The overall observational strategy is similar for each of the eight targets in the Large HST programme (GO-12473; principal investigator D.K.S.), which have been presented for WASP-19b14, HAT-P-1b12, 13, WASP-12b3, WASP-31b4 and WASP-6b11 with the details summarized here and applied to the remaining targets HAT-P-12b, WASP-17b and WASP-39b. We observed two transits of each target with the HST STIS G430L grating, and one with the STIS G750L. The G430L and G750L data sets contain typically 43 to 53 spectra, which span either four or five spacecraft orbits and were taken with a wide 52 arcsec × 2 arcsec slit to minimize slit light losses. Both gratings have resolutions of R of λ/Δλ = 530–1,040 (~2 pixels is 5.5 Å for G430L and ~2 pixels is 9.8 Å for G750L). The G430L grating covers the wavelength range from 2,900 Å to 5,700 Å, while the G750L grating covers 5,240 Å to 10,270 Å. The visits of HST were scheduled such that the third and/or fourth spacecraft orbits contain the transit, providing good coverage between second and third contact, as well as an out-of-transit baseline time series before and after the transit. Exposure times of 279 s were used in conjunction with a 128-pixel-wide sub-array, which reduces the readout time between exposures to 21 s, providing a 93% overall duty cycle. The STIS data set was pipeline-reduced with the latest version of CALSTIS, and cleaned for cosmic ray detections with a customized procedure11. The G750L data set was defringed using contemporaneous fringe flats. The spectral aperture extraction was done with IRAF, using a 13-pixel-wide aperture with no background subtraction, which minimizes the out-of-transit standard deviation of the white-light curves. The extracted spectra were then Doppler-corrected to a common rest frame through cross-correlation, which helped remove sub-pixel wavelength shifts in the dispersion direction. The STIS spectra were then used to create both a white-light photometric time series and custom wavelength bands covering the spectra, integrating the appropriate wavelength flux from each exposure for different bandpasses. Observations of HAT-P-1b and WASP-31b were also conducted in the infrared with the HST WFC3 instrument as part of GO-12473 and are detailed in refs 4 and 13. The observations use the infrared G141 grism in forward spatial scan mode over five HST orbits. Spatial scanning is done by slewing the telescope in the cross-dispersion direction during integration in a similar manner for each exposure, which increases the duty cycle and greatly increases the counts obtained per exposure. We used the ‘ima’ outputs from the CALWFC3 pipeline, which performs reference pixel subtraction, zero-read and dark current subtraction, and a nonlinearity correction. For the spectral extraction, we trimmed a wide box around each spectral image, with the spectra extracted using custom routines from the programming language IDL, similar to IRAF’s procedure from the APALL program. The aperture width was determined by minimizing the standard deviation of the fitted white-light curve. The aperture was traced around a computed centring profile, which was found to be consistent in the y axis with an error of <0.1 pixels. Background subtraction was applied using a clean region of the untrimmed image. For wavelength calibration, direct images were taken in the F139M narrow-band filter at the beginning of the observations. We assumed that all pixels in the same column have the same effective wavelength, as the spatial scan varied in the x-axis direction by less than one pixel, resulting in a spectral range from 1.1 μm to 1.7 μm. This wavelength range was later restricted to avoid the strongly sloped edges of the grism response, which results in much lower signal-to-noise light curves. For the comparative study, we also included the WFC3 observations for WASP-19b14, HD 209458b1, HAT-P-12b2 and WASP-17b31 (GO-12181; principal investigator D.D.). The WFC3 observations of WASP-12b3 were also included (GO-12230; principal investigator M. R. Swain), as was HD 189733b5 (GO-12881; principal investigator P. R. McCullough). The WFC3 observations of WASP-12b, WASP-17b, WASP-19b and HAT-P-12b were observed in stare mode, rather than with spatial scanning, and therefore have generally poorer overall photometric precision. See Extended Data Table 1 for a list of all observations. The eight targets in the large HST survey were also all covered by Spitzer transit observations as part of an Exploration Science Programme (90092; principal investigator J.-M. Désert) obtained using the Infrared Array Camera (IRAC) instrument with the 3.6-μm and 4.5-μm channels in subarray mode (32 × 32 pixels). Photometry was extracted from the basic calibrated FITS data cubes, produced by the IRAC pipeline after dark subtraction, flat-fielding, linearization and flux calibration. The images contain 64 exposures taken in sequence and have per-image integration times of 1.92 s. Both channels generally show a strong ramp feature at the beginning of the time series, and we elected to trim the first ~20 min of data to allow the detector to stabilize. We performed outlier filtering for hot (energetic) or cold (low-count values) pixels in the data by examining the time series of each pixel and subtracted the background flux from each image11. We measured the position of the star on the detector in each image incorporating the flux-weighted centroiding method using the background subtracted pixels from each image, for a circular region with a radius of 3 pixels centred on the approximate position of the star. We extracted photometric measurements from our data using both aperture photometry from a grid of apertures ranging from 1.5 to 3.5 pixels (in increments of 0.1) and time-variable aperture photometry. The best result was selected by measuring the flux scatter of the out-of-transit portion of the light curves for both channels after filtering the data for 5σ outliers with a width of 20 data points. All the transit light curves were modelled with analytical transit models15. For the white-light curves, the central transit time, orbital inclination, stellar density, planet-to-star radius contrast, stellar baseline flux and instrument systematic trends were fitted simultaneously. The period was initially fixed to a literature value, before being updated, with our final fits adopting the values obtained from an updated transit ephemeris. Both G430L transits were fitted simultaneously with a common inclination, stellar density and planet-to-star radius contrast. The results from the HST white-light curve and Spitzer fits were then used in conjunction with literature results to refine the orbital ephemeris and overall planetary system properties. To account for the effects of limb-darkening on the transit light curve, we adopted the four-parameter nonlinear limb-darkening law, calculating the coefficients with stellar models32, 33. As in our past STIS studies, we applied orbit-to-orbit flux corrections by fitting for a low-order polynomial to the photometric time series phased on the HST orbital period. The baseline flux level of each visit was free to vary in time linearly, described by two fit parameters. In addition, for the G750L we found it justified by the Bayesian Information Criteria34 to also linearly fit for two further systematic trends which correlated with the x and y detector positions of the spectra, as determined from a linear spectral trace in IRAF. The orders of the fit polynomials were statistically justified based on the Bayesian Information Criteria34, and the systematic trends were fitted simultaneously with the transit parameters. The errors on each data point were initially set to the pipeline values, which are dominated by photon noise but also includes readout noise. The best-fitting parameters were determined simultaneously with a Levenberg–Marquardt least-squares algorithm35 using the unbinned data. After the initial fits, the uncertainties for each data point were rescaled based on the standard deviation of the residuals and any measured systematic errors correlated in time (‘red noise’), thus taking into account any underestimated errors calculated by the reduction pipeline in the data points. The uncertainties on the fitted parameters were calculated using the covariance matrix from the Levenberg–Marquardt algorithm, which assumes that the probability space around the best-fitting solution is well described by a multivariate Gaussian distribution and equivalent results were found when using an Markov Chain Monte Carlo analysis36. Inspection of the two-dimensional probability distributions from both methods indicated that there were no significant correlations between the planet-to-star radius contrasts and systematic trend parameters. In an additional analysis step compared to our previous results4, 11, 12, we also marginalized over the systematic models37 for the spectra of WASP-17b, WASP-39b, HAT-P-1b, HAT-P-12b and HD 209458b. Under this approach, we effectively averaged the results obtained from a suite of systematics models in a coherent manner. For each systematic model used to correct the data, we calculated the evidence of fit, which is then used to apply a weight to the parameter of interest (R /R ) measured using that model. In doing so, we marginalized over our uncertainty as to selecting which model is actually the ‘correct’ model. For the STIS data we included all combinations of factors up to the 4th order in both HST phase, 3rd order in detector positions x and y, 3rd order in wavelength shift, and 1st order in time. For the WFC3 data, our grid of parameterized models includes all combinations of factors up to the fourth order in both HST phase, to correct for ‘HST breathing’ effects, and up to the fourth order in wavelength shift, in addition to the visit-long linear trend. In addition, we also included exponential HST phase models, with a linear and squared planetary phase trend. For the Spitzer data, we included all combinations of the x and y positions of the stellar point spread function on the detector, including the cross-product from polynomials of x and y up to a second-order. We note that the best-fitting systematics models for HST and Spitzer are generally well constrained and the marginalized results were very similar to those based on model selection by the Bayesian Information Criteria34. For HD 209458b, lightcurve analyses and marginalization were performed using Gaussian process models38. Owing to the flexibility of Gaussian process models, a broad range of systematics behaviours can be captured without the need to provide an explicit functional form. The results of a single Gaussian process model are thus comparable to marginalizing over many simpler parametric systematics models, as was done for the other lightcurves37. The synthetic spectra17, 39 used for this study include isothermal models as well as those with a self-consistent treatment of radiative transfer and chemical equilibrium of neutral and ionic species. Chemical mixing ratios and opacities were calculated assuming local thermochemical equilibrium accounting for condensation and thermal ionization but not photoionization40, 41, 42, 43, for both solar metallicity and sub-solar metallicity abundances. A simplified treatment adding in small aerosol haze particles was performed by including a Rayleigh scattering opacity (that is, σ = σ (λ/λ )−4) that had a cross-section which was 10×, 100× and 1,000×the cross-section of molecular hydrogen gas (σ = 5.31 × 10−27 cm2 at λ = 350 nm; ref. 44). Similarly, to include the effects of a flat cloud deck we included a wavelength-independent cross-section, which was 1×, 10× and 100× the cross-section of molecular hydrogen gas at 350 m (see Extended Data Fig. 4). To enable a direct comparison between planets, the transmission spectra have been plotted on a common scale by dividing the measured wavelength-dependent altitude of the transmission spectra, z(λ), by the planet’s atmospheric scale height (H , the vertical distance over which the gas pressure drops by a factor of e) estimated using the equilibrium temperature. The analytical relation for the wavelength-dependent transit-measured altitude z(λ) of a hydrostatic atmosphere is44: where ε is the abundance of the absorbing or scattering species, P is the pressure at a reference altitude, σ(λ) is the wavelength-dependent cross-section, τ is the optical thickness at the effective transit-measured radius, k is Boltzmann’s constant, T is the local gas temperature, μ is the mean mass of the atmospheric particles, g the planetary surface gravity, R the planetary radius, and H = kT/μg is the atmospheric pressure scale height. The altitude difference measured between two wavelength regions (λ and λ′) in a transmission spectrum is proportional to the quantity: where α is the absorption plus scattering extinction coefficient: Thus, the quantity ΔZ = z(λ) − z(λ′) is related to the ratio of the total scattering plus absorption of the atoms and molecules between the wavelength regions λ and λ′, and we use the quantity ΔZ /H = ln(α/α′) as a metric to intercompare the atmospheric extinction for the different planets in our survey. Note that the temperature and scale height of the upper atmosphere can differ from the equilibrium value, especially at high altitudes where hot upper layers in hot Jupiters have been found45, 46, 47, 48. We defined indices around three main wavelength regions (see Table 1). We used a blue-optical band consisting of the G430L grating, which is sensitive between 0.3 μm and 0.57 μm and roughly covers the Johnson U and B photometric bandpasses. This wavelength region is almost always exclusively dominated by scattering for clear, cloudy and hazy exoplanets (see Extended Data Fig. 4). The second is a near-infrared band between 1.22 μm and 1.33 μm, which has overlap with the Johnson J photometric band, and is located between the strong H O absorption bands centred around 1.15 μm and 1.4 μm. This wavelength region is sensitive to the scattering continuum in hazy, cloudy and highly sub-solar models and the H O continuum in clear atmospheres with abundances near solar (see Extended Data Fig. 4). We also used a third wavelength region in the mid-infrared between 3 μm and 5 μm, which overlaps with the Johnson L and M photometric bandpasses and consists of the two Spitzer IRAC photometric channels 1 and 2. This wavelength region is highly sensitive to strong H O, CO and CH absorption bands, which are the main active molecular species expected in hot Jupiters17, 18, 19, and only sensitive to scattering in the cloudiest cases, making it an overall effective measure of the total molecular extinction (see Extended Data Fig. 4). From the data, ΔZ was measured taking the difference between the planet radius measured in the blue-optical HST data using the G430L grating (UB, wavelengths 0.3–0.57 μ m) and the weighted-average value of the radii measured in Spitzer IRAC photometric channels 1 and 2 (LM, wavelengths 3–5 μ m). Δ Z was measured similarly, although using the near-infrared WFC3 data (J, wavelengths 1.22–1.33 μ m). In addition, we also measured the amplitude of the near-infrared H O absorption band using the WFC3 spectra (see Table 1), measuring the average radii in a band containing strong H O absorption (between 1.34 μm and 1.49 μm) compared to an adjacent band between strong H O features (1.22 μm and 1.33 μm). The measured H O amplitude for each exoplanet was then divided by the value predicted by atmospheric models17, 39 calculated for each planet using a planet-averaged temperature–pressure profile assuming clear atmospheres and solar abundances. From Fig. 3, a likely inverse correlation is seen between the H O amplitude and the ΔZ /H index, with the Spearman’s rank correlation coefficient measured to be − 0.76 which has a false alarm probability of 2.8%. We note that this false alarm probability is not the probability that the water depletion scenario is correct, as that is excluded with Fig. 3 to a much higher degree (5.9σ significance). A much weaker inverse correlation of − 0.48 is found with ΔZ in Extended Data Fig. 2, although that has a high false alarm probability of 23%. As stellar activity can affect the measurement of a transmission spectrum, we photometrically monitored the activity levels of our target stars with the Cerro Tololo Inter-American Observatory (CTIO) 1.3-m telescope for the southern targets14 and the Tennessee State University Celestron 14-inch (C14) Automated Imaging Telescope (AIT) located at Fairborn Observatory in Arizona for the northern targets49. All but two of our targets showed low levels of stellar activity, with observed photometric variations or upper limits which are sufficiently small that their effects on measuring the transmission spectra are minimal compared to the measurement errors3, 4, 11, 12, 13. The two most active stars in the survey, WASP-19A and HD 189733A, were corrected for occulted and un-occulted star spots10, 14. As no contemporaneous photometric monitoring of WASP-19A is available for the July 2011 WFC3 spectra from ref. 14, we matched the spectra to the spot-corrected transit depth of R /R = 0.14019 ± 0.00073 as measured using HST WFC3 on 12 June 2014 from GO-13431 (principal investigator C. M. Huitson), which had simultaneous CTIO activity monitoring. We also normalized the differential transit depths of the WFC3 spectra5 to a transit depth value consistent with ref. 10, which has a uniform treatment between the HST and Spitzer data sets of system parameters, limb-darkening and activity correction. As effects of stellar activity could potentially mimic an optical scattering slope in a transmission spectra5, 10, 28, 45, we searched for a relationship between the activity levels of the stars in our survey and the presence of a strong optical slope. If stellar activity were the main cause of the enhanced optical slopes, rather than scattering by hazes or clouds, then it is expected that highly active stars would have higher levels of spots and plages, and should show preferentially larger transmission spectral slopes. As an additional measure of stellar activity, we used the strength of the Ca II H and K emission lines as a stellar activity indicator (logR′ ), as measured by Keck HIRES50, 51; see Table 1. We searched for a correlation with the chromospheric activity index logR′ , as it is correlated with the stellar photometric variability52 and can be used to quantify stars with low activity levels, for which the photometric variations would be undetectable. We found no significant correlation with logR′ activity and either the presence of haze or the strength of optical transmission spectral slope, as measured with the Δ Z index (Extended Data Fig. 3). This suggests that the effects of stellar activity are not the overall cause of the strong optical slopes seen in some of the transmission spectra. There are also other indications that stellar activity does not have a dominant role. For one, while changing stellar activity levels should have an effect on the transmission spectra, no significant variations were seen between the three epochs of the HST STIS spectra, which has an overlapping wavelength region, for all of our targets, including active stars. In addition, the atmospheric temperature can be derived by measuring the transmission spectral slope in an atmosphere dominated by Rayleigh scattering3, 4, 11, 45, and the temperatures found fitting a Rayleigh scattering slope for HD 189733b, HAT-P-12b and WASP-6b (1,340 ± 150 K, 1,010 ± 80 K and 973 ± 144 K, respectively) are in good agreement with the planetary temperatures T expected (1,196 K, 958 K and 1,183 K, respectively). This agreement is consistent with the atmospheric temperature, rather than stellar activity, being probed by the scattering haze. For these three stars, where HAT-P-12 has a much lower activity than the other two, the individual activity levels would have to be finely tuned for the spectral slopes to mimic the planetary temperatures. In addition to condensation chemistry53, hazes can also form through photochemical processes resulting in hydrocarbon aerosols54. This process is more effective for cooler exoplanets55 and the incident stellar ultraviolet irradiation also plays an important factor in hydrocarbon formation54. Our results indicate no correlation with the presence of haze to either the atmospheric temperature or levels of ultraviolet irradiation (as traced by stellar activity indicators), which generally favours condensation chemistry over photochemical processes as the general source of the observed hazes and clouds. We have opted not to make the customized IDL codes used to produce the spectra publicly available owing to their undocumented intricacies.
News Article | September 12, 2016
In spite of a significant imbalance between male and female leaders in business, new research from the University at Buffalo's School of Management suggests that in collaborative work environments where women are outnumbered, they often emerge as the natural group leader. The findings fly in the face of the reality of the U.S. workforce, where many fail to recognize the extent of the female leadership gap. Women represent just 3% of new CEOs in the U.S., 5.1% of Fortune 1000 CEOs, and 4% of Standard and Poor’s 500 CEOs. A recent survey by the Rockefeller Foundation also found that nine in 10 respondents thought there were more female business leaders than there really are, and further research by the W. P. Carey School of Business at Arizona State University found that those women are more likely to be targeted by shareholder activism. "We tend to see the man as more leader-like than the woman," says lead author Jim Lemoine, in a video interview by UB School of Management. "What we were interested in in this research were exceptions to the rule." In the study, researchers assigned nearly 1,000 participants to small groups and asked them to complete a series of tasks, later polling them on who emerged as the natural leader of their group. The study was replicated with participants of varying ages over both long and short-term periods. When the groups communicated a lot, or were more "extroverted" in Lemoine’s words, women were more likely to emerge as leaders. They were also more likely to emerge as leaders when the groups were predominantly male. "When a group is composed of lots of extroverted people, they talk more," he says. "They’re actually getting to understand each other’s strengths and weaknesses and who may be the better leader beyond this diversity demographics stuff." This getting-to-know-each-other phase is key to gender leadership balance, says Lemoine. "It makes the environment less masculine, more balanced, and gives everyone a chance to play on equal footing," he says. Lemoine adds that when he advises companies, he often encourages them to ignore strategy talk at first and instead spend some time getting to know the other people in the room. "When we think of men, we think independent, aggressive, competitive risk takers, which is for a lot of people a stereotypical view of a leader," he says. "When we think of women, we tend to think—true or not—more helpful, more cooperative, more caring." Lemoine explains that in spite of centuries of gender imbalance, he finally sees the tide beginning to turn in favor of female leaders. That is because when people are asked what kind of leader they want to work for today, the typical answer has evolved to describe stereotypically female characteristics. As he puts it: People tend to answer this more now, ‘I would like to work for someone who is ethical,’ ‘I would like to work for someone who really cares about me, who understands me, who trains me, who puts me first, who’s very authentic. As our ideas of what a leader is changes, so do our ideas change of who a leader can be, so really the future is looking bright for more gender equality for who becomes a leader. In other words, one of the key strategies for breaking the gender leadership gap in the workplace could be simple conversation between team members, in a setting that gives every member of the team a level playing field.
News Article | August 26, 2016
There’s no need to reinvent the genetic wheel. That’s one lesson of a new study that looks to the saliva of humans, gorillas, orangutans, macaques and African green monkeys for insights into evolution. The research, published on Aug. 25 in Scientific Reports, examined a gene called MUC7 that tells the body how to create a salivary protein of the same name. The protein, which is long and thin, forms the backbone of a bottlebrush-shaped molecule that helps to give spit its slimy, sticky consistency. The study found that within the MUC7 gene, instructions for building important components of the bottlebrush were repeated multiple times in each of the five primate species studied. Gorillas had the fewest copies of this information (four to five), while African green monkeys had the most (11 to 12). Humans fell somewhere in between, with five to six. Through an in-depth analysis of MUC7’s evolutionary history, the researchers concluded that having numerous copies of the repeated instructions likely conferred an evolutionary advantage to primates — possibly by enhancing important traits of saliva such as its lubricity and, perhaps even more importantly, its ability to bind to microbes (a capability that may help curb disease). Evolution can favor the expansion of tried-and-true genetic tools, in addition to the development of totally new ones, says University at Buffalo biologist Omer Gokcumen, who led the study together with Stefan Ruhl, a salivary researcher in UB’s oral biology department. “You don’t always have to invent a new tool,” says Gokcumen, PhD, an assistant professor of biological sciences in UB's College of Arts and Sciences. “Sometimes, you just need to amplify the tool you already have.” In the case of MUC7, repeating key genetic instructions over and over resulted in longer, denser proteins, which are likely better at performing two protective tasks: lubricating the mouth — which facilitates talking, chewing and other vital functions — and latching onto microbes, an action that’s thought to expedite the removal of disease-causing pathogens from the oral cavity. The genetic instructions that are repeated within the MUC7 gene are what scientists call tandem repeats — short strings of DNA found multiple times inside the gene. The new study shows that as primates evolved, the DNA in their MUC7 tandem repeats sometimes changed in places (a normal part of evolution). But the genetic material stayed the same in one key way: Pieces of DNA that told the body how to make the amino acids serine and threonine, two vital building blocks of the bottlebrush backbone, persisted in all primates. The directions for creating serine and threonine were found in the same location in tandem repeats across humans, gorillas, orangutans, macaques and African green monkeys. The likelihood of this happening at random is small, which hints that those genetic sequences provided an evolutionary advantage to their hosts, Gokcumen says. This hypothesis is bolstered by the crucial role that serine and threonine play in the MUC7 protein’s function. Within MUC7, the two compounds act as anchoring points for sugar molecules, which protrude from the protein backbone like the bristles of a brush. It’s these bristles that carry out the important task of binding to microbes. The research elucidates how tandem repeats may serve as modular building blocks for rapid evolutionary adaptation. “Tandem repeats may be a major way that many different genes in the genome quickly adapt to their environments,” says Duo “Erica” Xu, the study’s first author and a PhD student in biological sciences in the UB College of Arts and Sciences. The research builds on the groundbreaking work of scientists in UB’s oral biology department, who discovered the MUC7 protein more than 30 years ago and sequenced the MUC7 gene, says Ruhl, a professor in that same department, which is part of the UB School of Dental Medicine. “Saliva is an important body fluid which has been for a long time underappreciated by mainstream biomedical science,” Ruhl says. “It is amazing to see the research on MUC7 take off again with modern technology. In the next few years, we expect to learn a lot more about the importance of saliva for human health through such cross-disciplinary studies with evolutionary geneticists.” The research team also included scientists from the Foundation of Research and Technology in Greece and the University of Minnesota Twin Cities.
News Article | August 22, 2016
Babies that seem to get upset more easily and take longer to calm down may be at higher risk for obesity while babies that exhibit more “cuddliness” and calm down easily are less likely at risk, according to a University at Buffalo study. The purpose of the research, published in Childhood Obesity, is to explore new ways to identify infants at risk for becoming overweight or obese in order to intervene as early as possible. “The research tells us that differences in behavior begin as early as infancy and those differences can influence health behaviors that impact future health risks,” said Kai Ling Kong, PhD, first author and assistant professor of pediatrics in the Jacobs School of Medicine and Biomedical Sciences at UB. Kong conducts research in the Division of Behavioral Medicine in the UB Department of Pediatrics. In the study, 105 infants from nine to 18 months old were taught to press a button to earn a reward. They completed the task twice, and received either a piece of their favorite food as a reward or 10 seconds of a non-food reward, such as blowing bubbles, watching a Baby Einstein DVD or hearing music. Parents were instructed to say only specific phrases while the child completed the task. As the task went on, it became increasingly difficult for the infant to earn the reward as they had to press the button more times. The amount of “work” they were willing to do was calculated by counting the number of times the child was willing to press the button to get the reward. The child’s temperament was assessed through a detailed, 191-question online questionnaire that parents completed. “We found that infants that rated higher on what we call cuddliness — the baby’s expression of enjoyment and molding of the body to being held — had lower food reinforcement,” explained Kong. “That means they were willing to work more for a non-food reward versus a food reward. So an infant who enjoyed being held closely by a caregiver was less motivated to work for food.” The researchers measured cuddliness by asking parents specific questions such as, “When being held, how often did your baby pull away or kick?” and “While being fed on your lap, how often did your baby snuggle even after they were done?” Infants who rated high on how quickly they could recover from crying or being distressed also were less motivated to work for food compared to non-food alternatives. Conversely, infants who rated lower on cuddliness and who took longer to recover from distress and arousal, had higher food reinforcement -- that is, they were willing to work harder for a food reward. Kong said that correlating these differences in temperament with their relative food reinforcement will help researchers identify ways to encourage healthier diets among the youngest individuals. Parents who identify these characteristics in their infants also can benefit, she said. “If a parent sees high relative food reinforcement in their child, it is not cause for immediate concern,” she said. Instead, she noted, the parent could evaluate their child’s relationship to food, encouraging the child to engage in activities other than eating, especially as a reward. “Using rewards other than food, such as a trip to the playground or engaging in active play with their parents, may help reduce the child’s tendency to find pleasure in food,” she said. Making available a wide array of toys, activities and playmates so food isn’t the main focus and sole source of pleasure also can be beneficial. Kong added that children can learn healthier lifestyles when parents model healthy behaviors themselves, pay close attention to children’s satiety cues (noting when they are full) and don’t immediately use food to comfort a child who is crying or fussing.
News Article | April 8, 2016
Now, the technology is moving forward under a licensing agreement that the university inked with ZOETIC Pharmaceuticals, an early stage drug development company in Amherst, New York. ZOETIC plans to commercialize the technology by partnering with pharmaceutical and biotechnology companies whose products can be enhanced by the nanoparticle. "This technology can improve the performance and safety of biologic drugs, provide new treatments that correct the root cause of autoimmune diseases and significantly increase the success rates of gene therapy," said Sven Beushausen, chief scientific officer at ZOETIC. The nanoparticle was invented in the laboratory of Sathy V. Balu-Iyer, PhD, professor of pharmaceutical science in the UB School of Pharmacy and Pharmaceutical Sciences. ZOETIC sees the nanoparticle as a boon for biologic drugs, which are genetically-engineered proteins derived from human genes. When chronically administered, biologic drugs often elicit immune responses resulting in the production of antidrug antibodies. These antibodies reduce the effectiveness of the drug, which can exacerbate the disease, create the need for increased dosing or switching of drugs, and subject patients to potential life-threatening complications. Doctors are increasingly prescribing biologic drugs to treat Type 1 diabetes, rheumatoid arthritis and other autoimmune diseases. The nanoparticle, which has been proven successful in preclinical research, could improve the treatment of these ailments by teaching the body not to mount an immune response to the antigens that provoke autoimmune responses. The nanoparticle also has applications in gene therapy, which is the transplantation of normal genes into a patient to correct genetic disorders. Examples of gene therapy include coagulation factors FVIII and FIX for the treatment of hemophilia A and B, and lysosomal storage diseases like Pompe's Disease where patients have a deficiency in the enzyme acid-alpha-glucosidase. A limitation of gene therapy is that many patients mount immune responses that detect and eliminate the therapy soon after it enters the body. The nanoparticle can significantly reduce that response by acting as a spy (stealthily preventing the body from recognizing and neutralizing the therapy) and a shield (guarding the drugs from being metabolized in the body), which ultimately allows the body to accept the therapy. While ZOETIC is seeking external partners, it will continue to work with UB, which is providing the company with startup support and a home at tenX, a co-working space operated by UB's Office of Science Technology Transfer and Economic Outreach at Baird Research Park. "ZOETIC is an exciting new addition to Buffalo's fast-growing life sciences economy," said UB Vice Provost Robert Genco, who serves as director of UB STOR. "We are looking forward to a long and fruitful relationship with the company as it works toward commercializing this very promising technology created by University at Buffalo researchers."