A statistical analysis of audience sound absorption (A)

The absorption produced by the audience in concert halls is considered a random variable. Beranek's proposal [L. L. Beranek, Music, Acoustics and Architecture (Wiley, New York, 1962), p. 543] that audience absorption is proportional to the area they occupy and not to their number is subjected to a statistical hypothesis test. A two variable linear regression model of the absorption with audience area and residual area as regressor variables is postulated for concert halls without added absorptive materials. Since Beranek's contention amounts to the statement that audience absorption is independent of the seating density, the test of the hypothesis lies in categorizing halls by seating density and examining for significant differences among slopes of regression planes of the different categories. Such a test shows that Beranek's hypothesis can be accepted. It is also shown that the audience area is a better predictor of the absorption than the audience number. The absorption coefficients and their 95% confidence limits are given for the audience and residual areas. A critique of the regression model is presented.

HDL subspecies : association with low HDL-C, obesity, and metabolic syndrome

AIMS An independent, powerful coronary heart disease (CHD) predictor is a low level of high-density lipoprotein cholesterol (HDL-C). Discoidal preβ-HDL particles and large HDL2 particles are the primary cholesterol acceptors in reverse cholesterol transport, a key anti-atherogenic HDL mechanism. The quality of HDL subspecies may provide better markers of HDL functionality than does HDL-C alone. We aimed I) to study whether alterations in the HDL subspecies profile exist in low-HDL-C subjects II) to explore the relationship of any changes in HDL subspecies profile in relation to atherosclerosis and metabolic syndrome; III) to elucidate the impact of genetics and acquired obesity on HDL subspecies distribution. SUBJECTS The study consisted of 3 cohorts: A) Finnish families with low HDL-C and premature CHD (Study I: 67 subjects with familial low HDL-C and 64 controls; Study II: 83 subjects with familial low HDL-C, 65 family members with normal HDL-C, and 133 controls); B) a cohort of 113 low- and 133 high-HDL-C subjects from the Health 2000 Health Examination Survey carried out in Finland (Study III); and C) a Finnish cohort of healthy young adult twins (52 monozygotic and 89 dizygotic pairs) (Study IV). RESULTS AND CONCLUSIONS The subjects with familial low HDL-C had a lower preβ-HDL concentration than did controls, and the low-HDL-C subjects displayed a dramatic reduction (50-70%) in the proportion of large HDL2b particles. The subjects with familial low HDL-C had increased carotid atherosclerosis measured as intima-media-thickness (IMT), and HDL2b particles correlated negatively with IMT. The reduction in both key cholesterol acceptors, preβ-HDL and HDL2 particles, supports the concept of impaired reverse cholesterol transport contributing to the higher CHD risk in low-HDL-C subjects. The family members with normal HDL-C and the young adult twins with acquired obesity showed a reduction in large HDL2 particles and an increase in small HDL3 particles, which may be the first changes leading to the lowering of HDL-C. The low-HDL-C subjects had a higher serum apolipoprotein E (apoE) concentration, which correlated positively with the metabolic syndrome components (waist circumference, TG, and glucose), highlighting the need for a better understanding of apoE metabolism in human atherosclerosis. In the twin study, the increase in small HDL3b particles was associated with obesity independent of genetic effects. The heritability estimate, of 73% for HDL-C and 46 to 63% for HDL subspecies, however, demonstrated a strong genetic influence. These results suggest that the relationship between obesity and lipoproteins depends on different elements in each subject. Finally, instead of merely elevating HDL-C, large HDL2 particles and discoidal preβ-HDL particles may provide beneficial targets for HDL-targeted therapy.

High-risk patients and high-risk grafts in infrainguinal bypass for critical limb ischaemia

The main purpose of revascularization procedures for critical limb ischaemia (CLI) is to preserve the leg and sustain the patient s ambulatory status. Other goals are ischaemic pain relief and healing of ischaemic ulcers. Patients with CLI are usually old and have several comorbidities affecting the outcome. Revascularization for CLI is meaningless unless both life and limb are preserved. Therefore, the knowledge of both patient- and bypass-related risk factors is of paramount importance in clinical decision-making, patient selection and resource allocation. The aim of this study was to identify patient- and graft-related predictors of impaired outcome after infrainguinal bypass for CLI. The purpose was to assess the outcome of high-risk patients undergoing infrainguinal bypass and to evaluate the usefulness of specific risk scoring methods. The results of bypasses in the absence of optimal vein graft material were also evaluated, and the feasibility of the new method of scaffolding suboptimal vein grafts was assessed. The results of this study showed that renal insufficiency - not only renal failure but also moderate impairment in renal function - seems to be a significant risk factor for both limb loss and death after infrainguinal bypass in patients with CLI. Low estimated GFR (PIENEMPI KUIN 30 ml/min/1.73 m2) is a strong independent marker of poor prognosis. Furthermore, estimated GFR is a more accurate predictor of survival and leg salvage after infrainguinal bypass in CLI patients than serum creatinine level alone. We also found out that the life expectancy of octogenarians with CLI is short. In this patient group endovascular revascularization is associated with a better outcome than bypass in terms of survival, leg salvage and amputation-free survival especially in presence of coronary artery disease. This study was the first one to demonstrate that Finnvasc and modified Prevent III risk scoring methods both predict the long-term outcome of patients undergoing both surgical and endovascular infrainguinal revascularization for CLI. Both risk scoring methods are easy to use and might be helpful in clinical practice as an aid in preoperative patient selection and decision-making. Similarly than in previous studies, we found out that a single-segment great saphenous vein graft is superior to any other autologous vein graft in terms of mid-term patency and leg salvage. However, if optimal vein graft is lacking, arm vein conduits are superior to prosthetic grafts especially in infrapopliteal bypasses for CLI. We studied also the new method of scaffolding suboptimal quality vein grafts and found out that this method may enable the use of vein grafts of compromised quality otherwise unsuitable for bypass grafting. The remarkable finding was that patients with the combination of high operative risk due to severe comorbidities and risk graft have extremely poor survival, suggesting that only relatively fit patients should undergo complex bypasses with risk grafts. The results of this study can be used in clinical practice as an aid in preoperative patient selection and decision-making. In the future, the need of vascular surgery will increase significantly as the elderly and diabetic population increases, which emphasises the importance of focusing on those patients that will gain benefit from infrainguinal bypass. Therefore, the individual risk of the patient, ambulatory status, outcome expectations, the risk of bypass procedure as well as technical factors such as the suitability of outflow anatomy and the available vein material should all be assessed and taken into consideration when deciding on the best revascularization strategy.

Lypsylehmän energiataseen mallintaminen tuotoskauden alkuvaiheessa

Energiataseen mallinnus on osa KarjaKompassi-hankkeeseen liittyvää kehitystyötä. Tutkielman tavoitteena oli kehittää lypsylehmän energiatasetta etukäteen ennustavia ja tuotoskauden aikana saatavia tietoja hyödyntäviä matemaattisia malleja. Selittävinä muuttujina olivat dieetti-, rehu-, maitotuotos-, koelypsy-, elopaino- ja kuntoluokkatiedot. Tutkimuksen aineisto kerättiin 12 Suomessa tehdyistä 8 – 28 laktaatioviikon pituisesta ruokintakokeesta, jotka alkoivat heti poikimisen jälkeen. Mukana olleista 344 lypsylehmästä yksi neljäsosa oli friisiläis- ja loput ayshire-rotuisia. Vanhempien lehmien päätiedosto sisälsi 2647 havaintoa (koe * lehmä * laktaatioviikko) ja ensikoiden 1070. Aineisto käsiteltiin SAS-ohjelmiston Mixed-proseduuria käyttäen ja poikkeavat havainnot poistettiin Tukeyn menetelmällä. Korrelaatioanalyysillä tarkasteltiin energiataseen ja selittävien muuttujien välisiä yhteyksiä. Energiatase mallinnettiin regressioanalyysillä. Laktaatiopäivän vaikutusta energiataseeseen selitettiin viiden eri funktion avulla. Satunnaisena tekijänä mallissa oli lehmä kokeen sisällä. Mallin sopivuutta aineistoon tarkasteltiin jäännösvirheen, selitysasteen ja Bayesin informaatiokriteerin avulla. Parhaat mallit testattiin riippumattomassa aineistossa. Laktaatiopäivän vaikutusta energiataseeseen selitti hyvin Ali-Schaefferin funktio, jota käytettiin perusmallina. Kaikissa energiatasemalleissa vaihtelu kasvoi laktaatioviikosta 12. alkaen, kun havaintojen määrä väheni ja energiatase muuttui positiiviseksi. Ennen poikimista käytettävissä olevista muuttujista dieetin väkirehuosuus ja väkirehun syönti-indeksi paransivat selitysastetta ja pienensivät jäännösvirhettä. Ruokinnan onnistumista voidaan seurata maitotuotoksen, maidon rasvapitoisuuden ja rasva-valkuaissuhteen tai EKM:n sisältävillä malleilla. EKM:n vakiointi pienensi mallin jäännösvirhettä. Elopaino ja kuntoluokka olivat heikkoja selittäjiä. Malleja voidaan hyödyntää karjatason ruokinnan suunnittelussa ja seurannassa, mutta yksittäisen lehmän energiataseen ennustamiseen ne eivät sovellu.

A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio HR] = 2.4; 95% CI = 1.4-3.8; p < 0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1-2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4-2.8; p < 0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04-1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.

Endophytic fungal communities in woody perennials of three tropical forest types of the Western Ghats, southern India

Fungal endophytes of tropical trees are expected to be exceptionally species rich as a consequence of high tree diversity in the tropics and the purported host restriction among the endophytes. Based on this premise, endophytes have been regarded as a focal group for estimating fungal numbers because their possible hyperdiverse nature would reflect significantly global fungal diversity. We present our consolidated ten-year work on 75 dicotyledonous tree hosts belonging to 33 families and growing in three different types of tropical forests of the NBR in the Western Ghats, southern India. We conclude that endophyte diversity in these forests is limited due to loose host affiliations among endophytes. Some endophytes have a wide host range and colonize taxonomically disparate hosts suggesting adaptations in them to counter a variety of defense chemicals in their hosts. Furthermore, such polyphagous endophytes dominate the endophyte assemblages of different tree hosts. Individual leaves may be densely colonized but only by a few endophyte species. It appears that the environment (the type of forest in this case) has a larger role in determining the endophyte assemblage of a plant host than the taxonomy of the host plant. Thus, different tropical plant communities have to be studied for their endophyte diversity to test the generalization that endophytes are hyperdiverse in the tropics, estimate their true species richness, and use them as a predictor group for more accurate assessment of global fungal diversity.

Post-seizure EEG fractal dimension of first ECT predicts antidepressant response at two weeks

Background: Duration of seizure by itself is an insufficient criterion for a therapeutically adequate seizure in ECT. Therefore, measures of seizure EEG other than its duration need to be explored as indices of seizure adequacy and predictors of treatment response. We measured the EEG seizure using a geometrical method-fractal dimension (FD) and examined if this measure predicted remission. Methods: Data from an efficacy study on melancholic depressives (n = 40) is used for the present exploration. They received thrice or once weekly ECTs, each schedule at two energy levels - high or low energy level. FD was computed for early-, mid- and post-seizure phases of the ictal EEG. Average of the two channels was used for analysis. Results: Two-thirds of the patients (n = 25) were remitted at the end of 2 weeks. As expected, a significantly higher proportion of patients receiving thrice weekly ECT remitted than in patients receiving once weekly ECT. Smaller post-seizure FD at first ECT is the only variable which predicted remission status after six ECTs. within the once weekly ECT group too, smaller post-seizure FD was associated with remission status. Conclusions: Post-seizure FD is proposed as a novel measure of seizure adequacy and predictor of treatment response. Clinical implications: Seizure measures at first ECT may guide selection of ECT schedule to optimize ECT. Limitations: The study examined short term antidepressant effects only. The results may not be generalized to medication-resistant depressives. (C) 1999 Elsevier Science B.V. All rights reserved.

A new parallel overlapped domain decomposition method for nonlinear dynamic finite element analysis

In this paper a new parallel algorithm for nonlinear transient dynamic analysis of large structures has been presented. An unconditionally stable Newmark-beta method (constant average acceleration technique) has been employed for time integration. The proposed parallel algorithm has been devised within the broad framework of domain decomposition techniques. However, unlike most of the existing parallel algorithms (devised for structural dynamic applications) which are basically derived using nonoverlapped domains, the proposed algorithm uses overlapped domains. The parallel overlapped domain decomposition algorithm proposed in this paper has been formulated by splitting the mass, damping and stiffness matrices arises out of finite element discretisation of a given structure. A predictor-corrector scheme has been formulated for iteratively improving the solution in each step. A computer program based on the proposed algorithm has been developed and implemented with message passing interface as software development environment. PARAM-10000 MIMD parallel computer has been used to evaluate the performances. Numerical experiments have been conducted to validate as well as to evaluate the performance of the proposed parallel algorithm. Comparisons have been made with the conventional nonoverlapped domain decomposition algorithms. Numerical studies indicate that the proposed algorithm is superior in performance to the conventional domain decomposition algorithms. (C) 2003 Elsevier Ltd. All rights reserved.

Correction for Bias in Downscaling GCM Simulations for Hydrologic Impact Assessment

In a statistical downscaling model, it is important to remove the bias of General Circulations Model (GCM) outputs resulting from various assumptions about the geophysical processes. One conventional method for correcting such bias is standardisation, which is used prior to statistical downscaling to reduce systematic bias in the mean and variances of GCM predictors relative to the observations or National Centre for Environmental Prediction/ National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data. A major drawback of standardisation is that it may reduce the bias in the mean and variance of the predictor variable but it is much harder to accommodate the bias in large-scale patterns of atmospheric circulation in GCMs (e.g. shifts in the dominant storm track relative to observed data) or unrealistic inter-variable relationships. While predicting hydrologic scenarios, such uncorrected bias should be taken care of; otherwise it will propagate in the computations for subsequent years. A statistical method based on equi-probability transformation is applied in this study after downscaling, to remove the bias from the predicted hydrologic variable relative to the observed hydrologic variable for a baseline period. The model is applied in prediction of monsoon stream flow of Mahanadi River in India, from GCM generated large scale climatological data.

3D Image Reconstruction From Truncated Helical Cone Beam Projection Data - A Linear Prediction Approach

With the introduction of 2D flat-panel X-ray detectors, 3D image reconstruction using helical cone-beam tomography is fast replacing the conventional 2D reconstruction techniques. In 3D image reconstruction, the source orbit or scanning geometry should satisfy the data sufficiency or completeness condition for exact reconstruction. The helical scan geometry satisfies this condition and hence can give exact reconstruction. The theoretically exact helical cone-beam reconstruction algorithm proposed by Katsevich is a breakthrough and has attracted interest in the 3D reconstruction using helical cone-beam Computed Tomography.In many practical situations, the available projection data is incomplete. One such case is where the detector plane does not completely cover the full extent of the object being imaged in lateral direction resulting in truncated projections. This result in artifacts that mask small features near to the periphery of the ROI when reconstructed using the convolution back projection (CBP) method assuming that the projection data is complete. A number of techniques exist which deal with completion of missing data followed by the CBP reconstruction. In 2D, linear prediction (LP)extrapolation has been shown to be efficient for data completion, involving minimal assumptions on the nature of the data, producing smooth extensions of the missing projection data.In this paper, we propose to extend the LP approach for extrapolating helical cone beam truncated data. The projection on the multi row flat panel detectors has missing columns towards either ends in the lateral direction in truncated data situation. The available data from each detector row is modeled using a linear predictor. The available data is extrapolated and this completed projection data is backprojected using the Katsevich algorithm. Simulation results show the efficacy of the proposed method.

Fuzzy inference based data preprocessing for VBR video traffic prediction

Prediction of variable bit rate compressed video traffic is critical to dynamic allocation of resources in a network. In this paper, we propose a technique for preprocessing the dataset used for training a video traffic predictor. The technique involves identifying the noisy instances in the data using a fuzzy inference system. We focus on three prediction techniques, namely, linear regression, neural network and support vector regression and analyze their performance on H.264 video traces. Our experimental results reveal that data preprocessing greatly improves the performance of linear regression and neural network, but is not effective on support vector regression.

Dynamics between patent latent variables and patent price

This paper focuses on studying the relationship between patent latent variables and patent price. From the existing literature, seven patent latent variables, namely age, generality, originality, foreign filings, technology field, forward citations, and backward citations were identified as having an influence on patent value. We used Ocean Tomo's patent auction price data in this study. We transformed the price and the predictor variables (excluding the dummy variables) to its logarithmic value. The OLS estimates revealed that forward citations and foreign filings were positively correlated to price. Both the variables jointly explained 14.79% of the variance in patent pricing. We did not find sufficient evidence to come up with any definite conclusions on the relationship between price and the variables such as age, technology field, generality, backward citations and originality. The Heckman two-stage sample selection model was used to test for selection bias. (C) 2011 Elsevier Ltd. All rights reserved.

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R (H) in a river basin at monthly scale. Uncertainty in the future projections of R (H) is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R (H) are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978-2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978-2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R (H) is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.

Reproductive queue without overt conflict in the primitively eusocial wasp Ropalidia marginata

Colonies of the primitively eusocial wasp Ropalidia marginata consist of a single egg layer (queen) and a number of non-egg-laying workers. Although the queen is a docile individual, not at the top of the behavioral dominance hierarchy of the colony, she maintains complete reproductive monopoly. If the queen is lost or removed, one and only one of the workers potential queen (PQ)] becomes hyperaggressive and will become the next queen of the colony. The PQ is almost never challenged because she first becomes hyperaggressive and then gradually loses her aggression, develops her ovaries, and starts laying eggs. Although we are unable to identify the PQ when the queen is present, she appears to be a ``cryptic heir designate.'' Here, we show that there is not just one heir designate but a long reproductive queue and that PQs take over the role of egg-laying, successively, without overt conflict, as the queen or previous PQs are removed. The dominance rank of an individual is not a significant predictor of its position in the succession hierarchy. The age of an individual is a significant predictor, but it is not a perfect predictor because PQs often bypass older individuals to become successors. We suggest that such a predesignated reproductive queue that is implemented without overt conflict is adaptive in the tropics, where conspecific usurpers from outside the colony, which can take advantage of the anarchy prevailing in a queenless colony and invade it, are likely to be present throughout the year.

When good neighbors don't need fences: temporal landscape partitioning among baboon social groups

Intraspecific competition is a key factor shaping space-use strategies and movement decisions in many species, yet how and when neighbors utilize shared areas while exhibiting active avoidance of one another is largely unknown. Here, we investigated temporal landscape partitioning in a population of wild baboons (Papio cynocephalus). We used global positioning system (GPS) collars to synchronously record the hourly locations of five baboon social groups for similar to 900 days, and we used behavioral, demographic, and life history data to measure factors affecting use of overlap areas. Annual home ranges of neighboring groups overlapped substantially, as predicted (baboons are considered non-territorial), but home ranges overlapped less when space use was assessed over shorter time scales. Moreover, neighboring groups were in close spatial proximity to one another on fewer days than predicted by a null model, suggesting an avoidance-based spacing pattern. At all time scales examined (monthly, biweekly, and weekly), time spent in overlap areas was greater during time periods when groups fed on evenly dispersed, low-quality foods. The percent of fertile females in social groups was negatively correlated with time spent in overlap areas only during weekly time intervals. This suggests that broad temporal changes in ecological resources are a major predictor of how intensively overlap areas are used, and groups modify these ecologically driven spacing patterns at short time scales based on female reproductive status. Together, these findings offer insight into the economics of territoriality by highlighting the dynamics of spacing patterns at differing time scales.