Heavy-tailed Independent Component Analysis

Independent component analysis (ICA) is the problem of efficiently recovering a matrix $A \in \mathbb{R}^{n\times n}$ from i.i.d. observations of $X=AS$ where $S \in \mathbb{R}^n$ is a random vector with mutually independent coordinates. This problem has been intensively studied, but all existing efficient algorithms with provable guarantees require that the coordinates $S_i$ have finite fourth moments. We consider the heavy-tailed ICA problem where we do not make this assumption, about the second moment. This problem also has received considerable attention in the applied literature. In the present work, we first give a provably efficient algorithm that works under the assumption that for constant $\gamma > 0$, each $S_i$ has finite $(1+\gamma)$-moment, thus substantially weakening the moment requirement condition for the ICA problem to be solvable. We then give an algorithm that works under the assumption that matrix $A$ has orthogonal columns but requires no moment assumptions. Our techniques draw ideas from convex geometry and exploit standard properties of the multivariate spherical Gaussian distribution in a novel way.Comment: 30 page

Combinatorial Pharyngeal Taste Coding for Feeding Avoidance in Adult Drosophila.

Taste drives appropriate food preference and intake. In Drosophila, taste neurons are housed in both external and internal organs, but the latter have been relatively underexplored. Here, we report that Poxn mutants with a minimal taste system of pharyngeal neurons can avoid many aversive tastants, including bitter compounds, acid, and salt, suggesting that pharyngeal taste is sufficient for rejecting intake of aversive compounds. Optogenetic activation of selected pharyngeal bitter neurons during feeding events elicits changes in feeding parameters that can suppress intake. Functional dissection experiments indicate that multiple classes of pharyngeal neurons are involved in achieving behavioral avoidance, by virtue of being inhibited or activated by aversive tastants. Tracing second-order pharyngeal circuits reveals two main relay centers for processing pharyngeal taste inputs. Together, our results suggest that the pharynx can control the ingestion of harmful compounds by integrating taste input from different classes of pharyngeal neurons

Multiparametric MRI and [18F]fluorodeoxyglucose positron emission tomography imaging is a potential prognostic imaging biomarker in recurrent glioblastoma

Purpose/objectivesMultiparametric advanced MR and [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging may be important biomarkers for prognosis as well for distinguishing recurrent glioblastoma multiforme (GBM) from treatment-related changes.Methods/materialsWe retrospectively evaluated 30 patients treated with chemoradiation for GBM and underwent advanced MR and FDG-PET for confirmation of tumor progression. Multiparametric MRI and FDG-PET imaging metrics were evaluated for their association with 6-month overall (OS) and progression-free survival (PFS) based on pathological, radiographic, and clinical criteria.Results17 males and 13 females were treated between 2001 and 2014, and later underwent FDG-PET at suspected recurrence. Baseline FDG-PET and MRI imaging was obtained at a median of 7.5 months [interquartile range (IQR) 3.7–12.4] following completion of chemoradiation. Median follow-up after FDG-PET imaging was 10 months (IQR 7.2–13.0). Receiver-operator characteristic curve analysis identified that lesions characterized by a ratio of the SUVmax to the normal contralateral brain (SUVmax/NB index) >1.5 and mean apparent diffusion coefficient (ADC) value of ≤1,400 × 10−6 mm2/s correlated with worse 6-month OS and PFS. We defined three patient groups that predicted the probability of tumor progression: SUVmax/NB index >1.5 and ADC ≤1,400 × 10−6 mm2/s defined high-risk patients (n = 7), SUVmax/NB index ≤1.5 and ADC >1,400 × 10−6 mm2/s defined low-risk patients (n = 11), and intermediate-risk (n = 12) defined the remainder of the patients. Median OS following the time of the FDG-PET scan for the low, intermediate, and high-risk groups were 23.5, 10.5, and 3.8 months (p < 0.01). Median PFS were 10.0, 4.4, and 1.9 months (p = 0.03). Rates of progression at 6-months in the low, intermediate, and high-risk groups were 36, 67, and 86% (p = 0.04).ConclusionRecurrent GBM in the molecular era is associated with highly variable outcomes. Multiparametric MR and FDG-PET biomarkers may provide a clinically relevant, non-invasive and cost-effective method of predicting prognosis and improving clinical decision making in the treatment of patients with suspected tumor recurrence

Compiler-aided systematic construction of large-scale DNA strand displacement circuits using unpurified components

Biochemical circuits made of rationally designed DNA molecules are proofs of concept for embedding control within complex molecular environments. They hold promise for transforming the current technologies in chemistry, biology, medicine and material science by introducing programmable and responsive behaviour to diverse molecular systems. As the transformative power of a technology depends on its accessibility, two main challenges are an automated design process and simple experimental procedures. Here we demonstrate the use of circuit design software, combined with the use of unpurified strands and simplified experimental procedures, for creating a complex DNA strand displacement circuit that consists of 78 distinct species. We develop a systematic procedure for overcoming the challenges involved in using unpurified DNA strands. We also develop a model that takes synthesis errors into consideration and semi-quantitatively reproduces the experimental data. Our methods now enable even novice researchers to successfully design and construct complex DNA strand displacement circuits

Gastroesophageal reflux and PPI exposure alter gut microbiota in very young infants

ImportanceInfants with symptomatic Gastroesophageal reflux are treated with pharmacological therapy that includes proton pump inhibitors (PPI) with clinical improvement. The alterations to gut microbiome profiles in comparison to infants without reflux is not known.ObjectiveTo determine the effect of PPI therapy on gut bacterial richness, diversity, and proportions of specific taxa in infants when compared to infants not exposed to acid suppressive therapy.Design, setting, and participantsThis cohort study was conducted at the Stony Brook Hospital in Stony Brook, NY between February 2016, and June 2019. Infants meeting inclusion criteria were enrolled in a consecutive fashion.ResultsA total of 76 Infants were recruited and 60 were enrolled in the study, Twenty nine infants met clinical criteria for reflux and were treated with PPI therapy: median [IQR] gestation: 38.0 weeks [34.7–39.6 weeks]; median [IQR] birthweight: 2.95 Kg [2.2–3.4]; 14 [46.7%] male) and 29 infant were healthy controls median [IQR] gestation: 39.1 weeks [38–40 weeks]; median [IQR] birthweight: 3.3 Kg [2.2–3.4]; 17 [58.6%] male); 58 stool samples from 58 infants were analyzed. There were differences in Shannon diversity between the reflux and control groups. The reflux group that was exposed to PPI therapy had increased relative abundance of a diverse set of genera belonging to the phylum Firmicutes. On the other hand, the control group microbiota was dominated by Bifidobacterium, and a comparatively lower level of enrichment and abundance of microbial taxa was observed in this group of infants.Conclusions and relevanceWe observed significant differences in both α- and β-diversity of the microbiome, when the two groups of infants were compared. The microbiome in the reflux group had more bacterial taxa and the duration of PPIs exposure was clearly associated with the diversity and abundance of gut microbes. These findings suggest that PPI exposure among infants results in early enrichment of the intestinal microbiome

Compiler-aided systematic construction of large-scale DNA strand displacement circuits using unpurified components

Biochemical circuits made of rationally designed DNA molecules are proofs of concept for embedding control within complex molecular environments. They hold promise for transforming the current technologies in chemistry, biology, medicine and material science by introducing programmable and responsive behaviour to diverse molecular systems. As the transformative power of a technology depends on its accessibility, two main challenges are an automated design process and simple experimental procedures. Here we demonstrate the use of circuit design software, combined with the use of unpurified strands and simplified experimental procedures, for creating a complex DNA strand displacement circuit that consists of 78 distinct species. We develop a systematic procedure for overcoming the challenges involved in using unpurified DNA strands. We also develop a model that takes synthesis errors into consideration and semi-quantitatively reproduces the experimental data. Our methods now enable even novice researchers to successfully design and construct complex DNA strand displacement circuits

Single Cell Profiling of Circulating Tumor Cells: Transcriptional Heterogeneity and Diversity from Breast Cancer Cell Lines

BACKGROUND: To improve cancer therapy, it is critical to target metastasizing cells. Circulating tumor cells (CTCs) are rare cells found in the blood of patients with solid tumors and may play a key role in cancer dissemination. Uncovering CTC phenotypes offers a potential avenue to inform treatment. However, CTC transcriptional profiling is limited by leukocyte contamination; an approach to surmount this problem is single cell analysis. Here we demonstrate feasibility of performing high dimensional single CTC profiling, providing early insight into CTC heterogeneity and allowing comparisons to breast cancer cell lines widely used for drug discovery. METHODOLOGY/PRINCIPAL FINDINGS: We purified CTCs using the MagSweeper, an immunomagnetic enrichment device that isolates live tumor cells from unfractionated blood. CTCs that met stringent criteria for further analysis were obtained from 70% (14/20) of primary and 70% (21/30) of metastatic breast cancer patients; none were captured from patients with non-epithelial cancer (n = 20) or healthy subjects (n = 25). Microfluidic-based single cell transcriptional profiling of 87 cancer-associated and reference genes showed heterogeneity among individual CTCs, separating them into two major subgroups, based on 31 highly expressed genes. In contrast, single cells from seven breast cancer cell lines were tightly clustered together by sample ID and ER status. CTC profiles were distinct from those of cancer cell lines, questioning the suitability of such lines for drug discovery efforts for late stage cancer therapy. CONCLUSIONS/SIGNIFICANCE: For the first time, we directly measured high dimensional gene expression in individual CTCs without the common practice of pooling such cells. Elevated transcript levels of genes associated with metastasis NPTN, S100A4, S100A9, and with epithelial mesenchymal transition: VIM, TGFß1, ZEB2, FOXC1, CXCR4, were striking compared to cell lines. Our findings demonstrate that profiling CTCs on a cell-by-cell basis is possible and may facilitate the application of 'liquid biopsies' to better model drug discovery

Logical Analysis of Data (LAD) model for the early diagnosis of acute ischemic stroke

<p>Abstract</p> <p>Background</p> <p>Strokes are a leading cause of morbidity and the first cause of adult disability in the United States. Currently, no biomarkers are being used clinically to diagnose acute ischemic stroke. A diagnostic test using a blood sample from a patient would potentially be beneficial in treating the disease.</p> <p>Results</p> <p>A classification approach is described for differentiating between proteomic samples of stroke patients and controls, and a second novel predictive model is developed for predicting the severity of stroke as measured by the National Institutes of Health Stroke Scale (NIHSS). The models were constructed by applying the Logical Analysis of Data (LAD) methodology to the mass peak profiles of 48 stroke patients and 32 controls. The classification model was shown to have an accuracy of 75% when tested on an independent validation set of 35 stroke patients and 25 controls, while the predictive model exhibited superior performance when compared to alternative algorithms. In spite of their high accuracy, both models are extremely simple and were developed using a common set consisting of only 3 peaks.</p> <p>Conclusion</p> <p>We have successfully identified 3 biomarkers that can detect ischemic stroke with an accuracy of 75%. The performance of the classification model on the validation set and on cross-validation does not deteriorate significantly when compared to that on the training set, indicating the robustness of the model. As in the case of the LAD classification model, the results of the predictive model validate the function constructed on our support-set for approximating the severity scores of stroke patients. The correlation and root mean absolute error of the LAD predictive model are consistently superior to those of the other algorithms used (Support vector machines, C4.5 decision trees, Logistic regression and Multilayer perceptron).</p

A Sensitive Search for Supernova Emission Associated with the Extremely Energetic and Nearby GRB 221009A

We report observations of the optical counterpart of the long gamma-ray burst (LGRB) GRB 221009A. Due to the extreme rarity of being both nearby ($z = 0.151$) and highly energetic ($E_{\gamma,\mathrm{iso}} \geq 10^{54}$ erg), GRB 221009A offers a unique opportunity to probe the connection between massive star core collapse and relativistic jet formation across a very broad range of $\gamma$-ray properties. Adopting a phenomenological power-law model for the afterglow and host galaxy estimates from high-resolution Hubble Space Telescope imaging, we use Bayesian model comparison techniques to determine the likelihood of an associated SN contributing excess flux to the optical light curve. Though not conclusive, we find moderate evidence ($K_{\rm{Bayes}}=10^{1.2}$) for the presence of an additional component arising from an associated supernova, SN 2022xiw, and find that it must be substantially fainter ($<$ 67% as bright at the 99% confidence interval) than SN 1998bw. Given the large and uncertain line-of-sight extinction, we attempt to constrain the supernova parameters ($M_{\mathrm{Ni}}$, $M_{\mathrm{ej}}$, and $E_{\mathrm{KE}}$) under several different assumptions with respect to the host galaxy's extinction. We find properties that are broadly consistent with previous GRB-associated SNe: $M_{\rm{Ni}}=0.05$ - $0.25 \, \rm{M_\odot}$, $M_{\rm{ej}}=3.5$ - $11.1 \, \rm{M_\odot}$, and $E_{\rm{KE}} = (1.6$ - $5.2) \times 10^{52} \, \rm{erg}$. We note that these properties are weakly constrained due to the faintness of the supernova with respect to the afterglow and host emission, but we do find a robust upper limit on the $M_{\rm{Ni}}$ of $M_{\rm{Ni}}<0.36\, \rm{M_\odot}$. Given the tremendous range in isotropic gamma-ray energy release exhibited by GRBs (7 orders of magnitude), the SN emission appears to be decoupled from the central engine in these systems.Comment: 18 pages, accepted to ApJL, 4 tables, 5 figures. Updated abstract in Previe