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Introduction: The vertebral bone quality (VBQ) score that is based on non-contrast enhanced T1-weighted MRI was recently introduced as a novel measure of bone quality in the lumbar spine and shown to be a significant predictor of healthy versus osteopenic/osteoporotic bone. Research question: This study aimed to assess possible correlations between the VBQ score and the functional cross-sectional area (FCSA) of psoas and lumbar spine extensor muscles. Material and methods: Patients who underwent fusion surgery between 2014 and 2017 and had lumbar MRI and CT scans within 6 months prior to surgery were included. The FCSA was assessed at L3-L5 using a pixel intensity threshold method. The VBQ score was calculated by dividing the signal intensity (SI) of the vertebrae L1-L4 through the SI of the cerebrospinal fluid at L3. Volumetric bone mineral density (vBMD) was assessed by quantitative CT. Results: 80 patients (58.8% female, median age 68.8 years) were included. Overall prevalence of osteopenia/osteoporosis was 66.3%, with no significant differences between men and women. The mean (SD) VBQ score was significantly smaller in men, at 2.26 (0.45) versus women at 2.59 (0.39) (p = 0.001). After adjusting for age and BMI, a significant negative correlation was seen between the VBQ score and psoas FCSA at L3 (β = −0.373; p = 0.022), but only in men. Conclusion: Our results highlight sex differences in the VBQ score that were not demonstrated by vBMD and suggest a potential role of this novel measure to assess not only bone quality, but also spinal muscle quantity.

Background: Long-term diabetes mellitus may disrupt skin homeostasis resulting in dermatological disorders. These manifestations may be predictors of diabetes and markers for the progression of the disease or the effectiveness of therapeutic management.The aim of the work: This study was carried out to assess the prevalence of cutaneous disorders in diabetic patients and to determine the associations between cutaneous disorders and the patient's risk factors in the Damietta governorate.Patients and Methods: This cross-sectional study was conducted on 947 diabetic patients. All the study participants were subjected to a questionnaire for socio-demographic characteristics, a detailed medical history, dermatological examination, and assessing body mass index [BMI] and glycated hemoglobin.Results: Of the total of 947 diabetic patients examined for cutaneous manifestations, 595[62.8%] had one or more diabetes associated cutaneous disorders. Common cutaneous manifestations were cutaneous infections [27%], acrochordons [19.2%], pruritus [18.2%], xerosis [16.6%], acanthosis nigricans [16.2%], scleroderma-like skin changes [8.2%], and diabetic dermopathy [7.7%]. Longer duration of diabetes, poor controlled hemoglobin A1C and obesity were independent risk factors of Skin manifestations strongly associated with our studied diabetic patients.Conclusion: As patients with diabetes have a high prevalence of cutaneous disorders, that can emerge prior to the diagnosis of diabetes and may reflect glycemic control, awareness of these manifestations will aid in the prompt and comprehensive care of both dermatoses and diabetes.

Coronavirus disease 2019 (COVID-19) has been reported to affect nearly all body systems. Kidney affection has been observed in several studies. The effect of COVID-19 on renal function is beyond that occurring in pneumonia or severe respiratory distress cases. Renal affection is attributed to several factors, including the mechanism of viral injury. Patients with preexisting kidney injury are at increased risk of infection. Early detection and management are crucial to avoid morbidity and mortality, prevent the spread and contamination of hemodialysis Units. Early detection and treatment of kidney involvement in COVID-19 are vital to avoid increased morbidity and mortality. Proper selection of drugs and fluid management is vital in cases with kidney involvement. This review aims to discuss the clinical and pathophysiological affection of the kidney in COVID-19.

Background and aim Diabetes mellitus is a metabolic disorder affecting a wide range of the population all over the world. Microvascular and macrovascular complications are very common in diabetic patients with high incidence of affection of the coronary arteries. Rapid and efficient management in cases (especially diabetics) complaining of chest pain at the emergency department is a matter of great importance to decrease the incidence of subsequent complications. Our aim was to predict coronary artery diseases in type-2 diabetic patients with typical chest pain with normal initial cardiac investigations at the emergency room by using computed tomography (CT) coronary angiography. Patients and methods A total number of 100 patients were included in this study and classified into two groups: 80 patients with diabetes mellitus (group A) and 20 nondiabetic patients (group B) presented with chest pain. Complete history was taken from the cases with performing of complete physical examination, and routine laboratory and radiological investigations. Additionally, echocardiography and multislice CT-coronary angiography (coronary computed tomography angiography) were done for all cases. Also, calcium score was calculated. Results CT angiography revealed affection in 19 (23.8%) cases of the cases in group A with no affected cases in group B with a statistically significant difference between the two groups. There was a high statistically significant agreement between the findings of the CT angiography and coronary angiography results (P

Cooperative learning (CL) refers to teaching methods in which students work in small groups to help one another learn and improve their learning outcomes. Often CL is described by five basic elements: (1) positive interdependence, (2) individual accountability, (3) promotive interaction, (4) social skills and (5) group processing. The positive effects of CL have been extensively documented. The quality of implementation, mostly determined by application of the five basic elements of CL, has been shown to be significantly related to the effectiveness of the methods. However, due to the complex demands that designing CL sequences places on teachers, the question of how and why they implement CL methods is not trivial. The present study used an explanatory mixed methods design with sequential phases (quantitative–qualitative) to investigate the implementation of CL in school practice. A survey, structured interviews with teachers and classroom observations rated on an observation scale including indicators of the basic elements of CL were used to gather data in a total of 49 German classrooms. Results show that the implementation quality of CL lessons was rather low. Only 7% of the observed teachers implemented the basic elements. Even group goals and individual accountability, the two most important elements of CL, were implemented in only 17% of the lessons observed. Survey results indicated that implementation quality is related to teachers' evaluation of CL with regard to its appropriateness for different learning goals (r =.40*) and diverse students (r =.36*). Qualitative analysis of the teacher interviews analysed by thematic coding showed differences between teachers with high and low implementation quality regarding their beliefs. Teachers with high implementation quality see more value in social learning processes and feel more responsible for the success of CL. The results show a theory–practice gap and point to the relevance of beliefs for CL implementation. [ABSTRACT FROM AUTHOR]

Study Design. Retrospective longitudinal study. Objective. To investigate the association between lumbar intervertebral disk degeneration (DD) and the vertebral bone quality (VBQ) score. Background. The VBQ score that is based on magnetic resonance imaging has been proposed as a measure of lumbar spine bone quality and is a significant predictor of healthy versus osteoporotic bone. However, the role of segmental contributing factors on VBQ is unknown. Methods. Nonsurgical patients who underwent repeated lumbar magnetic resonance imaging scans, at least three years apart primarily for low back pain were retrospectively included. VBQ was assessed as previously described. DD was assessed using the Pfirrmann grading (PFG) scale. PFG grades were summarized as PFGL1-4 for the upper three lumbar disk levels, as PFGL4-S1 for the lower two lumbar disc levels, and as PFGL1-S1 for all lumbar disc levels. Multivariable linear mixed models were used with adjustments for age, sex, race, body mass index, and the clustering of repeated measurements. Results. A total of 350 patients (54.6% female, 85.4% White) were included in the final analysis, with a median age at baseline of 60.1 years and a body mass index of 25.8 kg/m2. VBQ significantly increased from 2.28 at baseline to 2.36 at follow-up (P = 0.001). In the unadjusted analysis, a significant positive correlation was found between PFGL1-4, PFGL1-S1, and VBQ at baseline (P < 0.05) that increased over time (P < 0.005). In the adjusted multivariable analysis, PFGL1-4 (β = -0.0195; P = 0.021), PFGL4-S1 (β = -0.0310; P = 0.007), and PFGL1-S1 (β = -0.0160; P = 0.012) were independently and negatively associated with VBQ. Conclusions. More advanced and long-lasting DD is associated with lower VBQ indicating less bone marrow fat content and potentially stronger bone. VBQ score as a marker of bone quality seems affected by DD. [ABSTRACT FROM AUTHOR]

Deep learning architectures are being increasingly adopted for human activity recognition using radar technology. A majority of these architectures are based on convolutional neural networks (CNNs) and accept radar micro-Doppler signatures as input. The state-of-the-art CNN-based models employ batch normalization (BN) to optimize network training and improve generalization. In this paper, we present whitening-aided CNN models for classifying human activities with radar sensors. We replace BN layers in a CNN model with whitening layers, which is shown to improve the model’s accuracy by not only centering and scaling activations, similar to BN, but also decorrelating them. We also exploit the rotational freedom afforded by whitening matrices to align the whitened activations in the latent space with the corresponding activity classes. Using real data measurements of six different activities, we show that whitening provides superior performance over BN in terms of classification accuracy for a CNN-based classifier. This demonstrates the potential of whitening-aided CNN models to provide enhanced human activity recognition with radar sensors.

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since 2014 July. This paper describes the second data release from this phase, and the 14th from SDSS overall (making this Data Release Fourteen or DR14). This release makes the data taken by SDSS-IV in its first two years of operation (2014-2016 July) public. Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey; the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data-driven machine-learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from the SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS web site (www.sdss.org) has been updated for this release and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020 and will be followed by SDSS-V.

We present an anisotropic analysis of baryon acoustic oscillation (BAO) signal from the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 14 quasar sample. The sample consists of 147 000 quasars distributed over a redshift range of 0.8 < z < 2.2. We apply the redshift weights technique to the clustering of quasars in this sample and achieve a 4.6 per cent measurement of the angular distance measurement DM at z = 2.2 and Hubble parameter H at z = 0.8. We parametrize the distance-redshift relation, relative to a fiducial model, as a Taylor series. The coefficients of this expansion are used to reconstruct the distance-redshift relation and obtain distance and Hubble parameter measurements at all redshifts within the redshift range of the sample. Reporting the result at two characteristic redshifts, we determine DM(z = 1) = 3405 ± 305 (rd/rd, fid)Mpc, H(z = 1) = 120.7 ± 7.3 (rd, fid/rd) kms-1 Mpc-1 andDM(z=2)=5325±249 (rd/rd, fid)Mpc, H(z = 2) = 189.9 ± 32.9 (rd, fid/rd) km s-1 Mpc-1. These measurements are highly correlated. We assess the outlook of BAO analysis from the final quasar sample by testing the method on a set of mocks that mimic the noise level in the final sample. We demonstrate on these mocks that redshift weighting shrinks the measurement error by over 25 per cent on average. We conclude redshift weighting can bring us closer to the cosmological goal of the final quasar sample.

This study seeks to achieve the main objective, which is to identify Free software for detecting plagiarism of texts in the digital environment and to know the effectiveness of software for detecting plagiarism in the digital environment to do so.

Abstract Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium’s therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.

Abstract Ecologists have long debated the properties that confer stability to complex, species‐rich ecological networks. Species‐level soil food webs are large and structured networks of central importance to ecosystem functioning. Here, we conducted an analysis of the stability properties of an up‐to‐date set of theoretical soil food web models that account both for realistic levels of species richness and the most recent views on the topological structure (who is connected to whom) of these food webs. The stability of the network was best explained by two factors: strong correlations between interaction strengths and the blocked, nonrandom trophic structure of the web. These two factors could stabilize our model food webs even at the high levels of species richness that are typically found in soil, and that would make random systems very unstable. Also, the stability of our soil food webs is well‐approximated by the cascade model. This result suggests that stability could emerge from the hierarchical structure of the functional organization of the web. Our study shows that under the assumption of equilibrium and small perturbations, theoretical soil food webs possess a topological structure that allows them to be complex yet more locally stable than their random counterpart. In particular, results strongly support the general hypothesis that the stability of rich and complex soil food webs is mostly driven by correlations in interaction strength and the organization of the soil food web into functional groups. The implication is that in real‐world food web, any force disrupting the functional structure and distribution pattern of interaction strengths (i.e., energy fluxes) of the soil food webs will destabilize the dynamics of the system, leading to species extinction and major changes in the relative abundances of species.

Abstract Background Chronic obstructive pulmonary disease (COPD) is accompanied by substantial systemic dysregulations that comprise systemic inflammation and neurohormonal activation in addition to many neurological involvements, such as cerebrovascular diseases, polyneuropathies, motor neuron diseases, and cognitive impairment has been described in COPD patients. The aim of the current work was to detect the subclinical affection of the peripheral nervous system in patients with stable COPD. Results Forty COPD patients and 30 subjects as healthy control were enrolled. All included subjects were submitted to complete medical history, clinical evaluations, investigations in the form of; arterial blood gases, spirometry, in addition to nerve conduction study. The patients with COPD were classified according to GOLD criteria for severity in grade I in 7.5% grade II in 62.5%, grade III in 20%, and grade IV in 10%. The results show a statistically significant increase in distal latency and a statistically significant decrease of amplitude and conduction velocity in patients with COPD when compared to the examined nerves of control group. The demyelinating nerve affection was the most common. Conclusion In COPD patients the peripheral nervous system could be affected subclinically once the severity of COPD increased and the patient should be neurophysiologically observed for early recognition of peripheral nervous system affection.

Abstract Adjacent segment disorders are prevalent in patients following a spinal fusion surgery. Postoperative alterations in the adjacent segment biomechanics play a role in the etiology of these conditions. While experimental approaches fail to directly quantify spinal loads, previous modeling studies have numerous shortcomings when simulating the complex structures of the spine and the pre/postoperative mechanobiology of the patient. The biomechanical effects of the L4–L5 fusion surgery on muscle forces and adjacent segment kinetics (compression, shear, and moment) were investigated using a validated musculoskeletal model. The model was driven by in vivo kinematics for both preoperative (intact or severely degenerated L4–L5) and postoperative conditions while accounting for muscle atrophies. Results indicated marked changes in the kinetics of adjacent L3–L4 and L5–S1 segments (e.g., by up to 115% and 73% in shear loads and passive moments, respectively) that depended on the preoperative L4–L5 disc condition, postoperative lumbopelvic kinematics and, to a lesser extent, postoperative changes in the L4–L5 segmental lordosis and muscle injuries. Upper adjacent segment was more affected post-fusion than the lower one. While these findings identify risk factors for adjacent segment disorders, they indicate that surgical and postoperative rehabilitation interventions should focus on the preservation/restoration of patient’s normal segmental kinematics.

Abstract In this pandemic SARS-CoV-2 crisis, any attempt to contain and eliminate the virus will also stop its spread and consequently decrease the risk of severe illness and death. While ozone treatment has been suggested as an effective disinfection process, no precise mechanism of action has been previously reported. This study aimed to further investigate the effect of ozone treatment on SARS-CoV-2. Therefore, virus collected from nasopharyngeal and oropharyngeal swab and sputum samples from symptomatic patients was exposed to ozone for different exposure times. The virus morphology and structure were monitored and analyzed through Atomic Force Microscopy (AFM), Transmission Electron Microscopy (TEM), Atomic Absorption Spectroscopy (AAS), and ATR-FTIR. The obtained results showed that ozone treatment not only unsettles the virus morphology but also alters the virus proteins’ structure and conformation through amino acid disturbance and Zn ion release from the virus non-structural proteins. These results could provide a clearer pathway for virus elimination and therapeutics preparation.

In this paper, we introduce and analytically demonstrate a novel biosensor based on the light-matter interaction in a classic topological photonic crystal (PC) heterostructure, which consists of two opposite-facing 5-period PCs separated by a microfluidic channel. Because of the excitation of topological edge mode (TEM) at the interface of the two PCs, the strong coupling between incident light and TEM produces a high-quality resonance peak, which can be used to detect very small changes in the refractive index of biomaterials such as Jurkat Cells inside the microfluidic channel. The proposed biosensor has a sensitivity as high as 240 nm/RIU and figure of merit (FOM) higher than 250.

Given the world’s growing population and energy demand, modern methods are developed to contribute to generating alternative energies. They aim to maintain the renewability of the supplied energy and decrease environmental contaminations. Biogas is a renewable energy carrier that has recently been under consideration in Iran. One objective of such plans is to find proper locations for installing and running the existing potentials and infrastructures. In this paper, Tehran, Iran is selected as the study area which is ranked the 1st in population density and proper infrastructures available here are accessible. According to the widespread poultry and cow-breading farms in this province, bovine and aviculture excreta are considered as raw materials in producing biogas. An inference network was established in this research for evaluating the process taking into account the infrastructural parameters, geomorphological constraints, resource availability factors, and limiting parameters such as protected/prohibited areas.In this paper, the fuzzy method was used to standardize the data and the fuzzy-analytical hierarchy process method was employed to weight the locating criteria in the geographical information system. The evaluation outcomes suggested certain zones in southern parts of the province in which the industrial livestock farms become frequently widespread and the suburb areas of smaller cities on the eastern part of the province are the most proper areas for this purpose.

Background & aim: Human papillomavirus is the most common sexually transmitted disease and the main risk factor for malignant and malignant lesions in the cervix. The aim of the present study was to determine and evaluate the prevalence of human papillomavirus in women with abnormal pap smear referred to Shahid Mofatteh specialized clinic by Multiplex PCR & Reverse Dot Blot Hybridization in Yasuj, Iran. Methods: The present descriptive-analytical study was conducted during the years 2015-2016. A total of 67 abnormal cervical cytology specimens including 51 ASC-US specimens, 9 LSIL specimens and 7 HSIL specimens were collected and analyzed by Multiplex PCR & Reverse Dot Blot Hybridization. The collected data were analyzed using chi-square test Result: The overall prevalence of human papillomavirus (HPV) was 61.2%. 36 (87.8%) women were infected with high-risk types of human papillomavirus, 26 (63.4%) with low-risk types and 2 (4.9%) with unknown types. The most common high-risk types were HPV 39 (43.9%), HPV66 (31.7%), HPV31 (26.8%) and HPV18 (14.6%), respectively. HPV16 was detected in only 2 samples (4.9%). Conclusion: The results of the present study indicated a high prevalence of papillomavirus infection in women with abnormal Pap smear. In addition, different high-risk genotypes of this virus were identified compared to other studies in Iran. Further samples from wider geographical areas are needed to study the molecular epidemiology and genotypes of human papillomavirus.