β-1,3-d-Glucan is a ubiquitous glucose polymer produced by plants, bacteria, and most fungi. It has been used as a diagnostic tool in patients with invasive mycoses via a highly-sensitive reagent consisting of the blood coagulation system of horseshoe crab. However, no method is currently available for measuring β-1,6-glucan, another primary β-glucan structure of fungal polysaccharides. Herein, we describe the development of an economical and highly-sensitive and specific assay for β-1,6-glucan using a modified recombinant endo-β-1,6-glucanase having diminished glucan hydrolase activity. The purified β-1,6-glucanase derivative bound to the β-1,6-glucan pustulan with a KD of 16.4 nm. We validated the specificity of this β-1,6-glucan probe by demonstrating its ability to detect cell wall β-1,6-glucan from both yeast and hyphal forms of the opportunistic fungal pathogen Candida albicans, without any detectable binding to glucan lacking the long β-1,6-glucan branch. We developed a sandwich ELISA-like assay with a low limit of quantification for pustulan (1.5 pg/ml), and we successfully employed this assay in the quantification of extracellular β-1,6-glucan released by >250 patient-derived strains of different Candida species (including Candida auris) in culture supernatant in vitro. We also used this assay to measure β-1,6-glucan in vivo in the serum and in several organs in a mouse model of systemic candidiasis. Our work describes a reliable method for β-1,6-glucan detection, which may prove useful for the diagnosis of invasive fungal infections.

¹⁸F-PI-2620 is a next generation tau positron emission tomography (PET)-tracer that has demonstrated ability to image the spatial distribution of suspected tau pathology. The objective of this study was to assess the tracer biodistribution, dosimetry and quantitative methods of ¹⁸F-PI-2620 in the human brain. Full kinetic modelling approaches to quantify tau load were investigated. Non-invasive kinetic modeling approaches and semi-quantitative methods were evaluated against the full tracer kinetics. Finally, the reproducibility of PET measurements from test and retest scans was assessed. Methods: Three healthy controls (HC) and 4 Alzheimer disease (AD) subjects underwent two dynamic PET scans including arterial sampling. Distribution volume ratio (DVR) was estimated using full tracer kinetics (2 Tissue Compartment (2TC) models, Logan Graphical Analysis (LGA)) and non-invasive kinetic models (Non-Invasive Logan Graphical Analysis (NI-LGA) and the multilinear reference tissue model (MRTM2)). Standardized uptake value ratio (SUVR) was determined at different imaging windows after injection. Correlation between DVR and SUVR, effect size (Cohen’s d) and test-retest variability (TRV) were evaluated. Additionally, 6 HC subjects received one tracer administration and underwent whole-body PET for dosimetry calculation. Organ doses and the whole-body effective dose were calculated using OLINDA 2.0. Results: Strong correlation was found across different kinetic models (R2 >0.97) and between DVR(2TC) and SUVRs between 30 to 90 min with R2>0.95. Secular equilibrium was reached around 40 min post injection (p.i.) in most regions and subjects. The TRV and effect size for the SUVR across different regions was similar at 30-60 min (TRV=3.8%, d=3.80), 45-75 min (TRV=4.3%, d=3.77) and 60-90 min (TRV=4.9%, d=3.73) and increased at later time points. Elimination was via the hepatobiliary and urinary system. The whole-body effective dose was determined to be 33.3±2.1 μSv/MBq for an adult female and 33.1±1.4 μSv/MBq for an adult male with a 1.5 hour urinary bladder voiding interval. Conclusion: ¹⁸F-PI-2620 exhibits fast kinetics, suitable dosimetry and low TRV. DVR measured using the 2TC model with arterial sampling correlated strongly with DVR measured by NI-LGA, MRTM2 and SUVR. SUVR can be used for ¹⁸F-PI-2620 PET quantification of tau deposits avoiding arterial blood sampling. Static ¹⁸F-PI-2620 PET scans between 45-75min p.i. provide excellent quantification accuracy, large effect size and low TRV.

Rationale: To conduct a retrospective study comparing three ¹²³I-FP-CIT-SPECT quantitative methods in patients with neurodegenerative syndromes as referenced to neuropathological findings. Methods: ¹²³I-FP-CIT-SPECT and neuropathological findings among patients with neurodegenerative syndromes from the Mayo Alzheimer's Disease Research Center and Mayo Clinic Study of Aging were examined. Three ¹²³I-FP-CIT-SPECT quantitative assessment Methods: MIMneuro (MIM Software Inc.), DaTQUANT (GE Healthcare), and manual region of interest (ROI) creation on an Advantage Workstation (GE Healthcare) were compared to neuropathological findings describing the presence or absence of Lewy body disease (LBD). Striatum to background ratios (SBRs) generated by DaTQUANT were compared to the calculated SBRs of the manual method and MIMneuro. The left and right SBRs for caudate, putamen and striatum were evaluated with the manual method. For DaTQUANT and MIMneuro the left, right, total and average SBRs and z-scores for whole striatum, caudate, putamen, anterior putamen, and posterior putamen were calculated. Results: The cohort included 24 patients [20 (83%) male, aged 75.4 +/- 10.0 at death]. The antemortem clinical diagnoses were Alzheimer’s disease dementia (ADem, N = 6), probable dementia with Lewy bodies (pDLB, N = 12), mixed ADem/pDLB (N = 1), Parkinson’s disease with mild cognitive impairment (N = 2), corticobasal syndrome (N = 1), idiopathic rapid eye movement sleep behavior disorder (iRBD) (N = 1) and behavioral variant frontotemporal dementia (N = 1). Seventeen (71%) had LBD pathology. All three ¹²³I-FP-CIT-SPECT quantitative methods had area under the receiver operating characteristics (AUROC) values above 0.93 and up to 1.000 (p<0.001) and showed excellent discrimination between LBD and non-LBD patients in each region assessed, p<.001. There was no significant difference between the accuracy of the regions in discriminating the two groups, with good discrimination for both caudate and putamen. Conclusion: All three ¹²³I-FP-CIT-SPECT quantitative methods showed excellent discrimination between LBD and non-LBD patients in each region assessed, using both SBRs and z-scores.

Nutritional studies rely on various biological specimens for fatty acid (FA) determination, yet it is unclear how levels of serum non-esterified FA (NEFAs) correlate with other circulating lipid pools. Here, we used a high throughput method (< 4 min/sample) based on multisegment injection-non-aqueous-capillary electrophoresis–mass spectrometry (MSI-NACE-MS) to investigate whether specific serum NEFAs have utility as biomarkers of dietary fat intake in women. We first  identified circulating NEFAs correlated with long-term/habitual food intake among pregnant women with contrasting dietary patterns (n=50). Acute changes in serum NEFA trajectories were also studied in non-pregnant women (n=18) following high-dose (5 g/day) fish oil (FO) supplementation or isoenergetic sunflower oil placebo over 56 days. In the cross-sectional study, serum omega-3 (-3) FA correlated with self-reported total -3 daily intake, notably eicosapentaenoic acid (EPA) as its NEFA (r=0.46; p=0.001), whereas pentadecanoic acid was associated with full-fat dairy intake (r=0.43; p=0.002), outcomes consistent with results from  total FA serum hydrolysates. In the intervention cohort, serum -3 NEFAs increased 2.5-fold from baseline within 28 days following FO supplementation, and this increase was most pronounced for EPA (p=0.0004). Unlike for docosahexaenoic acid, circulating EPA as its NEFA also strongly correlated to EPA concentrations measured from erythrocyte phospholipid hydrolysates (r=0.66; p=4.6 x 10-10), and was better suited to detect dietary non-adherence. We conclude that MSI-NACE-MS offers a rapid method to quantify serum NEFAs and objectively monitor dietary fat intake in women that is complementary to diet records or food frequency questionnaires.

Lipid kinases and phosphatases play key roles in cell signaling and regulation, and are implicated in many human diseases, and are hence thus attractive targets for drug development. Currently, no direct in vitro activity assay is available for these important enzymes, which hampers mechanistic studies as well as high-throughput screening of small molecule modulators. Here we report a highly sensitive and quantitative assay employing a ratiometric fluorescence sensor that directly and specifically monitors the real-time concentration change of a single lipid species. Due Because of to its modular design, the assay system can be applied to a wide variety of lipid kinases and phosphatases, including Class I phosphoinositide 3-kinase (PI3K) and phosphatase and tensin homolog (PTEN). When applied to PI3K, the assay provided the newdetailed mechanistic information about the product inhibition and substrate acyl acyl-chain selectivity of PI3K and allowed enabled rapid evaluation of its small molecule inhibitors. We also used this assay to quantitatively determine the substrate specificity of PTEN, providing new insight into its physiological functionThe assay also quantitatively determined the substrate specificity of PTEN, thereby providing new insight into its physiological function. In summary, we have developed a fluorescence-based real-time assay for PI3K and PTEN that we anticipate could be adapted to measure the activities of other lipid kinases and phosphatases with high sensitivity and accuracy.

Mass spectrometry (MS) assisted lipidomic tissue analysis is a valuable tool to assess sphingolipid metabolism dysfunction in disease. These analyses can reveal potential pharmacological targets or direct mechanistic studies to better understand the molecular underpinnings and influence of sphingolipid metabolism alterations on disease etiology. But procuring sufficient human tissues for adequately powered studies can be challenging. Therefore, biorepositories, which hold large collections of cryopreserved human tissues, are an ideal retrospective source of specimens. However, this resource has been vastly underutilized by lipid biologists, as the components of optimal cutting temperature compound (OCT) used in cryopreservation are incompatible with MS analyses. Here, we report results indicating that OCT also interferes with protein quantification assays, and that the presence of OCT impacts the quantification of extracted sphingolipids by LC–ESI–MS/MS. We developed and validated a simple and inexpensive method that removes OCT from OCT-embedded tissues. Our results indicate that removal of OCT from cryopreserved tissues does not significantly affect the accuracy of sphingolipid measurements with LC–ESI–MS/MS. We used the validated method to analyze sphingolipid alterations in tumors compared with normal adjacent uninvolved lung tissues from individuals with lung cancer, and to determine the long-term stability of sphingolipids in OCT-cryopreserved normal lung tissues. We show that lung cancer tumors have significantly altered sphingolipid profiles and that sphingolipids are stable for up to 16 years in OCT-cryopreserved normal lung tissues. This validated sphingolipidomic OCT-removal protocol should be a valuable addition to the lipid biologist’s toolbox.

Electrospray ionization is today the most widely used ionization technique in chemical and bio-chemical analysis. Interfaced with a mass spectrometer it allows to investigate the molecular composition of liquid samples. With electrospray a large variety of chemical substances can be ionized. There is no limitation in mass which enables even the investigation of large non-covalent protein complexes. Its high ionization efficiency profoundly changed bio-molecular sciences because proteins can be identified and quantified on trace amounts in a high throughput fashion. This review article focusses mainly on the exploration of the underlying ionization mechanism. Some ionization characteristics are discussed which are related to this mechanism. Typical spectra of peptides, proteins and non-covalent complexes are shown and the quantitative character of spectra is highlighted. Finally the possibilities and limitations in measuring the association constant of bivalent non-covalent complexes are described.

A fundamental feature of the eukaryotic cell membrane is the asymmetric arrangement of lipids in its two leaflets. A cell invests significant energy to maintain this asymmetry and uses it to regulate important biological processes, such as apoptosis and vesiculation. The dynamic coupling of the inner or cytoplasmic and outer or exofacial leaflets is a challenging open question in membrane biology. Here, we combined fluorescence lifetime imaging microscopy (FLIM) with imaging total internal reflection fluorescence correlation spectroscopy (ITIR-FCS) to differentiate the dynamics and organization of the two leaflets of live mammalian cells. We characterized the biophysical properties of fluorescent analogs of phosphatidylcholine, sphingomyelin, and phosphatidylserine in the plasma membrane of two mammalian cell lines (CHO-K1 and RBL-2H3). Because of their specific transverse membrane distribution, these probes allowed leaflet-specific investigation of the plasma membrane. We compared the results of the two methods having different temporal and spatial resolution. Fluorescence lifetimes of fluorescent lipid analogs were in ranges characteristic for the liquid ordered phase in the outer leaflet and for the liquid disordered phase in the inner leaflet. The observation of a more fluid inner leaflet was supported by free diffusion in the inner leaflet, with high average diffusion coefficients. The liquid ordered phase in the outer leaflet was accompanied by slower diffusion and diffusion with intermittent transient trapping. Our results show that the combination of FLIM and ITIR-FCS with specific fluorescent lipid analogs is a powerful tool for investigating lateral and transbilayer characteristics of plasma membrane in live cell lines.

APOA5 is a low-abundance exchangeable apolipoprotein that plays critical roles in human triglyceride (TG) metabolism. Indeed, aberrations in the plasma concentration or structure of APOA5 are linked to hypertriglyceridemia, hyperchylomicronemia, myocardial infarction risk, obesity, and coronary artery disease. While it has been successfully produced at low yield in bacteria, the resulting protein had limitations for structure-function studies due to its low solubility under physiological buffer conditions. We hypothesized that the yield and solubility of recombinant APOA5 could be increased by: i) engineering a fusion protein construct in a codon optimized expression vector, ii) optimizing an efficient refolding protocol, and iii) screening buffer systems at physiological pH. The result was a high-yield (25 mg/l) bacterial expression system that produces lipid-free APOA5 soluble at concentrations of up to 10 mg/ml at a pH of 7.8 in bicarbonate buffers. Physical characterization of lipid-free APOA5 indicated that it exists as an array of multimers in solution, and far UV circular dichroism analyses show differences in total α-helicity between acidic and neutral pH buffering conditions. The protein was functional in that it bound and emulsified multilamellar dimyristoyl-phosphatidylcholine vesicles and could inhibit postprandial plasma TG accumulation when injected into C57BL/6J mice orally gavaged with Intralipid.

Elevated levels of triglyceride-rich lipoproteins (TRLs), both fasting and postprandial, are associated with increased risk for atherosclerosis. However, guidelines for treatment are defined solely by fasting lipid levels, even though postprandial lipids may be more informative. In the postprandial state, circulating lipids consist of dietary fat transported from the intestine in chylomicrons (CMs; containing ApoB48) and fat transported from the liver in VLDL (containing ApoB100). Research into the roles of endogenous versus dietary fat has been hindered because of the difficulty in separating these particles by ultracentrifugation. CM fractions have considerable contamination from VLDL (purity, 10%). To separate CMs from VLDL, we produced polyclonal antibodies against ApoB100 and generated immunoaffinity columns. TRLs isolated by ultracentrifugation of plasma were applied to these columns, and highly purified CMs were collected (purity, 90–94%). Overall eight healthy unmedicated adult volunteers (BMI, 27.2 ± 1.4 kg/m²; fasting triacylglycerol, 102.6 ± 19.5 mg/dl) participated in a feeding study, which contained an oral stable-isotope tracer (1-¹³C acetate). We then used this technique on plasma samples freshly collected during an 8 h human feeding study from a subset of four subjects. We analyzed fractionated lipoproteins by Western blot, isolated and derivatized triacylglycerols, and calculated fractional de novo lipogenesis. The results demonstrated effective separation of postprandial lipoproteins and substantially improved purity compared with ultracentrifugation protocols, using the immunoaffinity method. This method can be used to better delineate the role of dietary sugar and fat on postprandial lipids in cardiovascular risk and explore the potential role of CM remnants in atherosclerosis.

Analyzing global steroid metabolism in humans can shed light on the etiologies of steroid-related diseases. However, existing methods require large amounts of serum and lack the evaluation of accuracy. Here, we developed an LC/MS/MS method for the simultaneous quantification of 12 steroid hormones: testosterone, pregnenolone, progesterone, androstenedione, corticosterone, 11-deoxycortisol, cortisol, 17-hydroxypregnenolone, 17-hydroxyprogesterone, dehydroepiandrosterone, estriol, and estradiol. Steroids and spiked internal standards in 100 μl serum were extracted by protein precipitation and liquid-liquid extraction. The organic phase was dried by evaporation, and isonicotinoyl chloride was added for steroid derivatization, followed by evaporation under nitrogen and redissolution in 50% methanol. Chromatographic separation was performed on a reverse-phase PFP column, and analytes were detected on a triple quadrupole mass spectrometer with ESI. The lower limits of quantification ranged from 0.005 ng/ml for estradiol to 1 ng/ml for cortisol. Apparent recoveries of steroids at high, medium, and low concentrations in quality control samples were between 86.4% and 115.0%. There were limited biases (–10.7% to 10.5%) between the measured values and the authentic values, indicating that the method has excellent reliability. An analysis of the steroid metabolome in pregnant women highlighted the applicability of the method in clinical serum samples. We conclude that the LC/MS/MS method reported here enables steroid metabolome analysis with high accuracy and reduced serum consumption, indicating that it may be a useful tool in both clinical and scientific laboratory research.

Steroids that contain a 3-hydroxyl group (3-OH steroids) are widely distributed in nature. During analysis with ESI-MS, they easily become dehydrated while in the protonated form, resulting in the production of several precursor ions and leading to low sensitivity of detection. To address this analytical challenge, here, we developed a method for the quantitation of 3-OH steroids by LC-MS/MS coupled with post-column addition of lithium (Li) ions to the mobile phase. The Li ion has a high affinity for the keto group of steroids, stabilizing their structures during ionization and permitting detection of analytes exclusively as the lithiated form. This not only improved the intensities of the precursor ions, but also promoted the formation of typical lithiated fragment ions. This improvement made the quantitation by multiple reaction monitoring more sensitive and reliable, as evidenced by 1.53–188 times enhanced detection sensitivity of 13 steroids that contained at least one keto and two hydroxyl groups or one keto and one 5-olefinic double bond, among 16 different 3-OH steroids. We deployed our newly developed method for profiling steroids in mouse brain tissue and identified six steroids in one tissue sample. Among these, 16-hydroxyestrone, tetrahydrocorticosterone, and 17α-hydroxypregnenolone were detected for the first time in the mouse brain. In summary, the method described here enables the detection of lithiated steroids by LC-MS/MS, including three 3-OH steroids not previously reported in the mouse brain. We anticipate that this new method may allow the determination of 3-OH steroids in different brain regions.

This article focuses on the establishment of an accurate and sensitive quantitation method for the analysis of furan fatty acids. In particular, the sensitivity of GC/MS and UPLC/ESI/MS/MS was compared for the identification and quantification of furan fatty acids. Different methylation methods were tested with respect to GC/MS analysis. Special attention needs to be paid to the methylation of furan fatty acids, as acidic catalysts might lead to the degradation of the furan ring. GC/MS analysis in full-scan mode demonstrated that the limit of quantitation was 10 μM. UPLC/ESI/MS/MS in multiple reaction monitoring mode displayed a higher detection sensitivity than GC/MS. Moreover, the identification of furan fatty acids with charge-reversal derivatization was tested in the positive mode with two widely used pyridinium salts. Significant oxidation was unexpectedly observed using N-(4-aminomethylphenyl) pyridinium as a derivatization agent. The formed 3-acyl-oxymethyl-1-methylpyridinium iodide derivatized by 2-bromo-1-methylpyridinium iodide and 3-carbinol-1-methylpyridinium iodide improved the sensitivity more than 2,000-fold compared with nonderivatization in the negative mode by UPLC/ESI/MS/MS. This charge-reversal derivatization enabled the targeted quantitation of furan fatty acids in human plasma. Thus, it is anticipated that this protocol could greatly contribute to the clarification of pathological mechanisms related to furan fatty acids and their metabolites.

The hydrolysis of triglycerides in triglyceride-rich lipoproteins by LPL is critical for the delivery of triglyceride-derived fatty acids to tissues, including heart, skeletal muscle, and adipose tissues. Physiologically active LPL is normally bound to the endothelial cell protein glycosylphosphatidylinositol-anchored high-density lipoprotein binding protein 1 (GPIHBP1), which transports LPL across endothelial cells, anchors LPL to the vascular wall, and stabilizes LPL activity. Disruption of LPL-GPIHBP1 binding significantly alters triglyceride metabolism and lipid partitioning. In this study, we modified the NanoLuc® Binary Technology split-luciferase system to develop a novel assay that monitors the binding of LPL to GPIHBP1 on endothelial cells in real time. We validated the specificity and sensitivity of the assay using endothelial lipase and a mutant version of LPL and found that this assay reliably and specifically detected the interaction between LPL and GPIHBP1. We then interrogated various endogenous and exogenous inhibitors of LPL-mediated lipolysis for their ability to disrupt the binding of LPL to GPIHBP1. We found that angiopoietin-like (ANGPTL)4 and ANGPTL3-ANGPTL8 complexes disrupted the interactions of LPL and GPIHBP1, whereas the exogenous LPL blockers we tested (tyloxapol, poloxamer-407, and tetrahydrolipstatin) did not. We also found that chylomicrons could dissociate LPL from GPIHBP1 and found evidence that this dissociation was mediated in part by the fatty acids produced by lipolysis. These results demonstrate the ability of this assay to monitor LPL-GPIHBP1 binding and to probe how various agents influence this important complex.

Site-specific recombinases, such as Cre, are a widely used tool for genetic lineage tracing in the fields of developmental biology, neural science, stem cell biology, and regenerative medicine. However, nonspecific cell labeling by some genetic Cre tools remains a technical limitation of this recombination system, which has resulted in data misinterpretation and led to many controversies in the scientific community. In the past decade, to enhance the specificity and precision of genetic targeting, researchers have used two or more orthogonal recombinases simultaneously for labeling cell lineages. Here, we review the history of cell-tracing strategies and then elaborate on the working principle and application of a recently developed dual genetic lineage-tracing approach for cell fate studies. We place an emphasis on discussing the technical strengths and caveats of different methods, with the goal to develop more specific and efficient tracing technologies for cell fate mapping. Our review also provides several examples for how to use different types of DNA recombinase–mediated lineage-tracing strategies to improve the resolution of the cell fate mapping in order to probe and explore cell fate–related biological phenomena in the life sciences.

Fertility awareness based methods of contraception are increasingly being used for pregnancy prevention. In the US, the proportion of contraceptive users who choose such methods has grown from 1% in 2008 to approximately 3% in 2014.  Relative to other methods of pregnancy prevention, however, substantial misinformation exists around fertility...

London : D. Bogue, 1881.

Manchester : J. Heywood, 1888.

London : Chemist and Druggist, 1898.

Edinburgh : Young J. Pentland, 1886.

London : Longmans, Green, 1895.

Rockville, Maryland : National Institute of Drug Abuse, 1985.

We study the least-squares regression problem over a Hilbert space, covering nonparametric regression over a reproducing kernel Hilbert space as a special case. We first investigate regularized algorithms adapted to a projection operator on a closed subspace of the Hilbert space. We prove convergence results with respect to variants of norms, under a capacity assumption on the hypothesis space and a regularity condition on the target function. As a result, we obtain optimal rates for regularized algorithms with randomized sketches, provided that the sketch dimension is proportional to the effective dimension up to a logarithmic factor. As a byproduct, we obtain similar results for Nystr"{o}m regularized algorithms. Our results provide optimal, distribution-dependent rates that do not have any saturation effect for sketched/Nystr"{o}m regularized algorithms, considering both the attainable and non-attainable cases, in the well-conditioned regimes. We then study stochastic gradient methods with projection over the subspace, allowing multi-pass over the data and minibatches, and we derive similar optimal statistical convergence results.

We study derivative-free methods for policy optimization over the class of linear policies. We focus on characterizing the convergence rate of these methods when applied to linear-quadratic systems, and study various settings of driving noise and reward feedback. Our main theoretical result provides an explicit bound on the sample or evaluation complexity: we show that these methods are guaranteed to converge to within any pre-specified tolerance of the optimal policy with a number of zero-order evaluations that is an explicit polynomial of the error tolerance, dimension, and curvature properties of the problem. Our analysis reveals some interesting differences between the settings of additive driving noise and random initialization, as well as the settings of one-point and two-point reward feedback. Our theory is corroborated by simulations of derivative-free methods in application to these systems. Along the way, we derive convergence rates for stochastic zero-order optimization algorithms when applied to a certain class of non-convex problems.

We develop an encompassing framework for matching, covariate balancing, and doubly-robust methods for causal inference from observational data called generalized optimal matching (GOM). The framework is given by generalizing a new functional-analytical formulation of optimal matching, giving rise to the class of GOM methods, for which we provide a single unified theory to analyze tractability and consistency. Many commonly used existing methods are included in GOM and, using their GOM interpretation, can be extended to optimally and automatically trade off balance for variance and outperform their standard counterparts. As a subclass, GOM gives rise to kernel optimal matching (KOM), which, as supported by new theoretical and empirical results, is notable for combining many of the positive properties of other methods in one. KOM, which is solved as a linearly-constrained convex-quadratic optimization problem, inherits both the interpretability and model-free consistency of matching but can also achieve the $sqrt{n}$-consistency of well-specified regression and the bias reduction and robustness of doubly robust methods. In settings of limited overlap, KOM enables a very transparent method for interval estimation for partial identification and robust coverage. We demonstrate this in examples with both synthetic and real data.

Umberto Amato, Anestis Antoniadis, Italia De Feis.

Source: Statistics Surveys, Volume 14, 32--70.

Abstract:
We present and compare some nonparametric estimation methods (wavelet and/or spline-based) designed to recover a one-dimensional piecewise-smooth regression function in both a fixed equidistant or not equidistant design regression model and a random design model. Wavelet methods are known to be very competitive in terms of denoising and compression, due to the simultaneous localization property of a function in time and frequency. However, boundary assumptions, such as periodicity or symmetry, generate bias and artificial wiggles which degrade overall accuracy. Simple methods have been proposed in the literature for reducing the bias at the boundaries. We introduce new ones based on adaptive combinations of two estimators. The underlying idea is to combine a highly accurate method for non-regular functions, e.g., wavelets, with one well behaved at boundaries, e.g., Splines or Local Polynomial. We provide some asymptotic optimal results supporting our approach. All the methods can handle data with a random design. We also sketch some generalization to the multidimensional setting. To study the performance of the proposed approaches we have conducted an extensive set of simulations on synthetic data. An interesting regression analysis of two real data applications using these procedures unambiguously demonstrates their effectiveness.

George Karabatsos, Fabrizio Leisen.

Source: Statistics Surveys, Volume 12, 66--104.

Abstract:
We are living in the big data era, as current technologies and networks allow for the easy and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible modeling approach which is attractive for describing the complexity of these datasets. These models often exhibit a likelihood function which is intractable due to the large sample size, high number of parameters, or functional complexity. Approximate Bayesian Computational (ABC) methods provides likelihood-free methods for performing statistical inferences with Bayesian models defined by intractable likelihood functions. The vastity of the literature on ABC methods created a need to review and relate all ABC approaches so that scientists can more readily understand and apply them for their own work. This article provides a unifying review, general representation, and classification of all ABC methods from the view of approximate likelihood theory. This clarifies how ABC methods can be characterized, related, combined, improved, and applied for future research. Possible future research in ABC is then outlined.

Michael Fop, Thomas Brendan Murphy.

Source: Statistics Surveys, Volume 12, 18--65.

Abstract:
Model-based clustering is a popular approach for clustering multivariate data which has seen applications in numerous fields. Nowadays, high-dimensional data are more and more common and the model-based clustering approach has adapted to deal with the increasing dimensionality. In particular, the development of variable selection techniques has received a lot of attention and research effort in recent years. Even for small size problems, variable selection has been advocated to facilitate the interpretation of the clustering results. This review provides a summary of the methods developed for variable selection in model-based clustering. Existing R packages implementing the different methods are indicated and illustrated in application to two data analysis examples.

Zeinab Mashreghi, David Haziza, Christian Léger.

Source: Statistics Surveys, Volume 10, 1--52.

Abstract:
We review bootstrap methods in the context of survey data where the effect of the sampling design on the variability of estimators has to be taken into account. We present the methods in a unified way by classifying them in three classes: pseudo-population, direct, and survey weights methods. We cover variance estimation and the construction of confidence intervals for stratified simple random sampling as well as some unequal probability sampling designs. We also address the problem of variance estimation in presence of imputation to compensate for item non-response.

José A. Ferreira.

Source: Statistics Surveys, Volume 9, 106--208.

Abstract:
This article provides a concise and essentially self-contained exposition of some of the most important models and non-parametric methods for the analysis of observational data, and a substantial number of illustrations of their application. Although for the most part our presentation follows P. Rosenbaum’s book, “Observational Studies”, and naturally draws on related literature, it contains original elements and simplifies and generalizes some basic results. The illustrations, based on simulated data, show the methods at work in some detail, highlighting pitfalls and emphasizing certain subjective aspects of the statistical analyses.

Aki Vehtari, Janne Ojanen.

Source: Statistics Surveys, Volume 8, , 1--1.

Abstract:
Errata for “A survey of Bayesian predictive methods for model assessment, selection and comparison” by A. Vehtari and J. Ojanen, Statistics Surveys , 6 (2012), 142–228. doi:10.1214/12-SS102.

Aki Vehtari, Janne Ojanen

Source: Statist. Surv., Volume 6, 142--228.

Abstract:
To date, several methods exist in the statistical literature for model assessment, which purport themselves specifically as Bayesian predictive methods. The decision theoretic assumptions on which these methods are based are not always clearly stated in the original articles, however. The aim of this survey is to provide a unified review of Bayesian predictive model assessment and selection methods, and of methods closely related to them. We review the various assumptions that are made in this context and discuss the connections between different approaches, with an emphasis on how each method approximates the expected utility of using a Bayesian model for the purpose of predicting future data.

Nancy Heckman

Source: Statist. Surv., Volume 6, 113--141.

Abstract:
The popular cubic smoothing spline estimate of a regression function arises as the minimizer of the penalized sum of squares $sum_{j}(Y_{j}-mu(t_{j}))^{2}+lambda int_{a}^{b}[mu''(t)]^{2},dt$, where the data are $t_{j},Y_{j}$, $j=1,ldots,n$. The minimization is taken over an infinite-dimensional function space, the space of all functions with square integrable second derivatives. But the calculations can be carried out in a finite-dimensional space. The reduction from minimizing over an infinite dimensional space to minimizing over a finite dimensional space occurs for more general objective functions: the data may be related to the function $mu$ in another way, the sum of squares may be replaced by a more suitable expression, or the penalty, $int_{a}^{b}[mu''(t)]^{2},dt$, might take a different form. This paper reviews the Reproducing Kernel Hilbert Space structure that provides a finite-dimensional solution for a general minimization problem. Particular attention is paid to the construction and study of the Reproducing Kernel Hilbert Space corresponding to a penalty based on a linear differential operator. In this case, one can often calculate the minimizer explicitly, using Green’s functions.

Gregory J. Matthews, Ofer Harel

Source: Statist. Surv., Volume 5, 1--29.

Abstract:
There is an ever increasing demand from researchers for access to useful microdata files. However, there are also growing concerns regarding the privacy of the individuals contained in the microdata. Ideally, microdata could be released in such a way that a balance between usefulness of the data and privacy is struck. This paper presents a review of proposed methods of statistical disclosure control and techniques for assessing the privacy of such methods under different definitions of disclosure.

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Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Recently, many models for text summarization have been proposed. Most of those models were evaluated using recall-oriented understudy for gisting evaluation (ROUGE) scores. However, as ROUGE scores are computed based on n-gram overlap, they do not reflect semantic meaning correspondences between generated and reference summaries. Because Korean is an agglutinative language that combines various morphemes into a word that express several meanings, ROUGE is not suitable for Korean summarization. In this paper, we propose evaluation metrics that reflect semantic meanings of a reference summary and the original document, Reference and Document Aware Semantic Score (RDASS). We then propose a method for improving the correlation of the metrics with human judgment. Evaluation results show that the correlation with human judgment is significantly higher for our evaluation metrics than for ROUGE scores.

Uplift modeling is a predictive modeling technique that estimates the user-level incremental effect of a treatment using machine learning models. It is often used for targeting promotions and advertisements, as well as for the personalization of product offerings. In these applications, there are often hundreds of features available to build such models. Keeping all the features in a model can be costly and inefficient. Feature selection is an essential step in the modeling process for multiple reasons: improving the estimation accuracy by eliminating irrelevant features, accelerating model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnostics capability. However, feature selection methods for uplift modeling have been rarely discussed in the literature. Although there are various feature selection methods for standard machine learning models, we will demonstrate that those methods are sub-optimal for solving the feature selection problem for uplift modeling. To address this problem, we introduce a set of feature selection methods designed specifically for uplift modeling, including both filter methods and embedded methods. To evaluate the effectiveness of the proposed feature selection methods, we use different uplift models and measure the accuracy of each model with a different number of selected features. We use both synthetic and real data to conduct these experiments. We also implemented the proposed filter methods in an open source Python package (CausalML).

Subhadeep Paul, Yuguo Chen.

Source: The Annals of Statistics, Volume 48, Number 1, 230--250.

Abstract:
We consider the problem of estimating a consensus community structure by combining information from multiple layers of a multi-layer network using methods based on the spectral clustering or a low-rank matrix factorization. As a general theme, these “intermediate fusion” methods involve obtaining a low column rank matrix by optimizing an objective function and then using the columns of the matrix for clustering. However, the theoretical properties of these methods remain largely unexplored. In the absence of statistical guarantees on the objective functions, it is difficult to determine if the algorithms optimizing the objectives will return good community structures. We investigate the consistency properties of the global optimizer of some of these objective functions under the multi-layer stochastic blockmodel. For this purpose, we derive several new asymptotic results showing consistency of the intermediate fusion techniques along with the spectral clustering of mean adjacency matrix under a high dimensional setup, where the number of nodes, the number of layers and the number of communities of the multi-layer graph grow. Our numerical study shows that the intermediate fusion techniques outperform late fusion methods, namely spectral clustering on aggregate spectral kernel and module allegiance matrix in sparse networks, while they outperform the spectral clustering of mean adjacency matrix in multi-layer networks that contain layers with both homophilic and heterophilic communities.

Mathieu Gerber, Nicolas Chopin, Nick Whiteley.

Source: The Annals of Statistics, Volume 47, Number 4, 2236--2260.

Abstract:
We study convergence and convergence rates for resampling schemes. Our first main result is a general consistency theorem based on the notion of negative association, which is applied to establish the almost sure weak convergence of measures output from Kitagawa’s [ J. Comput. Graph. Statist. 5 (1996) 1–25] stratified resampling method. Carpenter, Ckiffird and Fearnhead’s [ IEE Proc. Radar Sonar Navig. 146 (1999) 2–7] systematic resampling method is similar in structure but can fail to converge depending on the order of the input samples. We introduce a new resampling algorithm based on a stochastic rounding technique of [In 42nd IEEE Symposium on Foundations of Computer Science ( Las Vegas , NV , 2001) (2001) 588–597 IEEE Computer Soc.], which shares some attractive properties of systematic resampling, but which exhibits negative association and, therefore, converges irrespective of the order of the input samples. We confirm a conjecture made by [ J. Comput. Graph. Statist. 5 (1996) 1–25] that ordering input samples by their states in $mathbb{R}$ yields a faster rate of convergence; we establish that when particles are ordered using the Hilbert curve in $mathbb{R}^{d}$, the variance of the resampling error is ${scriptstylemathcal{O}}(N^{-(1+1/d)})$ under mild conditions, where $N$ is the number of particles. We use these results to establish asymptotic properties of particle algorithms based on resampling schemes that differ from multinomial resampling.

Michael E. Sobel, Martin A. Lindquist.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 452--472.

Abstract:
Neuroscientists often use functional magnetic resonance imaging (fMRI) to infer effects of treatments on neural activity in brain regions. In a typical fMRI experiment, each subject is observed at several hundred time points. At each point, the blood oxygenation level dependent (BOLD) response is measured at 100,000 or more locations (voxels). Typically, these responses are modeled treating each voxel separately, and no rationale for interpreting associations as effects is given. Building on Sobel and Lindquist ( J. Amer. Statist. Assoc. 109 (2014) 967–976), who used potential outcomes to define unit and average effects at each voxel and time point, we define and estimate both “point” and “cumulated” effects for brain regions. Second, we construct a multisubject, multivoxel, multirun whole brain causal model with explicit parameters for regions. We justify estimation using BOLD responses averaged over voxels within regions, making feasible estimation for all regions simultaneously, thereby also facilitating inferences about association between effects in different regions. We apply the model to a study of pain, finding effects in standard pain regions. We also observe more cerebellar activity than observed in previous studies using prevailing methods.

Zhonghua Liu, Ian Barnett, Xihong Lin.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 433--451.

Abstract:
Principal component analysis (PCA) is a popular method for dimension reduction in unsupervised multivariate analysis. However, existing ad hoc uses of PCA in both multivariate regression (multiple outcomes) and multiple regression (multiple predictors) lack theoretical justification. The differences in the statistical properties of PCAs in these two regression settings are not well understood. In this paper we provide theoretical results on the power of PCA in genetic association testings in both multiple phenotype and SNP-set settings. The multiple phenotype setting refers to the case when one is interested in studying the association between a single SNP and multiple phenotypes as outcomes. The SNP-set setting refers to the case when one is interested in studying the association between multiple SNPs in a SNP set and a single phenotype as the outcome. We demonstrate analytically that the properties of the PC-based analysis in these two regression settings are substantially different. We show that the lower order PCs, that is, PCs with large eigenvalues, are generally preferred and lead to a higher power in the SNP-set setting, while the higher-order PCs, that is, PCs with small eigenvalues, are generally preferred in the multiple phenotype setting. We also investigate the power of three other popular statistical methods, the Wald test, the variance component test and the minimum $p$-value test, in both multiple phenotype and SNP-set settings. We use theoretical power, simulation studies, and two real data analyses to validate our findings.

Seth Flaxman, Michael Chirico, Pau Pereira, Charles Loeffler.

Source: The Annals of Applied Statistics, Volume 13, Number 4, 2564--2585.

Abstract:
We propose a generic spatiotemporal event forecasting method which we developed for the National Institute of Justice’s (NIJ) Real-Time Crime Forecasting Challenge (National Institute of Justice (2017)). Our method is a spatiotemporal forecasting model combining scalable randomized Reproducing Kernel Hilbert Space (RKHS) methods for approximating Gaussian processes with autoregressive smoothing kernels in a regularized supervised learning framework. While the smoothing kernels capture the two main approaches in current use in the field of crime forecasting, kernel density estimation (KDE) and self-exciting point process (SEPP) models, the RKHS component of the model can be understood as an approximation to the popular log-Gaussian Cox Process model. For inference, we discretize the spatiotemporal point pattern and learn a log-intensity function using the Poisson likelihood and highly efficient gradient-based optimization methods. Model hyperparameters including quality of RKHS approximation, spatial and temporal kernel lengthscales, number of autoregressive lags and bandwidths for smoothing kernels as well as cell shape, size and rotation, were learned using cross validation. Resulting predictions significantly exceeded baseline KDE estimates and SEPP models for sparse events.

Chanmin Kim, Michael J. Daniels, Joseph W. Hogan, Christine Choirat, Corwin M. Zigler.

Source: The Annals of Applied Statistics, Volume 13, Number 3, 1927--1956.

Abstract:
Emission control technologies installed on power plants are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relationships between emissions, ambient air pollution and human health, many of these relationships have never been empirically verified. The goal of this paper is to develop new statistical methods to quantify these relationships. We frame this problem as one of mediation analysis to evaluate the extent to which the effect of a particular control technology on ambient pollution is mediated through causal effects on power plant emissions. Since power plants emit various compounds that contribute to ambient pollution, we develop new methods for multiple intermediate variables that are measured contemporaneously, may interact with one another, and may exhibit joint mediating effects. Specifically, we propose new methods leveraging two related frameworks for causal inference in the presence of mediating variables: principal stratification and causal mediation analysis. We define principal effects based on multiple mediators, and also introduce a new decomposition of the total effect of an intervention on ambient pollution into the natural direct effect and natural indirect effects for all combinations of mediators. Both approaches are anchored to the same observed-data models, which we specify with Bayesian nonparametric techniques. We provide assumptions for estimating principal causal effects, then augment these with an additional assumption required for causal mediation analysis. The two analyses, interpreted in tandem, provide the first empirical investigation of the presumed causal pathways that motivate important air quality regulatory policies.

Cristiano Villa, Jeong Eun Lee.

Source: Bayesian Analysis, Volume 15, Number 2, 533--558.

Abstract:
In this work we propose a novel model prior for variable selection in linear regression. The idea is to determine the prior mass by considering the worth of each of the regression models, given the number of possible covariates under consideration. The worth of a model consists of the information loss and the loss due to model complexity. While the information loss is determined objectively, the loss expression due to model complexity is flexible and, the penalty on model size can be even customized to include some prior knowledge. Some versions of the loss-based prior are proposed and compared empirically. Through simulation studies and real data analyses, we compare the proposed prior to the Scott and Berger prior, for noninformative scenarios, and with the Beta-Binomial prior, for informative scenarios.

Susan Gruber, Mark J. van der Laan.

Source: Statistical Science, Volume 34, Number 1, 82--85.

Abstract:
Dorie and co-authors (DHSSC) are to be congratulated for initiating the ACIC Data Challenge. Their project engaged the community and accelerated research by providing a level playing field for comparing the performance of a priori specified algorithms. DHSSC identified themes concerning characteristics of the DGP, properties of the estimators, and inference. We discuss these themes in the context of targeted learning.

Elizabeth A. Stuart

Source: Statist. Sci., Volume 25, Number 1, 1--21.

Abstract:
When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970s, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods—or developing methods related to matching—do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.

The general field of quantitative biology has advanced significantly on the back of recent improvements in both sequencing technology and proteomics methods. The development of high-throughput, short-read sequencing has revolutionized RNA-based expression studies, while improvements in proteomics methods have enabled quantitative studies to attain better resolution. Here we introduce methods to undertake global analyses of gene expression through RNA and protein quantification in Xenopus embryos and tissues.

Determining the concentration of protein samples generally is accomplished either by measuring the UV absorbance at 280 nm or by reacting the protein quantitatively with dyes and/or metal ions (Bradford, Lowry, or BCA assays). For purified proteins, UV absorbance remains the most popular method because it is fast, convenient, and reproducible; it does not consume the protein; and it requires no additional reagents, standards, or incubations. No method of protein concentration determination is perfect because each is subject to a different set of constraints such as interference of buffer components and contaminating proteins in direct UV determination (A₂₈₀) or reactivity of individual proteins and buffer components with the detecting reagents in colorimetric assays. In cases in which protein concentration is critical (e.g., determination of catalytic rate constants for an enzyme), it may be advisable to compare the results of several assays.

New findings on a state initiative in Wisconsin suggest that to make the most out of smaller class sizes in the early grades, teachers should focus on basic skills when they have one-on-one contact with students, ask children to discuss and demonstrate what they know, and have a firm, but nurturing,

Known rates of nonsuicidal self-injury, hurting oneself without the intent to die, are between ~7% and 24% in samples of early adolescents and older adolescents, yet research has not reported rates for youth younger than 11 years old.

Children as young as 7 years old report engaging in nonsuicidal self-injury. There is a grade by gender interaction for nonsuicidal self-injury, such that ninth-grade girls report the greatest rates of engagement and do so by cutting themselves. (Read the full article)