Lasting effects of workplace strength training for neck/shoulder/arm pain among laboratory technicians:Natural experiment with 3-year follow-up

Objectives. This study investigated long-term effects and implementation processes of workplace strength training for musculoskeletal disorders. Methods. 333 and 140 laboratory technicians from private and public sector companies, respectively, replied to a 3-year follow-up questionnaire subsequent to a 1-year randomized controlled trial (RCT) with high-intensity strength training for prevention and treatment of neck, shoulder, and arm pain. Being a natural experiment, the two participating companies implemented and modified the initial training program in different ways during the subsequent 2 years after the RCT. Results. At 3-year follow-up the pain reduction in neck, shoulder, elbow, and wrist achieved during the first year was largely maintained at both companies. However, the private sector company was rated significantly better than the public sector company in (1) training adherence, (2) training culture, that is, relatively more employees trained at the workplace and with colleagues, (3) self-reported health changes, and (4) prevention of neck and wrist pain development among initially pain-free employees. Conclusions. This natural experiment shows that strength training can be implemented successfully at different companies during working hours on a long-term basis with lasting effects on pain in neck, shoulder, and arm

Improved detection of chemical substances from colorimetric sensor data using probabilistic machine learning

We present a data-driven machine learning approach to detect drug- and explosives-precursors using colorimetric sensor technology for air-sampling. The sensing technology has been developed in the context of the CRIM-TRACK project. At present a fullyintegrated portable prototype for air sampling with disposable sensing chips and automated data acquisition has been developed. The prototype allows for fast, user-friendly sampling, which has made it possible to produce large datasets of colorimetric data for different target analytes in laboratory and simulated real-world application scenarios. To make use of the highly multi-variate data produced from the colorimetric chip a number of machine learning techniques are employed to provide reliable classification of target analytes from confounders found in the air streams. We demonstrate that a data-driven machine learning method using dimensionality reduction in combination with a probabilistic classifier makes it possible to produce informative features and a high detection rate of analytes. Furthermore, the probabilistic machine learning approach provides a means of automatically identifying unreliable measurements that could produce false predictions. The robustness of the colorimetric sensor has been evaluated in a series of experiments focusing on the amphetamine pre-cursor phenylacetone as well as the improvised explosives pre-cursor hydrogen peroxide. The analysis demonstrates that the system is able to detect analytes in clean air and mixed with substances that occur naturally in real-world sampling scenarios. The technology under development in CRIM-TRACK has the potential as an effective tool to control traf- ficking of illegal drugs, explosive detection, or in other law enforcement applications.EU FP7 Grant Agreement Number 31320

Predicting physiological imbalance in Holstein dairy cows by three different sets of milk biomarkers

Blood biomarkers may be used to detect physiological imbalance and potential disease. However, blood sampling is difficult and expensive, and not applicable in commercial settings. Instead, individual milk samples are readily available at low cost, can be sampled easily and analysed instantly. The present observational study sampled blood and milk from 234 Holstein dairy cows from experimental herds in six European countries. The objective was to compare the use of three different sets of milk biomarkers for identification of cows in physiological imbalance and thus at risk of developing metabolic or infectious diseases. Random forests was used to predict body energy balance (EBAL), index for physiological imbalance (PI-index) and three clusters differentiating the metabolic status of cows created on basis of concentrations of plasma glucose, β-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA) and serum IGF-1. These three metabolic clusters were interpreted as cows in balance, physiological imbalance and “intermediate cows” with physiological status in between. The three sets of milk biomarkers used for prediction were: milk Fourier transform mid-IR (FT-MIR) spectra, 19 immunoglobulin G (IgG) N-glycans and 8 milk metabolites and enzymes (MME). Blood biomarkers were sampled twice; around 14 days after calving (days in milk (DIM)) and around 35 DIM. MME and FT-MIR were sampled twice weekly 1−50 DIM whereas IgG N-glycan were measured only four times. Performances of EBAL and PI-index predictions were measured by coefficient of determination (R2cv) and root mean squared error (RMSEcv) from leave-one-cow-out cross-validation (cv). For metabolic clusters, performance was measured by sensitivity, specificity and global accuracy from this cross-validation. Best prediction of PI-index was obtained by MME (R2cv = 0.40 (95 % CI: 0.29−0.50) at 14 DIM and 0.35 (0.23−0.44) at 35 DIM) while FT-MIR showed a better performance than MME for prediction of EBAL (R2cv = 0.28 (0.24−0.33) vs 0.21 (0.18−0.25)). Global accuracies of predicting metabolic clusters from MME and FT-MIR were at the same level ranging from 0.54 (95 % CI: 0.39−0.68) to 0.65 (0.55−0.75) for MME and 0.51 (0.37−0.65) to 0.68 (0.53−0.81) for FT-MIR. R2cv and accuracies were lower for IgG N-glycans. In conclusion, neither EBAL nor PI-index were sufficiently well predicted to be used as a management tool for identification of risk cows. MME and FT-MIR may be used to predict the physiological status of the cows, while the use of IgG N-glycans for prediction still needs development. Nevertheless, accuracies need to be improved and a larger training data set is warranted

Detection of large deletions in the LDL receptor gene with quantitative PCR methods

BACKGROUND: Familial Hypercholesterolemia (FH) is a common genetic disease and at the molecular level most often due to mutations in the LDL receptor gene. In genetically heterogeneous populations, major structural rearrangements account for about 5% of patients with LDL receptor gene mutations. METHODS: In this study we tested the ability of two different quantitative PCR methods, i.e. Real-Time PCR and Multiplex Ligation-Dependent Probe Amplification (MLPA), to detect deletions in the LDL receptor gene. We also reassessed the contribution of major structural rearrangements to the mutational spectrum of the LDL receptor gene in Denmark. RESULTS: With both methods it was possible to discriminate between one and two copies of the LDL receptor gene exon 5, but the MLPA method was cheaper, and it was far more accurate and precise than Real-Time PCR. In five of 318 patients with an FH phenotype, MLPA analysis revealed five different deletions in the LDL receptor gene. CONCLUSION: The MLPA method was accurate, precise and at the same time effective in screening a large number of FH patients for large deletions in the LDL receptor gene

Genomic characterization of five deletions in the LDL receptor gene in Danish Familial Hypercholesterolemic subjects

BACKGROUND: Familial Hypercholesterolemia is a common autosomal dominantly inherited disease that is most frequently caused by mutations in the gene encoding the receptor for low density lipoproteins (LDLR). Deletions and other major structural rearrangements of the LDLR gene account for approximately 5% of the mutations in many populations. METHODS: Five genomic deletions in the LDLR gene were characterized by amplification of mutated alleles and sequencing to identify genomic breakpoints. A diagnostic assay based on duplex PCR for the exon 7 – 8 deletion was developed to discriminate between heterozygotes and normals, and bioinformatic analyses were used to identify interspersed repeats flanking the deletions. RESULTS: In one case 15 bp had been inserted at the site of the deleted DNA, and, in all five cases, Alu elements flanked the sites where deletions had occurred. An assay developed to discriminate the wildtype and the deletion allele in a simple duplex PCR detected three FH patients as heterozygotes, and two individuals with normal lipid values were detected as normal homozygotes. CONCLUSION: The identification of the breakpoints should make it possible to develop specific tests for these mutations, and the data provide further evidence for the role of Alu repeats in intragenic deletions

Density functional theory based screening of ternary alkali-transition metal borohydrides: A computational material design project

The dissociation of molecules, even the most simple hydrogen molecule, cannot be described accurately within density functional theory because none of the currently available functionals accounts for strong on-site correlation. This problem led to a discussion of properties that the local Kohn-Sham potential has to satisfy in order to correctly describe strongly correlated systems. We derive an analytic expression for the nontrivial form of the Kohn-Sham potential in between the two fragments for the dissociation of a single bond. We show that the numerical calculations for a one-dimensional two-electron model system indeed approach and reach this limit. It is shown that the functional form of the potential is universal, i.e., independent of the details of the two fragments.We acknowledge funding by the Spanish MEC (Grant No. FIS2007-65702-C02-01), “Grupos Consolidados UPV/EHU del Gobierno Vasco” (Grant No. IT-319-07), and the European Community through e-I3 ETSF project (Grant Agreement No. 211956).Peer reviewe