Random Shifts Research Articles

Automatic Bone Marrow Cell Identification and Classification By Deep Neural Network

Purpose Differential counting of blood cells is the basis of diagnostic hematology. In many circumstances, identification of cells in bone marrow smears is the golden standard for diagnosis. Presently, methods for automatic differential counting of peripheral blood are readily available commercially. However, morphological assessment and differential counting of bone marrow smears are still performed manually. This procedure is tedious, time-consuming and laden with high inter-operator variation. In recent years, deep neural networks have proven useful in many medical image recognition tasks, such as diagnosis of diabetic retinopathy, and detection of cancer metastasis in lymph nodes. However, there has been no published work on using deep neural networks for complete differential counting of entire bone marrow smear. In this work, we present the results of using deep convolutional neural network for automatic differential counting of bone marrow nucleated cells. Materials & Methods The bone marrow smears from patients with either benign or malignant disorders in National Taiwan University Hospital were recruited in this study. The bone marrow smears are stained with Liu's stain, a modified Romanowsky stain. Digital images of the bone marrow smears were taken using 1000x oil immersion lens and 20MP color CCD camera on a single microscope with standard illumination and white-balance settings. The contour of each nucleated cell was artificially defined. These cells were then divided into a training/validation set and a test set. Each cell was then classified into 1 of the 11 categories (blast, promyelocyte, neutrophilic myelocyte, neutrophilic metamyelocyte, neutrophils, eosinophils and precursors, basophil, monocyte and precursors, lymphocyte, erythroid lineage cells, and invalid cell). In training/validation set, the classification of each cell was annotated once by experienced medical technician or hematologist. The annotated dataset was used to train a Path-Aggregation Network for instance segmentation task. In test set, cell classification was annotated by three medical technicians or hematologists; only over 2/3 consensus was regarded as valid. After the neural network model was fully trained, the ability of the model to classify and detect bone marrow nucleated cells was evaluated in terms of precision, recall and accuracy. During the model training, we used group normalization and stochastic gradient descent optimizer for training. Random noise, Gaussian blur, rotation, contrast and color shift were also used as means for data augmentation. Results The digital images of 150 bone marrow aspirate smears were taken for this study. They included 61 for acute leukemia, 39 for lymphoma, 2 for myelodysplastic syndrome (MDS), 2 for myeloproliferative neoplasm (MPN), 10 for MDS/MPN, 12 for multiple myeloma, 4 for hemolytic anemia, 9 for aplastic anemia, 8 for infectious etiology and 3 for solid cancers. The final data contained 5927 images and 187730 nucleated bone marrow cells, which were divided into 2 sets: 5630 images containing 170966 cells as the training/validation set, and 297 images containing 16764 cells as the test set. Among the 16764 cells annotated in test set, 15676 cells (93.6 %) reached over 2/3 consensus. The trained neural network achieved 0.832 recall and 0.736 precision for cell detection task, 0.79 mean intersection over union (IOU) for cell segmentation task, mean average precision of 0.659 and accuracy of 0.801 for cell classification. For individual cell categories, the model performs the best with "erythroid-lineage-cells" (0.971 recall, 0.935 precision) and the worst with "monocyte-and-precursors" (0.825 recall, 0.337 precision). Conclusions We have created the largest and the most comprehensive annotated bone marrow smear image dataset for deep neural network training. Compared with previous works, our approach is more practical for clinical application because it is able to take in an entire field of smear and generate differential counts without any other preprocessing steps. Current results are highly encouraging. With continued expansion of dataset, our model would be more precise and clinically useful. Figure Disclosures Yeh: aether AI: Other: CEO and co-founder. Yang:aether AI: Employment. Tien:Novartis: Honoraria; Daiichi Sankyo: Honoraria; Celgene: Research Funding; Roche: Honoraria; Johnson &Johnson: Honoraria; Alexion: Honoraria; BMS: Honoraria; Roche: Research Funding; Celgene: Honoraria; Pfizer: Honoraria; Abbvie: Honoraria. Hsu:aether AI: Employment.

Adaptive Calibration of Electrode Array Shifts Enables Robust Myoelectric Control.

The objective of this work is to develop a novel method for adaptive calibration of the electrode array shifts toward achieving robust myoelectric pattern-recognition control. This work is inspired by the idea of image object detection when high-density surface electromyogram signals recorded from a two-dimensional electrode array carry rich spatial information and can serve as a muscular activation image. A convolutional neural network involving transfer learning (from a network for image recognition) is used to learn muscular activity patterns at an original/baseline position of an electrode array. The shift of an electrode array can be estimated by identifying and matching partially overlapped regions between the training images of muscular activation and the testing images at any other position, in an unsupervised manner. Given the calibration of an electrode array shift, the identification of muscular activity patterns is implemented accordingly. The performance of the proposed method was evaluated with data recorded by a 10 × 10 electrode array placed over the forearm extensors of 10 subjects performing 6 wrist and finger extension tasks. The proposed method achieved high task classification accuracies around 95% and outperformed five traditional methods under conditions with multiple designated shifts in offline experiments and a random shift in online testing. The proposed method is demonstrated to be a promising solution for the automatic and adaptive calibration of electrode array shifts. This work will enhance the robustness of myoelectric control systems.

Image Reconstruction from Digital Holograms Obtained by Specifying Random Phase Shifts

A new method for image reconstruction from a series of digital holograms obtained by stepped phase shift with random values of phase shifts is under consideration. It is shown that images reconstructed from a digital hologram in a Fresnel region at a distance matching a distance from the object to the hologram consist of two parts, one of which is a clear image of the object and the other one being formed by incorrect phase shifts consists of a set of defocused images of the object. At the same time, the quality of the reconstructed image is slightly reduced. This effect eliminates the necessity to use precision systems for setting the phase shift and significantly reduces requirements for stabilizing the optical device.

DEEP LEARNING FOR MORPHOLOGICAL CLASSIFICATION OF GALAXIES FROM SDSS

We present the results of applying deep convolutional neural network to the images of redshift-limited ( z < 0 . 1) sample of ∼ 300000 galaxies from the SDSS DR9. We aimed to classify galaxies into the two classes: Elliptical and Spiral. To create the training sample, we used a set of ∼ 6000 galaxies from our previous work with visually inspected morphologi- cal types, and also added 80000 well-confirmed galaxies from Galaxy Zoo 2 dataset, that were also classified visually. With a given sample of ∼ 86000 galaxies, we used the deep neural network, namely Xception, to provide a classification of g-r-i composite images (25 arcsec in each axis in size) of galaxies. Keeping in the mind a relatively small training dataset, we provided the data augmentation (horizontal and vertical flips, random shifts on ± 10 pixels , and rotations within 180 degrees), that was randomly applied to the images during learning. The data augmentation is a key technique within our algorithm to display the variative nature of the observed galaxies, and avoid overfitting problem. We compared our classification result with the Support Vector Machine (SVM) classification performed on the SDSS photometric data (absolute magnitudes, colour indices, inverse concentration index, ratios of semiaxes, etc.), and proposed a method to learn the benefits from both approaches (Deep Learning and photometric classification). We show the common mistakes of both algorithms, and propose to stack these two approaches to block these mistakes, with a main goal to increase the overall classification quality of SDSS galaxies.

A new fast double-chaotic based Image encryption scheme

In this paper, a new scheme for image encryption based on a double chaotic pseudo random generator (DCPG), simple XOR and shift rotations operations is proposed. The DCPG is a combination of both Tent and Chebyshev chaotic and so it needs three values of control parameters which are used as shared secret keys. The encryption consists of two rounds. In the first round, the hash value of the input image is computed using SHA-512. This value also is used as a forth shared secret key and from which, 4 amounts of the shift rotations are extracted. A pseudorandom sequence is generated using the proposed DCPG with the same size of the input image. This sequence and also the input image are divided into blocks of size 1 × 8. Each block of input image is processed with the corresponding block of the pseudorandom sequence using simple shift rotation and XOR operation. To extend the effect of the original image one pixel change to all the pixels of the cipher image, a second round of XOR operation is added. The proposed scheme has many advantages. It is highly secure due to two reasons. Firstly, it uses four secret keys for encryption which provides a large key space to overcome all types of brute force attacks. Secondly, the amounts of shift rotation used are input image dependent which achieves a strong resistance against chosen plaintext attacks. Also, it is more efficient compared to other recently existing schemes as it consists only of two rounds of simple operations. Security analysis of scheme has been provided. Based on the results, our scheme is highly secure with a reduced encryption time and so it can be used for many applications which require real time secure image communications.

Ptychographic modulation engine: a low-cost DIY microscope add-on for coherent super-resolution imaging

Imaging of biological cells and tissues often relies on fluorescent labels, which offer high contrast with molecular specificity. The use of exogenous labeling agents, however, may alter the normal physiology of the bio-specimens. Complementary to the established fluorescence microscopy, label-free quantitative phase imaging provides an objective morphological measurement tool for bio-specimens and is free of variability introduced by contrast agents. Here, we report a simple and low-cost microscope add-on, termed ptychographic modulation engine (PME), for super-resolution quantitative phase imaging. In this microscope add-on module, we attach a diffuser to a 3D-printed holder that can be mechanically moved to different x-y positions. We then use two vibrational motors to introduce random positional shifts to the diffuser. The add-on module can be placed between the objective lens and the specimen in most existing microscope platforms. Thanks to the diffuser modulation process, the otherwise inaccessible high-resolution object information can now be encoded into the captured images. In the ptychographic phase retrieval process, we jointly recover the complex object wavefront, the complex diffuser profile, and the unknown positional shifts of the diffuser. We demonstrate a four-fold resolution gain over the diffraction limit of the employed 2× objective lens. We also test our approach for in vitro cell imaging, where we are able to adjust the focus after the data has been captured. The reported microscope add-on provides a turnkey solution for super-resolution quantitative phase imaging. It may find applications in label-free bio-imaging where both large field-of-view and high resolution are needed.

Partially coherent Bessel vortex superposition with linear charge increase and aligned maxima

In this paper, the generation of a partially coherent optical vortex composed of the incoherent superposition of N coaxial Bessel vortex beams with linearly increasing topological charges and aligned intensity maxima is proposed. To generate this beam, concentric ring slits with increasing topological charges and random phase shifts are optically Fourier transformed. The radii of the ring slits are chosen in such a way that the maxima of their corresponding Bessel functions are coincident. It is demonstrated that the resulting beam radius is independent of the topological charges involved in the superposition and the beam is propagation invariant. The Bessel vortex addition is experimentally generated by means of a phase-only liquid crystal spatial light modulator.

Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range

Phosphorylation is one of the main regulators of cellular signaling typically occurring in flexible parts of folded proteins and in intrinsically disordered regions. It can have distinct effects on the chemical environment as well as on the structural properties near the modification site. Secondary chemical shift analysis is the main NMR method for detection of transiently formed secondary structure in intrinsically disordered proteins (IDPs) and the reliability of the analysis depends on an appropriate choice of random coil model. Random coil chemical shifts and sequence correction factors were previously determined for an Ac-QQXQQ-NH2-peptide series with X being any of the 20 common amino acids. However, a matching dataset on the phosphorylated states has so far only been incompletely determined or determined only at a single pH value. Here we extend the database by the addition of the random coil chemical shifts of the phosphorylated states of serine, threonine and tyrosine measured over a range of pH values covering the pKas of the phosphates and at several temperatures (www.bio.ku.dk/sbinlab/randomcoil). The combined results allow for accurate random coil chemical shift determination of phosphorylated regions at any pH and temperature, minimizing systematic biases of the secondary chemical shifts. Comparison of chemical shifts using random coil sets with and without inclusion of the phosphoryl group, revealed under/over estimations of helicity of up to 33%. The expanded set of random coil values will improve the reliability in detection and quantification of transient secondary structure in phosphorylation-modified IDPs.

Unambiguous Identification of Pyroglutamate in Full-Length Biopharmaceutical Monoclonal Antibodies by NMR Spectroscopy.

Biotherapeutic proteins are an indispensable class of pharmaceuticals that present a high degree of structural complexity and are prone to chemical modifications during production, processing, and storage, which have to be tightly controlled. Pyroglutamate (pGlu), a cyclization product of N-terminal Gln or Glu residues, is a widespread post-translational modification in proteins, including monoclonal antibodies (mAbs). The unambiguous identification and quantification of this modification in proteins is challenging, since the mass difference of -17 Da or -18 Da, when formed from Gln or Glu, respectively, is not unique. Moreover, deamidation and dehydration occur not only during cyclization to pGlu, but also during other reactions leading to different types of modifications, like succinimide or isopeptide bond moieties due to cross-linking between Asn or Gln and Lys side chains. Here we report the unambiguous identification and quantification of pGlu in intact mAbs with natural isotope distribution by NMR spectroscopy. The assignment of all 1H, 13C and 15N random coil chemical shifts of pGlu in short reference peptides led to the identification of unique chemical shift pairs that are distinct from the random coil chemical shifts of the natural amino-acid residues. These characteristic correlations are suited for the detection of pGlu in denatured proteins. We achieved complete denaturation of mAbs using a straightforward protocol, and could detect and quantify pGlu, in agreement with available mass spectrometric data. The application to the mAbs rituximab and adalimumab illustrates the potential of our approach for the characterization of biotherapeutics containing isotopes at natural abundance.

Концепции культурных и социальных поворотов и их эвристические возможности

&lt;p&gt;Понятие «поворот» эвристически значимо для характеристики нового пути или новых путей в исследовании процессов и явлений. С помощью концепции поворотов сегодня конструируется и упорядочивается ткань научного дискурса. Поворот фиксирует не просто случайный сдвиг, а закономерный или найденный автором новый тренд либо изменение тренда. На наш взгляд, социальные повороты, наряду с культурными, способствуют социальному конструированию реальности, открывая пласт социальных проблем и выводов. В одних случаях поворот характеризует в большей степени новый методологический подход, а в других — новую онтологию или попытку новой рефлексии существующих реалий.&lt;/p&gt;&lt;p&gt;Используя понятие «поворот», мы обращаемся к либеральному и социал-демократическому поворотам, а также к повороту переосмысления европейской современности путем нахождения ее новых форм. Попытка поворота состоит в поисках нового ракурса исследовательской деятельности. Эвристический потенциал, стоящий за переходом к поворотам, велик, но не может быть переоценен, поскольку не все значимые изменения характеризуются как повороты, как не все повороты являются концептуально важными.&lt;/p&gt;

A new kind of chaotic diffusion: anti-persistent random walks of explosive dissipative solitons

The solitons that exist in nonlinear dissipative media have properties very different from the ones that exist in conservative media and are modeled by the nonlinear Schrödinger equation. One of the surprising behaviors of dissipative solitons is the occurrence of explosions: sudden transient enlargements of a soliton, which as a result induce spatial shifts. In this work using the complex Ginzburg–Landau equation in one dimension, we address the long-time statistics of these apparently random shifts. We show that the motion of a soliton can be described as an anti-persistent random walk with a corresponding oscillatory decay of the velocity correlation function. We derive two simple statistical models, one in discrete and one in continuous time, which explain the observed behavior. Our statistical analysis benchmarks a future microscopic theory of the origin of this new kind of chaotic diffusion.

Chromatic dispersion immune microwave photonic phase shifter based on double-sideband modulation.

A chromatic dispersion (CD) immune microwave photonic phase shifter (MPPS) based on double-sideband (DSB) modulation is proposed and demonstrated. An optical spectrum processor introduces the phase shift to the MPPS. The DSB signals along two orthogonal polarizations are demodulated to two RF signals with both quadrature amplitude and phase items, transferring the CD-induced power fading to the phase item of the synthetic RF signals. Experimental results show that the RF signals over 14-25GHz obtain random phase shift in 360° range without a power fading point (PFP) after passing through a dispersion compensation fiber with CD of -331 ps/nm. The phase variation and power variation of the phase-shifted signal are <±5.7° and <±0.9 dB, respectively, at the original PFP at 16GHz.

Random Walks and Measures on Hilbert Space that are Invariant with Respect to Shifts and Rotations

We study random walks in a Hilbert space H and their applications to representations of solutions to Cauchy problems for differential equations whose initial conditions are number-valued functions on the Hilbert space H. Examples of such representations of solutions to various evolution equations in the case of a finite-dimensional space H are given. Measures on a Hilbert space that are invariant with respect to shifts are considered for constructing such representations in infinite-dimensional Hilbert spaces. According to a theorem of A. Weil, there is no Lebesgue measure on an infinite-dimensional Hilbert space. We study a finitely additive analog of the Lebesgue measure, namely, a nonnegative, finitely additive measure λ defined on the minimal ring of subsets of an infinite-dimensional Hilbert space H containing all infinite-dimensional rectangles whose products of sides converge absolutely; this measure is invariant with respect to shifts and rotations in the Hilbert space H. We also consider finitely additive analogs of the Lebesgue measure on the spaces lp, 1 ≤ p ≤ ∞, and introduce the Hilbert space \( \mathcal{H} \) of complex-valued functions on the Hilbert space H that are square integrable with respect to a shift-invariant measure λ. We also obtain representations of solutions to the Cauchy problem for the diffusion equation in the space H and the Schrodinger equation with the coordinate space H by means of iterations of the mathematical expectations of random shift operators in the Hilbert space \( \mathcal{H} \).

Random coil shifts of posttranslationally modified amino acids

Most eukaryotic proteins are modified during and/or after translation, regulating their structure, function and localisation. The role of posttranslational modifications (PTMs) in both normal cellular processes and in diseases is already well recognised and methods for detection of PTMs and generation of specifically modified proteins have developed rapidly over the last decade. However, structural consequences of PTMs and their specific effects on protein dynamics and function are not well understood. Furthermore, while random coil NMR chemical shifts of the 20 standard amino acids are available and widely used for residue assignment, dihedral angle predictions and identification of structural elements or propensity, they are not available for most posttranslationally modified amino acids. Here, we synthesised a set of random coil peptides containing common naturally occurring PTMs and determined their random coil NMR chemical shifts under standardised conditions. We highlight unique NMR signatures of posttranslationally modified residues and their effects on neighbouring residues. This comprehensive dataset complements established random coil shift datasets of the 20 standard amino acids and will facilitate identification and assignment of posttranslationally modified residues. The random coil shifts will also aid in determination of secondary structure elements and prediction of structural parameters of proteins and peptides containing PTMs.

Accurate and fast two-step phase shifting algorithm based on principle component analysis and Lissajous ellipse fitting with random phase shift and no pre-filtering.

To achieve high measurement accuracy with less computational time-in-phase shifting interferometry, a random phase-shifting algorithm based on principal component analysis and Lissajous ellipse fitting (PCA&LEF) is proposed. It doesn't need pre-filtering and can obtain relatively accurate phase distribution with only two phase shifted interferograms and less computational time and is suitable for different background intensity, modulation amplitude distributions and noises. Moreover, it can obtain absolutely accurate result when the background intensity and modulation amplitude are perfect and can partly suppress the effect of imperfect background intensity and modulation amplitude. Last but not least, it removes the restriction that PCA needs more than three interferograms with well-distributed phase shifts to subtract relatively accurate mean. The simulations and experiments verify the correctness and feasibility of PCA&LEF.

The Least-squares Algorithm for Interferograms with Random Phase Shifts

In surface shape measurement with phase-shifting interferometry, the extraction accuracy of initial phase is significant and it will influence the correct of measurement result. A least-squares algorithm is proposed to solve the parameters of interferograms with random and known phase shifts. It can rapidly extract the initial phase, modulation amplitude and background intensity without any iteration. Numerical simulations and optical experiments are implemented to demonstrate the performance of the proposed method. The simulation and experiment results show that the presented method is effective, rapid and accurate.

On Mode-Spacing Division of a Frequency Comb by Temporal Phase Modulation

Mode-spacing division of a phase-coherent optical frequency comb is numerically investigated using temporal phase modulation of a periodic optical pulse train. Different schemes, including Talbot and random phase modulations, are investigated and compared, i.e., concerning the tunability of mode-spacing division factors and the robustness to potential practical deviations on the modulation phase profiles. Talbot phase modulation provides a versatile technique for mode-spacing division of a frequency comb; however, the approach is found to be sensitive to phase deviations under certain design parameters. The results show the difficulty in obtaining experimentally the frequency shift by half mode spacing that is predicted by theory for odd division factors. In contrast, generalized random phase modulation methods, proposed here for the first time, utilize random 0-π or multilevel phase profiles to provide interesting alternative methods for implementation of mode-spacing division of a frequency comb. The use of random 0-π phase modulation simplifies the practical implementation of the scheme as it requires inducing only 0 and π phase shifts. The use of random multilevel phase modulation generalizes the approach by using multiple levels of random phase shifts. Both random phase modulation approaches enable successful mode-spacing division by any designed division factor, and they show a relatively lower sensitivity to phase deviations in the modulation functions.

Quantum electrodynamics of a superconductor–insulator phase transition

A chain of Josephson junctions implements one of the simplest many-body models undergoing a superconductor-insulator (SI) quantum phase transition between states with zero and infinite resistance. Apart from zero resistance, the superconducting state is necessarily accompanied by a sound-like mode due to collective oscillations of the phase of the complex-valued order parameter. Exciting this phase mode results in transverse photons propagating along the chain. Surprisingly little is known about the fate of this mode upon entering the insulating state, where the order parameter's amplitude remains non-zero, but the phase ordering is "melted" by quantum fluctuations. Here we report momentum-resolved radio-frequency spectroscopy of collective modes in nanofabricated chains of Al/AlOx/Al tunnel junctions. We find that the phase mode survives remarkably far into the insulating regime, such that $\textrm{M}\Omega$-resistance chains carry $\textrm{GHz}$-frequency alternating currents as nearly ideal superconductors. The insulator reveals itself through broadening and random frequency shifts of collective mode resonances, originated from intrinsic interactions. By pushing the chain parameters deeper into the insulating state, we achieved propagation with the speed of light down to $8\times 10^5~\textrm{m/s}$ and the wave impedance up to $23~\textrm{k}\Omega$. The latter quantity exceeds the predicted critical impedance by an order of magnitude, which opens the problem of quantum electrodynamics of a Bose glass insulator for both theory and experiment. Notably, the effective fine structure constant of such a 1D vacuum exceeds a unity, promising transformative applications to quantum science and technology.

ВІДТВОРЕННЯ СИГНАЛІВ ТА ЗОБРАЖЕНЬ ЗА БІСПЕКТРОМ У ПРИСУТНОСТІ ШУМІВ

In a number of practical applications of digital signal processing, the process under study may include correlated spectral components or phase coupling. Extracting the phase relationships provides very important and useful information for the correct understanding, analysis, and description of the properties of physical phenomena generating these processes. However, such information is irretrievably lost when using classical methods of signal processing using energy statistics, i.e. second-order statistics. Obtaining estimates of signal parameters and analyzing them using third-order correlation functions and bispectrum makes it possible to learn much more about signal properties than when using conventional correlation functions. Estimating the bispectral density (third order spectral density), in contrast to estimating the energy spectrum, makes it possible not only to describe the characteristics of the observed process correctly, but also to preserve and, if necessary, extract the phase characteristics of the component, which includes the observed process. Therefore, in a number of applied tasks of telecommunications, as well as tasks of image processing and other bisection analysis, often serves as an effective tool of signal processing. The aim of the article is to study the feasibility of using a recursive algorithm when restoring a waveform and image by bispectrum in the noise environment. The following types of signals were selected for the study: rectangular, triangular, Gaussian impulses and signal-triplet. They were distorted with additive white Gaussian noise Test image was distorted by additive white Gaussian, pulsed, Poisson and multiplicative noises. Analysis of the signal recovery results indicates that as the noise power increases, the quality of the recovery decreases. The effect of random signal shift does not affect the shape of the recovered signal. Analysis of the image recovery results indicates image recovery, but this algorithm introduces distortions in the form of an offset

On the performance of CUSUM control charts for monitoring the coefficient of variation with measurement errors

In this paper, we investigate the effect of the measurement error on the performance of the cumulative sum (CUSUM) control charts monitoring the coefficient of variation. The measurement errors are supposed to follow a linear covariate error model. The obtained results show that the precision error ratio and the accuracy error have negative impact on the chart performance. Moreover, in order to overcome the difficulty in predetermining a specific value for the process shift size, we suggest to optimize the parameters of the charts according to the random shift size in a given interval. The robustness of the proposed method is studied. An example is given to illustrate the use of the CUSUM charts on a real quality control problem from sintering process.