Practical design considerations for secondary air injection in wood-burning cookstoves: An experimental study

Billions of households worldwide cook using biomass fires and suffer from the toxic smoke emitted into their homes. Laboratory studies of wood-burning cookstoves demonstrate that secondary air injection can greatly reduce the emission of harmful air pollution, but these experimental advancements are not easily translated into practical cookstove designs that can be widely adopted. In this study, we use a modular cookstove platform to experimentally quantify the practical secondary air injection design requirements (e.g., flow rate, pressure, and temperature) to reduce mass emissions of particulate matter (PM), carbon monoxide (CO), and black carbon (BC) by at least 90% relative to a traditional cooking fire. Over the course of 111 experimental trials, we illuminate the physical mechanisms that drive emission reductions, and outline fundamental design principles to optimize cookstove performance. Using the experimental data, we demonstrate that low-cost (<$10) fans and blowers are available to drive the secondary flow, and can be independently powered using an inexpensive thermoelectric generator mounted nearby. Furthermore, size-resolved PM measurements show that secondary air injection inhibits particle growth, but the total number of particles generated remains relatively unaffected. We discuss the potential impacts for human health and investigate methods to mitigate the PM formation mechanisms that persist

Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes.

Aims/hypothesisWhile genome-wide association studies (GWASs) have been successful in identifying novel variants associated with various diseases, it has been much more difficult to determine the biological mechanisms underlying these associations. Expression quantitative trait loci (eQTL) provide another dimension to these data by associating single nucleotide polymorphisms (SNPs) with gene expression. We hypothesised that integrating SNPs known to be associated with type 2 diabetes with eQTLs and coexpression networks would enable the discovery of novel candidate genes for type 2 diabetes.MethodsWe selected 32 SNPs associated with type 2 diabetes in two or more independent GWASs. We used previously described eQTLs mapped from genotype and gene expression data collected from 1,008 morbidly obese patients to find genes with expression associated with these SNPs. We linked these genes to coexpression modules, and ranked the other genes in these modules using an inverse sum score.ResultsWe found 62 genes with expression associated with type 2 diabetes SNPs. We validated our method by linking highly ranked genes in the coexpression modules back to SNPs through a combined eQTL dataset. We showed that the eQTLs highlighted by this method are significantly enriched for association with type 2 diabetes in data from the Wellcome Trust Case Control Consortium (WTCCC, p = 0.026) and the Gene Environment Association Studies (GENEVA, p = 0.042), validating our approach. Many of the highly ranked genes are also involved in the regulation or metabolism of insulin, glucose or lipids.Conclusions/interpretationWe have devised a novel method, involving the integration of datasets of different modalities, to discover novel candidate genes for type 2 diabetes

Six façons de mettre à profit l’expertise des professionnels en technopédagogie

We underutilize the knowledge and skills of Instructional Design and Technology (IDT) professionals, despite the frequent challenges in implementing learning technologies in medical education. This is largely due to a lack of understanding among stakeholders regarding the expertise of IDT professionals and their role in technology implementation processes. We seek to improve technology implementation outcomes by explaining the IDT field’s foundational tenets of a systems perspective and disciplined approach, clarifying the role that IDT professionals can play in educational technology initiatives, and providing guidance on how to foster productive collaborations in pursuit of effective technology-enhanced learning.Bien que souvent confrontés aux défis de la mise en oeuvre des technologies éducatives en éducation médicale, nous sous-utilisons les connaissances et les compétences technopédagogues. Cela est largement dû à un manque de compréhension, chez les parties prenantes, de l’expertise de ces professionnels et de leur rôle dans les processus de mise en œuvre des technologies. Dans le but d’améliorer les impacts de la mise en œuvre des technologies, nous expliquons les principes fondamentaux de la technopédagogie, à savoir une perspective systémique et une approche rigoureuse, tout en clarifiant le rôle que les technopédagogues peuvent jouer dans les initiatives technologiques éducatives. Nous proposons également des conseils sur la manière de favoriser des collaborations productives en vue d’un apprentissage efficace optimisé par les technologies

Origin of lymph node-derived lymphocytes in human hepatic allografts

Hepatic allograft-derived lymph nodes were examined in the post-transplant period on order to determine the origin of lymphocytes and structural elements of the lymph node. Histologic assessment and immunohistochemical studies verified that T-cell infiltration of donor lymph nodes by recipient-derived lymphocytes occurred early in the post-transplant period. These T cells bore T-cell activation markers, e.g. TAC receptor and HLA-DR antigens. In addition, functional analysis demonstrated alloreactive T cells in secondary proliferation assays. The pattern of alloreactivity in these assays was dependent upon the phenotypic make-up (and therefore origin) of the lymphocytes within the lymph node. A gradual shift in predominance of donor-derived lymphocytes to recipient-derived lymphocytes occurred, but even late in the post-transplant course the stromal elements and a residium of lymphocytes within the lymph nodes continued to bear donor HLA antigens. The possible role of these 'passenger' lymphocytes in allograft immunity is discussed

A functional role for small-conductance calcium-activated potassium channels in sensory pathways including nociceptive processes

We investigated the role of small-conductance calcium-activated potassium (SK) and intermediate-conductance calcium-activated potassium channels in modulating sensory transmission from peripheral afferents into the rat spinal cord. Subunit-specific antibodies reveal high levels of SK3 immunoreactivity in laminas I, II, and III of the spinal cord. Among dorsal root ganglion neurons, both peripherin-positive (C-type) and peripherin-negative (A-type) cells show intense SK3 immunoreactivity. Furthermore, dorsal root-stimulated sensory responses recorded in vitro are inhibited when SK channel activity is increased with 1-ethyl-2-benzimidazolinone (1-EBIO). In vivo electrophysiological recordings show that neuronal responses to naturally evoked nociceptive and nonnociceptive stimuli increase after application of the selective SK channel blocker 8,14-diaza-1,7( 1,4)-diquinolinacyclotetradecaphanedium ditrifluoroacetate (UCL 1848), indicating that SK channels are normally active in moderating afferent input. Conversely, neuronal responses evoked by mechanical stimuli are inhibited when SK channel activity is increased with 1-EBIO. These effects are reversed by the subsequent application of UCL 1848. Our data demonstrate that SK channels have an important role in controlling sensory input into the spinal cord

Interval State Estimation in Active Distribution Systems Considering Multiple Uncertainties.

Distribution system state estimation (DSSE) plays a significant role for the system operation management and control. Due to the multiple uncertainties caused by the non-Gaussian measurement noise, inaccurate line parameters, stochastic power outputs of distributed generations (DG), and plug-in electric vehicles (EV) in distribution systems, the existing interval state estimation (ISE) approaches for DSSE provide fairly conservative estimation results. In this paper, a new ISE model is proposed for distribution systems where the multiple uncertainties mentioned above are well considered and accurately established. Moreover, a modified Krawczyk-operator (MKO) in conjunction with interval constraint-propagation (ICP) algorithm is proposed to solve the ISE problem and efficiently provides better estimation results with less conservativeness. Simulation results carried out on the IEEE 33-bus, 69-bus, and 123-bus distribution systems show that the our proposed algorithm can provide tighter upper and lower bounds of state estimation results than the existing approaches such as the ICP, Krawczyk-Moore ICP(KM-ICP), Hansen, and MKO

The mechanical properties and toughening mechanisms of an epoxy polymer modified with polysiloxane-based core-shell particles

AbstractAn epoxy resin, cured using an anhydride hardener, has been modified by the addition of pre-formed polysiloxane core-shell rubber (S-CSR) particles with a mean diameter of 0.18 μm. The glass transition temperature, Tg, of the cured unmodified epoxy polymer was 148 °C, and this was unchanged after the addition of the S-CSR particles. The polysiloxane rubber particles had a Tg of about −100 °C. Atomic force microscopy showed that the S-CSR particles were well-dispersed in the epoxy polymer. The addition of the S-CSR particles reduced the Young's modulus and tensile strength of the epoxy polymer, but at 20 °C the fracture energy, GIc, increased from 117 J/m2 for the unmodified epoxy to 947 J/m2 when 20 wt% of the S-CSR particles were incorporated. Fracture tests were also performed at −55 °C, −80 °C, and −109 °C. The results showed that the measured fracture energy of the S-CSR-modified epoxy polymers decreased significantly below room temperature. For example, at −109 °C, a fracture energy of 481 J/m2 was measured using 20 wt% of S-CSR particles. Nevertheless, this value of toughness still represented a major increase compared with the unmodified epoxy polymer, which possessed a value of GIc of 174 J/m2 at this very low test temperature. Thus, a clear fact that emerged was that the addition to the epoxy polymer of the S-CSR particles may indeed lead to significant toughening of the epoxy, even at temperatures as low as about −100 °C. The toughening mechanisms induced by the S-CSR particles were identified as (a) localised plastic shear-band yielding around the particles and (b) cavitation of the particles followed by plastic void growth of the epoxy polymer. These mechanisms were modelled using the Hsieh et al. approach [33,49] and the values of GIc of the S-CSR-modified epoxy polymers at the different test temperatures were calculated. Excellent agreement was found between the predictions and the experimentally measured fracture energies. Further, the experimental and modelling results of the present study indicated that the extent of plastic void growth was suppressed at low temperatures for the S-CSR-modified epoxy polymers, but that the localised shear-band yielding mechanism was relatively insensitive to the test temperature

Turnip mosaic potyvirus probably first spread to Eurasian brassica crops from wild orchids about 1000 years ago

Turnip mosaic potyvirus (TuMV) is probably the most widespread and damaging virus that infects cultivated brassicas worldwide. Previous work has indicated that the virus originated in western Eurasia, with all of its closest relatives being viruses of monocotyledonous plants. Here we report that we have identified a sister lineage of TuMV-like potyviruses (TuMV-OM) from European orchids. The isolates of TuMV-OM form a monophyletic sister lineage to the brassica-infecting TuMVs (TuMV-BIs), and are nested within a clade of monocotyledon-infecting viruses. Extensive host-range tests showed that all of the TuMV-OMs are biologically similar to, but distinct from, TuMV-BIs and do not readily infect brassicas. We conclude that it is more likely that TuMV evolved from a TuMV-OM-like ancestor than the reverse. We did Bayesian coalescent analyses using a combination of novel and published sequence data from four TuMV genes [helper component-proteinase protein (HC-Pro), protein 3(P3), nuclear inclusion b protein (NIb), and coat protein (CP)]. Three genes (HC-Pro, P3, and NIb), but not the CP gene, gave results indicating that the TuMV-BI viruses diverged from TuMV-OMs around 1000 years ago. Only 150 years later, the four lineages of the present global population of TuMV-BIs diverged from one another. These dates are congruent with historical records of the spread of agriculture in Western Europe. From about 1200 years ago, there was a warming of the climate, and agriculture and the human population of the region greatly increased. Farming replaced woodlands, fostering viruses and aphid vectors that could invade the crops, which included several brassica cultivars and weeds. Later, starting 500 years ago, inter-continental maritime trade probably spread the TuMV-BIs to the remainder of the world

Deep Learning networks with p-norm loss layers for spatial resolution enhancement of 3D medical images

Thurnhofer-Hemsi K., López-Rubio E., Roé-Vellvé N., Molina-Cabello M.A. (2019) Deep Learning Networks with p-norm Loss Layers for Spatial Resolution Enhancement of 3D Medical Images. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol 11487. Springer, ChamNowadays, obtaining high-quality magnetic resonance (MR) images is a complex problem due to several acquisition factors, but is crucial in order to perform good diagnostics. The enhancement of the resolution is a typical procedure applied after the image generation. State-of-the-art works gather a large variety of methods for super-resolution (SR), among which deep learning has become very popular during the last years. Most of the SR deep-learning methods are based on the min- imization of the residuals by the use of Euclidean loss layers. In this paper, we propose an SR model based on the use of a p-norm loss layer to improve the learning process and obtain a better high-resolution (HR) image. This method was implemented using a three-dimensional convolutional neural network (CNN), and tested for several norms in order to determine the most robust t. The proposed methodology was trained and tested with sets of MR structural T1-weighted images and showed better outcomes quantitatively, in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), and the restored and the calculated residual images showed better CNN outputs.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech