373 research outputs found
Stability of the photonic band gap in the presence of disorder
The photonic eigenmodes near a band gap of a type of one-dimensional disordered photonic crystal have been investigated statistically. For the system considered, it is found that the tail of the density of states entering the band gap is characterized by a certain penetration depth, which is proportional to the disorder parameter. A quantitative relation between the relative penetration depth, the relative width of the photonic band gap, and the disorder has been found. It is apparent that there is a certain level of disorder below which the probability of the appearance of photonic eigenstates at the center of the photonic band gap essentially vanishes. Below the threshold, the ensemble-averaged transmission at the center of the photonic band gap does not change significantly with increasing disorder, but above threshold it increases much more rapidly. A simple empirical formula has been obtained which describes how the logarithm of the transmission relates to the periodic refractive index modulation and the disorde
Β«ΠΠ΅ΠΎΠΆΠΈΠ΄Π°Π½Π½ΡΠ΅Β» ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ ΠΏΡΠΈ Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ ΡΠ΅Π²ΠΈΠ·ΠΈΡΡ
Background. Data from the national registers of arthroplasty showed that about 12% of hip and knee arthroplasty undergo revision within 10 years after the primary surgery. The leading cause of hip revisions is aseptic loosening of components, knee joint periprosthetic infection (PPI). Some of the infectious complications, including those related to mechanical causes, remain out of sight.
The aim of the study was to identify the frequency of unexpected infections during revision knee and hip arthroplasty performed for aseptic complications of any etiology.
Materials and Methods. 839 cases of revision arthroplasty of knee and hip joints were analyzed, including 485 aseptic revisions in 450 patients. Clinical, X-ray, laboratory (complete blood count and comprehensive metabolic panel, coagulation panel) methods, synovial fluid analysis and microbiological examination of punctures, including intraoperative ones, were used. The ICM and EBJIS (European Bone and Joint Infections Society) consensus recommendations were used as criteria for assessing the presence of infection.
Results. The average age of patients at the time of the revision was 61.7 years. The hip joint prevailed (59.4%), knee joint 40.6%. The growth of microorganisms in the intraoperative biomaterial was detected in 2.08% of observations: in 10 out of 287 patients after aseptic revision of the hip joints and in none of the 198 revisions of the knee joints. In 8 out of 10 cases, the causative agents were coagulase-negative staphylococci, including 6 MRSE; in two cases, anaerobic bacteria. All revisions were carried out by a one-stage method. Patients with detected PPI underwent systemic antibacterial therapy. At the stage of catamnesis, reinfection was assumed in one of the 10 identified cases of PPI, the patient did not show up for revision. In control 63% of the group of the other (aseptic) 470 patients, PPI developed in 4 cases, two-stage revisions were carried out.
Conclusions. The frequency of infections accidentally detected during aseptic revisions of large joints was 2.08%. Three-time examination of joint punctures, including intraoperative, provides additional opportunities for the diagnosis of PPI during aseptic revision, and also allows you to choose the optimal stage of revision treatment. The experience gained makes it possible in certain cases to perform one-stage revision in the treatment of PPI.ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ. ΠΠ°Π½Π½ΡΠ΅ ΠΌΠΈΡΠΎΠ²ΡΡ
ΡΠ΅Π³ΠΈΡΡΡΠΎΠ² Π°ΡΡΡΠΎΠΏΠ»Π°ΡΡΠΈΠΊΠΈ ΡΡΡΡΠ°Π²ΠΎΠ² ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΠΎΠΊΠΎΠ»ΠΎ 12% ΡΠ½Π΄ΠΎΠΏΡΠΎΡΠ΅Π·ΠΎΠ² ΡΠ°Π·ΠΎΠ±Π΅Π΄ΡΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΈ ΠΊΠΎΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΡΡΡΡΠ°Π²ΠΎΠ² ΠΏΠΎΠ΄Π²Π΅ΡΠ³Π°ΡΡΡΡ ΡΠ΅Π²ΠΈΠ·ΠΈΠΎΠ½Π½ΡΠΌ Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ²Π°ΠΌ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 10 Π»Π΅Ρ ΠΏΠΎΡΠ»Π΅ ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. ΠΠΈΠ΄ΠΈΡΡΡΡΠ°Ρ ΠΏΡΠΈΡΠΈΠ½Π° ΡΠ΅Π²ΠΈΠ·ΠΈΠΉ ΡΠ°Π·ΠΎΠ±Π΅Π΄ΡΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΡΡΡΠ°Π²Π° Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠ°ΡΡΠ°ΡΡΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΎΠ², ΠΊΠΎΠ»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠ΅ΡΠΈΠΏΡΠΎΡΠ΅Π·Π½Π°Ρ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΡ (ΠΠΠ). Π§Π°ΡΡΡ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΎΠ½Π½ΡΡ
ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ, Π² Ρ.Ρ. ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΠΌΠ΅Ρ
Π°Π½ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΏΡΠΈΡΠΈΠ½Π°ΠΌΠΈ, ΠΎΡΡΠ°Π΅ΡΡΡ Π²Π½Π΅ ΠΏΠΎΠ»Ρ Π·ΡΠ΅Π½ΠΈΡ Π²ΡΠ°ΡΠ΅ΠΉ. Π¦Π΅Π»ΡΡ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²ΡΡΠ²Π»Π΅Π½ΠΈΠ΅ ΡΠ°ΡΡΠΎΡΡ Π½Π΅ΠΎΠΆΠΈΠ΄Π°Π½Π½ΡΡ
ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ ΠΏΡΠΈ ΡΠ΅Π²ΠΈΠ·ΠΈΠΎΠ½Π½ΠΎΠΌ ΡΠ½Π΄ΠΎΠΏΡΠΎΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΊΠΎΠ»Π΅Π½Π½ΡΡ
ΠΈ ΡΠ°Π·ΠΎΠ±Π΅Π΄ΡΠ΅Π½Π½ΡΡ
ΡΡΡΡΠ°Π²ΠΎΠ², Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π½ΠΎΠΌ ΠΏΠΎ ΠΏΠΎΠ²ΠΎΠ΄Ρ Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ Π»ΡΠ±ΠΎΠΉ ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ. ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½ΠΎ 839 ΡΠ»ΡΡΠ°Π΅Π² ΡΠ΅Π²ΠΈΠ·ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠ½Π΄ΠΎΠΏΡΠΎΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠ»Π΅Π½Π½ΠΎΠ³ΠΎ (ΠΠ‘) ΠΈ ΡΠ°Π·ΠΎΠ±Π΅Π΄ΡΠ΅Π½Π½ΠΎΠ³ΠΎ (Π’ΠΠ‘) ΡΡΡΡΠ°Π²ΠΎΠ², Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ 485 Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Π²ΠΈΠ·ΠΈΠΉ Ρ 450 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². ΠΡΠΈΠΌΠ΅Π½ΡΠ»ΠΈΡΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΉ, ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ, Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠ½ΡΠΉ (ΠΎΠ±ΡΠΈΠΉ ΠΈ Π±ΠΈΠΎΡ
ΠΈΠΌΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°Π»ΠΈΠ·Ρ ΠΊΡΠΎΠ²ΠΈ, ΠΊΠΎΠ°Π³ΡΠ»ΠΎΠ³ΡΠ°ΠΌΠΌΠ°) ΠΌΠ΅ΡΠΎΠ΄Ρ, Π°Π½Π°Π»ΠΈΠ· ΡΠΈΠ½ΠΎΠ²ΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ ΠΈ ΠΌΠΈΠΊΡΠΎΠ±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ½ΠΊΡΠ°ΡΠΎΠ², Π² Ρ.Ρ. ΠΈΠ½ΡΡΠ°ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² ΠΎΡΠ΅Π½ΠΊΠΈ Π½Π°Π»ΠΈΡΠΈΡ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈ ΡΠ΅ΠΊΠΎΠΌΠ΅Π½Π΄Π°ΡΠΈΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΡΠ° ICM ΠΈ EBJIS (ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΠ΅ΡΡΠ²Π° ΠΏΠΎ ΠΈΠ½ΡΠ΅ΠΊΡΠΈΡΠΌ ΠΊΠΎΡΡΠ΅ΠΉ ΠΈ ΡΡΡΡΠ°Π²ΠΎΠ²). Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π‘ΡΠ΅Π΄Π½ΠΈΠΉ Π²ΠΎΠ·ΡΠ°ΡΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π½Π° ΠΌΠΎΠΌΠ΅Π½Ρ ΡΠ΅Π²ΠΈΠ·ΠΈΠΈ ΡΠΎΡΡΠ°Π²ΠΈΠ» 61,7 Π³ΠΎΠ΄Π°. ΠΠ° Π’ΠΠ‘ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ 59,4% ΡΠ΅Π²ΠΈΠ·ΠΈΠΎΠ½Π½ΡΡ
ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΉ, Π½Π° ΠΠ‘ 40,6%. Π ΠΎΡΡ ΠΌΠΈΠΊΡΠΎΠΎΡΠ³Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π² ΠΈΠ½ΡΡΠ°ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΠΎΠΌ Π±ΠΈΠΎΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π΅ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ Π² 2,08% Π½Π°Π±Π»ΡΠ΄Π΅Π½ΠΈΠΉ: Ρ 10 ΠΈΠ· 287 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠΎΡΠ»Π΅ Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π²ΠΈΠ·ΠΈΠΈ ΡΠ°Π·ΠΎΠ±Π΅Π΄ΡΠ΅Π½Π½ΡΡ
ΡΡΡΡΠ°Π²ΠΎΠ² ΠΈ Π½ΠΈ Π² ΠΎΠ΄Π½ΠΎΠΌ ΡΠ»ΡΡΠ°Π΅ ΠΈΠ· 198 ΡΠ΅Π²ΠΈΠ·ΠΈΠΉ ΠΊΠΎΠ»Π΅Π½Π½ΡΡ
ΡΡΡΡΠ°Π²ΠΎΠ². Π 8 ΠΈΠ· 10 ΡΠ»ΡΡΠ°Π΅Π² Π²ΠΎΠ·Π±ΡΠ΄ΠΈΡΠ΅Π»ΡΠΌΠΈ Π±ΡΠ»ΠΈ ΠΊΠΎΠ°Π³ΡΠ»Π°Π·ΠΎ-Π½Π΅Π³Π°ΡΠΈΠ²Π½ΡΠ΅ ΡΡΠ°ΡΠΈΠ»ΠΎΠΊΠΎΠΊΠΊΠΈ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π² 6 MRSE; Π² Π΄Π²ΡΡ
ΡΠ»ΡΡΠ°ΡΡ
Π°Π½Π°ΡΡΠΎΠ±Π½ΡΠ΅ Π±Π°ΠΊΡΠ΅ΡΠΈΠΈ. ΠΡΠ΅ ΡΠ΅Π²ΠΈΠ·ΠΈΠΈ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ ΠΎΠ΄Π½ΠΎΡΡΠ°ΠΏΠ½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ. ΠΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌ Ρ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½Π½ΠΎΠΉ ΠΠΠ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΠΈΡΡΠ΅ΠΌΠ½Π°Ρ Π°Π½ΡΠΈΠ±Π°ΠΊΡΠ΅ΡΠΈΠ°Π»ΡΠ½Π°Ρ ΡΠ΅ΡΠ°ΠΏΠΈΡ. ΠΠ° ΡΡΠ°ΠΏΠ΅ ΠΊΠ°ΡΠ°ΠΌΠ½Π΅Π·Π° Π² ΠΎΠ΄Π½ΠΎΠΌ ΠΈΠ· 10 Π²ΡΡΠ²Π»Π΅Π½Π½ΡΡ
ΡΠ»ΡΡΠ°Π΅Π² ΠΠΠ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π»Π°ΡΡ ΡΠ΅ΠΈΠ½ΡΠ΅ΠΊΡΠΈΡ, ΠΏΠ°ΡΠΈΠ΅Π½Ρ Π½Π° ΡΠ΅Π²ΠΈΠ·ΠΈΡ Π½Π΅ ΡΠ²ΠΈΠ»ΡΡ. ΠΡΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Π΅ 63% ΠΈΠ· Π³ΡΡΠΏΠΏΡ ΠΎΡΡΠ°Π»ΡΠ½ΡΡ
(Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
) 470 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π² 4 ΡΠ»ΡΡΠ°ΡΡ
ΡΠ°Π·Π²ΠΈΠ»Π°ΡΡ ΠΠΠ , ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Ρ Π΄Π²ΡΡ
ΡΡΠ°ΠΏΠ½ΡΠ΅ ΡΠ΅Π²ΠΈΠ·ΠΈΠΈ. ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π§Π°ΡΡΠΎΡΠ° ΠΈΠ½ΡΠ΅ΠΊΡΠΈΠΉ, ΡΠ»ΡΡΠ°ΠΉΠ½ΠΎ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½Π½ΡΡ
ΠΏΡΠΈ Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅Π²ΠΈΠ·ΠΈΡΡ
ΠΊΡΡΠΏΠ½ΡΡ
ΡΡΡΡΠ°Π²ΠΎΠ², ΡΠΎΡΡΠ°Π²ΠΈΠ»Π° Π² 2,08%. Π’ΡΠ΅Ρ
ΠΊΡΠ°ΡΠ½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠ½ΠΊΡΠ°ΡΠΎΠ² ΡΡΡΡΠ°Π²Π°, Π² Ρ.Ρ. ΠΈΠ½ΡΡΠ°ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΎΠ½Π½ΡΡ
, ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΠΠ ΠΏΡΠΈ Π°ΡΠ΅ΠΏΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π²ΠΈΠ·ΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΈΠ·Π±ΡΠ°ΡΡ ΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΡ ΡΡΠ°ΠΏΠ½ΠΎΡΡΡ ΡΠ΅Π²ΠΈΠ·ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π»Π΅ΡΠ΅Π½ΠΈΡ. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠΉ ΠΎΠΏΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΡ
ΡΠ»ΡΡΠ°ΡΡ
ΠΏΡΠΈ Π»Π΅ΡΠ΅Π½ΠΈΠΈ ΠΠΠ Π²ΡΠΏΠΎΠ»Π½ΡΡΡ ΠΎΠ΄Π½ΠΎΡΡΠ°ΠΏΠ½ΠΎΠ΅ ΡΠ΅ΡΠ½Π΄ΠΎΠΏΡΠΎΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅
Optimal affine image normalization approach for optical character recognition
Optical character recognition (OCR) in images captured from arbitrary angles requires preliminary normalization, i.e. a geometric transformation resulting in an image as if it was captured at an angle suitable for OCR. In most cases, a surface containing characters can be considered flat, and a pinhole model can be adopted for a camera. Thus, in theory, the normalization should be projective. Usually, the camera optical axis is approximately perpendicular to the document surface, so the projective normalization can be replaced with an affine one without a significant loss of accuracy. An affine image transformation is performed significantly faster than a projective normalization, which is important for OCR on mobile devices. In this work, we propose a fast approach for image normalization. It utilizes an affine normalization instead of a projective one if there is no significant loss of accuracy. The approach is based on a proposed criterion for the normalization accuracy: root mean square (RMS) coordinate discrepancies over the region of interest (ROI). The problem of optimal affine normalization according to this criterion is considered. We have established that this unconstrained optimization is quadratic and can be reduced to a problem of fractional quadratic functions integration over the ROI. The latter was solved analytically in the case of OCR where the ROI consists of rectangles. The proposed approach is generalized for various cases when instead of the affine transform its special cases are used: scaling, translation, shearing, and their superposition, allowing the image normalization procedure to be further accelerated.This work was partially financially supported by the Russian Foundation for Basic Research, projects 18-29-26035 and 17-29-03370
Modeling of spontaneous emission in presence of cylindrical nanoobjects: the scattering matrix approach
We propose a method of analysis of spontaneous emission of a quantum emitter (an atom, a luminescence center, a quantum dot) inside or in vicinity of a cylinder. At the focus of our method are analytical expressions for the scattering matrix of the cylindrical nanoobject. We propose the approach to electromagnetic field quantization based of eigenvalues and eigenvectors of the scattering matrix. The method is applicable for calculation and analysis of spontaneous emission rates and angular dependences of radiation for a set of different systems: semiconductor nanowires with quantum dots, plasmonic nanowires, cylindrical hollows in dielectrics and metals. Relative simplicity of the method allows obtaining analytical and semi-analytical expressions for both cases of radiation into external medium and into guided modes.The work has been supported by the Russian Science Foundation 21-12-00304
ΠΠ»Π³ΠΎΡΠΈΡΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΌΠ°ΡΡ-ΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ Π΄Π°Π½Π½ΡΡ Π΄Π»Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠ°Π½Π΅Π»ΠΈ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΠΏΠΎΠΈΡΠΊΠ° ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΌΠ΅ΡΠ°ΡΡΠ°Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈ ΡΠ°ΠΊΠ΅ ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ
A pathology diagnostic using molecular marker is a perspective direction of clinical medicine. Mass-spectrometry (MS) is a one of methods, which are used for obtaining information about molecular profiles. Selection of species, essential for classification βcase/control is an important task for data processing. Pipeline of data processing has been proposed using MS data, obtained during analysis of tumor breast tissue samples and health breast tissue samples, with the aim of metastasis marker selection. As a result, selection of lipid markers that belong to classes, related to metastasis and proliferation processes, makes it possible to create high sensitivity diagnostic model, based on logistic regression. The proposed method is applicable for data processing, obtained by MS analysis of other βomicsβ: metabolome, proteome, glycome.ΠΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΏΠΎ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΠΌ ΠΌΠ°ΡΠΊΠ΅ΡΠ°ΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ΠΌ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Ρ, Π² ΠΊΠΎΡΠΎΡΠΎΠΌ ΠΌΠ°ΡΡ-ΡΠΏΠ΅ΠΊΡΡΠΎΠΌΠ΅ΡΡΠΈΡ (ΠΠ‘) ΡΡΠ»ΡΠΆΠΈΡ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ· ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ², ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΡ
Π΄Π»Ρ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΎ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΡΡ
ΠΏΡΠΎΡΠΈΠ»ΡΡ
. Π ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ, ΠΈΠ³ΡΠ°ΡΡΠΈΡ
ΠΊΠ»ΡΡΠ΅Π²ΡΡ ΡΠΎΠ»Ρ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΡ/Π±ΠΎΠ»Π΅Π·Π½Ρ, Π²Π°ΠΆΠ½ΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΈΠΌΠ΅Π΅Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΡΠ°ΡΡΠΎ Π²ΠΊΠ»ΡΡΠ°ΡΡΠΈΡ
Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ ΡΠΎΡΠ΅Π½ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ. Π Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Π°Π»Π³ΠΎΡΠΈΡΠΌ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ Π΄Π°Π½Π½ΡΡ
ΠΠ‘, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΎΠΏΡΡ
ΠΎΠ»Π΅Π²ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ ΠΈ Π·Π΄ΠΎΡΠΎΠ²ΠΎΠΉ ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ, Ρ ΡΠ΅Π»ΡΡ Π²ΡΠ΄Π΅Π»Π΅Π½ΠΈΡ Π»ΠΈΠΏΠΈΠ΄Π½ΡΡ
ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΠΌΠ΅ΡΠ°ΡΡΠ°Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ Π΅Π³ΠΎ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΎΠ»ΡΡΠ΅Π½ Π½Π°Π±ΠΎΡ ΡΠΎΠ΅Π΄ΠΈΠ½Π΅Π½ΠΈΠΉ, ΠΎΡΠ½ΠΎΡΡΡΠΈΡ
ΡΡ ΠΊ ΠΊΠ»Π°ΡΡΠ°ΠΌ Π»ΠΈΠΏΠΈΠ΄ΠΎΠ², ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΠΏΡΠΎΡΠ΅ΡΡΠ°ΠΌΠΈ ΠΌΠ΅ΡΠ°ΡΡΠ°Π·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ»ΠΈΡΠ΅ΡΠ°ΡΠΈΠΈ, ΠΈ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΡ
ΠΏΠΎΡΡΡΠΎΠΈΡΡ Π²ΡΡΠΎΠΊΠΎΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΡΡ ΠΌΠΎΠ΄Π΅Π»Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π»ΠΎΠ³ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΈ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΌΠ΅ΡΠΎΠ΄ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎ ΠΏΡΠΈΠ³ΠΎΠ΄Π΅Π½ Π΄Π»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
ΠΠ‘, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΠΏΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠΈΠ»Ρ Π±ΠΈΠΎΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»Π° Π΄ΡΡΠ³ΠΎΠ³ΠΎ Π±Π°Π·ΠΈΡΠ° (ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΎΠΌ, ΠΏΡΠΎΡΠ΅ΠΎΠΌ, Π³Π»ΠΈΠΊΠΎΠΌ)
Current state of the research on optoacoustic fiber-optic ultrasonic transducers based on thermoelastic effect and fiber-optic interferometric receivers
The work is devoted to an overview of the current state of optoacoustic fiber-optic ultrasonic transducers based on thermoelastic effect and fiber-optic interference receivers, its scope, technologies and materials used, the advantages and disadvantages of different methods and the prospects for the development of the industry.The work has been supported by the Russian Science Foundation 21-12-00304
The triple-pomeron regime and the structure function of the pomeron in the diffractive deep inelastic scattering at very small x
Misprints and numerical coefficients corrected, a bit of phenomenology and
one figure added. The case for the linear evolution of the unitarized structure
functions made stronger.Comment: KFA-IKP(Th)-1993-17, Landau-16/93, 46 pages, 14 figures upon request
from N.Nikolaev, [email protected]
Towards a unified framework for identity documents analysis and recognition
Identity documents recognition is far beyond classical optical character recognition problems. Automated ID document recognition systems are tasked not only with the extraction of editable and transferable data but with performing identity validation and preventing fraud, with an increasingly high cost of error. A significant amount of research is directed to the creation of ID analysis systems with a specific focus for a subset of document types, or a particular mode of image acquisition, however, one of the challenges of the modern world is an increasing demand for identity document recognition from a wide variety of image sources, such as scans, photos, or video frames, as well as in a variety of virtually uncontrolled capturing conditions. In this paper, we describe the scope and context of identity document analysis and recognition problem and its challenges; analyze the existing works on implementing ID document recognition systems; and set a task to construct a unified framework for identity document recognition, which would be applicable for different types of image sources and capturing conditions, as well as scalable enough to support large number of identity document types. The aim of the presented framework is to serve as a basis for developing new methods and algorithms for ID document recognition, as well as for far more heavy challenges of identity document forensics, fully automated personal authentication and fraud prevention.This work was partially supported by the Russian Foundation for Basic Research (Project No. 18-29-03085 and 19-29-09055)
X-ray tomography: the way from layer-by-layer radiography to computed tomography
The methods of X-ray computed tomography allow us to study the internal morphological structure of objects in a non-destructive way. The evolution of these methods is similar in many respects to the evolution of photography, where complex optics were replaced by mobile phone cameras, and the computers built into the phone took over the functions of high-quality image generation. X-ray tomography originated as a method of hardware non-invasive imaging of a certain internal cross-section of the human body. Today, thanks to the advanced reconstruction algorithms, a method makes it possible to reconstruct a digital 3D image of an object with a submicron resolution. In this article, we will analyze the tasks that the software part of the tomographic complex has to solve in addition to managing the process of data collection. The issues that are still considered open are also discussed. The relationship between the spatial resolution of the method, sensitivity and the radiation load is reviewed. An innovative approach to the organization of tomographic imaging, called βreconstruction with monitoringβ, is described. This approach makes it possible to reduce the radiation load on the object by at least 2 β 3 times. In this work, we show that when X-ray computed tomography moves towards increasing the spatial resolution and reducing the radiation load, the software part of the method becomes increasingly important.This work was supported by Russian Foundation for Basic Research (Projects No.18-29-26033, 18-29-26020)
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