Rapid profiling of protein complex re-organization in perturbed systems

Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information on protein complexes and interaction networks. Methods based on co-fractionation paired with mass spectrometry have demonstrated the capability for deep biological insight but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. As such, there has been little uptake of this strategy beyond a few expert labs into the broader life science community despite rich biological information content. We present a rapid integrated experimental and computational workflow to assess the re-organization of protein complexes across multiple cellular states. It enables complex experimental designs requiring increased sample/condition numbers. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and a subsequent stimulation of macrophage cells with lipopolysaccharide. We observed massive proteome organization in functions related to signaling, cell adhesion, and extracellular matrix during differentiation, and less pronounced changes in processes related to innate immune response induced by the macrophage stimulation. We therefore establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization with broad utility in complex experimental designs.

Large-deviation asymptotics of condition numbers of random matrices

Let $\mathbf{X}$ be a $p\times n$ random matrix whose entries are independent and identically distributed real random variables with zero mean and unit variance. We study the limiting behaviors of the 2-normal condition number k(p,n) of $\mathbf{X}$ in terms of large deviations for large n, with p being fixed or $p=p(n)\rightarrow\infty$ with $p(n)=o(n)$ . We propose two main ingredients: (i) to relate the large-deviation probabilities of k(p,n) to those involving n independent and identically distributed random variables, which enables us to consider a quite general distribution of the entries (namely the sub-Gaussian distribution), and (ii) to control, for standard normal entries, the upper tail of k(p,n) using the upper tails of ratios of two independent $\chi^2$ random variables, which enables us to establish an application in statistical inference.

High-precision quantum algorithms for partial differential equations

Quantum computers can produce a quantum encoding of the solution of a system of differential equations exponentially faster than a classical algorithm can produce an explicit description. However, while high-precision quantum algorithms for linear ordinary differential equations are well established, the best previous quantum algorithms for linear partial differential equations (PDEs) have complexity poly(1/ϵ), where ϵ is the error tolerance. By developing quantum algorithms based on adaptive-order finite difference methods and spectral methods, we improve the complexity of quantum algorithms for linear PDEs to be poly(d,log⁡(1/ϵ)), where d is the spatial dimension. Our algorithms apply high-precision quantum linear system algorithms to systems whose condition numbers and approximation errors we bound. We develop a finite difference algorithm for the Poisson equation and a spectral algorithm for more general second-order elliptic equations.

A Critical Evaluation and Modification of the Padé–Laplace Method for Deconvolution of Viscoelastic Spectra

This paper shows that using the Padé–Laplace (PL) method for deconvolution of multi-exponential functions (stress relaxation of polymers) can produce ill-conditioned systems of equations. Analysis of different sets of generated data points from known multi-exponential functions indicates that by increasing the level of Padé approximants, the condition number of a matrix whose entries are coefficients of a Taylor series in the Laplace space grows rapidly. When higher levels of Padé approximants need to be computed to achieve stable modes for separation of exponentials, the problem of generating matrices with large condition numbers becomes more pronounced. The analysis in this paper discusses the origin of ill-posedness of the PL method and it was shown that ill-posedness may be regularized by reconstructing the system of equations and using singular value decomposition (SVD) for computation of the Padé table. Moreover, it is shown that after regularization, the PL method can deconvolute the exponential decays even when the input parameter of the method is out of its optimal range.

Prevalence and risk factors for diastasis recti abdominis: a review and proposal of a new anatomical variation

Purpose Diastasis recti abdominis (DRA) or rectus diastasis is an acquired condition in which the rectus muscles are separated by an abnormal distance along their length, but with no fascia defect. To data there is no consensus about risk factors for DRA. The aim of this article is to critically review the literature about prevalence and risk factor of DRA. Method A total of 13 papers were identified. Results The real prevalence of DRA is unknown because the prevalence rate varies with measurement method, measurement site and judgment criteria, but it is certainly an extremely frequent condition. Numbers of parity, BMI, diabetes are the most plausible risk factors. We identified a new anatomical variation in cadaveric dissection and in abdominal CT image evaluation: along the semilunar line the internal oblique aponeurosis could join the rectus sheath with only a posterior layer, so without a double layer (anterior and posterior) as usually described. We conducted a retrospective review of abdominal CT images and the presence of the posterior insertion only could be considered as a risk factor for DRA. Conclusion Further studies with large sample size, including nulliparous, primiparous, pluriparous and men too, are necessary for identify the real prevalence

Effect of Sucrose on Microtuber Induction and Inulin Accumulation in Jerusalem Artichoke (Helianthus tuberosus L.)

The effect of sucrose concentrations and photoperiod applying on microtuber induction and inulin accumulation of Jerusalem artichoke (Helianthus tuberosus L.) have conducted under in vitro condition. Numbers, lengths and weights of microtubers induced from the single node explants with 0.50 cm above stem node- and stem node- cutting was not significant difference. Concentration of sucrose (51.70, 60, 80, 100 and 108.20 g/l) containing in microtuber induction medium (MST) and photoperiod applying (10.30/13.70, 12/12, 16/8, 20/4 and 21.60/2.40 h light/dark) significant effected to numbers of microtubers (P ≤ 0.05). The optimized sucrose concentration and photoperiod applying for highest numbers of microtubers was 100 g/l and 20/4 h light/dark, respectively. The significant difference of inulin content (P ≤ 0.05) in microtuber induced from various conditions was determined. The microtubers induced on MST medium supplemented with 80 g/l sucrose under 16/8 h light/dark accumulated highest inulin content (324.84 ± 40.78 mg/ g dry weight) when compared with others. Data suggested that sucrose and light duration played role in microtuber induction and inulin accumulation of Jerusalem artichoke. Keywords: Inulin, Jerusalem artichoke, Microtuber, Photoperiod, Sucrose

Wavelet Numerical Solutions for a Class of Elliptic Equations with Homogeneous Boundary Conditions

This paper focuses on a method to construct wavelet Riesz bases with homogeneous boundary condition and use them to a kind of second-order elliptic equation. First, we construct the splines on the interval [0,1] and consider their approximation properties. Then we define the wavelet bases and illustrate the condition numbers of stiffness matrices are small and bounded. Finally, several numerical examples show that our approach performs efficiently.