The Transportation of Wood in Chutes

Artificial channels, known as chutes, in which logs and bolts may be transported down steep slopes by means of gravity, were devised several centuries ago in the mountainous regions of Europe and later were used by north American loggers, especially in New England, New York, and Pennsylvania. They operate most advantageously on grades that are far in excess of those on which wheeled vehicles or sleds can be used safely, and they are most serviceable for moving timber on terrain which is so steep or broken that the construction cost of suitable roads is prohibitive

The prevalence and distribution of the amyloidogenic transthyretin (TTR) V122I allele in Africa

Transthyretin (TTR) pV142I (rs76992529-A) is one of the 113 variants in the human TTR gene associated with systemic amyloidosis. It results from a G to A transition at a CG dinucleotide in the codon for amino acid 122 of the mature protein (TTR V122I). The allele frequency is 0.0173 in African Americans

Estimating dry biomass and plant nitrogen concentration in pre-Alpine grasslands with low-cost UAS-borne multispectral data – a comparison of sensors, algorithms, and predictor sets

Grasslands are an important part of pre-Alpine and Alpine landscapes. Despite the economic value and the significant role of grasslands in carbon and nitrogen (N) cycling, spatially explicit information on grassland biomass and quality is rarely available. Remotely sensed data from unmanned aircraft systems (UASs) and satellites might be an option to overcome this gap. Our study aims to investigate the potential of low-cost UAS-based multispectral sensors for estimating above-ground biomass (dry matter, DM) and plant N concentration. In our analysis, we compared two different sensors (Parrot Sequoia, SEQ; MicaSense RedEdge-M, REM), three statistical models (linear model; random forests, RFs; gradient-boosting machines, GBMs), and six predictor sets (i.e. different combinations of raw reflectance, vegetation indices, and canopy height). Canopy height information can be derived from UAS sensors but was not available in our study. Therefore, we tested the added value of this structural information with in situ measured bulk canopy height data. A combined field sampling and flight campaign was conducted in April 2018 at different grassland sites in southern Germany to obtain in situ and the corresponding spectral data. The hyper-parameters of the two machine learning (ML) approaches (RF, GBM) were optimized, and all model setups were run with a 6-fold cross-validation. Linear models were characterized by very low statistical performance measures, thus were not suitable to estimate DM and plant N concentration using UAS data. The non-linear ML algorithms showed an acceptable regression performance for all sensor–predictor set combinations with average (avg; cross-validated, cv) R2cv of 0.48, RMSEcv,avg of 53.0 g m2, and rRMSEcv,avg (relative) of 15.9 % for DM and with R2cv, avg of 0.40, RMSEcv,avg of 0.48 wt %, and rRMSEcv, avg of 15.2 % for plant N concentration estimation. The optimal combination of sensors, ML algorithms, and predictor sets notably improved the model performance. The best model performance for the estimation of DM (R2cv=0.67, RMSEcv=41.9 g m2, rRMSEcv=12.6 %) was achieved with an RF model that utilizes all possible predictors and REM sensor data. The best model for plant N concentration was a combination of an RF model with all predictors and SEQ sensor data (R2cv=0.47, RMSEcv=0.45 wt %, rRMSEcv=14.2 %). DM models with the spectral input of REM performed significantly better than those with SEQ data, while for N concentration models, it was the other way round. The choice of predictors was most influential on model performance, while the effect of the chosen ML algorithm was generally lower. The addition of canopy height to the spectral data in the predictor set significantly improved the DM models. In our study, calibrating the ML algorithm improved the model performance substantially, which shows the importance of this step

Community health workers as change agents in improving equity in birth outcomes in Detroit

We examined whether pairing pregnant women with community health workers improved pregnancy outcomes among 254 Black women with singleton pregnancies participating in the Women-Inspired Neighborhood (WIN) Network: Detroit using a case-control design. A subset (N = 63) of women were recontacted and asked about program satisfaction, opportunities, and health behaviors. Michigan Vital Statistics records were used to ascertain controls (N = 12,030) and pregnancy and infant health outcomes. Logistic and linear regression were used to examine the association between WIN Network participation and pregnancy and infant health outcomes. The WIN Network participants were less likely than controls to be admitted to the neonatal intensive care unit (odds ratio = 0.55, 95% CI 0.33-0.93) and had a longer gestational length (mean difference = 0.42, 95% CI 0.02-0.81). Community health workers also shaped participants\u27 view of opportunities to thrive. This study demonstrates that community health workers can improve pregnancy outcomes for Black women

Long period variables in 47 Tuc: direct evidence for lost mass

We have identified 22 new variable red giants in 47 Tuc and determined periods for another 8 previously known variables. All red giants redder than V-I_c=1.8 are variable at the limits of our detection threshold, which corresponds to delta V ~ 0.1 mag. This colour limit corresponds to a luminosity log L/L_sun=3.15 and it is considerably below the tip of the RGB at log L/L_sun=3.35. Linear non-adiabatic models without mass loss on the giant branch can not reproduce the observed PL laws for the low amplitude pulsators. Models that have undergone mass loss do reproduce the observed PL relations and they show that mass loss of the order of 0.3 M_sun occurs along the RGB and AGB. The linear pulsation periods do not agree well with the observed periods of the large amplitude Mira variables, which pulsate in the fundamental mode. The solution to this problem appears to be that the nonlinear pulsation periods in these low mass stars are considerably shorter than the linear pulsation periods due to a rearrangement of stellar structure caused by the pulsation. Both observations and theory show that stars evolve up the RGB and first part of the AGB pulsating in low order overtone modes, then switch to fundamental mode at high luminosities.Comment: 11 pages, accepted for publication in A&

Reaction Kinetics in Restricted Spaces

Reactions in restricted spaces rarely get stirred vigorously by convection and are thus controlled by diffusion. Furthermore, the compactness of the Brownian motion leads to both anomalous diffusion and anomalous reaction kinetics. Elementary binary reactions of the type A + A → Products, A + B → Products, and A + C → C + Products are discussed theoretically for both batch and steady‐state conditions. The anomalous reaction orders and time exponents (for the rate coefficients) are discussed for various situations. Global and local rate laws are related to particle distribution functions. Only Poissonian distributions guarantee the classical rate laws. Reactant self‐organization leads to interesting new phenomena. These are demonstrated by theory, simulations, and experiments. The correlation length of reactant production affects the self‐ordering length scale. These effects are demonstrated experimentally, including the stability of reactant segregation observed in chemical reactions in one‐dimensional spaces, e.g., capillaries and microcapillaries. The gap between the reactant A (cation) and B (anion) actually increases in time and extends over millimeters. Excellent agreement is found among theory, simulation, and experiment for the various scaling exponents.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101785/1/199100016_ftp.pd

ViVaMBC: estimating viral sequence variation in complex populations from illumina deep-sequencing data using model-based clustering

Background: Deep-sequencing allows for an in-depth characterization of sequence variation in complex populations. However, technology associated errors may impede a powerful assessment of low-frequency mutations. Fortunately, base calls are complemented with quality scores which are derived from a quadruplet of intensities, one channel for each nucleotide type for Illumina sequencing. The highest intensity of the four channels determines the base that is called. Mismatch bases can often be corrected by the second best base, i.e. the base with the second highest intensity in the quadruplet. A virus variant model-based clustering method, ViVaMBC, is presented that explores quality scores and second best base calls for identifying and quantifying viral variants. ViVaMBC is optimized to call variants at the codon level (nucleotide triplets) which enables immediate biological interpretation of the variants with respect to their antiviral drug responses. Results: Using mixtures of HCV plasmids we show that our method accurately estimates frequencies down to 0.5%. The estimates are unbiased when average coverages of 25,000 are reached. A comparison with the SNP-callers V-Phaser2, ShoRAH, and LoFreq shows that ViVaMBC has a superb sensitivity and specificity for variants with frequencies above 0.4%. Unlike the competitors, ViVaMBC reports a higher number of false-positive findings with frequencies below 0.4% which might partially originate from picking up artificial variants introduced by errors in the sample and library preparation step. Conclusions: ViVaMBC is the first method to call viral variants directly at the codon level. The strength of the approach lies in modeling the error probabilities based on the quality scores. Although the use of second best base calls appeared very promising in our data exploration phase, their utility was limited. They provided a slight increase in sensitivity, which however does not warrant the additional computational cost of running the offline base caller. Apparently a lot of information is already contained in the quality scores enabling the model based clustering procedure to adjust the majority of the sequencing errors. Overall the sensitivity of ViVaMBC is such that technical constraints like PCR errors start to form the bottleneck for low frequency variant detection

Comparative proteomic analysis of normal and tumor stromal cells by tissue on chip based mass spectrometry (toc-MS)

In carcinoma tissues, genetic and metabolic changes not only occur at the tumor cell level, but also in the surrounding stroma. This carcinoma-reactive stromal tissue is heterogeneous and consists e.g. of non-epithelial cells such as fibroblasts or fibrocytes, inflammatory cells and vasculature-related cells, which promote carcinoma growth and progression of carcinomas. Nevertheless, there is just little knowledge about the proteomic changes from normal connective tissue to tumor stroma. In the present study, we acquired and analysed specific protein patterns of small stromal sections surrounding head and neck cell complexes in comparison to normal subepithelial connective tissue. To gain defined stromal areas we used laser-based tissue microdissection. Because these stromal areas are limited in size we established the highly sensitive 'tissue on chip based mass spectrometry' (toc-MS). Therefore, the dissected areas were directly transferred to chromatographic arrays and the proteomic profiles were subsequently analysed with mass spectrometry. At least 100 cells were needed for an adequate spectrum. The locating of differentially expressed proteins enables a precise separation of normal and tumor stroma. The newly described toc-MS technology allows an initial insight into proteomic differences between small numbers of exactly defined cells from normal and tumor stroma