Sigmoidal Regression Research Articles

A tale of two algal blooms: Negative and predictable effects of two common bloom-forming macroalgae on seagrass and epiphytes

Recent evidence suggests macroalgal blooms may play a role in the worldwide decline in seagrass, but the shape of the functional relationship between seagrass health and dominant bloom-forming macroalgae is poorly characterized. We tested whether the impact of varying abundances of two cosmopolitan bloom-forming macroalgal genera caused linear/quasi-linear or sudden threshold changes in measures of eelgrass, Zostera marina, meadow health. We conducted two caging experiments in a shallow Z. marina bed (∼1 m depth) in Bodega Harbor, California, USA where we maintained six densities within the range of natural abundances of macroalgae, Ulva (0–4.0 kg m−2) and Gracilariopsis (0–2.0 kg m−2), as well as uncaged controls over a 10-week period. Shoot density, blade growth, and epiphyte load were measured every two weeks and algal treatments reset. We did not find support for threshold transitions between algal abundance and measures of seagrass bed health using sigmoidal and broken-stick regression analyses for each data set; these models are commonly used to identify threshold patterns in ecological shifts. Instead, final measurements of shoot density and epiphyte load were best modelled as linear or slightly non-linear declines with increasing Ulva abundance. A negative linear relationship also existed between shoot density and Gracilariopsis abundance and a trend towards linear negative effects on epiphyte load. The similar shape of these functional relationships across different types of algae suggests the relationship may be generalizable. At algal abundances that are commonly observed, we found smooth and predictable negative impacts to Z. marina by decline in shoot density and potential impacts to food webs by loss of epiphytes rather than sudden threshold shifts or “ecological surprises”. Our work contrasts with the growing body of literature suggesting highly non-linear shifts in response to human impact; thus, it is important to broaden understanding of shifts to more than just pattern but to the processes that drive different patterns of shifts.

Discovering predictive ensembles for transfer learning and meta-learning

Recent meta-learning approaches are oriented towards algorithm selection, optimization or recommendation of existing algorithms. In this article we show how data-tailored algorithms can be constructed from building blocks on small data sub-samples. Building blocks, typically weak learners, are optimized and evolved into data-tailored hierarchical ensembles. Good-performing algorithms discovered by evolutionary algorithm can be reused on data sets of comparable complexity. Furthermore, these algorithms can be scaled up to model large data sets. We demonstrate how one particular template (simple ensemble of fast sigmoidal regression models) outperforms state-of-the-art approaches on the Airline data set. Evolved hierarchical ensembles can therefore be beneficial as algorithmic building blocks in meta-learning, including meta-learning at scale.

Waste tire recycling process for production of steam activated carbon in a pilot plant

This work is focused on the production of activated carbon from waste tires by means of a two-steps process: steam gasification of starting material followed by steam activation of char. In particular the evaluation of the most appropriate hold time value for the activation step, in terms of quality and amount of activated carbons produced, and the energy balance of the overall process were performed. The hold time was choose as the best compromise between a high surface area of final product and a not excessively low burn-off value. Investigations were carried out through seven experimental tests on a pilot plant based on a rotary kiln reactor. Studies performed pointed out that, when other process parameters were kept constant (in steam gasification: temperature is 850°C, steam to feed ratio is 1, mean residence time of material is 6min, carrier flowrate of N2 is 0.9Nm3h−1; in steam activation: temperature is 920°C, steam to char ratio is 2, carrier flowrate of N2 is 1Nm3h−1), a hold time in activation step set to 3h is the best choice, resulting in a surface area of 786m2·g−1 and a burn-off value equal to 78.4%. Moreover, the energy balance of the overall process which goes from waste tires to activated carbon was performed: if 1kg·h−1 of waste tires are gasified and activated for three hours, an input power of 3.5kW is requested. After the experimental tests, a theoretical model was developed, based on the Arrhenius equation whose parameters were determined from a sigmoidal regression of the experimental data. The theoretical model showed results in good agreement with the experimental data, especially for hold times greater than 2h, and can represent a useful tool to make decision choices.

Accurate automatic titration procedure for low sharpness and dichroism in end point detection using digital movies as detection technique

This study addresses the challenge of performing fast and accurate titrations with end points that are difficult to identify. In this sense, the combination of digital movies (DMs) with a flow-batch analyzer (FBA) has shown to be a promising analytical tool. In a digital movie-based automatic titrator (DMB-AT), a DM is recorded by a webcam as the titrant is added in the mixing chamber (MC). The DM is then decompiled into frames that are ordered sequentially. In the first frame, the region of interest (ROI) of 257×255pixels is defined. The red (R) component is used to calculate the Pearson's correlation coefficient (r) between the first frame and each subsequent one. The titration curve is obtained in real time by plotting r values and the opening time of the titrant valve. The experimental titration curve is fitted in a sigmoidal regression, and the new titration curve is used to estimate the end point by means of the second derivative method. A software program written in Adobe AIR for a desktop platform programmed in ActionScript 3.0 controlled the opening time of the solenoid valves, gathered the analytical signal and carried out the analytical procedure in real time. The feasibility of the method was confirmed by determining the total hardness and the total alkalinity in mineral waters. The results were compared with classical batch titration and did not exhibit statistically significant differences when the paired t-test at the 95% confidence level was applied. The proposed method is able to process approximately 67 and 86samplesh−1 for total hardness and total alkalinity, respectively. Its precision was confirmed by overall relative standard deviation (RSD) values of less than 1.0% in both applications.

Use of morphological characteristics for calculating individual biomass in the kelp Ecklonia cava

Because kelp biomass contributes to the abundance and production of myriad animals and plants in coastal marine ecosystems, its measurement is critical to evaluating the impact of macroalgae on marine and aquatic habitats. We investigated the relationships among morphological characteristics to develop a non-destructive method for calculating Ecklonia cava biomass. Approximately 253 sporophytes of E. cava with various stipe size gradients were collected at five sites on a seasonal basis from November 2012 to August 2014. The relationships between morphological characteristics and individual weight were best fit by a non-linear regression equation, the sigmoidal regression equation. The length and diameter of the stipe, blade number, and total length accounted for more than 80% of the variation in individual weight. In particular, total length × blade number was highly correlated (r 2 = 0.94) correlated with individual weight. The non-destructive method was 3.4–6.7 times faster than destructive sampling method. Considering the cost and time of fieldwork, stipe diameter and length are suitable for large-scale long-term monitoring. We recommend the use of the total length × blade number variable to improve the accuracy of plant biomass estimation in monitoring programs.

Spray Deposition and Control of Botrytis cinerea on Grape Leaves and Bunches: Part 1 (Table Grapes)

Insufficient quantity but also quality of spray deposition on susceptible grapevine tissue (i.e. target sites) and favourable conditions for pathogens could lead to control failure during high disease pressure situations. To determine deposition quantity and quality benchmarks for biologically effective spray deposits, bunches and leaves of table grapes (Waltham Cross) were sprayed at various growth stages, using different application volumes of a mixture of fenhexamid and a fluorescent tracer pigment and subsequently dusted with dry conidia of Botrytis cinerea where after infection levels on pedicels, receptacles and leaves were determined. Pigment deposition quantity and quality were determined from photos of sprayed parts taken with a digital camera under a stereo microscope and black light illumination at ×30 or ×10 magnifications and assessed with digital image and Hoerl regression analyses. The deposition quantity resulting in 75% control of B. cinerea infection (FPC75 values) was calculated from biological efficacy curves (sigmoidal regression analyses) for leaves and for each growth stage, for pedicels and receptacles. Deposition quantity and quality measurements correlated favourably with Botrytis infection. An optimal deposition value for the control of B. cinerea was determined by increasing spray volume, however by increasing spray volume and deposition quantity or quality levels past this optimum will not significantly improve disease control further. It was indicated that efficacy of agricultural chemicals could be influenced by improving both deposition quantity and quality, quantifiable by digital image analyses of fluorescent pigment deposition. FPC75 values obtained in this study can be used as benchmarks to evaluate future spray application in vineyards.

Meteorological conditions determine the thermal-temporal position of the annual Botrytis bunch rot epidemic on <em>Vitis vinifera</em> L. cv. Riesling grapes

<p style="text-align: justify;"><strong>Aims</strong>:</p><p class="Tabelle" style="text-align: justify;">Under Central European climatic conditions, bunch rot caused by <em>Botrytis cinerea</em> occurs virtually every season on <em>Vitis vinifera</em> L. cv. Riesling grapes. Statistical investigations based on at least three annual disease severity assessments in 7 seasons (2007-2013) aimed at (i) simulating the disease progress and (ii) identifying meteorological conditions with predictive value for epidemics.</p><p class="Tabelle" style="text-align: justify;"><strong>Methods and results</strong>:</p><p class="Tabelle" style="text-align: justify;">Sigmoidal regression models were used to describe the disease progress as function of thermal time. Coefficients of determination were > 0.97. The thermal time adjusted pace of the epidemic was almost constant in all seasons while the point of time when 5% disease severity was reached varied among years. Window pane analyses showed that relatively low temperatures and wet conditions during bloom as well as relatively high temperatures and low precipitation sums around/after veraison were associated with thermal-temporally late epidemics.</p><p class="Tabelle" style="text-align: justify;"><strong>Conclusions</strong>:</p><p class="Tabelle" style="text-align: justify;">Environmental conditions determine the timing of annual bunch rot epidemics. Analyses indicate a strong link between meteorological conditions around grape bloom (probably affecting fruit set and cluster structure) and the predisposition of the grape clusters to bunch rot.</p><p class="Tabelle" style="text-align: justify;"><strong>Significance and impact of the study</strong>:</p><p class="Tabelle" style="text-align: justify;">The enhanced understanding of the effect of environmental conditions on the bunch rot epidemics supports growers to optimize control measures and is supposed to result in a Botrytis bunch rot model.</p>

Dormant‐Season Planting and Seed‐Dormancy Impacts on Switchgrass Establishment and Yield

ABSTRACTEstablishment failures linked to seed dormancy are a challenge to wide‐scale use of switchgrass (Panicum virgatum L.) for biomass feedstock and forage production. One prospective strategy for breaking dormancy is dormant‐season planting. The objectives of this study were to evaluate (i) three switchgrass dormant‐season planting dates (1 December, 1 February, and 15 March) vs. a growing‐season (1 May) control; (ii) two seeding rates (6.7 and 10.1 kg pure live seed [PLS] ha−1); and (iii) high‐ and low‐dormancy seed lots. Treatments were assigned in a split‐plot design with three replications at two locations in Tennessee in 2008 and 2009. Neither seeding rate nor seed‐dormancy level affected plant density or yield (P > 0.05). However, a seeding date × year interaction impacted first‐year density at both locations. Although patterns differed by year for the two locations, density of March plantings equaled or exceeded (P < 0.05) those at other dates for both locations and years. These variations in density did not carry over to impact yield in year two. A sigmoidal regression of seedling density vs. yield was significant (P < 0.001) albeit not strong (R2 = 0.13); yield response approached an asymptote above ∼8 plants m−2. Results suggest March planting dates, using standard seeding rate recommendations (6.7 kg PLS ha−1) irrespective of seed‐dormancy rates, may be more reliable than planting in May. Thus, a broader establishment window than traditionally used may be practical. However, results should be validated over a broader range of soils and climatic conditions, especially over a winter severity gradient.

Learning Topology and Dynamics of Large Recurrent Neural Networks

Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms. This paper studies how to identify all authentic connections and estimate system parameters of a recurrent network, given a sequence of node observations. This task becomes extremely challenging in modern network applications, because the available observations are usually very noisy and limited, and the associated dynamical system is strongly nonlinear. By formulating the problem as multivariate sparse sigmoidal regression, we develop simple-to-implement network learning algorithms, with rigorous convergence guarantee in theory, for a variety of sparsity-promoting penalty forms. A quantile variant of progressive recurrent network screening is proposed for efficient computation and allows for direct cardinality control of network topology in estimation. Moreover, we investigate recurrent network stability conditions in Lyapunov's sense, and integrate such stability constraints into sparse network learning. Experiments show excellent performance of the proposed algorithms in network topology identification and forecasting.

A novel model incorporating two variability sources for describing motor evoked potentials.

Motor evoked potentials (MEPs) play a pivotal role in transcranial magnetic stimulation (TMS), e.g., for determining the motor threshold and probing cortical excitability. Sampled across the range of stimulation strengths, MEPs outline an input-output (IO) curve, which is often used to characterize the corticospinal tract. More detailed understanding of the signal generation and variability of MEPs would provide insight into the underlying physiology and aid correct statistical treatment of MEP data. A novel regression model is tested using measured IO data of twelve subjects. The model splits MEP variability into two independent contributions, acting on both sides of a strong sigmoidal nonlinearity that represents neural recruitment. Traditional sigmoidal regression with a single variability source after the nonlinearity is used for comparison. The distribution of MEP amplitudes varied across different stimulation strengths, violating statistical assumptions in traditional regression models. In contrast to the conventional regression model, the dual variability source model better described the IO characteristics including phenomena such as changing distribution spread and skewness along the IO curve. MEP variability is best described by two sources that most likely separate variability in the initial excitation process from effects occurring later on. The new model enables more accurate and sensitive estimation of the IO curve characteristics, enhancing its power as a detection tool, and may apply to other brain stimulation modalities. Furthermore, it extracts new information from the IO data concerning the neural variability-information that has previously been treated as noise.

Production and characterization of chimeric anti-HLA monoclonal antibodies targeting public epitopes as tools for standardizations of the anti-HLA antibody detection

Two chimeric monoclonal antibodies (MoAbs) were designed with variable parts of the mouse antibodies W6/32 and F3.3, and the human constant parts C-gamma1 and C-kappa. These chimeric MoAbs are specific for HLA-class I and HLA-class II public epitopes, respectively. The anti-class I MoAb recognizes all HLA-class I tested so far, and the anti-class II MoAb recognize all HLA-DR, DP, and only DQ antigens of the DQ2 subgroup. These Moabs were used as positive controls in routine tests for the detection of IgG anti-HLA antibodies in the sera of patients (Luminex, flow cytometry, and complement-dependent lymphocytotoxicity assay: CD-LCT).In tests with the LABScreen MIX assay, serial dilutions of the two MoAbs have allowed to determine the thresholds of detection by the Boltzmann sigmoidal regression. The thresholds of detection in mean of fluorescence intensity (MFI) were 926 and 866 for the anti-class I MoAb and the anti-class II MoAb, respectively. The thresholds defined as mean+3SD of MFI values obtained with 30 negative control sera were 411 and 251 for the anti-class I beads and the anti-class II beads, respectively. For the daily validation of the anti-HLA IgG antibodies screening by LABScreen MIX we decided to use the positive control MoAbs at three concentrations (5, 50, and 500ng/ml) and we measured the repeatability (<10%) and reproducibility (<16%) of the method. Used as positive controls in tests with the LABScreen Single Antigen kit, the two MoAbs allowed to estimate daily the quality of beads coated with HLA antigens. The two anti HLA MoAbs were also used as positive control in the cross-match assay by flow cytometry or CD-LCT.In total these two chimeric anti-HLA-MoAbs, which have no equivalent so far, are valuable positive controls for the daily validations of most techniques used for the detection of anti-HLA antibodies according to the good practice guidelines for laboratories.

Do vegetation boundaries display smooth or abrupt spatial transitions along environmental gradients? Evidence from the prairie–forest biome boundary of historic Minnesota, USA

AbstractQuestionsTwo alternative mechanisms of abrupt vegetation change across ecological boundaries have been proposed: (1) concomitantly abrupt gradients in physical environmental variables and vegetation across the boundary, and (2) gradual environmental gradients that vegetation responds to in a non‐linear or threshold manner. Here, we evaluate spatial patterns of climate and vegetation across a grassland–forest biome boundary to examine evidence in favour of either of these alternatives.LocationMinnesota, USA.MethodsVegetation data represented the presence of prairie vs. forest vegetation in Minnesota from 1847 to 1908, generally prior to European settlement of the region, while the climatic variables represented an index of long‐term average moisture availability (precipitation minus potential evapotranspiration (P – PET). Using linear and sigmoidal regression models, we evaluated spatial patterns of change in vegetation, climate and vegetation–climate relationships across 22 transects (170–400 km) oriented perpendicular to the biome boundary. We also evaluated boundary characteristics in light of dominant topographical controls and position along the boundary.ResultsVegetation followed a sigmoidal pattern of change across the boundary, with mean boundary width of ca. 100 km. The P – PET increased by ca. 100 mm across the boundary following a comparatively smooth pattern of change. Climate–vegetation relationships were clearly non‐linear across the boundary, indicating these variables did not change in a common spatial pattern. Regional topographical controls modified relationships between vegetation and climate along the length of the boundary.ConclusionsOur results document strong non‐linear relationships between the presence of forest vegetation and its dominant climate control across a grassland–forest biome boundary. An average change of ca. 100 mm in P – PET moving across the boundary is about 40% of the long‐term mean annual range of this variable, suggesting that modest changes to P – PET may potentially cause substantial shifts in the location of the prairie–forest boundary.

Actividad fotodinámica de ftalocianina de aluminio (iii) y zinc (ii) en promastigotes de Leishmania

Photodynamic therapy is a two-step procedure, involving the use of photosensitizing agents followed by selective illumination of the target lesion with visible light. It produces highly reactive oxygen species and subsequent cellular damage. This study was designed to determine whether Leishmania chagasi and L. panamensis promastigotes were sensitive to photodynamic therapy in vitro. Leishmania promastigotes were treated with aluminium phthalocyanine chloride and zinc phthalocyanine photosensitizers before illumination with visible light at 670 nm. The parasite photoactivity was calculated by sigmoidal regression analysis. Leishmania chagasi promastigotes were highly photosensitive to aluminium phthalocyanine chloride treatment with effective inhibitory dose50 (ED50) concentration values of 0.0033, 0.0083 and 0.0093 microM upon exposure to 10.0, 5.0, and 2.5 J/cm2 light intensities respectively. By contrast, the activity of aluminium phthalocyanine chloride on L. panamensis was significantly lower (P < 0.01) with ED50 values of 0.17, 0.25, 0.34 microM at the same light intensities. Zinc phthalocyanine activity was significantly (P < 0.01) less active than aluminium phthalocyanine chloride on both strains of these two species and no differences in zinc phthalocyanine activity were found between them. A dose-response phototoxic effect with both phthalocyanines was observed. Parasite inhibition was not observed after aluminium phthalocyanine chloride or zinc phthalocyanine treatment in the dark. The reference drugs hexadecylphosphocholine and amphotericin B were not photoactive. Treatment of Leishmania promastigotes with aluminium phthalocyanine chloride and zinc phthalocyanine followed by illumination with visible light at 670 nm inhibited in vitro growth of promastigotes of L. chagasi and L. panamensis. Photodynamic therapy against Leishmania could be a promising strategy for leishmaniasis treatment.

Benghal Dayflower (Commelina benghalensis) Seed Viability in Soil

Benghal dayflower is an exotic weed species in the United States that is a challenge to manage in agricultural fields. Research was conducted in North Carolina, Georgia, and Florida to evaluate the longevity of buried Benghal dayflower seeds. Seeds were buried in the field for 2 to 60 mo at a depth of 20 cm in mesh bags containing soil native to each area. In North Carolina, decline of Benghal dayflower seed viability was described by a sigmoidal regression model, with seed size having no effect on viability. Seed viability at the initiation of the study was 81%. After burial, viability declined to 51% after 24 mo, 27% after 36 mo, and &lt; 1% after 42 mo. In Georgia, initial seed viability averaged 86% and declined to 63 and 33% at 12 and 24 mo, respectively. Burial of 36 mo or longer reduced seed viability to &lt; 2%. The relationship between Benghal dayflower seed viability and burial time was described by a sigmoidal regression model. In Florida, there was greater variability in Benghal dayflower seed viability than there was at the other locations. Seed viability at the first sampling date after 2 mo of burial was 63%. Although there were fluctuations during the first 24 mo, the regression model indicated approximately 60% of seed remained viable. After 34 mo of burial, seed viability was reduced to 46% and then rapidly fell to 7% at 39 mo, which was consistent with the decrease in seed viability at the other locations. Although there is a physical dormancy imposed by the seed coat of Benghal dayflower, which has been detected in previous studies, it appears that a decline in buried seed viability to minimal levels occurs within 39 to 48 mo in the southeastern United States, suggesting that management programs must prevent seed production for at least four growing seasons to severely reduce the Benghal dayflower soil seedbank.

Breakthrough analysis for water disinfection using silver nanoparticles coated resin beads in fixed-bed column

This study demonstrates the use of silver nanoparticles coated resin beads in deactivating microbes in drinking water in a column filtration system. The coated resin beads are characterized using X-ray diffraction (XRD), Fourier transform infra-red (FT-IR), scanning electron microscope (SEM), transmission electron microscope (TEM) and energy dispersive spectroscopy (EDS) to confirm the functional groups, morphology and the presence of silver nanoparticles on the surface of the resin. The performance of the coated resin is evaluated as a function of bed mass, initial bacterial concentration and flow rate using Escherichia coli as model microbial contaminant in water. The survival curves of E. coli are expressed as breakthrough curves (BTCs), which are modeled using sigmoidal regression equations to obtain relevant rate parameters. The number of bed volumes processed at breakthrough point and capacity of the bed are used as performance indicators. Results show that performance increases with a decrease in initial bacterial concentration, an increase in flow rate and an increase in bed mass.

Methods for exploring the morpho-functional relations of the aortic depressor nerve in experimental diabetes

The present study investigated morpho-functional relations of the aortic depressor nerve (ADN) 5, 15 and 120 days after the onset of streptozotocin-induced diabetes in rats. Time control animals received vehicle. Under pentobarbital anesthesia, ADN activity was recorded simultaneously with arterial pressure. After the recordings, nerves were prepared for light microscopy study and morphometry. ADN function was accessed by means of pressure-nerve activity curve (fitted by sigmoidal regression) and cross-spectral analysis between mean arterial pressure (MAP) and ADN activity. The relation between morphological (myelinated fibers number and density, total myelin area, total fiber area and percentage of occupancy) and functional (gain, signal/noise relation, frequency) parameters were accessed by linear regression analysis and correlation coefficient calculations. Functional parameters obtained by means of the sigmoidal regression curve as well as by cross-spectral analysis were similar in diabetic and control rats. Morphometric parameters of the ADN were similar between groups 5 days after the onset of diabetes. Average myelin area and myelinated fiber area were significantly smaller on diabetic rats 15 and 120 days after the onset of diabetes, being the myelinated fiber and respective axons area and diameter also smaller on 120 days group. Nevertheless, G ratio (ratio between axon and fiber diameter) was nearly 0.6 and not different between groups or experimental times. No significant relationship between morphological and functional parameters was detected in all experimental groups. The present study suggests that ADN diabetic neuropathy was time-dependent, with damage to myelinated fibers to be the primary event, not evidenced by physiological methods.

Influence of the albumin concentration and temperature on the lysis of human erythrocytes by sodium dodecyl sulfate

The stability of human erythrocytes to sodium dodecyl sulfate (SDS) was assessed spectrophotometrically in the presence of different concentrations of bovine serum albumin (BSA) and at different temperatures (27-45 °C). The absorbance at 540 nm (A₅₄₀) was correlated with the SDS concentration by sigmoidal regression based on the Boltzmann equation. Erythrocyte stability was characterized on the basis of the SDS concentration that induces hemolysis in 50% of the cells (D₅₀). Progressive increases in the albumin concentration led to increases in the D₅₀ value. The protective effect of BSA against SDS-induced hemolysis was attributed to the binding of the surfactant to the hydrophobic binding sites of this protein. The D₅₀ values decreased sigmoidally with an increase in the temperature. This trend, which could not be explained by changes in the spectral properties of hemoglobin, maybe due to heterogeneity in the erythrocyte population.

The approximate entropy of the electromyographic signals of tremor correlates with the osmotic fragility of human erythrocytes.

BackgroundThe main problem of tremor is the damage caused to the quality of the life of patients, especially those at more advanced ages. There is not a consensus yet about the origins of this disorder, but it can be examined in the correlations between the biological signs of aging and the tremor characteristics.MethodsThis work sought correlations between the osmotic fragility of erythrocytes and features extracted from electromyographic (EMG) activity resulting from physiological tremor in healthy patients (N = 44) at different ages (24-87 years). The osmotic fragility was spectrophotometrically evaluated by the dependence of hemolysis, provided by the absorbance in 540 nm (A54o), on the concentration of NaCl. The data were adjusted to curves of sigmoidal regression and characterized by the half transition point (H50), amplitude of lysis transition (dx) and values of A540 in the curve regions that characterize the presence of lysed (A1) and preserved erythrocytes (A2). The approximate entropy was estimated from EMG signals detected from the extensor carpi ulnaris muscle during the movement of the hand of subjects holding up a laser pen towards an Archimedes spiral, fixed in a whiteboard. The evaluations were carried out with the laser pen at rest, at the center of the spiral, and in movement from the center to the outside and from outside to the center. The correlations among the parameters of osmotic fragility, tremor and age were tested.ResultsNegative correlations with age were found for A1 and dx. With the hand at rest, a positive correlation with H50 was found for the approximate entropy. Negative correlations with H50 were found for the entropy with the hand in movement, as from the center to the outside or from the outside to the center of the spiral.ConclusionIn healthy individuals, the increase in the erythrocyte osmotic fragility was associated with a decrease in the approximate entropy for rest tremor and with an increase of the entropy for movement tremor. This suggests that the neuromuscular degeneration associated with tremor entails also the mechanisms involved in the breakdown of structural homeostasis of the erythrocyte membrane.

Production of bio-fuels (hydrogen and lipids) through a photofermentation process

The purple non-sulfur photosynthetic bacteria Rhodopseudomonas palustris (strain 42OL) was investigated for a co-production of both bio-H 2 and biodiesel (lipids). The investigation was carried out using malic and glutamic acids in a fed-batch cultivation system under continuous irradiances of 36, 56, 75, 151, 320, 500, and 803 W m −2. Boltzmann’s sigmoidal regression model was used to determine growth kinetic parameters during hydrogen photoevolution. The upper limit of volumetric hydrogen photoevolution was 15.5 ± 0.9 ml l −1 h −1. During the entire cultivation period (408 h), the highest average hydrogen production rate (HPR av) of 11.1 ± 3.1 ml l −1 h −1 was achieved at an irradiance of 320 W m −2. Biomasses stored at the end of each experimental set were analyzed in order to determine lipid content, which ranged from a minimum of 22 ± 1% to a maximum of 39 ± 2% of biomass dry weight.

Some Studies on Densification Behavior Modeling of Al–Pb Alloys Produced by Different Routes

The recent research into the alternate sliding bearing materials unequivocally point to the beneficial role of lead (Pb) in aluminum (Al). For such applications, powder metallurgical processing (P/M) of the alloys can result in homogeneous microstructure and controlled porosity. However, the cold die compaction used in P/M processing leads to inhomogeneous density distributions and non-uniform shrinkage or distortion during sintering. In order to control their final shape, the appropriate models are necessary for densification of composite powders. In the present work, the authors studied the effect of compaction pressure and BCR on densification behavior of five compositions of Al–Pb alloys processed through conventional ball milling and attrition milling routes. The premixed powder samples are subjected to compressibility test, and the changes in density are analyzed in cold die compaction. The microstructural evidence provided by the SEM photographs and the compressibility test data, together, confirms that attrition milling process and higher BCRs result in better densification in Al–Pb alloys, due to refined microstructure and improved homogeneity. Further, sigmoidal family nonlinear regression models are applied to the test data based on Levenberg–Marquardt method. Based on a two-stage validation of the best fitting mathematical models, this study establishes that Richards Model effectively predicts compact density within ±1% in most of the cases related to Al–Pb alloys.