Covariates of corticotropin-releasing hormone (CRH) concentrations in cerebrospinal fluid (CSF) from healthy humans

BACKGROUND: Define covariates of cerebrospinal corticotropin-releasing hormone (CRH) levels in normal humans. CRH(CSF )was measured in 9 normal subjects as part of an intensive study of physiological responses stressors in chronic pain and fatigue states. CRH(CSF )was first correlated with demographic, vital sign, HPA axis, validated questionnaire domains, baseline and maximal responses to pain, exercise and other stressors. Significant factors were used for linear regression modeling. RESULTS: Highly significant correlations were found despite the small number of subjects. Three models were defined: (a) CRH(CSF )with blood glucose and sodium (explained variance = 0.979, adjusted R(2 )= 0.958, p = 0.02 by 2-tailed testing); (b) CRH(CSF )with resting respiratory and heart rates (R(2 )= 0.963, adjusted R(2 )= 0.939, p = 0.007); and (c) CRH(CSF )with SF-36 Vitality and Multidimensional Fatigue Inventory Physical Fatigue domains (R(2 )= 0.859, adjusted R(2 )= 0.789, p = 0.02). CONCLUSIONS: Low CRH(CSF )was predicted by lower glucose, respiratory and heart rates, and higher sodium and psychometric constructs of well being. Responses at peak exercise and to other acute stressors were not correlated. CRH(CSF )may have reflected an overall, or chronic, set-point for physiological responses, but did not predict the reserves available to respond to immediate stressors

Sparsity and Incoherence in Compressive Sampling

We consider the problem of reconstructing a sparse signal $x^0\in\R^n$ from a limited number of linear measurements. Given $m$ randomly selected samples of $U x^0$, where $U$ is an orthonormal matrix, we show that $\ell_1$ minimization recovers $x^0$ exactly when the number of measurements exceeds $$m\geq \mathrm{Const}\cdot\mu^2(U)\cdot S\cdot\log n,$$ where $S$ is the number of nonzero components in $x^0$, and $\mu$ is the largest entry in $U$ properly normalized: $\mu(U) = \sqrt{n} \cdot \max_{k,j} |U_{k,j}|$. The smaller $\mu$, the fewer samples needed. The result holds for ``most'' sparse signals $x^0$ supported on a fixed (but arbitrary) set $T$. Given $T$, if the sign of $x^0$ for each nonzero entry on $T$ and the observed values of $Ux^0$ are drawn at random, the signal is recovered with overwhelming probability. Moreover, there is a sense in which this is nearly optimal since any method succeeding with the same probability would require just about this many samples

Cell nuclei detection using globally optimal active contours with shape prior

Cell nuclei detection in fluorescent microscopic images is an important and time consuming task for a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make this a challenging task for automated detection of individual nuclei using image analysis. This paper proposes a novel and robust detection method based on the active contour framework. The method exploits prior knowledge of the nucleus shape in order to better detect individual nuclei. The method is formulated as the optimization of a convex energy function. The proposed method shows accurate detection results even for clusters of nuclei where state of the art methods fail

Asymptotics of the Sketched Pseudoinverse

We take a random matrix theory approach to random sketching and show an asymptotic first-order equivalence of the regularized sketched pseudoinverse of a positive semidefinite matrix to a certain evaluation of the resolvent of the same matrix. We focus on real-valued regularization and extend previous results on an asymptotic equivalence of random matrices to the real setting, providing a precise characterization of the equivalence even under negative regularization, including a precise characterization of the smallest nonzero eigenvalue of the sketched matrix, which may be of independent interest. We then further characterize the second-order equivalence of the sketched pseudoinverse. Lastly, we propose a conjecture that these results generalize to asymptotically free sketching matrices, obtaining the resulting equivalence for orthogonal sketching matrices and comparing our results to several common sketches used in practice.Comment: 37 pages, 7 figure

Human neuroglobin protein in cerebrospinal fluid

BACKGROUND: Neuroglobin is a hexacoordinated member of the globin family of proteins. It is predominantly localized to various brain regions and retina where it may play a role in protection against ischemia and nitric oxide-induced neural injury. Cerebrospinal fluid was collected from 12 chronic regional or systemic pain and 5 control subjects. Proteins were precipitated by addition of 50% 0.2 N acetic acid, 50% ethanol, 0.02% sodium bisulfite. The pellet was extensively digested with trypsin. Peptides were separated by capillary liquid chromatography using a gradient from 95% water to 95% acetonitrile in 0.2% formic acid, and eluted through a nanoelectrospray ionization interface into a quadrapole – time-of-flight dual mass spectrometer (QToF2, Waters, Milford, MA). Peptides were sequenced (PepSeq, MassLynx v3.5) and proteins identified using MASCOT (®). RESULTS: Six different neuroglobin peptides were identified in various combinations in 3 of 9 female pain subjects, but none in male pain, or female or male control subjects. CONCLUSION: This is the first description of neuroglobin in cerebrospinal fluid. The mechanism(s) leading to its release in chronic pain states remain to be defined

Nova saznanja o neuralnoj regulaciji nosne sluznice kod ljudi

Nasal mucosa is innervated by multiple subsets of nociceptive, parasympathetic and sympathetic nerves. These play carefully coordinated roles in regulating glandular, vascular and other processes. These functions are vital for cleaning and humidifying ambient air before it is inhaled into the lungs. The recent recognition of distinct classes of nociceptive nerves with unique patterns of sensory receptors that include seven transmembrane G-protein coupled receptors, new families of transient receptor potential and voltage and calcium gated ion channels, and combinations of neurotransmitters that can be modulated during inflammation by neurotrophic factors has revolutionized our understanding of the complexity and subtlety of nasal innervation. These findings may provide a rational basis for responses to air temperature changes, culinary and botanical odorants ("aromatherapy"), and inhaled irritants in conditions as diverse as idiopathic nonallergic rhinitis, occupational rhinitis, hyposmia, and multiple chemical sensitivity.Nosnu sluznicu prožimaju višestruke podskupine nociceptivnih, parasimpatičkih i simpatičkih živaca koji imaju podrobno usklađene uloge u reguliranju žljezdanih, žilnih i drugih procesa. Ove funkcije su presudne za čišćenje i ovlaživanje zraka iz okoline prije negoli se udahne u pluća. Naše shvaćanje složene i fine naravi inervacije nosne sluznice radikalno se mijenja nedavnim prepoznavanjem različitih vrsta nociceptivnih živaca s jedinstvenim obrascima senzornih receptora koji obuhvaćaju sedam transmembranskih receptora vezanih s G-proteinom, nove porodice prolaznog receptorskog potencijala i napona te kalcijem ograničene (gated) ionske kanale i kombinacije neurotransmitora koje tijekom upale mogu mijenjati neurotropni čimbenici. Ovi nalazi mogli bi pružiti razumnu osnovu za odgovore na promjene u temperaturi zraka, kulinarske i botaničke mirise ("aromaterapija") i udisajne iritante u uvjetima tako ranovrsnim kao što su idiopatski nealergijski rinitis, profesionalni rinitis, hiposmija i višestruka kemijska osjetljivost

MomentumRNN: Integrating Momentum into Recurrent Neural Networks

Designing deep neural networks is an art that often involves an expensive search over candidate architectures. To overcome this for recurrent neural nets (RNNs), we establish a connection between the hidden state dynamics in an RNN and gradient descent (GD). We then integrate momentum into this framework and propose a new family of RNNs, called {\em MomentumRNNs}. We theoretically prove and numerically demonstrate that MomentumRNNs alleviate the vanishing gradient issue in training RNNs. We study the momentum long-short term memory (MomentumLSTM) and verify its advantages in convergence speed and accuracy over its LSTM counterpart across a variety of benchmarks. We also demonstrate that MomentumRNN is applicable to many types of recurrent cells, including those in the state-of-the-art orthogonal RNNs. Finally, we show that other advanced momentum-based optimization methods, such as Adam and Nesterov accelerated gradients with a restart, can be easily incorporated into the MomentumRNN framework for designing new recurrent cells with even better performance. The code is available at https://github.com/minhtannguyen/MomentumRNN.Comment: 21 pages, 11 figures, Accepted for publication at Advances in Neural Information Processing Systems (NeurIPS) 202