Electrochemical treatment of Poultry Slaughterhouse Wastewater using Iron and Aluminium Electrodes

Electrochemical process for the treatment of poultry slaughterhouse wastewater (PSWW) was studied. The effects of some key factors such as initial pH, current density, operating time and the kind of electrodes on the removal of chemical oxygen demand (COD), oil and grease, total suspended solids (TSS), total kjeldahl nitrogen (TKN) and total phosphors (TP) were investigated. It is clear that the process has a good efficiency. The highest removal amount of COD (95.6) was achieved with aluminium electrode (pH value between 2 and 3 and charge passed 20.34x10(3) colons (current density 0.014 A cm(-2)). 95.3 of oil and grease was removed in the same conditions, of course, in the case of iron electrode. The maximum removal efficiency for TKN and TP were 77.8 and 89.6 respectively (pH 3, charge passed 30.51x10(3) colons and with aluminium). Consequently electrocoagulation is comparatively suitable process for PSWW treatment

Longitudinal clustering analysis and prediction of Parkinson\u27s disease progression using radiomics and hybrid machine learning

Background: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson\u27s disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. Methods: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. Results: We identified 3 distinct progression trajectories. Hotelling\u27s t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. Conclusions: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data

Robust identification of Parkinson\u27s disease subtypes using radiomics and hybrid machine learning

OBJECTIVES: It is important to subdivide Parkinson\u27s disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features. METHODS: We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson\u27s Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples. RESULTS: When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling\u27s T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations. CONCLUSION: Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features

Mechanical Properties of Boehmite Evaluated by Atomic Force Microscopy Experiments and Molecular Dynamic Finite Element Simulations

Boehmite nanoparticles show great potential in improving mechanical properties of fiber reinforced polymers. In order to predict the properties of nanocomposites, knowledge about the material parameters of the constituent phases, including the boehmite particles, is crucial. In this study, the mechanical behavior of boehmite is investigated using Atomic Force Microscopy (AFM) experiments and Molecular Dynamic Finite Element Method (MDFEM) simulations. Young's modulus of the perfect crystalline boehmite nanoparticles is derived from numerical AFM simulations. Results of AFM experiments on boehmite nanoparticles deviate significantly. Possible causes are identified by experiments on complementary types of boehmite, that is, geological and hydrothermally synthesized samples, and further simulations of imperfect crystals and combined boehmite/epoxymodels. Under certain circumstances, the mechanical behavior of boehmite was found to be dominated by inelastic effects that are discussed in detail in the present work. The studies are substantiated with accompanying X-ray diffraction and Raman experiments.DFG/FOR/202

Direct Nanoscale Imaging of Evolving Electric Field Domains in Quantum Structures

The external performance of quantum optoelectronic devices is governed by the spatial profiles of electrons and potentials within the active regions of these devices. For example, in quantum cascade lasers (QCLs), the electric field domain (EFD) hypothesis posits that the potential distribution might be simultaneously spatially nonuniform and temporally unstable. Unfortunately, there exists no prior means of probing the inner potential profile directly. Here we report the nanoscale measured electric potential distribution inside operating QCLs by using scanning voltage microscopy at a cryogenic temperature. We prove that, per the EFD hypothesis, the multi-quantum-well active region is indeed divided into multiple sections having distinctly different electric fields. The electric field across these serially-stacked quantum cascade modules does not continuously increase in proportion to gradual increases in the applied device bias, but rather hops between discrete values that are related to tunneling resonances. We also report the evolution of EFDs, finding that an incremental change in device bias leads to a hopping-style shift in the EFD boundary – the higher electric field domain expands at least one module each step at the expense of the lower field domain within the active region.Natural Sciences and Engineering Research Council of CanadaCanadian Foundation for InnovationCMC Microsystems (Firm)Ontario Research Foundatio

Assessment of Organophosphorus Pesticide Residues in Water and Sediment Collected from the Southern Caspian Sea

Pollution of water resources by uncontrolled pesticide use is a serious health and environmental issue. In this study, concentrations of three organophosphorus pesticides (diazinon, malathion, and azinphos-methyl) in water and sediment samples from five estuaries (Sefidrud, Chalus, Babolrud, Tajan, and Gorganrud) along the Caspian Sea were investigated. Samples were collected from surface water and sediment during summer to autumn, and pesticides were analysed by gas chromatography-mass spectrometry (GC-MS). Results indicated that salinity and turbidity in Gorganrud were higher (salinity: range 2–8%; turbidity: range 1–9%) compared to other stations. Higher diazinon (water: 0.08±0.06, sediment: 0.04±0.01), malathion (water: 0.09±0.06, sediment: 0.05±0.01) and azinphos-methyl (water: 0.1±0.08, sediment: 0.06± 0.02) concentrations were observed in the Tajan river compared to other stations. Mean concentrations of diazinon, malathion and azinphos-methyl pesticides were higher in the summer compared to the autumn. Azinphos-methyl concentrations were higher than sediment quality guidelines (SQGs), which warrants ongoing monitoring. Our research provides insights into the presence of organophosphate pesticides (OPs) in rivers that enter into the Caspian Sea. Further work to better understand the environmental pollution of OPs in the Caspian Sea is ongoing

Adding marrow adiposity and cortical porosity to femoral neck areal bone mineral density improves the discrimination of women with nonvertebral fractures from controls

Advancing age is accompanied by a reduction in bone formation and remodeling imbalance, which produces microstructural deterioration. This may be partly caused by a diversion of mesenchymal cells towards adipocytes rather than osteoblast lineage cells. We hypothesized that microstructural deterioration would be associated with an increased marrow adiposity, and each of these traits would be independently associated with nonvertebral fractures and improve discrimination of women with fractures from controls over that achieved by femoral neck (FN) areal bone mineral density (aBMD) alone. The marrow adiposity and bone microstructure were quantified from HR‐pQCT images of the distal tibia and distal radius in 77 women aged 40 to 70 years with a recent nonvertebral fracture and 226 controls in Melbourne, Australia. Marrow fat measurement from HR‐pQCT images was validated using direct histologic measurement as the gold standard, at the distal radius of 15 sheep, with an agreement (R2 = 0.86, p < 0.0001). Each SD higher distal tibia marrow adiposity was associated with 0.33 SD higher cortical porosity, and 0.60 SD fewer, 0.24 SD thinner, and 0.72 SD more‐separated trabeculae (all p < 0.05). Adjusted for age and FN aBMD, odds ratios (ORs) (95% CI) for fracture per SD higher marrow adiposity and cortical porosity were OR, 3.39 (95% CI, 2.14 to 5.38) and OR, 1.79 (95% CI, 1.14 to 2.80), respectively. Discrimination of women with fracture from controls improved when cortical porosity was added to FN aBMD and age (area under the receiver‐operating characteristic curve [AUC] 0.778 versus 0.751, p = 0.006) or marrow adiposity was added to FN aBMD and age (AUC 0.825 versus 0.751, p = 0.002). The model including FN aBMD, age, cortical porosity, trabecular thickness, and marrow adiposity had an AUC = 0.888. Results were similar for the distal radius. Whether marrow adiposity and cortical porosity indices improve the identification of women at risk for fractures requires validation in prospective studies. © 2019 American Society for Bone and Mineral Research

Associations between the lipid profile and the lumbar spine bone mineral density and trabecular bone score in elderly Iranian individuals participating in the Bushehr Elderly Health Program: a population-based study

Summary: We hypothesized that the lipid profile or dyslipidemia may have an influence on the bone mineral density and bone microstructure in an elderly Iranian population. The results of this study showed some significant associations between the serum lipid levels and the lumbar spine and femoral areal bone mineral densities and the trabecular bone score (TBS). Purpose: Serum lipid abnormalities are possible risk factors for cardiovascular diseases and osteoporosis. Our aim was to evaluate the associations between the lipid profile and the areal bone mineral density (aBMD) and trabecular bone score in an elderly Iranian population. Methods: The study subjects included 2426 elderly women and men participating in the second stage of the Bushehr Elderly Health program, a population-based prospective cohort study. The aBMDs of the lumbar spine and femoral neck and the lumbar spine texture were measured using dual-energy X-ray absorptiometry and the TBS algorithm, respectively. The associations between the lipid profiles and the aBMDs and TBSs were examined using multivariable linear regression analyses stratified by sex and adjusted for potential confounders. Results: In men, we found negative correlations between the lumbar spine aBMD and TBS and the total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels (TC: p < 0.001 and p < 0.006, HDL-C: p = 0.002 and p = 0.004, and LDL-C: p < 0.001 and p < 0.009, respectively). However, only the HDL-C level was negatively associated with the aBMD in women (p = 0.016). A positive and statistically significant correlation was found between the serum triglyceride (TG) level and the aBMD in the women (p < 0.001). The TG level and the TBS were not statistically significantly correlated in either sex, and the TBS was not correlated with any of the lipid values in women. Conclusion: The results of this study showed some significant but generally weak associations between the lipid profile and the aBMD. The associations that were significant for both the men and the women included positive associations between the TG level and the femoral neck aBMD, as well as the HDL-C level and the femoral neck and lumbar spine aBMD

Grand multiparity associations with low bone mineral density and degraded trabecular bone pattern

Introduction: Pregnancy is associated with changes in bone remodeling and calcium metabolism, which may increase the risk of fragility fracture after menopause. We hypothesized that in postmenopausal women, with history of grand multiparity, the magnitude of trabecular bone deterioration is associated with number of deliveries. Methods: 1217 women aged 69.2 ± 6.4 years, from the Bushehr Elderly Health (BEH) program were recruited. The areal bone mineral density (aBMD) of the lumbar spine and femoral neck and trabecular bone score (TBS) of 916 postmenopausal women, with grand multiparity defined as more than 4 deliveries, were compared with those of 301 postmenopausal women with 4 or fewer deliveries. The association of multiparity with aBMDs and TBS were evaluated after adjustment for possible confounders including age, years since menopause, body mass index, and other relevant parameters. Results: The aBMD of femoral neck (0.583 ± 0.110 vs. 0.603 ± 0.113 g/cm2), lumbar spine (0.805 ± 0.144 vs. 0.829 ± 0.140 g/cm2) and TBS (1.234 ± 0.086 vs. 1.260 ± 0.089) were significantly lower in women with history of grand multiparity than others. In the multiple regression analysis, after adjusting for confounders, the negative association did persist for lumbar spine aBMD (beta = −0.02, p value = 0.01), and the TBS (beta = −0.01, p value = 0.03), not for femoral neck aBMD. Conclusion: We infer that grand multiparity have deleterious effects on the aBMD and the trabecular pattern of the lumbar spine