In bottom-up, label-free discovery proteomics, biological samples are acquired in a data-dependent (DDA) or data-independent (DIA) manner, with peptide signals recorded in an intact (MS1) and fragmented (MS2) form. While DDA has only the MS1 space for quantification, DIA contains both MS1 and MS2 at high quantitative quality. DIA profiles of complex biological matrices such as tissues or cells can contain quantitative interferences, and the interferences at the MS1 and the MS2 signals are often independent. When comparing biological conditions, the interferences can compromise the detection of differential peptide or protein abundance and lead to false positive or false negative conclusions.

We hypothesized that the combined use of MS1 and MS2 quantitative signals could improve our ability to detect differentially abundant proteins. Therefore, we developed a statistical procedure incorporating both MS1 and MS2 quantitative information of DIA. We benchmarked the performance of the MS1-MS2-combined method to the individual use of MS1 or MS2 in DIA using four previously published controlled mixtures, as well as in two previously unpublished controlled mixtures. In the majority of the comparisons, the combined method outperformed the individual use of MS1 or MS2. This was particularly true for comparisons with low fold changes, few replicates, and situations where MS1 and MS2 were of similar quality. When applied to a previously unpublished investigation of lung cancer, the MS1-MS2-combined method increased the coverage of known activated pathways.

Since recent technological developments continue to increase the quality of MS1 signals (e.g. using the BoxCar scan mode for Orbitrap instruments), the combination of the MS1 and MS2 information has a high potential for future statistical analysis of DIA data.

Quantitative proteomics by mass spectrometry is widely used in biomarker research and basic biology research for investigation of phenotype level cellular events. Despite the wide application, the methodology for statistical analysis of differentially expressed proteins has not been unified. Various methods such as t-test, linear model and mixed effect models are used to define changes in proteomics experiments. However, none of these methods consider the specific structure of MS-data. Choices between methods, often originally developed for other types of data, are based on compromises between features such as statistical power, general applicability and user friendliness. Furthermore, whether to include proteins identified with one peptide in statistical analysis of differential protein expression varies between studies. Here we present DEqMS, a robust statistical method developed specifically for differential protein expression analysis in mass spectrometry data. In all datasets investigated there is a clear dependence of variance on the number of PSMs or peptides used for protein quantification. DEqMS takes this feature into account when assessing differential protein expression. This allows for a more accurate data-dependent estimation of protein variance and inclusion of single peptide identifications without increasing false discoveries. The method was tested in several datasets including E.coli proteome spike-in data, using both label-free and TMT-labelled quantification. In comparison to previous statistical methods used in quantitative proteomics, DEqMS showed consistently better accuracy in detecting altered protein levels compared to other statistical methods in both label-free and labelled quantitative proteomics data. DEqMS is available as an R package in Bioconductor.

Multidrug resistance (MDR) in cancer arises from cross-resistance to structurally- and functionally-divergent chemotherapeutic drugs. In particular, MDR is characterized by increased expression and activity of ATP-binding cassette (ABC) superfamily transporters. Sphingolipids are substrates of ABC proteins in cell signaling, membrane biosynthesis, and inflammation, for example, and their products can favor cancer progression. Glucosylceramide (GlcCer) is a ubiquitous glycosphingolipid (GSL) generated by glucosylceramide synthase, a key regulatory enzyme encoded by the UDP-glucose ceramide glucosyltransferase (UGCG) gene. Stressed cells increase de novo biosynthesis of ceramides, which return to sub-toxic levels after UGCG mediates incorporation into GlcCer. Given that cancer cells seem to mobilize UGCG and have increased GSL content for ceramide clearance, which ultimately contributes to chemotherapy failure, here we investigated how inhibition of GSL biosynthesis affects the MDR phenotype of chronic myeloid leukemias. We found that MDR is associated with higher UGCG expression and with a complex GSL profile. UGCG inhibition with the ceramide analog d-threo-1-(3,4,-ethylenedioxy)phenyl-2-palmitoylamino-3-pyrrolidino-1-propanol (EtDO-P4) greatly reduced GSL and monosialotetrahexosylganglioside levels, and co-treatment with standard chemotherapeutics sensitized cells to mitochondrial membrane potential loss and apoptosis. ABC subfamily B member 1 (ABCB1) expression was reduced, and ABCC-mediated efflux activity was modulated by competition with nonglycosylated ceramides. Consistently, inhibition of ABCC-mediated transport reduced the efflux of exogenous C6-ceramide. Overall, UGCG inhibition impaired the malignant glycophenotype of MDR leukemias, which typically overcomes drug resistance through distinct mechanisms. This work sheds light on the involvement of GSL in chemotherapy failure, and its findings suggest that targeted GSL modulation could help manage MDR leukemias.

Jay S. Skyler
Feb 1, 2017; 66:241-255
Perspectives in Diabetes

The life cycle of malaria parasites in both their mammalian host and mosquito vector consists of multiple developmental stages that ensure proper replication and progeny survival. The transition between these stages is fueled by nutrients scavenged from the host and fed into specialized metabolic pathways of the parasite. One such pathway is used by Plasmodium falciparum, which causes the most severe form of human malaria, to synthesize its major phospholipids, phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine. Much is known about the enzymes involved in the synthesis of these phospholipids, and recent advances in genetic engineering, single-cell RNA-Seq analyses, and drug screening have provided new perspectives on the importance of some of these enzymes in parasite development and sexual differentiation and have identified targets for the development of new antimalarial drugs. This Minireview focuses on two phospholipid biosynthesis enzymes of P. falciparum that catalyze phosphoethanolamine transmethylation (PfPMT) and phosphatidylserine decarboxylation (PfPSD) during the blood stages of the parasite. We also discuss our current understanding of the biochemical, structural, and biological functions of these enzymes and highlight efforts to use them as antimalarial drug targets.

Long-term hyperglycemia in patients with diabetes leads to human serum albumin (HSA) glycation, which may impair HSA function as a transport protein and affect the therapeutic efficacy of anticoagulants in patients with diabetes. In this study, a novel mass spectrometry approach was developed to reveal the differences in the profiles of HSA glycation sites between patients with diabetes and healthy subjects. K199 was the glycation site most significantly changed in patients with diabetes, contributing to different interactions of glycated HSA and normal HSA with two types of anticoagulant drugs, heparin and warfarin. An in vitro experiment showed that the binding affinity to warfarin became stronger when HSA was glycated, while HSA binding to heparin was not significantly influenced by glycation. A pharmacokinetic study showed a decreased level of free warfarin in the plasma of diabetic rats. A preliminary retrospective clinical study also revealed that there was a statistically significant difference in the anticoagulant efficacy between patients with diabetes and patients without diabetes who had been treated with warfarin. Our work suggests that larger studies are needed to provide additional specific guidance for patients with diabetes when they are administered anticoagulant drugs or drugs for treating other chronic diseases.

Glucagon-like peptide 1 (GLP-1) mimetics are effective drugs for treatment of type 2 diabetes, and there is consequently extensive interest in increasing endogenous GLP-1 secretion and L-cell abundance. Here we identify G-protein–coupled bile acid receptor 1 (GPBAR1) as a selective regulator of intestinal L-cell differentiation. Lithocholic acid and the synthetic GPBAR1 agonist, L3740, selectively increased L-cell density in mouse and human intestinal organoids and elevated GLP-1 secretory capacity. L3740 induced expression of Gcg and transcription factors Ngn3 and NeuroD1. L3740 also increased the L-cell number and GLP-1 levels and improved glucose tolerance in vivo. Further mechanistic examination revealed that the effect of L3740 on L cells required intact GLP-1 receptor and serotonin 5-hydroxytryptamine receptor 4 (5-HT4) signaling. Importantly, serotonin signaling through 5-HT4 mimicked the effects of L3740, acting downstream of GLP-1. Thus, GPBAR1 agonists and other powerful GLP-1 secretagogues facilitate L-cell differentiation through a paracrine GLP-1–dependent and serotonin-mediated mechanism.

Though some Reds fans were disappointed that Billy Hamilton wasn't brought back this season, it didn't take long for Cincinnati to find another dynamic player who also brings flash and unpredictability. That would be outfielder Yasiel Puig.

It wasn't just that there were new faces when Reds camp opened Tuesday with the reporting of pitchers and catchers for physicals, there was a different vibe that could be felt in the hallways and clubhouse.

In glioma patients, differentiation between tumor progression (TP) and treatment-related changes (TRCs) remains challenging. Difficulties in classifying imaging alterations may result in a delay or an unnecessary discontinuation of treatment. PET using O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET) has been shown to be a useful tool for detecting TP and TRCs. Methods: We retrospectively evaluated 127 consecutive patients with World Health Organization grade II–IV glioma who underwent ¹⁸F-FET PET imaging to distinguish between TP and TRCs. ¹⁸F-FET PET findings were verified by neuropathology (40 patients) or clinicoradiologic follow-up (87 patients). Maximum tumor-to-brain ratios (TBR_max) of ¹⁸F-FET uptake and the slope of the time–activity curves (20–50 min after injection) were determined. The diagnostic accuracy of ¹⁸F-FET PET parameters was evaluated by receiver-operating-characteristic analysis and ² testing. The prognostic value of ¹⁸F-FET PET was estimated using the Kaplan–Meier method. Results: TP was diagnosed in 94 patients (74%) and TRCs in 33 (26%). For differentiating TP from TRCs, receiver-operating-characteristic analysis yielded an optimal ¹⁸F-FET TBR_max cutoff of 1.95 (sensitivity, 70%; specificity, 71%; accuracy, 70%; area under the curve, 0.75 ± 0.05). The highest accuracy was achieved by a combination of TBR_max and slope (sensitivity, 86%; specificity, 67%; accuracy, 81%). However, accuracy was poorer when tumors harbored isocitrate dehydrogenase (IDH) mutations (91% in IDH-wild-type tumors, 67% in IDH-mutant tumors, P < 0.001). ¹⁸F-FET PET results correlated with overall survival (P < 0.001). Conclusion: In our neurooncology department, the diagnostic performance of ¹⁸F-FET PET was convincing but slightly inferior to that of previous reports.

Posttreatment high-grade gliomas are usually monitored with contrast-enhanced MRI, but its diagnostic accuracy is limited as it cannot adequately distinguish between true tumor progression and treatment-related changes. According to recent Response Assessment in Neuro-Oncology recommendations, PET overcomes this limitation. However, it is currently unknown which tracer yields the best results. Therefore, a systematic review and metaanalysis were performed to compare the diagnostic accuracy of the different PET tracers in differentiating tumor progression from treatment-related changes in high-grade glioma patients. Methods: PubMed, Web of Science, and Embase were searched systematically. Study selection, data extraction, and quality assessment were performed independently by 2 authors. Metaanalysis was performed using a bivariate random-effects model when at least 5 studies were included. Results: The systematic review included 39 studies (11 tracers). ¹⁸F-FDG (12 studies, 171 lesions) showed a pooled sensitivity and specificity of 84% (95% confidence interval, 72%–92%) and 84% (95% confidence interval, 69%–93%), respectively. O-(2-¹⁸F-fluoroethyl)-l-tyrosine (¹⁸F-FET) (7 studies, 172 lesions) demonstrated a sensitivity of 90% (95% confidence interval, 81%–95%) and specificity of 85% (95% confidence interval, 71%–93%). For S-¹¹C-methyl)-l-methionine (¹¹C-MET) (8 studies, 151 lesions), sensitivity was 93% (95% confidence interval, 80%–98%) and specificity was 82% (95% confidence interval, 68%–91%). The numbers of included studies for the other tracers were too low to combine, but sensitivity and specificity ranged between 93%–100% and 0%–100%, respectively, for ¹⁸F-FLT; 85%–100% and 72%–100%, respectively, for 3,4-dihydroxy-6-¹⁸F-fluoro-l-phenylalanine (¹⁸F-FDOPA); and 100% and 70%–88%, respectively, for ¹¹C-choline. Conclusion: ¹⁸F-FET and ¹¹C-MET, both amino-acid tracers, showed a comparably higher sensitivity than ¹⁸F-FDG in the differentiation between tumor progression and treatment-related changes in high-grade glioma patients. The evidence for other tracers is limited; thus, ¹⁸F-FET and ¹¹C-MET are preferred when available. Our results support the incorporation of amino-acid PET tracers for the treatment evaluation of high-grade gliomas.

OBJECTIVE

To study whether the effects of intensive glycemic control on major vascular outcomes (a composite of major macrovascular and major microvascular events), all-cause mortality, and severe hypoglycemia events differ among participants with different levels of 10-year risk of atherosclerotic cardiovascular disease (ASCVD) and hemoglobin A_1c (HbA_1c) at baseline.

With the Trump administration having announced the end of the DACA program, Congress is facing growing calls to protect unauthorized immigrants who came to the U.S. as children. This fact sheet examines DREAM Act bills introduced in Congress as of mid-2017, offering estimates of who might earn conditional legal status—and ultimately legal permanent residence—based on educational, professional, and other requirements in the legislation.

2017 saw the introduction of several bills—two of them by Senate Republicans in the weeks following the Trump administration’s announcement that it would terminate the Deferred Action for Childhood Arrivals (DACA) program—that would provide a pathway to conditional and then legal permanent residence to unauthorized immigrants brought to the United States as children, if they meet a range of educational, professional, and other criteria.

Dementia.

London : Walton & Maberly, 1866.

Berlin : G. Reimer, 1899.

London : J. Murray, 1877.

Manchester : J. Heywood, 1888.

London : Adlard, 1904.

Yichong Wu, Tiejun Li, Xiaoping Liu, Luonan Chen.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1269--1301.

Abstract:
Detecting the change of biological interaction networks is of great importance in biological and medical research. We proposed a simple loss function, named as CrossFDTL, to identify the network change or differential network by estimating the difference between two precision matrices under Gaussian assumption. The CrossFDTL is a natural fusion of the D-trace loss for the considered two networks by imposing the $ell _{1}$ penalty to the differential matrix to ensure sparsity. The key point of our method is to utilize the cross variables, which correspond to the sum and difference of two precision matrices instead of using their original forms. Moreover, we developed an efficient minimization algorithm for the proposed loss function and further rigorously proved its convergence. Numerical results showed that our method outperforms the existing methods in both accuracy and convergence speed for the simulated and real data.

Fabien Panloup, Samy Tindel, Maylis Varvenne.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1075--1136.

Abstract:
In this paper we consider the drift estimation problem for a general differential equation driven by an additive multidimensional fractional Brownian motion, under ergodic assumptions on the drift coefficient. Our estimation procedure is based on the identification of the invariant measure, and we provide consistency results as well as some information about the convergence rate. We also give some examples of coefficients for which the identifiability assumption for the invariant measure is satisfied.

Abbas Pak, M. E. Ghitany, Mohammad Reza Mahmoudi.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 894--914.

Abstract:
This paper focuses on Bayesian estimation of the parameters and reliability function of the power Lindley distribution by using various symmetric and asymmetric loss functions. Assuming suitable priors on the parameters, Bayes estimates are derived by using squared error, linear exponential (linex) and general entropy loss functions. Since, under these loss functions, Bayes estimates of the parameters do not have closed forms we use lindley’s approximation technique to calculate the Bayes estimates. Moreover, we obtain the Bayes estimates of the parameters using a Markov Chain Monte Carlo (MCMC) method. Simulation studies are conducted in order to evaluate the performances of the proposed estimators under the considered loss functions. Finally, analysis of a real data set is presented for illustrative purposes.

Christian Olivera, Ciprian Tudor.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 3, 520--531.

Abstract:
Via a special transform and by using the techniques of the Malliavin calculus, we analyze the density of the solution to a stochastic differential equation with unbounded drift.

Rodrigo Citton P. dos Reis, Enrico A. Colosimo, Gustavo L. Gilardoni.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 374--396.

Abstract:
In the data analysis from multiple repairable systems, it is usual to observe both different truncation times and heterogeneity among the systems. Among other reasons, the latter is caused by different manufacturing lines and maintenance teams of the systems. In this paper, a hierarchical model is proposed for the statistical analysis of multiple repairable systems under different truncation times. A reparameterization of the power law process is proposed in order to obtain a quasi-conjugate bayesian analysis. An empirical Bayes approach is used to estimate model hyperparameters. The uncertainty in the estimate of these quantities are corrected by using a parametric bootstrap approach. The results are illustrated in a real data set of failure times of power transformers from an electric company in Brazil.

A deep neural network has relieved the burden of feature engineering by human experts, but comparable efforts are instead required to determine an effective architecture. On the other hands, as the size of a network has over-grown, a lot of resources are also invested to reduce its size. These problems can be addressed by sparsification of an over-complete model, which removes redundant parameters or connections by pruning them away after training or encouraging them to become zero during training. In general, however, these approaches are not fully differentiable and interrupt an end-to-end training process with the stochastic gradient descent in that they require either a parameter selection or a soft-thresholding step. In this paper, we propose a fully differentiable sparsification method for deep neural networks, which allows parameters to be exactly zero during training, and thus can learn the sparsified structure and the weights of networks simultaneously using the stochastic gradient descent. We apply the proposed method to various popular models in order to show its effectiveness.

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty. We cast POMDP filtering and planning problems as two closely related Sequential Monte Carlo (SMC) processes, one over the real states and the other over the future optimal trajectories, and combine the merits of these two parts in a new model named the DualSMC network. In particular, we first introduce an adversarial particle filter that leverages the adversarial relationship between its internal components. Based on the filtering results, we then propose a planning algorithm that extends the previous SMC planning approach [Piche et al., 2018] to continuous POMDPs with an uncertainty-dependent policy. Crucially, not only can DualSMC handle complex observations such as image input but also it remains highly interpretable. It is shown to be effective in three continuous POMDP domains: the floor positioning domain, the 3D light-dark navigation domain, and a modified Reacher domain.

Simplicity is the ultimate sophistication. Differentiable Architecture Search (DARTS) has now become one of the mainstream paradigms of neural architecture search. However, it largely suffers from several disturbing factors of optimization process whose results are unstable to reproduce. FairDARTS points out that skip connections natively have an unfair advantage in exclusive competition which primarily leads to dramatic performance collapse. While FairDARTS turns the unfair competition into a collaborative one, we instead impede such unfair advantage by injecting unbiased random noise into skip operations' output. In effect, the optimizer should perceive this difficulty at each training step and refrain from overshooting on skip connections, but in a long run it still converges to the right solution area since no bias is added to the gradient. We name this novel approach as NoisyDARTS. Our experiments on CIFAR-10 and ImageNet attest that it can effectively break the skip connection's unfair advantage and yield better performance. It generates a series of models that achieve state-of-the-art results on both datasets.

Javier Cárcamo, Antonio Cuevas, Luis-Alberto Rodríguez.

Source: Bernoulli, Volume 26, Number 3, 2143--2175.

Abstract:
We show that various functionals related to the supremum of a real function defined on an arbitrary set or a measure space are Hadamard directionally differentiable. We specifically consider the supremum norm, the supremum, the infimum, and the amplitude of a function. The (usually non-linear) derivatives of these maps adopt simple expressions under suitable assumptions on the underlying space. As an application, we improve and extend to the multidimensional case the results in Raghavachari ( Ann. Statist. 1 (1973) 67–73) regarding the limiting distributions of Kolmogorov–Smirnov type statistics under the alternative hypothesis. Similar results are obtained for analogous statistics associated with copulas. We additionally solve an open problem about the Berk–Jones statistic proposed by Jager and Wellner (In A Festschrift for Herman Rubin (2004) 319–331 IMS). Finally, the asymptotic distribution of maximum mean discrepancies over Donsker classes of functions is derived.

Cristina Butucea, Amandine Dubois, Martin Kroll, Adrien Saumard.

Source: Bernoulli, Volume 26, Number 3, 1727--1764.

Abstract:
We address the problem of non-parametric density estimation under the additional constraint that only privatised data are allowed to be published and available for inference. For this purpose, we adopt a recent generalisation of classical minimax theory to the framework of local $alpha$-differential privacy and provide a lower bound on the rate of convergence over Besov spaces $mathcal{B}^{s}_{pq}$ under mean integrated $mathbb{L}^{r}$-risk. This lower bound is deteriorated compared to the standard setup without privacy, and reveals a twofold elbow effect. In order to fulfill the privacy requirement, we suggest adding suitably scaled Laplace noise to empirical wavelet coefficients. Upper bounds within (at most) a logarithmic factor are derived under the assumption that $alpha$ stays bounded as $n$ increases: A linear but non-adaptive wavelet estimator is shown to attain the lower bound whenever $pgeq r$ but provides a slower rate of convergence otherwise. An adaptive non-linear wavelet estimator with appropriately chosen smoothing parameters and thresholding is shown to attain the lower bound within a logarithmic factor for all cases.

Wensheng Wang, Zhonggen Su, Yimin Xiao.

Source: Bernoulli, Volume 26, Number 2, 1410--1430.

Abstract:
We establish the exact moduli of non-differentiability of Gaussian random fields with stationary increments. As an application of the result, we prove that the uniform Hölder condition for the maximum local times of Gaussian random fields with stationary increments obtained in Xiao (1997) is optimal. These results are applicable to fractional Riesz–Bessel processes and stationary Gaussian random fields in the Matérn and Cauchy classes.

Ilya Pavlyukevich, Georgiy Shevchenko.

Source: Bernoulli, Volume 26, Number 2, 1381--1409.

Abstract:
In this paper, we study the Stratonovich stochastic differential equation $mathrm{d}X=|X|^{alpha }circ mathrm{d}B$, $alpha in (-1,1)$, which has been introduced by Cherstvy et al. ( New J. Phys. 15 (2013) 083039) in the context of analysis of anomalous diffusions in heterogeneous media. We determine its weak and strong solutions, which are homogeneous strong Markov processes spending zero time at $0$: for $alpha in (0,1)$, these solutions have the form egin{equation*}X_{t}^{ heta }=((1-alpha)B_{t}^{ heta })^{1/(1-alpha )},end{equation*} where $B^{ heta }$ is the $ heta $-skew Brownian motion driven by $B$ and starting at $frac{1}{1-alpha }(X_{0})^{1-alpha }$, $ heta in [-1,1]$, and $(x)^{gamma }=|x|^{gamma }operatorname{sign}x$; for $alpha in (-1,0]$, only the case $ heta =0$ is possible. The central part of the paper consists in the proof of the existence of a quadratic covariation $[f(B^{ heta }),B]$ for a locally square integrable function $f$ and is based on the time-reversion technique for Markovian diffusions.

Pasha Tkachov.

Source: Bernoulli, Volume 26, Number 2, 1354--1380.

Abstract:
The aim of this paper is to prove stability of traveling waves for integro-differential equations connected with branching Markov processes. In other words, the limiting law of the left-most particle of a (time-continuous) branching Markov process with a Lévy non-branching part is demonstrated. The key idea is to approximate the branching Markov process by a branching random walk and apply the result of Aïdékon [ Ann. Probab. 41 (2013) 1362–1426] on the limiting law of the latter one.

Richard A. Davis, Mikkel Slot Nielsen, Victor Rohde.

Source: Bernoulli, Volume 26, Number 2, 799--827.

Abstract:
In this paper, we introduce a model, the stochastic fractional delay differential equation (SFDDE), which is based on the linear stochastic delay differential equation and produces stationary processes with hyperbolically decaying autocovariance functions. The model departs from the usual way of incorporating this type of long-range dependence into a short-memory model as it is obtained by applying a fractional filter to the drift term rather than to the noise term. The advantages of this approach are that the corresponding long-range dependent solutions are semimartingales and the local behavior of the sample paths is unaffected by the degree of long memory. We prove existence and uniqueness of solutions to the SFDDEs and study their spectral densities and autocovariance functions. Moreover, we define a subclass of SFDDEs which we study in detail and relate to the well-known fractionally integrated CARMA processes. Finally, we consider the task of simulating from the defining SFDDEs.

Human ventral temporal cortex (VTC) is critical for visual recognition. It is thought that this ability is supported by large-scale patterns of activity across VTC that contain information about visual categories. However, it is unknown how category representations in VTC are organized at the submillimeter scale and across cortical depths. To fill this gap in knowledge, we measured BOLD responses in medial and lateral VTC to images spanning 10 categories from five domains (written characters, bodies, faces, places, and objects) at an ultra-high spatial resolution of 0.8 mm using 7 Tesla fMRI in both male and female participants. Representations in lateral VTC were organized most strongly at the general level of domains (e.g., places), whereas medial VTC was also organized at the level of specific categories (e.g., corridors and houses within the domain of places). In both lateral and medial VTC, domain-level and category-level structure decreased with cortical depth, and downsampling our data to standard resolution (2.4 mm) did not reverse differences in representations between lateral and medial VTC. The functional diversity of representations across VTC partitions may allow downstream regions to read out information in a flexible manner according to task demands. These results bridge an important gap between electrophysiological recordings in single neurons at the micron scale in nonhuman primates and standard-resolution fMRI in humans by elucidating distributed responses at the submillimeter scale with ultra-high-resolution fMRI in humans.

SIGNIFICANCE STATEMENT Visual recognition is a fundamental ability supported by human ventral temporal cortex (VTC). However, the nature of fine-scale, submillimeter distributed representations in VTC is unknown. Using ultra-high-resolution fMRI of human VTC, we found differential distributed visual representations across lateral and medial VTC. Domain representations (e.g., faces, bodies, places, characters) were most salient in lateral VTC, whereas category representations (e.g., corridors/houses within the domain of places) were equally salient in medial VTC. These results bridge an important gap between electrophysiological recordings in single neurons at a micron scale and fMRI measurements at a millimeter scale.

The action potential (AP) waveform controls the opening of voltage-gated calcium channels and contributes to the driving force for calcium ion flux that triggers neurotransmission at presynaptic nerve terminals. Although the frog neuromuscular junction (NMJ) has long been a model synapse for the study of neurotransmission, its presynaptic AP waveform has never been directly studied, and thus the AP waveform shape and propagation through this long presynaptic nerve terminal are unknown. Using a fast voltage-sensitive dye, we have imaged the AP waveform from the presynaptic terminal of male and female frog NMJs and shown that the AP is very brief in duration and actively propagated along the entire length of the terminal. Furthermore, based on measured AP waveforms at different regions along the length of the nerve terminal, we show that the terminal is divided into three distinct electrical regions: A beginning region immediately after the last node of Ranvier where the AP is broadest, a middle region with a relatively consistent AP duration, and an end region near the tip of nerve terminal branches where the AP is briefer. We hypothesize that these measured changes in the AP waveform along the length of the motor nerve terminal may explain the proximal-distal gradient in transmitter release previously reported at the frog NMJ.

SIGNIFICANCE STATEMENT The AP waveform plays an essential role in determining the behavior of neurotransmission at the presynaptic terminal. Although the frog NMJ is a model synapse for the study of synaptic transmission, there are many unknowns centered around the shape and propagation of its presynaptic AP waveform. Here, we demonstrate that the presynaptic terminal of the frog NMJ has a very brief AP waveform and that the motor nerve terminal contains three distinct electrical regions. We propose that the changes in the AP waveform as it propagates along the terminal can explain the proximal-distal gradient in transmitter release seen in electrophysiological studies.

Hydrocephalus is a pathologic condition associated with various brain diseases, including Alzheimer's disease (AD). Dysfunctional ependymal cells (EpCs) are believed to contribute to the development of hydrocephalus. It is thus of interest to investigate EpCs' development and function. Here, we report that vacuolar protein sorting-associated protein 35 (VPS35) is critical for EpC differentiation, ciliogenesis, and survival, and thus preventing neonatal hydrocephalus. VPS35 is abundantly expressed in EpCs. Mice with conditional knock-out (cKO) of Vps35 in embryonic (Vps35^GFAP-Cre and Vps35^Emx1-Cre) or postnatal (Vps35^Foxj1-CreER) EpC progenitors exhibit enlarged lateral ventricles (LVs) and hydrocephalus-like pathology. Further studies reveal marked reductions in EpCs and their cilia in both Vps35^GFAP-Cre and Vps35^Foxj1-CreER mutant mice. The reduced EpCs appear to be due to impairments in EpC differentiation and survival. Additionally, both Vps35^GFAP-Cre and Vps35^Foxj1-CreER neonatal pups exhibit increased cell proliferation and death largely in a region close to LV-EpCs. Many microglia close to the mutant LV-EpC region become activated. Depletion of the microglia by PLX3397, an antagonist of colony-stimulating factor 1 receptor (CSF1R), restores LV-EpCs and diminishes the pathology of neonatal hydrocephalus in Vps35^Foxj1-CreER mice. Taken together, these observations suggest unrecognized functions of Vps35 in EpC differentiation, ciliogenesis, and survival in neonatal LV, and reveal pathologic roles of locally activated microglia in EpC homeostasis and hydrocephalus development.

SIGNIFICANCE STATEMENT This study reports critical functions of vacuolar protein sorting-associated protein 35 (VPS35) not only in promoting ependymal cell (EpC) differentiation, ciliogenesis, and survival, but also in preventing local microglial activation. The dysfunctional EpCs and activated microglia are likely to induce hydrocephalus.

Behavior can be guided by neuronal activity in visual, auditory, or somatosensory cerebral cortex, depending on task requirements. In contrast to this flexible access of cortical signals, several observations suggest that behaviors depend more on neurons in later areas of visual cortex than those in earlier areas, although neurons in earlier areas would provide more reliable signals for many tasks. We recorded from neurons in different levels of visual cortex of 2 male rhesus monkeys while the animals did a visual discrimination task and examined trial-to-trial correlations between neuronal and behavioral responses. These correlations became stronger in primary visual cortex as neuronal signals in that area became more reliable relative to the other areas. The results suggest that the mechanisms that read signals from cortex might access any cortical area depending on the relative value of those signals for the task at hand.

SIGNIFICANCE STATEMENT Information is encoded by the action potentials of neurons in various cortical areas in a hierarchical manner such that increasingly complex stimulus features are encoded in successive stages. The brain must extract information from the response of appropriate neurons to drive optimal behavior. A widely held view of this decoding process is that the brain relies on the output of later cortical areas to make decisions, although neurons in earlier areas can provide more reliable signals. We examined correlations between perceptual decisions and the responses of neurons in different levels of monkey visual cortex. The results suggest that the brain may access signals in any cortical area depending on the relative value of those signals for the task at hand.

Persistent alterations in neuronal activity elicit homeostatic plastic changes in synaptic transmission and/or intrinsic excitability. However, it is unknown whether these homeostatic processes operate in concert or at different temporal scales to maintain network activity around a set-point value. Here we show that chronic neuronal hyperactivity, induced by M-channel inhibition, triggered intrinsic and synaptic homeostatic plasticity at different timescales in cultured hippocampal pyramidal neurons from mice of either sex. Homeostatic changes of intrinsic excitability occurred at a fast timescale (1–4 h) and depended on ongoing spiking activity. This fast intrinsic adaptation included plastic changes in the threshold current and a distal relocation of FGF14, a protein physically bridging Na_v1.6 and K_v7.2 channels along the axon initial segment. In contrast, synaptic adaptations occurred at a slower timescale (~2 d) and involved decreases in miniature EPSC amplitude. To examine how these temporally distinct homeostatic responses influenced hippocampal network activity, we quantified the rate of spontaneous spiking measured by multielectrode arrays at extended timescales. M-Channel blockade triggered slow homeostatic renormalization of the mean firing rate (MFR), concomitantly accompanied by a slow synaptic adaptation. Thus, the fast intrinsic adaptation of excitatory neurons is not sufficient to account for the homeostatic normalization of the MFR. In striking contrast, homeostatic adaptations of intrinsic excitability and spontaneous MFR failed in hippocampal GABAergic inhibitory neurons, which remained hyperexcitable following chronic M-channel blockage. Our results indicate that a single perturbation such as M-channel inhibition triggers multiple homeostatic mechanisms that operate at different timescales to maintain network mean firing rate.

SIGNIFICANCE STATEMENT Persistent alterations in synaptic input elicit homeostatic plastic changes in neuronal activity. Here we show that chronic neuronal hyperexcitability, induced by M-type potassium channel inhibition, triggered intrinsic and synaptic homeostatic plasticity at different timescales in hippocampal excitatory neurons. The data indicate that the fast adaptation of intrinsic excitability depends on ongoing spiking activity but is not sufficient to provide homeostasis of the mean firing rate. Our results show that a single perturbation such as M-channel inhibition can trigger multiple homeostatic processes that operate at different timescales to maintain network mean firing rate.

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