Fruit and vegetable intake and risk of incident of type 2 diabetes: results from the consortium on health and ageing network of cohorts in Europe and the United States (CHANCES)

BACKGROUND/OBJECTIVES: There is limited information to support definitive recommendations concerning the role of diet in the development of type 2 Diabetes mellitus (T2DM). The results of the latest meta-analyses suggest that an increased consumption of green leafy vegetables may reduce the incidence of diabetes, with either no association or weak associations demonstrated for total fruit and vegetable intake. Few studies have, however, focused on older subjects.

SUBJECTS/METHODS: The relationship between T2DM and fruit and vegetable intake was investigated using data from the NIH-AARP study and the EPIC Elderly study. All participants below the age of 50 and/or with a history of cancer, diabetes or coronary heart disease were excluded from the analysis. Multivariate logistic regression analysis was used to calculate the odds ratio of T2DM comparing the highest with the lowest estimated portions of fruit, vegetable, green leafy vegetables and cabbage intake.

RESULTS: Comparing people with the highest and lowest estimated portions of fruit, vegetable or green leafy vegetable intake indicated no association with the risk of T2DM. However, although the pooled OR across all studies showed no effect overall, there was significant heterogeneity across cohorts and independent results from the NIH-AARP study showed that fruit and green leafy vegetable intake was associated with a reduced risk of T2DM OR 0.95 (95% CI 0.91,0.99) and OR 0.87 (95% CI 0.87,0.90) respectively.

CONCLUSIONS: Fruit and vegetable intake was not shown to be related to incident T2DM in older subjects. Summary analysis also found no associations between green leafy vegetable and cabbage intake and the onset of T2DM. Future dietary pattern studies may shed light on the origin of the heterogeneity across populations.European Journal of Clinical Nutrition advance online publication, 17 August 2016; 

Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al 2 O 3 /SiO 2, Na 2 O/Al 2 O 3, Na 2 O/H 2 O, and Na/[Na+K], more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated alumina-silica natural products using alkaline hydroxide, and alkaline silicate. The research results are in agreement with mechanism of geopolymerization.


Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)MT.1943-5533.0000829

An overview of the effect of probiotics and exercise on mood and associated health conditions

The present paper provides a review of the current knowledge relating to the health benefits of probiotics, specially focused on the effects they may have together with physical exercise on mood disorders and related chronic medical conditions. With both these conditions being a substantial contributor to the global disease burden any alternative therapy must be considered. Probiotics influence the gut microbiota through a complex network of events which can influence mechanisms leading to development of mood disorders such as depression and anxiety. Similarly, through a complex interaction between psychological and neurobiological mechanisms, exercise has been found to play a key role in mood enhancement.

TRIB2 in normal and malignant hematopoiesis - a transcription factor network

Hematopoiesis is the tightly controlled and complex process in which the entire blood system is formed and maintained by a rare pool of hematopoietic stem cells (HSCs), and its dysregulation results in the formation of leukaemia. TRIB2, a member of the Tribbles family of serine/threonine pseudokinases, has been implicated in a variety of cancers and is a potent murine oncogene that induces acute myeloid leukaemia (AML) in vivo via modulation of the essential myeloid transcription factor CCAAT-enhancer binding protein α (C/EBPα). C/EBPα, which is crucial for myeloid cell differentiation, is commonly dysregulated in a variety of cancers, including AML. Two isoforms of C/EBPα exist - the full-length p42 isoform, and the truncated oncogenic p30 isoform. TRIB2 has been shown to selectively degrade the p42 isoform of C/EBPα and induce p30 expression in AML. In this study, overexpression of the p30 isoform in a bone marrow transplant (BMT) leads to perturbation of myelopoiesis, and in the presence of physiological levels of p42, this oncogene exhibited weak transformative ability. It was also shown by BMT that despite their degradative relationship, expression of C/EBPα was essential for TRIB2 mediated leukaemia. A conditional mouse model was used to demonstrate that oncogenic p30 cooperates with TRIB2 to reduce disease latency, only in the presence of p42. At the molecular level, a ubiquitination assay was used to show that TRIB2 degrades p42 by K48-mediated proteasomal ubiquitination and was unable to ubiquitinate p30. Mutation of a critical lysine residue in the C-terminus of C/EBPα abrogated TRIB2 mediated C/EBPα ubiquitination suggesting that this site, which is frequently mutated in AML, is the site at which TRIB2 mediates its degradative effects. The TRIB2-C/EBPα axis was effectively targeted by proteasome inhibition. AML is a very difficult disease to target therapeutically due to the extensive array of chromosomal translocations and genetic aberrations that contribute to the disease. The cell from which a specific leukaemia arises, or leukaemia initiating cell (LIC), can affect the phenotype and chemotherapeutic response of the resultant disease. The LIC has been elucidated for some common oncogenes but it is unknown for TRIB2. The data presented in this thesis investigate the ability of the oncogene TRIB2 to transform hematopoietic stem and progenitor cells in vitro and in vivo. TRIB2 overexpression conferred in vitro serially replating ability to all stem and progenitor cells studied. Upon transplantation, only TRIB2 overexpressing HSCs and granulocyte/macrophage progenitors (GMPs) resulted in the generation of leukaemia in vivo. TRIB2 induced a mature myeloid leukaemia from the GMP, and a mixed lineage leukaemia from the HSC. As such the role of TRIB2 in steady state hematopoiesis was also explored using a Trib2-/- mouse and it was determined that loss of Trib2 had no effect on lineage distribution in the hematopoietic compartment under steady-state conditions. The process of hematopoiesis is controlled by a host of lineage restricted transcription factors. Recently members of the Nuclear Factor 1 family of transcription factors (NFIA, NFIB, NFIC and NFIX) have been implicated in hematopoiesis. Little is known about the role of NFIX in lineage determination. Here we describe a novel role for NFIX in lineage fate determination. In human and murine datasets the expression of Nfix was shown to decrease as cells differentiated along the lymphoid pathway. NFIX overexpression resulted in enhanced myelopoiesis in vivo and in vitro and a block in B cell development at the pre-pro-B cell stage. Loss of NFIX resulted in disruption of myeloid and lymphoid differentiation in vivo. These effects on stem and progenitor cell fate correlated with changes in the expression levels of key transcription factors involved in hematopoietic differentiation including a 15-fold increase in Cebpa expression in Nfix overexpressing cells. The data presented support a role for NFIX as an important transcription factor influencing hematopoietic lineage specification. The identification of NFIX as a novel transcription factor influencing lineage determination will lead to further study of its role in hematopoiesis, and contribute to a better understanding of the process of differentiation. Elucidating the relationship between TRIB2 and C/EBPα not only impacts on our understanding of the pathophysiology of AML but is also relevant in other cancer types including lung and liver cancer. Thus in summary, the data presented in this thesis provide important insights into key areas which will facilitate the development of future therapeutic approaches in cancer treatment.

Effect of the cation on the interactions between alkyl methyl imidazolium chloride ionic liquids and water

A systematic study of the interactions between water and alkyl methyl imidazolium chloride ionic liquids at 298.2 K, based on activity coefficients estimated from water activity measurements in the entire solubility range, is presented. The results show that the activity coefficients of water in the studied ILs are controlled by the hydrophilicity of the cation and the cation-anion interaction. To achieve a deeper understanding on the interactions between water and the ILs, COSMO-RS and FTIR spectroscopy were also applied. COSMO-RS was used to predict the activity coefficient of water in the studied ionic liquids along with the excess enthalpies, suggesting the formation of complexes between three molecules of water and one IL molecule. On the basis of quantum-chemical calculations, it is found that cation-anion interaction plays an important role upon the ability of the IL anion to interact with water. The changes in the peak positions/band areas of OH vibrational modes of water as a function of IL concentration were investigated, and the impact of the cation on the hydrogen-bonding network of water is identified and discussed.

Time-Reversal Massive Multipath Effect and Bandwidth Heterogeneity

The proliferation of new mobile communication devices, such as smartphones and tablets, has led to an exponential growth in network traffic. The demand for supporting the fast-growing consumer data rates urges the wireless service providers and researchers to seek a new efficient radio access technology, which is the so-called 5G technology, beyond what current 4G LTE can provide. On the other hand, ubiquitous RFID tags, sensors, actuators, mobile phones and etc. cut across many areas of modern-day living, which offers the ability to measure, infer and understand the environmental indicators. The proliferation of these devices creates the term of the Internet of Things (IoT). For the researchers and engineers in the field of wireless communication, the exploration of new effective techniques to support 5G communication and the IoT becomes an urgent task, which not only leads to fruitful research but also enhance the quality of our everyday life. Massive MIMO, which has shown the great potential in improving the achievable rate with a very large number of antennas, has become a popular candidate. However, the requirement of deploying a large number of antennas at the base station may not be feasible in indoor scenarios. Does there exist a good alternative that can achieve similar system performance to massive MIMO for indoor environment? In this dissertation, we address this question by proposing the time-reversal technique as a counterpart of massive MIMO in indoor scenario with the massive multipath effect. It is well known that radio signals will experience many multipaths due to the reflection from various scatters, especially in indoor environments. The traditional TR waveform is able to create a focusing effect at the intended receiver with very low transmitter complexity in a severe multipath channel. TR's focusing effect is in essence a spatial-temporal resonance effect that brings all the multipaths to arrive at a particular location at a specific moment. We show that by using time-reversal signal processing, with a sufficiently large bandwidth, one can harvest the massive multipaths naturally existing in a rich-scattering environment to form a large number of virtual antennas and achieve the desired massive multipath effect with a single antenna. Further, we explore the optimal bandwidth for TR system to achieve maximal spectral efficiency. Through evaluating the spectral efficiency, the optimal bandwidth for TR system is found determined by the system parameters, e.g., the number of users and backoff factor, instead of the waveform types. Moreover, we investigate the tradeoff between complexity and performance through establishing a generalized relationship between the system performance and waveform quantization in a practical communication system. It is shown that a 4-bit quantized waveforms can be used to achieve the similar bit-error-rate compared to the TR system with perfect precision waveforms. Besides 5G technology, Internet of Things (IoT) is another terminology that recently attracts more and more attention from both academia and industry. In the second part of this dissertation, the heterogeneity issue within the IoT is explored. One of the significant heterogeneity considering the massive amount of devices in the IoT is the device heterogeneity, i.e., the heterogeneous bandwidths and associated radio-frequency (RF) components. The traditional middleware techniques result in the fragmentation of the whole network, hampering the objects interoperability and slowing down the development of a unified reference model for the IoT. We propose a novel TR-based heterogeneous system, which can address the bandwidth heterogeneity and maintain the benefit of TR at the same time. The increase of complexity in the proposed system lies in the digital processing at the access point (AP), instead of at the devices' ends, which can be easily handled with more powerful digital signal processor (DSP). Meanwhile, the complexity of the terminal devices stays low and therefore satisfies the low-complexity and scalability requirement of the IoT. Since there is no middleware in the proposed scheme and the additional physical layer complexity concentrates on the AP side, the proposed heterogeneous TR system better satisfies the low-complexity and energy-efficiency requirement for the terminal devices (TDs) compared with the middleware approach.

Development of combined acidification and separation of slurry: effect on slurry fractions composition and gaseous emission

Doutoramento em Engenharia do Ambiente - Instituto Superior de Agronomia - UL

Effect of Pavement-Vehicle Interaction on Highway Fuel Consumption and Emission

Vehicle fuel consumption and emission are two important effectiveness measurements of sustainable transportation development. Pavement plays an essential role in goals of fuel economy improvement and greenhouse gas (GHG) emission reduction. The main objective of this dissertation study is to experimentally investigate the effect of pavement-vehicle interaction (PVI) on vehicle fuel consumption under highway driving conditions. The goal is to provide a better understanding on the role of pavement in the green transportation initiates. Four study phases are carried out. The first phase involves a preliminary field investigation to detect the fuel consumption differences between paired flexible-rigid pavement sections with repeat measurements. The second phase continues the field investigation by a more detailed and comprehensive experimental design and independently investigates the effect of pavement type on vehicle fuel consumption. The third study phase calibrates the HDM-IV fuel consumption model with data collected in the second field phase. The purpose is to understand how pavement deflection affects vehicle fuel consumption from a mechanistic approach. The last phase applies the calibrated HDM-IV model to Florida’s interstate network and estimates the total annual fuel consumption and CO2 emissions on different scenarios. The potential annual fuel savings and emission reductions are derived based on the estimation results. Statistical results from the two field studies both show fuel savings on rigid pavement compared to flexible pavement with the test conditions specified. The savings derived from the first phase are 2.50% for the passenger car at 112km/h, and 4.04% for 18-wheel tractor-trailer at 93km/h. The savings resulted from the second phase are 2.25% and 2.22% for passenger car at 93km/h and 112km/h, and 3.57% and 3.15% for the 6-wheel medium-duty truck at 89km/h and 105km/h. All savings are statistically significant at 95% Confidence Level (C.L.). From the calibrated HDM-IV model, one unit of pavement deflection (1mm) on flexible pavement can cause an excess fuel consumption by 0.234-0.311 L/100km for the passenger car and by 1.123-1.277 L/100km for the truck. The effect is more evident at lower highway speed than at higher highway speed. From the network level estimation, approximately 40 million gallons of fuel (combined gasoline and diesel) and 0.39 million tons of CO2 emission can be saved/reduced annually if all Florida’s interstate flexible pavement are converted to rigid pavement with the same roughness levels. Moreover, each 1-mile of flexible-rigid conversion can result in a reduction of 29 thousand gallons of fuel and 258 tons of CO2 emission yearly.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.

The effect on CO2 emissions of taxing truck distance in retail transports

To finance transportation infrastructure and to address social and environmental negative externalities of road transports, several countries have recently introduced or consider a distance based tax on trucks. In competitive retail and transportation markets, such tax can be expected to lower the demand and thereby reduce CO2 emissions of road transports. However, as we show in this paper, such tax might also slow down the transition towards e-tailing. Considering that previous research indicates that a consumer switching from brick-and-mortar shopping to e-tailing reduces her CO2 emissions substantially, the direction and magnitude of the environmental net effect of the tax is unclear. In this paper, we assess the net effect in a Swedish regional retail market where the tax not yet is in place. We predict the net effect on CO2 emissions to be positive, but off-set by about 50% because of a slower transition to e-tailing.

Characterisation of the sewage microbiome in four European cities throughout the COVID-19 pandemic by means of ecological and network modelling

The purpose of this work was to investigate possible patterns occurring in the sewage bacterial content of four cities (Bologna, Budapest, Rome, Rotterdam) over time (March 2020 - November 2021), also considering the possible effects of the lockdown periods due to the COVID-19 pandemic. The sewage metagenomics data were provided within VEO (Versatile Emerging infectious disease Observatory) project. The first analysis was the evaluation of the between samples diversity, looking for (dis)similarities among the cities, as well as among different time periods (seasonality). To this aim, we computed both similarity networks and Principal Coordinate Analysis (PCoA) plots based on the Bray-Curtis metric. Then, the alpha-biodiversity of the samples was estimated by means of different diversity indices. By looking at the temporal behaviour of the biodiversity in the four cities, we noticed an abrupt decrease in both Rome and Budapest in the Summer of 2020, that is related to: the prevalence of some species when the minimum occurred, and the change in correlations among species (studied via correlation networks), which is enriched in the period of minimum biodiversity. Rotterdam samples seem to be very different with respect to those from the other cities, as confirmed by PCoA. Moreover, the Rotterdam time series is proved to be stable and stationary also in terms of biodiversity. The low variability in the Rotterdam samples seems to be related to the species of Pseudomonas genus, which are highly variable and plentiful in the other cities, but are not among the most abundant in Rotterdam. Also, we observed that no seasonality effect emerged from the time series of the four cities. Regarding the impact of lockdown periods due to the COVID-19 pandemic, from the limited data available no effect on the time series considered emerges. More samples will be soon available and these analyses will be performed also on them, so that the possible effects of lockdowns may be studied.

Obesity And Inflammation And The Effect On The Hematopoietic System.

Bone marrow is organized in specialized microenvironments known as 'marrow niches'. These are important for the maintenance of stem cells and their hematopoietic progenitors whose homeostasis also depends on other cell types present in the tissue. Extrinsic factors, such as infection and inflammatory states, may affect this system by causing cytokine dysregulation (imbalance in cytokine production) and changes in cell proliferation and self-renewal rates, and may also induce changes in the metabolism and cell cycle. Known to relate to chronic inflammation, obesity is responsible for systemic changes that are best studied in the cardiovascular system. Little is known regarding the changes in the hematopoietic system induced by the inflammatory state carried by obesity or the cell and molecular mechanisms involved. The understanding of the biological behavior of hematopoietic stem cells under obesity-induced chronic inflammation could help elucidate the pathophysiological mechanisms involved in other inflammatory processes, such as neoplastic diseases and bone marrow failure syndromes.

Structural Connectivity Of The Default Mode Network And Cognition In Alzheimer׳s Disease.

Disconnectivity between the Default Mode Network (DMN) nodes can cause clinical symptoms and cognitive deficits in Alzheimer׳s disease (AD). We aimed to examine the structural connectivity between DMN nodes, to verify the extent in which white matter disconnection affects cognitive performance. MRI data of 76 subjects (25 mild AD, 21 amnestic Mild Cognitive Impairment subjects and 30 controls) were acquired on a 3.0T scanner. ExploreDTI software (fractional Anisotropy threshold=0.25 and the angular threshold=60°) calculated axial, radial, and mean diffusivities, fractional anisotropy and streamline count. AD patients showed lower fractional anisotropy (P=0.01) and streamline count (P=0.029), and higher radial diffusivity (P=0.014) than controls in the cingulum. After correction for white matter atrophy, only fractional anisotropy and radial diffusivity remained significantly lower in AD compared to controls (P=0.003 and P=0.05). In the parahippocampal bundle, AD patients had lower mean and radial diffusivities (P=0.048 and P=0.013) compared to controls, from which only radial diffusivity survived for white matter adjustment (P=0.05). Regression models revealed that cognitive performance is also accounted for by white matter microstructural values. Structural connectivity within the DMN is important to the execution of high-complexity tasks, probably due to its relevant role in the integration of the network.

Effect Of Platform Connection And Abutment Material On Stress Distribution In Single Anterior Implant-supported Restorations: A Nonlinear 3-dimensional Finite Element Analysis.

Although various abutment connections and materials have recently been introduced, insufficient data exist regarding the effect of stress distribution on their mechanical performance. The purpose of this study was to investigate the effect of different abutment materials and platform connections on stress distribution in single anterior implant-supported restorations with the finite element method. Nine experimental groups were modeled from the combination of 3 platform connections (external hexagon, internal hexagon, and Morse tapered) and 3 abutment materials (titanium, zirconia, and hybrid) as follows: external hexagon-titanium, external hexagon-zirconia, external hexagon-hybrid, internal hexagon-titanium, internal hexagon-zirconia, internal hexagon-hybrid, Morse tapered-titanium, Morse tapered-zirconia, and Morse tapered-hybrid. Finite element models consisted of a 4×13-mm implant, anatomic abutment, and lithium disilicate central incisor crown cemented over the abutment. The 49 N occlusal loading was applied in 6 steps to simulate the incisal guidance. Equivalent von Mises stress (σvM) was used for both the qualitative and quantitative evaluation of the implant and abutment in all the groups and the maximum (σmax) and minimum (σmin) principal stresses for the numerical comparison of the zirconia parts. The highest abutment σvM occurred in the Morse-tapered groups and the lowest in the external hexagon-hybrid, internal hexagon-titanium, and internal hexagon-hybrid groups. The σmax and σmin values were lower in the hybrid groups than in the zirconia groups. The stress distribution concentrated in the abutment-implant interface in all the groups, regardless of the platform connection or abutment material. The platform connection influenced the stress on abutments more than the abutment material. The stress values for implants were similar among different platform connections, but greater stress concentrations were observed in internal connections.

The Effect Of Pelvic Floor Muscle Training Alone Or In Combination With Electrostimulation In The Treatment Of Sexual Dysfunction In Women With Multiple Sclerosis.

Sexual dysfunction (SD) affects up to 80% of multiple sclerosis (MS) patients and pelvic floor muscles (PFMs) play an important role in the sexual function of these patients. The objective of this paper is to evaluate the impact of a rehabilitation program to treat lower urinary tract symptoms on SD of women with MS. Thirty MS women were randomly allocated to one of three groups: pelvic floor muscle training (PFMT) with electromyographic (EMG) biofeedback and sham neuromuscular electrostimulation (NMES) (Group I), PFMT with EMG biofeedback and intravaginal NMES (Group II), and PFMT with EMG biofeedback and transcutaneous tibial nerve stimulation (TTNS) (Group III). Assessments, before and after the treatment, included: PFM function, PFM tone, flexibility of the vaginal opening and ability to relax the PFMs, and the Female Sexual Function Index (FSFI) questionnaire. After treatment, all groups showed improvements in all domains of the PERFECT scheme. PFM tone and flexibility of the vaginal opening was lower after the intervention only for Group II. All groups improved in arousal, lubrication, satisfaction and total score domains of the FSFI questionnaire. This study indicates that PFMT alone or in combination with intravaginal NMES or TTNS contributes to the improvement of SD.