Nonparametric Hamiltonian Monte Carlo

Probabilistic programming uses programs to express generative models whose posterior probability is then computed by built-in inference engines. A challenging goal is to develop general purpose inference algorithms that work out-of-the-box for arbitrary programs in a universal probabilistic programming language (PPL). The densities defined by such programs, which may use stochastic branching and recursion, are (in general) nonparametric, in the sense that they correspond to models on an infinite-dimensional parameter space. However standard inference algorithms, such as the Hamiltonian Monte Carlo (HMC) algorithm, target distributions with a fixed number of parameters. This paper introduces the Nonparametric Hamiltonian Monte Carlo (NP-HMC) algorithm which generalises HMC to nonparametric models. Inputs to NP-HMC are a new class of measurable functions called "tree representable", which serve as a language-independent representation of the density functions of probabilistic programs in a universal PPL. We provide a correctness proof of NP-HMC, and empirically demonstrate significant performance improvements over existing approaches on several nonparametric examples.Comment: Updated plots (after fixing minor bugs in the implementation) compared to the published version in Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021. The conclusions of the version published at ICML 2021 are not affecte

Nonparametric Involutive Markov Chain Monte Carlo

A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL). We present the nonparametric involutive Markov chain Monte Carlo (NP-iMCMC) algorithm as a method for constructing MCMC inference algorithms for nonparametric models expressible in universal PPLs. Building on the unifying involutive MCMC framework, and by providing a general procedure for driving state movement between dimensions, we show that NP-iMCMC can generalise numerous existing iMCMC algorithms to work on nonparametric models. We prove the correctness of the NP-iMCMC sampler. Our empirical study shows that the existing strengths of several iMCMC algorithms carry over to their nonparametric extensions. Applying our method to the recently proposed Nonparametric HMC, an instance of (Multiple Step) NP-iMCMC, we have constructed several nonparametric extensions (all of which new) that exhibit significant performance improvements.Comment: Updated plots (after fixing minor bugs in the implementation) compared to the published version in Proceedings of the 39th International Conference on Machine Learning, PMLR 162:14802-14859, 2022. The conclusions of the version published at ICML 2022 are not affecte

Densities of almost-surely terminating probabilistic programs are differentiable almost everywhere

We study the differential properties of higher-order statistical probabilistic programs with recursion and conditioning. Our starting point is an open problem posed by Hongseok Yang: what class of statistical probabilistic programs have densities that are differentiable almost everywhere? To formalise the problem, we consider Statistical PCF (SPCF), an extension of call-by-value PCF with real numbers, and constructs for sampling and conditioning. We give SPCF a sampling-style operational semantics a la Borgstrom et al., and study the associated weight (commonly referred to as the density) function and value function on the set of possible execution traces. Our main result is that almost-surely terminating SPCF programs, generated from a set of primitive functions (e.g. the set of analytic functions) satisfying mild closure properties, have weight and value functions that are almost-everywhere differentiable. We use a stochastic form of symbolic execution to reason about almost-everywhere differentiability. A by-product of this work is that almost-surely terminating deterministic (S)PCF programs with real parameters denote functions that are almost-everywhere differentiable. Our result is of practical interest, as almost-everywhere differentiability of the density function is required to hold for the correctness of major gradient-based inference algorithms

A framework of environmental mitigation for the convention and exhibition centers in the China greater Bay area

The numerous participants in convention and exhibition (C&E) events and the consequently huge consumption of direct and indirect resources have increased the environmental pressure on C&E centers to implement environmentally friendly practices and procedures. This paper explores the innovative methods adopted by green-certified C&E centers and synthesizes a reference framework of environmental mitigation practices for the C&E sector in the Greater Bay Area, which was recently designated as a major regional development area in China. Eleven green-certified C&E centers were selected to establish a comprehensive and indicative framework containing 59 actual environmental practices in three major categories. Suggestions made by 12 experts for modifying the fit of the constructed framework to suit the local geographical and climatic situations of C&E centers in the China Greater Bay Area were examined

Intrinsic Disorder in BAP1 and Its Association with Uveal Melanoma.

BACKGROUND: Specific subvariants of uveal melanoma (UM) are associated with increased rates of metastasis compared to other subvariants. BRCA1 (BReast CAncer gene 1)-associated protein-1 (BAP1) is encoded by a gene that has been linked to aggressive behavior in UM. METHODS: We evaluated BAP1 for the presence of intrinsically disordered protein regions (IDPRs) and its protein-protein interactions (PPI). We evaluated specific sequence-based features of the BAP1 protein using a set of bioinformatic databases, predictors, and algorithms. RESULTS: We show that BAP1\u27s structure contains extensive IDPRs as it is highly enriched in proline residues (the most disordered amino acid; p-value \u3c 0.05), the average percent of predicted disordered residues (PPDR) was 57.34%, and contains 9 disorder-based binding sites (ie. molecular recognition features (MoRFs)). BAP1\u27s intrinsic disorder allows it to engage in a complex PPI network with at least 49 partners (p-value \u3c 1.0 × 10-16). CONCLUSION: These findings show that BAP1 contains IDPRs and an intricate PPI network. Mutations in UM that are associated with the BAP1 gene may alter the function of the IDPRs embedded into its structure. These findings develop the understanding of UM and may provide a target for potential novel therapies to treat this aggressive neoplasm

Intrinsic Disorder in PRAME and Its Role in Uveal Melanoma

Introduction The PReferentially expressed Antigen in MElanoma (PRAME) protein has been shown to be an independent biomarker for increased risk of metastasis in Class 1 uveal melanomas (UM). Intrinsically disordered proteins and regions of proteins (IDPs/IDPRs) are proteins that do not have a well-defined three-dimensional structure and have been linked to neoplastic development. Our study aimed to evaluate the presence of intrinsic disorder in PRAME and the role these structureless regions have in PRAME( +) Class 1 UM. Methods A bioinformatics study to characterize PRAME’s propensity for the intrinsic disorder. We first used the AlphaFold tool to qualitatively assess the protein structure of PRAME. Then we used the Compositional Profiler and a set of per-residue intrinsic disorder predictors to quantify the intrinsic disorder. The Database of Disordered Protein Prediction (D2P2) platform, IUPred, FuzDrop, fIDPnn, AUCpred, SPOT-Disorder2, and metapredict V2 allowed us to evaluate the potential functional disorder of PRAME. Additionally, we used the Search Tool for the Retrieval of Interacting Genes (STRING) to analyze PRAME\u27s potential interactions with other proteins. Results Our structural analysis showed that PRAME contains intrinsically disordered protein regions (IDPRs), which are structureless and flexible. We found that PRAME is significantly enriched with serine (p-value \u3c 0.05), a disorder-promoting amino acid. PRAME was found to have an average disorder score of 16.49% (i.e., moderately disordered) across six per-residue intrinsic disorder predictors. Our IUPred analysis revealed the presence of disorder-to-order transition (DOT) regions in PRAME near the C-terminus of the protein (residues 475–509). The D2P2 platform predicted a region from approximately 140 and 175 to be highly concentrated with post-translational modifications (PTMs). FuzDrop predicted the PTM hot spot of PRAME to be a droplet-promoting region and an aggregation hotspot. Finally, our analysis using the STRING tool revealed that PRAME has significantly more interactions with other proteins than expected for randomly selected proteins of the same size, with the ability to interact with 84 different partners (STRING analysis result: p-value \u3c 1.0 × 10–16; model confidence: 0.400). Conclusion Our study revealed that PRAME has IDPRs that are possibly linked to its functionality in the context of Class 1 UM. The regions of functionality (i.e., DOT regions, PTM sites, droplet-promoting regions, and aggregation hotspots) are localized to regions of high levels of disorder. PRAME has a complex protein–protein interaction (PPI) network that may be secondary to the structureless features of the polypeptide. Our findings contribute to our understanding of UM and suggest that IDPRs and DOT regions in PRAME may be targeted in developing new therapies for this aggressive cancer

The Grizzly, September 25, 1987

Wild Weekend: Tippler Topples, Vandals Varnish, Class Cutters Cavort • Sororities to Begin Formal Rushing Season • Freshmen Find Fun on Campus • Letters: Unholy Parent\u27s Day Irks Jews; Old Men\u27s Life Bad News; Students Have Bills to Pay, Too • Freshman AIDS Orientation • Domestic Violence an Issue • Cameron a Pro Habla-ing • House Bill 749 • Victorious Volleyballers • Soccer\u27s Hoover Earns Athlete of the Week • Football Falls to F&M • Scabs to Score for NFL? • Cross Country Running to the Top • Hockey Lashes LaSalle • Busie Bodys Rehearse • Lantern Announces Deadline • All Greeks Not Geeks • Nautical Natives Sailing with Club Revival • Fat Fear: Freshman Fifteen Thickens Frosh • Ills a Problem Already • E-burg Offers Basic Grub • It\u27s Your Future • CAB Gets Some Public Relations • As Members Drop, the Show Must Go On • Entertainment: Ursinus Stylehttps://digitalcommons.ursinus.edu/grizzlynews/1193/thumbnail.jp

Surveillance of emerging drugs of abuse in Hong Kong: Validation of an analytical tool

© 2015, Hong Kong Academy of Medicine Press. All rights reserved. Objective: To validate a locally developed chromatography-based method to monitor emerging drugs of abuse whilst performing regular drug testing in abusers. Design: Cross-sectional study. Setting: Eleven regional hospitals, seven social service units, and a tertiary level clinical toxicology laboratory in Hong Kong. Participants: A total of 972 drug abusers and high-risk individuals were recruited from acute, rehabilitation, and high-risk settings between 1 November 2011 and 31 July 2013. A subset of the participants was of South Asian ethnicity. In total, 2000 urine or hair specimens were collected. Main outcome measures: Proof of concept that surveillance of emerging drugs of abuse can be performed whilst conducting routine drug of abuse testing in patients. Results: The method was successfully applied to 2000 samples with three emerging drugs of abuse detected in five samples: PMMA (paramethoxymethamphetamine), TFMPP [1-(3-trifluoromethylphenyl)piperazine], and methcathinone. The method also detected conventional drugs of abuse, with codeine, methadone, heroin, methamphetamine, and ketamine being the most frequently detected drugs. Other findings included the observation that South Asians had significantly higher rates of using opiates such as heroin, methadone, and codeine; and that ketamine and cocaine had significantly higher detection rates in acute subjects compared with the rehabilitation population. Conclusions: This locally developed analytical method is a valid tool for simultaneous surveillance of emerging drugs of abuse and routine drug monitoring of patients at minimal additional cost and effort. Continued, proactive surveillance and early identification of emerging drugs will facilitate prompt clinical, social, and legislative management.Link_to_subscribed_fulltex