Bone turnover markers for early detection of fracture healing disturbances: A review of the scientific literature

Imaging techniques are the standard method for assessment of fracture healing processes. However, these methods are perhaps not entirely reliable for early detection of complications, the most frequent of these being delayed union and non-union. A prompt diagnosis of such disorders could prevent prolonged patient distress and disability. Efforts should be directed towards the development of new technologies for improving accuracy in diagnosing complications following bone fractures. The variation in the levels of bone turnover markers (BTMs) have been assessed with regard to there ability to predict impaired fracture healing at an early stage, nevertheless the conclusions of some studies are not consensual. In this article the authors have revised the potential of BTMs as early predictors of prognosis in adult patients presenting traumatic bone fractures but who did not suffer from osteopenia or postmenopausal osteoporosis. The available information from the different studies performed in this field was systematized in order to highlight the most promising BTMs for the assessment of fracture healing outcome.

Detection of road accident accumulation zones with a visual analytics approach

Nowadays, road accidents are a major public health problem, which increase is forecasted if road safety is not treated properly, dying about 1.2 million people every year around the globe. In 2012, Portugal recorded 573 fatalities in road accidents, on site, revealing the largest decreasing of the European Union for 2011, along with Denmark. Beyond the impact caused by fatalities, it was calculated that the economic and social costs of road accidents weighted about 1.17% of the Portuguese gross domestic product in 2010. Visual Analytics allows the combination of data analysis techniques with interactive visualizations, which facilitates the process of knowledge discovery in sets of large and complex data, while the Geovisual Analytics facilitates the exploration of space-time data through maps with different variables and parameters that are under analysis. In Portugal, the identification of road accident accumulation zones, in this work named black spots, has been restricted to annual fixed windows. In this work, it is presented a dynamic approach based on Visual Analytics techniques that is able to identify the displacement of black spots on sliding windows of 12 months. Moreover, with the use of different parameterizations in the formula usually used to detect black spots, it is possible to identify zones that are almost becoming black spots. Through the proposed visualizations, the study and identification of countermeasures to this social and economic problem can gain new grounds and thus the decision- making process is supported and improved.

Numerical simulation of the reflow soldering process

Dissertação de mestrado integrado em Mechanical Engineering

Development of an enzyme-linked immunosorbent assay for detection of IgM antibodies to Babesia bigemina in cattle

A crude antigenic preparation of Babesia bigemina was used to develop an ELISA for the detection of IgM antibodies. Optimal dilutions of the antigen, using positive and negative reference sera, were determined by checkerboard titrations. Negative sera from cattle imported from tick-free areas, serum samples collected from infected B. bigemina cattle were used to validate the test. The specificity was 94% and sensitivity of the Elisa 87.5%. Sera from 385 cattle deriving from areas free from tick-borne diseases, which were submitted to a preimmunization process, were screened by this technique. The Elisa detected seroconversion on the 14th day post-inoculation in animals either infested with Boophilus microplus ticks (infected with B. bigemina), or inoculated with B. bigemina infected blood. Antibody titers decreased after day 33; however, all animals remained positive until the end of the experiment (124 days). The ELISA described may prove to be an appropriate serological test for the detection of IgM antibodies against B. bigemina.

Detection of active oxalate-carbonate pathway ecosystems in the Amazon Basin: Global implications of a natural potential C sink

The oxalate-carbonate pathway (OCP) is a biogeochemical process, which has been described in Milicia excelsa tree ecosystems of Africa. This pathway involves biological and geological parameters at different scales: oxalate, as a by-product of photosynthesis, is oxidized by oxalotrophic bacteria leading to a local pH increase, and eventually to carbonate accumulation through time in previously acidic and carbonate-free tropical soils. Former studies have shown that this pedogenic process can potentially lead to the formation of an atmospheric carbon sink. Considering that 80% of plant species are known to produce oxalate, it is reasonable to assume that M. excelsa is not the only tree that can support OCP ecosystems. The search for similar conditions on another continent led us to South America, in an Amazon forest ecosystem (Alto Beni, Bolivia). This area was chosen because of the absence of local inherited carbonate in the bedrock, as well as its expected acidic soil conditions. Eleven tree species and associated soils were tested positive for the presence of carbonate with a more alkaline soil pH close to the tree than at a distance from it. A detailed study of Pentaplaris davidsmithii and Ceiba speciosa trees showed that oxalotrophy impacted soil pH in a similar way to at African sites (at least with 1 pH unit increasing). African and South American sites display similar characteristics regarding the mineralogical assemblage associated with the OCP, except for the absence of weddellite. The amount of carbonate accumulated is 3 to 4 times lower than the values measured in African sites related to M. excelsa ecosystems. Still, these secondary carbonates remain critical for the continental carbon cycle, as they are unexpected in the acidic context of Amazonian soils. Therefore, the present study demonstrates the existence of an active OCP in South America. The three critical components of an operating OCP are the presence of: i) local alkalinization, ii) carbonate accumulations, and iii) oxalotrophic bacteria, which were identified associated to the oxalogenic tree C. speciosa. If the question of a potential carbon sink related to oxalotrophic-oxalogenic ecosystems in the Amazon Basin is still pending, this study highlights the implication of OCP ecosystems on carbon and calcium biogeochemical coupled cycles. As previously mentioned for M. excelsa tree ecosystems in Africa, carbonate accumulations observed in the Bolivian tropical forest could be extrapolated to part or the whole Amazon Basin and might constitute an important reservoir that must be taken into account in the global carbon balance of the Tropics.

Detection of fingermarks by colloidal gold (MMD/SMD): beyond the pH 3 limit

This work is part of a continuing goal to improve the multimetal deposition technique (MMD), as well as the single-metal deposition (SMD), to make them more robust, more user-friendly, and less labour-intensive. Indeed, two major limitations of the MMD/SMD were identified: (1) the synthesis of colloidal gold, which is quite labour-intensive, and (2) the sharp decrease in efficiency observed when the pH of the working solution is increased above pH 3. About the synthesis protocol, it has been simplified so that there is no more need to monitor the temperature during the synthesis. The efficiency has also been improved by adding aspartic acid, conjointly with sodium citrate, during the synthesis of colloidal gold. This extends the range of pH for which it is possible to detect fingermarks in the frame of the MMD/SMD. The operational range is now extended from 2 to 6.7, compared to 2-3 for the previous formulations. The increased robustness of the working solution may improve the ability of the technique to process substrates that tend to increase the pH of the solution after their immersion.

Detection of matchings in a sequence of underwater images through texture analysis

This paper presents an approach to ameliorate the reliability of the correspondence points relating two consecutive images of a sequence. The images are especially difficult to handle, since they have been acquired by a camera looking at the sea floor while carried by an underwater robot. Underwater images are usually difficult to process due to light absorption, changing image radiance and lack of well-defined features. A new approach based on gray-level region matching and selective texture analysis significantly improves the matching reliability

Abstracting qualitative information for process supervision

Supervisory systems evolution makes the obtaining of significant information from processes more important in the way that the supervision systems' particular tasks are simplified. So, having signal treatment tools capable of obtaining elaborate information from the process data is important. In this paper, a tool that obtains qualitative data about the trends and oscillation of signals is presented. An application of this tool is presented as well. In this case, the tool, implemented in a computer-aided control systems design (CACSD) environment, is used in order to give to an expert system for fault detection in a laboratory plant

Infornut® Process; improves accessibility to diagnosis and nutritional support for the malnourished hospitalized patient; impact on management indicators; two-year assessment.

Introduction: The high prevalence of disease-related hospital malnutrition justifies the need for screening tools and early detection in patients at risk for malnutrition, followed by an assessment targeted towards diagnosis and treatment. At the same time there is clear undercoding of malnutrition diagnoses and the procedures to correct it Objectives: To describe the INFORNUT program/ process and its development as an information system. To quantify performance in its different phases. To cite other tools used as a coding source. To calculate the coding rates for malnutrition diagnoses and related procedures. To show the relationship to Mean Stay, Mortality Rate and Urgent Readmission; as well as to quantify its impact on the hospital Complexity Index and its effect on the justification of Hospitalization Costs. Material and methods: The INFORNUT® process is based on an automated screening program of systematic detection and early identification of malnourished patients on hospital admission, as well as their assessment, diagnoses, documentation and reporting. Of total readmissions with stays longer than three days incurred in 2008 and 2010, we recorded patients who underwent analytical screening with an alert for a medium or high risk of malnutrition, as well as the subgroup of patients in whom we were able to administer the complete INFORNUT® process, generating a report for each.

Large-system analysis of a CDMA dynamic channel under a Markovian input process

We study the minimum mean square error (MMSE) and the multiuser efficiency η of large dynamic multiple access communication systems in which optimal multiuser detection is performed at the receiver as the number and the identities of active users is allowed to change at each transmission time. The system dynamics are ruled by a Markov model describing the evolution of the channel occupancy and a large-system analysis is performed when the number of observations grow large. Starting on the equivalent scalar channel and the fixed-point equation tying multiuser efficiency and MMSE, we extend it to the case of a dynamic channel, and derive lower and upper bounds for the MMSE (and, thus, for η as well) holding true in the limit of large signal–to–noise ratios and increasingly large observation time T.

Detection of moving groups among early type stars

Our procedure to detect moving groups in the solar neighbourhood (Chen et al., 1997) in the four-dimensional space of the stellar velocity components and age has been improved. The method, which takes advantadge of non-parametric estimators of density distribution to avoid any a priori knowledge of the kinematic properties of these stellar groups, now includes the effect of observational errors on the process to select moving group stars, uses a better estimation of the density distribution of the total sample and field stars, and classifies moving group stars using all the available information. It is applied here to an accurately selected sample of early-type stars with known radial velocities and Strömgren photometry. Astrometric data are taken from the HIPPARCOS catalogue (ESA, 1997), which results in an important decrease in the observational errors with respect to ground-based data, and ensures the uniformity of the observed data. Both the improvement of our method and the use of precise astrometric data have allowed us not only to confirm the existence of classical moving groups, but also to detect finer structures that in several cases can be related to kinematic properties of nearby open clusters or associations.

On The Spot Damage Detection Methodology for Highway Bridges, July 2010

Vibration-based damage identification (VBDI) techniques have been developed in part to address the problems associated with an aging civil infrastructure. To assess the potential of VBDI as it applies to highway bridges in Iowa, three applications of VBDI techniques were considered in this study: numerical simulation, laboratory structures, and field structures. VBDI techniques were found to be highly capable of locating and quantifying damage in numerical simulations. These same techniques were found to be accurate in locating various types of damage in a laboratory setting with actual structures. Although there is the potential for these techniques to quantify damage in a laboratory setting, the ability of the methods to quantify low-level damage in the laboratory is not robust. When applying these techniques to an actual bridge, it was found that some traditional applications of VBDI methods are capable of describing the global behavior of the structure but are most likely not suited for the identification of typical damage scenarios found in civil infrastructure. Measurement noise, boundary conditions, complications due to substructures and multiple material types, and transducer sensitivity make it very difficult for present VBDI techniques to identify, much less quantify, highly localized damage (such as small cracks and minor changes in thickness). However, while investigating VBDI techniques in the field, it was found that if the frequency-domain response of the structure can be generated from operating traffic load, the structural response can be animated and used to develop a holistic view of the bridge’s response to various automobile loadings. By animating the response of a field bridge, concrete cracking (in the abutment and deck) was correlated with structural motion and problem frequencies (i.e., those that cause significant torsion or tension-compression at beam ends) were identified. Furthermore, a frequency-domain study of operational traffic was used to identify both common and extreme frequencies for a given structure and loading. Common traffic frequencies can be compared to problem frequencies so that cost-effective, preventative solutions (either structural or usage-based) can be developed for a wide range of IDOT bridges. Further work should (1) perfect the process of collecting high-quality operational frequency response data; (2) expand and simplify the process of correlating frequency response animations with damage; and (3) develop efficient, economical, preemptive solutions to common damage types.

Nonlinear relaxation time and the detection of weak signals

Laser systems can be used to detect very weak optical signals. The physical mechanism is the dynamical process of the relaxation of a laser from an unstable state to a steady stable state. We present an analysis of this process based on the study of the nonlinear relaxation time. Our analytical results are compared with numerical integration of the stochastic differential equations that model this process.

American Cancer Society guideline for the early detection of prostate cancer: update 2010.

In 2009, the American Cancer Society (ACS) Prostate Cancer Advisory Committee began the process of a complete update of recommendations for early prostate cancer detection. A series of systematic evidence reviews was conducted focusing on evidence related to the early detection of prostate cancer, test performance, harms of therapy for localized prostate cancer, and shared and informed decision making in prostate cancer screening. The results of the systematic reviews were evaluated by the ACS Prostate Cancer Advisory Committee, and deliberations about the evidence occurred at committee meetings and during conference calls. On the basis of the evidence and a consensus process, the Prostate Cancer Advisory Committee developed the guideline, and a writing committee drafted a guideline document that was circulated to the entire committee for review and revision. The document was then circulated to peer reviewers for feedback, and finally to the ACS Mission Outcomes Committee and the ACS Board of Directors for approval. The ACS recommends that asymptomatic men who have at least a 10-year life expectancy have an opportunity to make an informed decision with their health care provider about screening for prostate cancer after they receive information about the uncertainties, risks, and potential benefits associated with prostate cancer screening. Prostate cancer screening should not occur without an informed decision-making process. Men at average risk should receive this information beginning at age 50 years. Men in higher risk groups should receive this information before age 50 years. Men should either receive this information directly from their health care providers or be referred to reliable and culturally appropriate sources. Patient decision aids are helpful in preparing men to make a decision whether to be tested.

An Experimental Validation of a Statistical-Based Damage Detection Approach, January 2011

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.