compounds in AML (MuSICAL); using an algorithm designed in-house, we screened all pairings of 384 FDA-approved compounds in less than 4000 wells by pooling drugs into 10 compounds per well. This approach maximised the probability of identifying new compound combinations with therapeutic potential while minimising cost, replication and redundancy. This screening strategy identified the triple combination of glimepiride, a sulfonylurea; pancuronium dibromide, a neuromuscular blocking agent; and vinblastine sulfate, a vinca alkaloid, as a potential therapy for paediatric AML. We envision that this approach can be used for a variety of disease-relevant screens allowing the efficient repurposing of drugs that can be rapidly moved into the clinic

Nano-encapsulation of a novel anti-Ran-GTPase peptide for blockade of regulator of chromosome condensation (RCC1) function in MDA-MB-231 breast cancer cells

Ran is a small ras-related GTPase and is highly expressed in aggressive breast carcinoma. Overexpression induces malignant transformation and drives metastatic growth. We have designed a novel series of anti-Ran-GTPase peptides, which prevents Ran hydrolysis and activation, and although they display effectiveness in silico, peptide activity is suboptimal in vitro due to reduced bioavailability and poor delivery. To overcome this drawback, we delivered an anti-Ran-GTPase peptide using encapsulation in PLGA-based nanoparticles (NP). Formulation variables within a double emulsion solvent evaporation technique were controlled to optimise physicochemical properties. NP were spherical and negatively charged with a mean diameter of 182–277 nm. Peptide integrity and stability were maintained after encapsulation and release kinetics followed a sustained profile. We were interested in the relationship between cellular uptake and poly(ethylene glycol) (PEG) in the NP matrix, with results showing enhanced in vitro uptake with increasing PEG content. Peptide-loaded, pegylated (10% PEG)-PLGA NP induced significant cytotoxic and apoptotic effects in MDA-MB-231 breast cancer cells, with no evidence of similar effects in cells pulsed with free peptide. Western blot analysis showed that encapsulated peptide interfered with the proposed signal transduction pathway of the Ran gene. Our novel blockade peptide prevented Ran activation by blockage of regulator of chromosome condensation 1 (RCC1) following peptide release directly in the cytoplasm once endocytosis of the peptide-loaded nanoparticle has occurred. RCC1 blockage was effective only when a nanoparticulate delivery approach was adopted