Long-term operation of a multi-channel cosmic muon system based on scintillation counters with MRS APD light readout

A Cosmic Ray Test Facility (CRTF) is the first large-scale implementation of a scintillation triggering system based on a new scintillation technique known as START. In START, the scintillation light is collected and transported by WLS optical fibers, while light detection is performed by pairs of avalanche photodiodes with the Metal-Resistor-Semiconductor structure operated in the Geiger mode (MRS APD). START delivers 100% efficiency of cosmic muon detection, while its intrinsic noise level is less than 10^{-2} Hz. CRTF, consisting of 160 START channels, has been continuously operated by the ALICE TOF collaboration for more than 25 000 hours, and has demonstrated a high level of stability. Fewer than 10% of MRS APDs had to be replaced during this period.Comment: Proceedings of NDIP-2008. 8 pages, 8 figures, 6 reference

Scintillation counter with MRS APD light readout

START, a high-efficiency and low-noise scintillation detector for ionizing particles, was developed for the purpose of creating a high-granular system for triggering cosmic muons. Scintillation light in START is detected by MRS APDs (Avalanche Photo-Diodes with Metal-Resistance-Semiconductor structure), operated in the Geiger mode, which have 1 mm^2 sensitive areas. START is assembled from a 15 x 15 x 1 cm^3 scintillating plastic plate, two MRS APDs and two pieces of wavelength-shifting optical fiber stacked in circular coils inside the plastic. The front-end electronic card is mounted directly on the detector. Tests with START have confirmed its operational consistency, over 99% efficiency of MIP registration and good homogeneity. START demonstrates a low intrinsic noise of about 10^{-2} Hz. If these detectors are to be mass-produced, the cost of a mosaic array of STARTs is estimated at a moderate level of 2-3 kUSD/m^2.Comment: 6 pages, 5 figure

Control of chronic excessive alcohol drinking by genetic manipulation of the Edinger-Westphal nucleus urocortin-1 neuropeptide system

Midbrain neurons of the centrally projecting Edinger-Westphal nucleus (EWcp) are activated by alcohol, and enriched with stress-responsive neuropeptide modulators (including the paralog of corticotropin-releasing factor, urocortin-1). Evidence suggests that EWcp neurons promote behavioral processes for alcohol-seeking and consumption, but a definitive role for these cells remains elusive. Here we combined targeted viral manipulations and gene array profiling of EWcp neurons with mass behavioral phenotyping in C57BL/6 J mice to directly define the links between EWcp-specific urocortin-1 expression and voluntary binge alcohol intake, demonstrating a specific importance for EWcp urocortin-1 activity in escalation of alcohol intake

START as the detector of choice for large-scale muon triggering systems

Further progress in building high-granular large-scale systems based on Scintillation Tiles with MRS APD light readout (START) became possible thanks to the creation of an improved version of MRS APD. The cost of the system may now be significantly reduced by using inexpensive extruded scintillator. More than 160 START samples were assembled based on this design modification and proved to possess 100% MIP detection efficiency and the intrinsic noise rate of less than 0.08 Hz. Long-term stability of START characteristics was confirmed after 3.5 months of operation

Universal Oligonucleotide Microarray for Sub-Typing of Influenza A Virus

A universal microchip was developed for genotyping Influenza A viruses. It contains two sets of oligonucleotide probes allowing viruses to be classified by the subtypes of hemagglutinin (H1–H13, H15, H16) and neuraminidase (N1–N9). Additional sets of probes are used to detect H1N1 swine influenza viruses. Selection of probes was done in two steps. Initially, amino acid sequences specific to each subtype were identified, and then the most specific and representative oligonucleotide probes were selected. Overall, between 19 and 24 probes were used to identify each subtype of hemagglutinin (HA) and neuraminidase (NA). Genotyping included preparation of fluorescently labeled PCR amplicons of influenza virus cDNA and their hybridization to microarrays of specific oligonucleotide probes. Out of 40 samples tested, 36 unambiguously identified HA and NA subtypes of Influenza A virus

Urocortin-1 within the Centrally-Projecting Edinger-Westphal Nucleus Is Critical for Ethanol Preference

Converging lines of evidence point to the involvement of neurons of the centrally projecting Edinger-Westphal nucleus (EWcp) containing the neuropeptide Urocortin-1 (Ucn1) in excessive ethanol (EtOH) intake and EtOH sensitivity. Here, we expanded these previous findings by using a continuous-access, two-bottle choice drinking paradigm (3%, 6%, and 10% EtOH vs. tap water) to compare EtOH intake and EtOH preference in Ucn1 genetic knockout (KO) and wild-type (WT) mice. Based on previous studies demonstrating that electrolytic lesion of the EWcp attenuated EtOH intake and preference in high-drinking C57BL/6J mice, we also set out to determine whether EWcp lesion would differentially alter EtOH consumption in Ucn1 KO and WT mice. Finally, we implemented well-established place conditioning procedures in KO and WT mice to determine whether Ucn1 and the corticotropin-releasing factor type-2 receptor (CRF-R2) were involved in the rewarding and aversive effects of EtOH (2 g/kg, i.p.). Results from these studies revealed that (1) genetic deletion of Ucn1 dampened EtOH preference only in mice with an intact EWcp, but not in mice that received lesion of the EWcp, (2) lesion of the EWcp dampened EtOH intake in Ucn1 KO and WT mice, but dampened EtOH preference only in WT mice expressing Ucn1, and (3) genetic deletion of Ucn1 or CRF-R2 abolished the conditioned rewarding effects of EtOH, but deletion of Ucn1 had no effect on the conditioned aversive effects of EtOH. The current findings provide strong support for the hypothesis that EWcp-Ucn1 neurons play an important role in EtOH intake, preference, and reward

Cross-Correlation Earthquake Precursors in the Hydrogeochemical and Geoacoustic Signals for the Kamchatka Peninsula

We propose a new type of earthquake precursor based on the analysis of correlation dynamics between geophysical signals of different nature. The precursor is found using a two-parameter cross-correlation function introduced within the framework of flicker-noise spectroscopy, a general statistical physics approach to the analysis of time series. We consider an example of cross-correlation analysis for water salinity time series, an integral characteristic of the chemical composition of groundwater, and geoacoustic emissions recorded at the G-1 borehole on the Kamchatka peninsula in the time frame from 2001 to 2003, which is characterized by a sequence of three groups of significant seismic events. We found that cross-correlation precursors took place 27, 31, and 35 days ahead of the strongest earthquakes for each group of seismic events, respectively. At the same time, precursory anomalies in the signals themselves were observed only in the geoacoustic emissions for one group of earthquakes.Comment: 21 pages, 5 figures, 1 table; to be published in "Acta Geophysica". arXiv admin note: substantial text overlap with arXiv:1101.147

Development of DNA-Biochip for Identification of Influenza A Virus Subtypes

Developed was the DNA-biochip to identify subtypes of influenza A virus, pathogenic for humans. Microchip was capable of detecting H1, H3, H5-subtypes of hemagglutinin (including H1-subtype of pandemic A/H1N1(2009) influenza virus ) and neuraminidase subtypes N1,N2 of influenza virus. This microchip was successfully tested on the strains of A/H5N1 highly pathogenic avian influenza virus, A/H1N1(2009) pandemic influenza virus, A/H1N1 and A/H3N2 seasonal influenza viruses

НОВЫЕ ПОДХОДЫ К РАЗРАБОТКЕ АЛГОРИТМОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ РАКА ЛЕГКОГО

The relevance of developing an intelligent automated diagnostic system (IADS) for lung cancer (LC) detection stems from the social significance of this disease and its leading position among all cancer diseases. Theoretically, the use of IADS is possible at a stage of screening as well as at a stage of adjusted diagnosis of LC. The recent approaches to training the IADS do not take into account the clinical and radiological classification as well as peculiarities of the LC clinical forms, which are used by the medical community. This defines difficulties and obstacles of using the available IADS. The authors are of the opinion that the closeness of a developed IADS to the «doctor’s logic» contributes to a better reproducibility and interpretability of the IADS usage results. Most IADS described in the literature have been developed on the basis of neural networks, which have several disadvantages that affect reproducibility when using the system. This paper proposes a composite algorithm using machine learning methods such as Deep Forest and Siamese neural network, which can be regarded as a more efficient approach for dealing with a small amount of training data and optimal from the reproducibility point of view. The open datasets used for training IADS include annotated objects which in some cases are not confirmed morphologically. The paper provides a description of the LIRA dataset developed by using the diagnostic results of St. Petersburg Clinical Research Center of Specialized Types of Medical Care (Oncology), which includes only computed tomograms of patients with the verified diagnosis. The paper considers stages of the machine learning process on the basis of the shape features, of the internal structure features as well as a new developed system of differential diagnosis of LC based on the Siamese neural networks. A new approach to the feature dimension reduction is also presented in the paper, which aims more efficient and faster learning of the system.Актуальность разработки интеллектуальной автоматизированной системы диагностики (ИАСД) рака легкого (РЛ) связана с социальной значимостью этого заболевания и его лидирующей позицией в структуре онкологической заболеваемости. Теоретически применение ИАСД возможно как на этапе скрининга, так и в уточненной диагностике РЛ. Применяемые подходы к обучению ИАСД не учитывают клинико-рентгенологическую классификацию и особенности клинических форм РЛ, используемые медицинским сообществом. С этим связаны трудности применения разрабатываемых в настоящее время систем. Авторы придерживаются мнения, что приближенность разрабатываемой ИАСД к «логике врача» способствует лучшей воспроизводимости и интерпретируемости результатов при ее использовании. Большинство описанных в литературе ИАСД созданы на основе нейронных сетей, которые обладают рядом недостатков, влияющих на воспроизводимость при использовании системы. Данная работа отражает применение комбинированного алгоритма с использованием методов машинного обучения, таких как глубокий лес и сиамская нейронная сеть, что является более эффективным подходом при малой выборке обучающих данных и оптимальным с точки зрения воспроизводимости. Открытые базы данных, применяемые при разработке ИАСД, включают размеченные, но в ряде случаев не подтвержденные морфологически находки. В статье приводится описание базы данных LIRA, созданной на материале Санкт-Петербургского клинического научно-практического центра специализированных видов медицинской помощи (онкологический), которая включает только компьютерные томограммы пациентов с верифицированным диагнозом. В статье описаны этапы машинного обучения по признакам формы, внутренней структуры, а также новая разработанная архитектура дифференциальной диагностики образований на основе сиамских нейронных сетей. Также отражен способ понижения размерности данных для более эффективного и быстрого обучения системы

АВТОМАТИЗИРОВАННАЯ СИСТЕМА ОБНАРУЖЕНИЯ ОБЪЕМНЫХ ОБРАЗОВАНИЙ В ЛЕГКИХ КАК ЭТАП РАЗВИТИЯ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА В ДИАГНОСТИКЕ РАКА ЛЕГКОГО

In the century of the fourth industrial revolution, there is a rapid progress of technological developments in medicine. Possibilities of collecting large amounts of digital information and the modern computer capacity growth are reasons for the increased attention to artificial intelligence (AI) and its role in the diagnostics and the prediction of diseases. In the diagnostics, AI aims to model the human intellectual activity, providing assistance to a practicing doctor in the processing of big data. Development of AI can be considered as a way for implementation and ensuring of national political and economic interests in the health care improvement. Lung cancer is on the first position of cancer incidences. This implies that the development and implementation of computed-aided systems for lung cancer diagnostic is very urgent and important. The article presents the results concerning the development of a computed-aided system for the lung nodule detection, which is based on the processing of computed tomography data. Perspectives of the AI application to the lung cancer diagnostics are discussed. There is a few information about a role of Russian developments in this area in foreign and domestic literature.В эпоху четвертой промышленной революции отмечается стремительный прогресс технологических разработок в области медицины. Возможности накопления больших объемов цифровой информации и рост производительности современных компьютеров стали причиной повышенного внимания к искусственному интеллекту (ИИ) и его роли в диагностике и прогнозировании заболеваний. В диагностике искусственный интеллект призван моделировать человеческую деятельность, которая считается интеллектуальной, обеспечивая помощь практикующему врачу в обработке больших объемов данных (big data). Развитие ИИ может быть рассмотрено как мера реализации и обеспечения национальных интересов политической и экономической направленности в развитии здравоохранения. Рак легкого занимает лидирующую позицию в структуре онкологической заболеваемости, это диктует актуальность разработки и внедрения автоматизированных систем диагностики (АСД), ориентированных именно на рак легкого как социально значимого заболевания. В статье приводятся сведения о результатах разработки автоматизированной системы обнаружения объемных образований в легких на основе обработки данных компьютерной томографии, отражена перспектива ее использования в диагностике рака легкого с помощью ИИ. В зарубежной и отечественной литературе пока нет достаточного количества сведений о месте российских разработок в этой области