Multivariate Techniques for Identifying Diffractive Interactions at the LHC

Close to one half of the LHC events are expected to be due to elastic or inelastic diffractive scattering. Still, predictions based on extrapolations of experimental data at lower energies differ by large factors in estimating the relative rate of diffractive event categories at the LHC energies. By identifying diffractive events, detailed studies on proton structure can be carried out. The combined forward physics objects: rapidity gaps, forward multiplicity and transverse energy flows can be used to efficiently classify proton-proton collisions. Data samples recorded by the forward detectors, with a simple extension, will allow first estimates of the single diffractive (SD), double diffractive (DD), central diffractive (CD), and non-diffractive (ND) cross sections. The approach, which uses the measurement of inelastic activity in forward and central detector systems, is complementary to the detection and measurement of leading beam-like protons. In this investigation, three different multivariate analysis approaches are assessed in classifying forward physics processes at the LHC. It is shown that with gene expression programming, neural networks and support vector machines, diffraction can be efficiently identified within a large sample of simulated proton-proton scattering events. The event characteristics are visualized by using the self-organizing map algorithm.

Direct detection of atmospheric particle formation using the Neutral cluster and Air Ion Spectrometer

Aerosol particles play an important role in the Earth s atmosphere and in the climate system: they scatter and absorb solar radiation, facilitate chemical processes, and serve as seeds for cloud formation. Secondary new particle formation (NPF) is a globally important source of these particles. Currently, the mechanisms of particle formation and the vapors participating in this process are, however, not truly understood. In order to fully explain atmospheric NPF and subsequent growth, we need to measure directly the very initial steps of the formation processes. This thesis investigates the possibility to study atmospheric particle formation using a recently developed Neutral cluster and Air Ion Spectrometer (NAIS). First, the NAIS was calibrated and intercompared, and found to be in good agreement with the reference instruments both in the laboratory and in the field. It was concluded that NAIS can be reliably used to measure small atmospheric ions and particles directly at the sizes where NPF begins. Second, several NAIS systems were deployed simultaneously at 12 European measurement sites to quantify the spatial and temporal distribution of particle formation events. The sites represented a variety of geographical and atmospheric conditions. The NPF events were detected using NAIS systems at all of the sites during the year-long measurement period. Various particle formation characteristics, such as formation and growth rates, were used as indicators of the relevant processes and participating compounds in the initial formation. In a case of parallel ion and neutral cluster measurements, we also estimated the relative contribution of ion-induced and neutral nucleation to the total particle formation. At most sites, the particle growth rate increased with the increasing particle size indicating that different condensing vapors are participating in the growth of different-sized particles. The results suggest that, in addition to sulfuric acid, organic vapors contribute to the initial steps of NPF and to the subsequent growth, not just later steps of the particle growth. As a significant new result, we found out that the total particle formation rate varied much more between the different sites than the formation rate of charged particles. The results infer that the ion-induced nucleation has a minor contribution to particle formation in the boundary layer in most of the environments. These results give tools to better quantify the aerosol source provided by secondary NPF in various environments. The particle formation characteristics determined in this thesis can be used in global models to assess NPF s climatic effects.

Detection, epidemiology and host spectrum of cowpox and Borna disease virus infections

Several orthopoxviruses (OPV) and Borna disease virus (BDV) are enveloped, zoonotic viruses with a wide geographical distribution. OPV antibodies cross-react, and former smallpox vaccination has therefore protected human populations from another OPV infection, rodent-borne cowpox virus (CPXV). Cowpox in humans and cats usually manifests as a mild, self-limiting dermatitis and constitutional symptoms, but it can be severe and even life-threatening in the immunocompromised. Classical Borna disease is a progressive meningoencephalomyelitis in horses and sheep known in central Europe for centuries. Nowadays the virus or its close relative infects humans and also several other species in central Europe and elsewhere, but the existence of human Borna disease with its suspected neuropsychiatric symptoms is controversial. The epidemiology of BDV is largely unknown, and the present situation is even more intriguing following the recent detection of several-million-year-old, endogenized BDV genes in primate and various other vertebrate genomes. The aims of this study were to elucidate the importance of CPXV and BDV in Finland and in possible host species, and particularly to 1) establish relevant methods for the detection of CPXV and other OPVs as well as BDV in Finland, 2) determine whether CPXV and BDV exist in Finland, 3) discover how common OPV immunity is in different age groups in Finland, 4) characterize possible disease cases and clarify their epidemiological context, 5) establish the hosts and possible reservoir species of these viruses and their geographical distribution in wild rodents, and 6) elucidate the infection kinetics of BDV in the bank vole. An indirect immunofluorescence assay and avidity measurement were established for the detection, timing and verification of OPV or BDV antibodies in thousands of blood samples from humans, horses, ruminants, lynxes, gallinaceous birds, dogs, cats and rodents. The mostly vaccine-derived OPV seroprevalence was found to decrease gradually according to the year of birth of the sampled human subjects from 100% to 10% in those born after 1977. On the other hand, OPV antibodies indicating natural contact with CPXV or other OPVs were commonly found in domestic and wild animals: the horse, cow, lynx, dog, cat and, with a prevalence occasionally even as high as 92%, in wild rodents, including some previously undetected species and new regions. Antibodies to BDV were detected in humans, horses, a dog, cats, and for the first time in wild rodents, such as bank voles (Myodes glareolus). Because of the controversy within the human Borna disease field, extra verification methods were established for BDV antibody findings: recombinant nucleocapsid and phosphoproteins were produced in Escherichia coli and in a baculovirus system, and peptide arrays were additionally applied. With these verification assays, Finnish human, equine, feline and rodent BDV infections were confirmed. Taken together, wide host spectra were evident for both OPV and BDV infections based on the antibody findings, and OPV infections were found to be geographically broadly distributed. PCR amplification methods were utilised for hundreds of blood and tissue samples. The methods included conventional, nested and real-time PCRs with or without the reverse transcription step and detecting four or two genes of OPVs and BDV, respectively. OPV DNA could be amplified from two human patients and three bank voles, whereas no BDV RNA was detected in naturally infected individuals. Based on the phylogenetic analyses, the Finnish OPV sequences were closely related although not identical to a Russian CPXV isolate, and clearly different from other CPXV strains. Moreover, the Finnish sequences only equalled each other, but the short amplicons obtained from German rodents were identical to monkeypox virus, in addition to German CPXV variants. This reflects the close relationship of all OPVs. In summary, RNA of the Finnish BDV variant could not be detected with the available PCR methods, but OPV DNA infrequently could. The OPV species infecting the patients of this study was proven to be CPXV, which is most probably also responsible for the rodent infections. Multiple cell lines and some newborn rodents were utilised in the isolation of CPXV and BDV from patient and wildlife samples. CPXV could be isolated from a child with severe, generalised cowpox. BDV isolation attempts from rodents were unsuccessful in this study. However, in parallel studies, a transient BDV infection of cells inoculated with equine brain material was detected, and BDV antigens discovered in archival animal brains using established immunohistology. Thus, based on several independent methods, both CPXV and BDV (or a closely related agent) were shown to be present in Finland. Bank voles could be productively infected with BDV. This experimental infection did not result in notable pathological findings or symptoms, despite the intense spread of the virus in the central and peripheral nervous system. Infected voles commonly excreted the virus in urine and faeces, which emphasises their possible role as a BDV reservoir. Moreover, BDV RNA was regularly reverse transcribed into DNA in bank voles, which was detected by amplifying DNA by PCR without reverse transcription, and verified with nuclease treatments. This finding indicates that BDV genes could be endogenized during an acute infection. Although further transmission studies are needed, this experimental infection demonstrated that the bank vole can function as a potential BDV reservoir. In summary, multiple methods were established and applied in large panels to detect two zoonoses novel to Finland: cowpox virus and Borna disease virus. Moreover, new information was obtained on their geographical distribution, host spectrum, epidemiology and infection kinetics.

Detection of clinical mastitis with the help of a thermal camera

Increasing dairy farm size and increase in automation in livestock production require that new methods are used to monitor animal health. In this study, a thermal camera was tested for its capacity to detect clinical mastitis. Mastitis was experimentally induced in 6 cows with 10 mu g of Escherichia coli lipopolysaccharide (LPS). The LPS was infused into the left forequarter of each cow, and the right forequarters served as controls. Clinical examination for systemic and local signs and sampling for indicators of inflammation in milk were carried out before morning and evening milking throughout the 5-d experimental period and more frequently on the challenge day. Thermal images of experimental and control quarters were taken at each sampling time from lateral and medial angles. The first signs of clinical mastitis were noted in all cows 2 h postchallenge and included changes in general appearance of the cows and local clinical signs in the affected udder quarter. Rectal temperature, milk somatic cell count, and electrical conductivity were increased 4 h postchallenge and milk N-acetyl-beta-D-glucosaminidase activity 8 h postchallenge. The thermal camera was successful in detecting the 1 to 1.5 degrees C temperature change on udder skin associated with clinical mastitis in all cows because temperature of the udder skin of the experimental and control quarters increased in line with the rectal temperature. Yet, local signs on the udder were seen before the rise in udder skin and body temperature. The udder represents a sensitive site for detection of any febrile disease using a noninvasive method. A thermal camera mounted in a milking or feeding parlor could detect temperature changes associated with clinical mastitis or other diseases in a dairy herd.

Detection and molecular epidemiology of tick-borne encephalitis virus infections

Pdf-file, link above

Change detection and controlling forest information using multi-temporal Landsat TM imagery.

A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each others, and several multitemporal images covering different geographic locations. The radiometricly calibrated difference images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field. The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.

Microbial identification by detection of ligation probes on DNA microarray

Microbes in natural and artificial environments as well as in the human body are a key part of the functional properties of these complex systems. The presence or absence of certain microbial taxa is a correlate of functional status like risk of disease or course of metabolic processes of a microbial community. As microbes are highly diverse and mostly notcultivable, molecular markers like gene sequences are a potential basis for detection and identification of key types. The goal of this thesis was to study molecular methods for identification of microbial DNA in order to develop a tool for analysis of environmental and clinical DNA samples. Particular emphasis was placed on specificity of detection which is a major challenge when analyzing complex microbial communities. The approach taken in this study was the application and optimization of enzymatic ligation of DNA probes coupled with microarray read-out for high-throughput microbial profiling. The results show that fungal phylotypes and human papillomavirus genotypes could be accurately identified from pools of PCR amplicons generated from purified sample DNA. Approximately 1 ng/μl of sample DNA was needed for representative PCR amplification as measured by comparisons between clone sequencing and microarray. A minimum of 0,25 amol/μl of PCR amplicons was detectable from amongst 5 ng/μl of background DNA, suggesting that the detection limit of the test comprising of ligation reaction followed by microarray read-out was approximately 0,04%. Detection from sample DNA directly was shown to be feasible with probes forming a circular molecule upon ligation followed by PCR amplification of the probe. In this approach, the minimum detectable relative amount of target genome was found to be 1% of all genomes in the sample as estimated from 454 deep sequencing results. Signal-to-noise of contact printed microarrays could be improved by using an internal microarray hybridization control oligonucleotide probe together with a computational algorithm. The algorithm was based on identification of a bias in the microarray data and correction of the bias as shown by simulated and real data. The results further suggest semiquantitative detection to be possible by ligation detection, allowing estimation of target abundance in a sample. However, in practise, comprehensive sequence information of full length rRNA genes is needed to support probe design with complex samples. This study shows that DNA microarray has the potential for an accurate microbial diagnostic platform to take advantage of increasing sequence data and to replace traditional, less efficient methods that still dominate routine testing in laboratories. The data suggests that ligation reaction based microarray assay can be optimized to a degree that allows good signal-tonoise and semiquantitative detection.