Generalizing Gaussian Smoothing for Random Search

Gaussian smoothing (GS) is a derivative-free optimization (DFO) algorithm that estimates the gradient of an objective using perturbations of the current parameters sampled from a standard normal distribution. We generalize it to sampling perturbations from a larger family of distributions. Based on an analysis of DFO for non-convex functions, we propose to choose a distribution for perturbations that minimizes the mean squared error (MSE) of the gradient estimate. We derive three such distributions with provably smaller MSE than Gaussian smoothing. We conduct evaluations of the three sampling distributions on linear regression, reinforcement learning, and DFO benchmarks in order to validate our claims. Our proposal improves on GS with the same computational complexity, and are usually competitive with and often outperform Guided ES and Orthogonal ES, two computationally more expensive algorithms that adapt the covariance matrix of normally distributed perturbations.Comment: This work was published at ICML 2022. This version contains some minor corrections and a link to a code repositor

Identification and stage-specific association with the translational apparatus of TbZFP3, a CCCH protein that promotes trypanosome life-cycle development

The post-transcriptional control of gene expression is becoming increasingly important in the understanding of regulated events in eukaryotic cells. The parasitic kinetoplastids have a unique reliance on such processes, because their genome is organized into polycistronic transcription units in which adjacent genes are not coordinately regulated. Indeed, the number of RNA-binding proteins predicted to be encoded in the genome of kinetoplastids is unusually large, invoking the presence of unique RNA regulators dedicated to gene expression in these evolutionarily ancient organisms. Here, we report that a small CCCH zinc finger protein, TbZFP3, enhances development between life-cycle stages in Trypanosoma brucei. Moreover, we demonstrate that this protein interacts both with the translational machinery and with other small CCCH proteins previously implicated in trypanosome developmental control. Antibodies to this protein also co-immunoprecipitate EP procyclin mRNA and encode the major surface antigen of insect forms of T. brucei. Strikingly, although TbZFP3 is constitutively expressed, it exhibits developmentally regulated association with polyribosomes, and mutational analysis demonstrates that this association is essential for the expression of phenotype. TbZFP3 is therefore a novel regulator of developmental events in kinetoplastids that acts at the level of the post-transcriptional control of gene expression

The post-transcriptional trans-acting regulator, TbZFP3, co-ordinates transmission-stage enriched mRNAs in Trypanosoma brucei

Post-transcriptional gene regulation is essential to eukaryotic development. This is particularly emphasized in trypanosome parasites where genes are co-transcribed in polycistronic arrays but not necessarily co-regulated. The small CCCH protein, TbZFP3, has been identified as a trans-acting post-transcriptional regulator of Procyclin surface antigen expression in Trypanosoma brucei. To investigate the wider role of TbZFP3 in parasite transmission, a global analysis of associating transcripts was carried out. Examination of a subset of the selected transcripts revealed their increased abundance through mRNA stabilization upon TbZFP3 ectopic overexpression, dependent upon the integrity of the CCCH zinc finger domain. Reporter assays demonstrated that this regulation was mediated through 3′-UTR sequences for two target transcripts. Global developmental expression profiling of the cohort of TbZFP3-selected transcripts revealed their significant enrichment in transmissible stumpy forms of the parasite. This analysis of the specific mRNAs selected by the TbZFP3mRNP provides evidence for a developmental regulon with the potential to co-ordinate genes important in parasite transmission

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Dual QM and MM Approach for Computing Equilibrium Isotope Fractionation Factor of Organic Species in Solution

A dual QM and MM approach for computing equilibrium isotope effects has been described. In the first partition, the potential energy surface is represented by a combined quantum mechanical and molecular mechanical (QM/MM) method, in which a solute molecule is treated quantum mechanically, and the remaining solvent molecules are approximated classically by molecular mechanics. In the second QM/MM partition, differential nuclear quantum effects responsible for the isotope effect are determined by a statistical mechanical double-averaging formalism, in which the nuclear centroid distribution is sampled classically by Newtonian molecular dynamics and the quantum mechanical spread of quantized particles about the centroid positions is treated using the path integral (PI) method. These partitions allow the potential energy surface to be properly represented such that the solute part is free of nuclear quantum effects for nuclear quantum mechanical simulations, and the double-averaging approach has the advantage of sampling efficiency for solvent configuration and for path integral convergence. Importantly, computational precision is achieved through free energy perturbation (FEP) theory to alchemically mutate one isotope into another. The PI-FEP approach is applied to model systems for the 18O enrichment found in cellulose of trees to determine the isotope enrichment factor of carbonyl compounds in water. The present method may be useful as a general tool for studying isotope fractionation in biological and geochemical systems

An Approach to Identify the Number of Clusters

In this technological age, vast amounts of data are generated. Various statistical methods are used to find patterns in data, including clustering. Many common methods for cluster analysis, such as k-means and Nonnegative Matrix Factorization, require input of the number of clusters in the data. However, usually that number is unknown. There exists a method that uses eigenvalues to compute the number of clusters, but sometimes it underestimates that number. In this paper, we propose a complementary method to identify the number of clusters. This method is used to analyze three data sets and gives fairly accurate estimates of the number of clusters.