Os cães não são apenas melhores amigos dos homens, são também muito amigos de bebês e crianças!
Nesta galeria que o Foto Shot vai compartilhar, você verá o quanto estes animais gostam de estar com crianças e principalmente com bebês!
Esta galeria possui 22 encantadoras imagens, que revelam um tocante carinho dos cães para com as crianças. Afinal de contas, os cães são considerados há muito tempo pelas pessoas como ajudantes, protetores e amigos muito leais.
Razão para incluir a Chia na dieta: Veja alguns exemplos dos seus benefícios e tire suas conclusões?
Imagem retirada do Google Sabemos que a alimentação é uma fonte importantíssima para o fortalecimento e bom funcionamento de nosso organismo. Portanto, devemos tratar o que comemos como nossos aliados diários! Porque a Chia auxilia na Dieta? A Chia controla o apetite! A semente “cresce” no estômago quando ingerida. Fator que prolonga a saciedade e evita aqueles conhecidos ataques à geladeira ou à padaria! Outra interessante função que o grão pode exercer em nosso organismo é a de regularizar nossas funções cerebrais, contribuído para a prevenção de depressão e manutenção de nosso bom humor! Não poderíamos deixar de citar algumas outras maravilhas que fazem com que a Chia seja realmente uma fonte de riquezas na natureza. Possui propriedade de melhoria do sistema circulatório e da produção de nossas defesas corporais. Os responsáveis pelo feito são a vitaminas do complexo B, a riboflavina, a niacina e a tiamina. Dica preciosa:
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If consumers have their way, self-driving cars will enable parents to keep tighter reins on teen motorists. A survey conducted by the College of Engineering at Carnegie Mellon University reveals that people are soundly in favor of putting parental controls in high-tech cars of the future. One thousand people, aged 18 to 70, were polled to learn which freedom-foiling attribute they deemed most important. Top Parental Controls
Roughly 84 percent all respondents wanted to control: a car's speed, the number of friends who can pile into the car and the driver's curfew time. Women (87 percent) were strongly in support of this capability, as were 91percent of people aged 66 to 70. Even 81 percent of the youngest polled, ages 18-24, favored these novel features. Implementing these types of control technologies could save lives, prevent injuries and reduce costs associated with accidents. In 2013, 2,524 teenagers perished in motor vehicles crashes, making vehicle accidents the leading cause of death for teenagers. Compared to older drivers and miles driven, teen drivers are three times more likely to be in a fatal wreck. Young, inexperienced drivers tend to speed and drive too fast for road conditions. Further, teens are more likely to crash when they have teen passengers in the car. When it comes to curtailing the distance teen drivers can travel, men (62 percent) and women (61percent) closely align on this point. This notion, however, did not resonate well with 18- to 24-year-olds. Only 54 percent of them opted for this feature, whereas nearly 65 percent of drivers aged 36 to 45 would constrain a car's geographic range. The one area where 18- to 24-year-olds outscored all other age groups was in their receptiveness to having a parental text display in the car. Surprisingly, 69 percent of the youngest respondents thought this was useful while only 53 percent of people aged 56 to 65 would consider this option. Women (63 percent) tended to be more receptive than men (57 percent) to this communication feature. About the survey: Carnegie Mellon, the birthplace of autonomous vehicle technology, has a 30-year history of advancing self-driving car technology for commercialization. The college polled 1,000 people to gain insight into what consumers are looking for in self-driving cars. In the survey, a self-driving car was defined as having sensors and computing technology that allows the car to safely travel without a driver controlling the steering wheel, gas and brake pedal. The vehicle would automatically move at safe speeds, keep a safe distance from surrounding cars, change traffic lanes, obey traffic signals and follow GPS directions to destinations. Story Source: The above story is based on materials provided by Carnegie Mellon. The original article was written by Sherry Stokes. Note: Materials may be edited for content and length.
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Chocolate. The results for the participants who consumed 60 percent cacao chocolate showed that the brain was more alert and attentive after consumption. Their blood pressure also increased for a short time. Credit: © joanna wnuk / Fotolia Larry Stevens eats a piece of high-cacao content chocolate every afternoon, which is in part because he has developed a taste for the unsweetened dark chocolate. It's also because research shows that it lowers blood pressure and his new study reveals that it improves attention, which is especially important when hitting that midday slump. "Chocolate is indeed a stimulant and it activates the brain in a really special way," said Stevens, a professor of psychological sciences at NAU. "It can increase brain characteristics of attention, and it also significantly affects blood pressure levels." The study, published in the journal NeuroRegulation and sponsored by the Hershey Company, is the first to examine the acute effects of chocolate on attentional characteristics of the brain and the first-ever study of chocolate consumption performed using electroencephalography, or EEG technology. EEG studies take images of the brain while it is performing a cognitive task and measure the brain activity. Historically, chocolate has been recognized as a vasodilator, meaning that it widens blood vessels and lowers blood pressure in the long run, but chocolate also contains some powerful stimulants. Stevens said his team wanted to investigate if people who consume chocolate would see an immediate stimulant effect. Stevens and his colleagues in the Department of Psychological Sciences performed the EEG study with 122 participants between the ages of 18 and 25 years old. The researchers examined the EEG levels and blood pressure effects of consuming a 60 percent cacao confection compared with five control conditions. Michelle Montopoli, an NAU alumna and student at the time of the study, led the EEG testing phase which included measuring serving sizes of the samples based on participant weight and packaging them so the participants were blind to what they were tasting. Constance Smith, professor of psychological sciences, assisted with the physiological analyses. The results for the participants who consumed the 60 percent cacao chocolate showed that the brain was more alert and attentive after consumption. Their blood pressure also increased for a short time. "A lot of us in the afternoon get a little fuzzy and can't pay attention, particularly students, so we could have a higher cacao content chocolate bar and it would increase attention," Stevens said. He added that a regular chocolate bar with high sugar and milk content won't be as good, it's the high-cacao content chocolate that can be found from most manufacturers that will have these effects. The most interesting results came from one of the control conditions, a 60 percent cacao chocolate which included L-theanine, an amino acid found in green tea that acts as a relaxant. This combination hasn't been introduced to the market yet, so you won't find it on the candy aisle. But it is of interest to Hershey and the researchers. "L-theanine is a really fascinating product that lowers blood pressure and produces what we call alpha waves in the brain that are very calm and peaceful," Stevens said. "We thought that if chocolate acutely elevates blood pressure, and L-theanine lowers blood pressure, then maybe the L-theanine would counteract the short-term hypertensive effects of chocolate." For participants who consumed the high-cacao content chocolate with L-theanine, researchers recorded an immediate drop in blood pressure. "It's remarkable. The potential here is for a heart healthy chocolate confection that contains a high level of cacao with L-theanine that is good for your heart, lowers blood pressure and helps you pay attention," Stevens said. Stevens hopes the results of this study will encourage manufacturers to investigate further and consider the health benefits of developing a chocolate bar made with high-cacao content and L-theanine. "People don't generally eat chocolate and think it's going to be healthy for them," Stevens said. He added that there is a possibility the millions of hypertension patients in the country could eat a bar of this heart healthy chocolate every afternoon and their blood pressure would drop into the normal range, and they would be more alert and attentive. Story Source: The above story is based on materials provided by Northern Arizona University. Note: Materials may be edited for content and length. Journal Reference:
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Facilities at WSU's Sleep and Performance Research Center allow for both scientific data collection and a restful night's sleep. Credit: Washington State University photo The difference between life and death in the operating room, on the battlefield or during a police shootout often comes down to the ability to adapt to the unexpected. Sleep deprivation may make it difficult to do so, according to a Washington State University study published this month in the journal Sleep. For the first time, WSU researchers created a laboratory experiment that simulates how sleep loss affects critical aspects of decision making in high-stakes, real-world situations. Their results provide a new understanding of how going without sleep for long periods can lead doctors, first responders, military personnel and others in a crisis situation to make catastrophic decisions. Overcoming challenge of lab research Recent history is full of examples of the sometimes devastating consequences of people operating without enough sleep. Investigations into the Chernobyl nuclear power plant meltdown in Ukraine, the grounding of the Exxon Valdez oil tanker and the explosion of the space shuttle Challenger all concluded that sleep-deprived operators played a role in causing the accidents. A long-standing conundrum for sleep scientists has been creating a controlled lab situation that sufficiently simulates the circumstances leading to severe lapses in real-world judgment. Previous laboratory research consistently showed sleep loss degrades attention, but its effects on demanding tests of cognition like decision making appeared to be relatively small. "So there has been a disconnect between decision making in the lab where the effects of sleep loss appeared to be minimal and decision making in the real world where sleep loss can lead to big problems," said Paul Whitney, WSU associate dean and professor of psychology. "Our goal was to bridge the gap and capture the essential elements of real-world decision making in a laboratory experiment." Adapting to feedback crucial In a natural context, decision making is a dynamic process that requires a person to learn what is going on nearby as a result of his or her actions and changing circumstances. A surgeon, for instance, might notice a change in a patient's vital signs midway through a procedure. The surgeon can then use this feedback decide a better course of action. "A novel aspect of this study was using a simple laboratory task that captures the essential aspect of real-world decision making of adapting to new information in a changing situation," said John Hinson, professor of psychology. "Prior studies of sleep loss and decision making have not realized how important adapting to changing circumstances is in determining when sleep loss will lead to decision making failures." Whitney, Hinson and Hans Van Dongen, director of the WSU Sleep and Performance Research Center at WSU Spokane, along with Melinda Jackson, now of the RMIT University, Victoria, Australia, recruited 26 healthy adults to take part in their study conducted at the Spokane sleep center. Thirteen of the participants were randomly selected to go 62 hours without sleep two days into the study while the other half of the group was allowed to rest. For six days and nights, the participants lived in a hotel-like laboratory where they performed a specially designed reversal learning task to test their ability to use feedback to guide future decisions. Mid-study switch confounds sleep deprived In the task, subjects were shown a series of numbers that, unknown to them, were pre-assigned to have either a "go" (response) or "no go" (non-response) value. They had less than a second to decide whether or not to respond to each number shown. Every time they correctly identified a number with a "go" value, they received a fictitious monetary reward. Errors resulted in a loss. After a while, both the sleep-deprived group and the controls started to catch on and selected the right numbers. Then the tricky part came. The researchers reversed the contingencies so that participants had to withhold a response to the "go" numbers and respond to the "no go" numbers. The switch confounded the sleep deprived participants. Even after being shown 40 numbers with reversed contingencies, they had almost zero success. On the other hand, the rested participants would catch on to the switch within 8-16 numbers. Implications of sleep-loss risk The data show that no matter how hard a person wants to make the right choice, sleep loss does something to the brain that simply prevents it from effectively using feedback. The study provides a new tool for investigating how sleep deprivation produces decision errors in real-life situations where information emerges over time. "People in high-stakes environments are held accountable for their actions when they are fatigued just like everyone else," Van Dongen said. "However, we now know that when someone is sleep-deprived their brain simply can't process feedback from their actions and changing circumstances. "Our findings tell us that putting sleep-deprived people in perilous environments is an inherently risky business and raises a number of medical, legal and financial implications," he said.
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The first layers (1 and 2) of a neural network trained to classify scenes seem to be tuned to geometric patterns of increasing complexity, but the higher layers (3 and 4) appear to be picking out particular classes of objects. Credit: Courtesy of the researchers Object recognition -- determining what objects are where in a digital image -- is a central research topic in computer vision. But a person looking at an image will spontaneously make a higher-level judgment about the scene as whole: It's a kitchen, or a campsite, or a conference room. Among computer science researchers, the problem known as "scene recognition" has received relatively little attention. Last December, at the Annual Conference on Neural Information Processing Systems, MIT researchers announced the compilation of the world's largest database of images labeled according to scene type, with 7 million entries. By exploiting a machine-learning technique known as "deep learning" -- which is a revival of the classic artificial-intelligence technique of neural networks -- they used it to train the most successful scene-classifier yet, which was between 25 and 33 percent more accurate than its best predecessor. At the International Conference on Learning Representations this weekend, the researchers will present a new paper demonstrating that, en route to learning how to recognize scenes, their system also learned how to recognize objects. The work implies that at the very least, scene-recognition and object-recognition systems could work in concert. But it also holds out the possibility that they could prove to be mutually reinforcing. "Deep learning works very well, but it's very hard to understand why it works -- what is the internal representation that the network is building," says Antonio Torralba, an associate professor of computer science and engineering at MIT and a senior author on the new paper. "It could be that the representations for scenes are parts of scenes that don't make any sense, like corners or pieces of objects. But it could be that it's objects: To know that something is a bedroom, you need to see the bed; to know that something is a conference room, you need to see a table and chairs. That's what we found, that the network is really finding these objects." Torralba is joined on the new paper by first author Bolei Zhou, a graduate student in electrical engineering and computer science; Aude Oliva, a principal research scientist, and Agata Lapedriza, a visiting scientist, both at MIT's Computer Science and Artificial Intelligence Laboratory; and Aditya Khosla, another graduate student in Torralba's group. Under the hood Like all machine-learning systems, neural networks try to identify features of training data that correlate with annotations performed by human beings -- transcriptions of voice recordings, for instance, or scene or object labels associated with images. But unlike the machine-learning systems that produced, say, the voice-recognition software common in today's cellphones, neural nets make no prior assumptions about what those features will look like. That sounds like a recipe for disaster, as the system could end up churning away on irrelevant features in a vain hunt for correlations. But instead of deriving a sense of direction from human guidance, neural networks derive it from their structure. They're organized into layers: Banks of processing units -- loosely modeled on neurons in the brain -- in each layer perform random computations on the data they're fed. But they then feed their results to the next layer, and so on, until the outputs of the final layer are measured against the data annotations. As the network receives more data, it readjusts its internal settings to try to produce more accurate predictions. After the MIT researchers' network had processed millions of input images, readjusting its internal settings all the while, it was about 50 percent accurate at labeling scenes -- where human beings are only 80 percent accurate, since they can disagree about high-level scene labels. But the researchers didn't know how their network was doing what it was doing. The units in a neural network, however, respond differentially to different inputs. If a unit is tuned to a particular visual feature, it won't respond at all if the feature is entirely absent from a particular input. If the feature is clearly present, it will respond forcefully. The MIT researchers identified the 60 images that produced the strongest response in each unit of their network; then, to avoid biasing, they sent the collections of images to paid workers on Amazon's Mechanical Turk crowdsourcing site, who they asked to identify commonalities among the images. Beyond category "The first layer, more than half of the units are tuned to simple elements -- lines, or simple colors," Torralba says. "As you move up in the network, you start finding more and more objects. And there are other things, like regions or surfaces, that could be things like grass or clothes. So they're still highly semantic, and you also see an increase." According to the assessments by the Mechanical Turk workers, about half of the units at the top of the network are tuned to particular objects. "The other half, either they detect objects but don't do it very well, or we just don't know what they are doing," Torralba says. "They may be detecting pieces that we don't know how to name. Or it may be that the network hasn't fully converged, fully learned." In ongoing work, the researchers are starting from scratch and retraining their network on the same data sets, to see if it consistently converges on the same objects, or whether it can randomly evolve in different directions that still produce good predictions. They're also exploring whether object detection and scene detection can feed back into each other, to improve the performance of both. "But we want to do that in a way that doesn't force the network to do something that it doesn't want to do," Torralba says. Story Source: The above story is based on materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty. Note: Materials may be edited for content and length.
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Much attention has been given to dedicated, perennial bioenergy crops to meet the revised Renewable Fuel Standard mandating production of 36 billion gallons of biofuel by the year 2022. Even so, concern remains over the impending need to convert as much as 30 million acres of U.S. crop land, which would include food crops, to land for perennial energy crops in order to meet that demand. Researchers realize that biomass feedstocks will need to come from many different sources, including crop residues, forest residues, and municipal waste, for example, said Marty Williams, a University of Illinois crop scientist and ecologist with the USDA-Agricultural Research Service. The use of double-cropping systems--a winter annual biomass crop is grown then harvested in the spring, followed by a summer annual crop--has been suggested as an additional option. Knowing that many large-seeded vegetables in the Midwest must be planted later than agronomic crops into warmer soils, Williams was interested in the possibility of developing a bioenergy feedstock/vegetable double-cropping system. He explained that no such system had been developed and tested yet. "Some vegetables have relatively short growing seasons, too. Rather than the standard fallow period for certain vegetables, what about integrating a bioenergy crop as a part of a double-cropping system?" Williams said. Williams chose a vegetable crop popular in the state of Illinois, pumpkin, to be used in the double-cropping system study. "We took a fairly simplistic look at comparing this bioenergy/vegetable double-cropping system with traditional vegetable production using processing pumpkin," Williams explained. "Illinois leads the nation in pumpkin production, providing some 90 percent of the processing pumpkin in the United States." Field trials were conducted over three environments. During the study, Williams compared crop productivity and weed communities in four different pumpkin production systems, varying in tillage, cover crop, and bioenergy feedstock/pumpkin double-cropping. A fall-planted rye (Secale cereale) mix was used as the biomass feedstock. "In the end, winter rye may not be the best feedstock crop to use," he explained. "It was more of a model crop for us for our system. It grows well and has several desirable traits. Seed is relatively inexpensive and the plant is hardy." Interestingly, the researchers saw pumpkin yields in the double-cropping system were comparable to conventional pumpkin production. However, the biomass feedstock also yielded an average of 4.4 tons per acre of dry biomass prior to pumpkin planting. "We saw a theoretical yield of 349 gallons of ethanol per acre, and a higher farm gate value than typical pumpkin production," Williams said. "It looks promising," he added. "The biomass yield wasn't as high as something like Miscanthus, but we're producing feedstock and not taking land away from food production. Moreover, the cropping systems were not optimized, such as for soil fertility, so our economic estimates are likely conservative." Overall, the biomass yield was comparable to that of 'Shawnee' switchgrass, but only one-half the yield of a hybrid switchgrass, the study reported. "Perhaps some of our vegetable-cropping systems could contribute to bioenergy production, while still producing veggies," Williams said. "Also, there may be certain vegetable crops that are better suited to double-cropping. Given the potential competition between food and fuel production globally, systems making contributions towards both goals appear worth further consideration," he added. Story Source: The above story is based on materials provided by University of Illinois College of Agricultural, Consumer and Environmental Sciences. The original article was written by Stephanie Henry. Note: Materials may be edited for content and length. Journal Reference:
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Some of the most popular social media sites are filled with images of extremely thin women that might be harmful to those who view them -- whether they are seeking them or not, according to research from the University of California, Davis. The images were often cropped to remove heads or focus on just a few body parts. Doctoral candidate Jannath Ghaznavi and associate professor Laramie Taylor in the Department of Communication examined about 300 photographs from Twitter and Pinterest postings that used the terms "thinspiration" and/or "thinspo" to tag images and ideas promoting extreme thinness and often casting eating disorders in a positive light. Their paper "Bones, body parts, and sex appeal: An analysis of #thinspiration images on popular social media" was recently published in Body Image: An International Journal of Research. "Imagine a teenage girl or even a young woman looking for inspiration using terms such as 'attractive,' 'fit,' or 'pretty,'" Ghaznavi said. "She will likely find images of headless, scantily clad, sexualized women and their body parts." Images from Twitter, popular among younger audiences, were most likely to be cropped to remove heads and focus on specific body parts compared to Pinterest, according to the study. The content analysis cannot speak to the effects of viewing the images, the researchers concede, but they point to studies that have shown repeated exposure to such content can result in body dissatisfaction and disordered eating attitudes. "A young woman looking at these image may think that's what she should look like," Ghaznavi said. "That could prompt these girls and women to resort to extreme dieting, excessive exercise or other harmful behaviors in order to achieve this thin ideal." Story Source: The above story is based on materials provided by University of California - Davis. Note: Materials may be edited for content and length. Journal Reference:
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Les photographes Daniel Taipale et Samuel Taipale sont deux frères originaires de Finlande. Sur leurs comptes Instagram respectifs, ils nous font voyager à travers la Finlande et la Norvège. Au regard de leurs clichés, nous passons par des paysages enneigés, des forêts de pins, des nuits étoilées ou encore illuminées par de sublimes aurores boréales. Des instantanées parfois oniriques.
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