Open Science: Tools, approaches, and implications

The Pacific Symposium on Biocomputing is an annual meeting whose topics are determined by proposals submitted by members of the community. This document is the proposal for a session on Open Science, submitted for consideration for the PSB meeting in 2009

Late-onset secondary pigmentary glaucoma following foldable intraocular lenses implantation in the ciliary sulcus: a long-term follow-up study

BACKGROUND: To review the long-term outcomes of eyes with secondary pigmentary glaucoma associated with the implantation of foldable intraocular lenses (IOL) in the ciliary sulcus. METHODS: The study retrospectively reviewed a series of cases who developed secondary pigmentary glaucoma after cataract operations. Data were collected from cases that were referred between 2002 and 2011. RESULTS: Ten eyes of 10 patients who developed secondary pigmentary glaucoma after foldable IOLs implantation in the sulcus were included in this study. Intraocular pressure (IOP) elevation was present in 2 eyes (20%) within the first 2 weeks following the initial cataract operation. The onset of glaucoma was delayed in the other 8 eyes (80%); the average onset time in these eyes was 21.9 ± 17.1 months after the initial cataract operation. Six eyes (60%) received surgical treatment because of large fluctuations and poor control of IOPs. Only 3 eyes (30%) achieved final visual acuities better than 20/40. CONCLUSION: Secondary pigmentary glaucoma accompanying the implantation of a foldable IOL in the ciliary sulcus may present as acute IOP elevation during the early postoperative period or, more commonly, late onset of IOP elevation accompanied by advanced glaucomatous optic nerve damage. Despite treatment, the visual prognosis for these patients can be poor. Placing a foldable IOL in the ciliary sulcus could pose a threat to the vision of the patients and long-term follow-up of IOP in these patients is necessary

Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues

<p>Abstract</p> <p>Background</p> <p>The emergence of structural genomics presents significant challenges in the annotation of biologically uncharacterized proteins. Unfortunately, our ability to analyze these proteins is restricted by the limited catalog of known molecular functions and their associated 3D motifs.</p> <p>Results</p> <p>In order to identify novel 3D motifs that may be associated with molecular functions, we employ an unsupervised, two-phase clustering approach that combines k-means and hierarchical clustering with knowledge-informed cluster selection and annotation methods. We applied the approach to approximately 20,000 cysteine-based protein microenvironments (3D regions 7.5 Å in radius) and identified 70 interesting clusters, some of which represent known motifs (<it>e.g</it>. metal binding and phosphatase activity), and some of which are novel, including several zinc binding sites. Detailed annotation results are available online for all 70 clusters at <url>http://feature.stanford.edu/clustering/cys</url>.</p> <p>Conclusions</p> <p>The use of microenvironments instead of backbone geometric criteria enables flexible exploration of protein function space, and detection of recurring motifs that are discontinuous in sequence and diverse in structure. Clustering microenvironments may thus help to functionally characterize novel proteins and better understand the protein structure-function relationship.</p

Semi-analytical stochastic study of radionuclide transport in the saturated zone below Yucca Mountain

The objective of this study is to predict radionuclide solute transport process in the saturated zone below the Yucca Mountain project area. Based on a stochastic perturbation approach, a numerical method of moments has been developed and used to predict the mean, variance and upper bound of the radionuclide mass flux through a control plane 5-km downstream of the footprint of the repository. This study enhances the analysis of the effect of medium’s heterogeneity on solute transport prediction, especially on prediction uncertainty

The Physical Properties of High-Mass Star-Forming Clumps: A Systematic Comparison of Molecular Tracers

We present observations of HCO+ and H^13CO+, N2H+, HCS+, HCN and HN^13C, SO and ^34SO, CCH, SO_2, and CH_3OH-E towards a sample of 27 high-mass clumps coincident with water maser emission. All transitions are observed with or convolved to nearly identical resolution (30"), allowing for inter-comparison of the clump properties derived from the mapped transitions. We find N2H+ emission is spatially differentiated compared to the dust and the other molecules towards a few very luminous cores (10 of 27) and the N2H+ integrated intensity does not correlate well with dust continuum flux. We calculate the effective excitation density, n_eff, the density required to excite a 1 K line in T_kin=20 K gas for each molecular tracer. The intensity of molecular tracers with larger effective excitation densities (n_eff > 10^5 cm^-3) appear to correlate more strongly with the submillimeter dust continuum intensity. The median sizes of the clumps are anti-correlated with the n_eff of the tracers (which span more than three orders of magnitude). Virial mass is not correlated with n_eff, especially where the lines are optically thick as the linewidths may be broadened significantly by non-virial motions. The median mass surface density and median volume density of the clumps is correlated with n_eff indicating the importance of understanding the excitation conditions of the molecular tracer when deriving the average properties of an ensemble of cores.Comment: 75 pages, 38 figure

The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation

SeqFEATURE, a tool for protein function annotation, models protein functions described by sequence motifs using a structural representation. The tool shows significantly improved performance over other methods when sequence and structural similarity are low

Discover and Cure: Concept-aware Mitigation of Spurious Correlation

Deep neural networks often rely on spurious correlations to make predictions, which hinders generalization beyond training environments. For instance, models that associate cats with bed backgrounds can fail to predict the existence of cats in other environments without beds. Mitigating spurious correlations is crucial in building trustworthy models. However, the existing works lack transparency to offer insights into the mitigation process. In this work, we propose an interpretable framework, Discover and Cure (DISC), to tackle the issue. With human-interpretable concepts, DISC iteratively 1) discovers unstable concepts across different environments as spurious attributes, then 2) intervenes on the training data using the discovered concepts to reduce spurious correlation. Across systematic experiments, DISC provides superior generalization ability and interpretability than the existing approaches. Specifically, it outperforms the state-of-the-art methods on an object recognition task and a skin-lesion classification task by 7.5% and 9.6%, respectively. Additionally, we offer theoretical analysis and guarantees to understand the benefits of models trained by DISC. Code and data are available at https://github.com/Wuyxin/DISC.Comment: ICML 202

D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion

The widespread deployment of Graph Neural Networks (GNNs) sparks significant interest in their explainability, which plays a vital role in model auditing and ensuring trustworthy graph learning. The objective of GNN explainability is to discern the underlying graph structures that have the most significant impact on model predictions. Ensuring that explanations generated are reliable necessitates consideration of the in-distribution property, particularly due to the vulnerability of GNNs to out-of-distribution data. Unfortunately, prevailing explainability methods tend to constrain the generated explanations to the structure of the original graph, thereby downplaying the significance of the in-distribution property and resulting in explanations that lack reliability. To address these challenges, we propose D4Explainer, a novel approach that provides in-distribution GNN explanations for both counterfactual and model-level explanation scenarios. The proposed D4Explainer incorporates generative graph distribution learning into the optimization objective, which accomplishes two goals: 1) generate a collection of diverse counterfactual graphs that conform to the in-distribution property for a given instance, and 2) identify the most discriminative graph patterns that contribute to a specific class prediction, thus serving as model-level explanations. It is worth mentioning that D4Explainer is the first unified framework that combines both counterfactual and model-level explanations. Empirical evaluations conducted on synthetic and real-world datasets provide compelling evidence of the state-of-the-art performance achieved by D4Explainer in terms of explanation accuracy, faithfulness, diversity, and robustness.Comment: Accepted at NeurIPS 2023, Camera Ready Versio