Breastfeeding, Bonding, and the Mother-infant Relationship

Mothers often report that breastfeeding is an enjoyable and emotionally beneficial experience they share with their infants. However, little research has investigated the role of feeding method in the development of the maternal bond and the mother-infant relationship. This study tested two hypotheses - the bonding hypothesis and the good-enough caregiver hypothesis - regarding the association of breastfeeding with maternal bonding and the mother-infant relationship. Using data from a longitudinal study of 570 mother-infant pairs, bonding and the quality of the mother-infant relationship were measured at 4 and 12 months. Although breastfeeding dyads tended to show higher quality relationships at 12 months, bottlefeeding dyads did not display poor quality or precarious relationships. Such results are encouraging For nonmaternal caregivers and mothers who bottlefeed their children

Enhancing food security in an era of global climate change

The goal of the workshop was to build a more strategic and integrated perspective on the threats and opportunities latent in the food / climate issue, and to discuss the hard challenges of moving forward toward common goals in a private, off-the-record setting. An executive session convened by the John F. Kennedy School of Government at Harvard University and the Venice International University on June 6-9, 2010 attracted more than 25 of the world’s leading experts from the fields of policy, science, and business to San Servolo Island for an intensive three day session (see text for a list of the participants). The discussions were off-the-record, with each participant present in his or her own capacity, rather than representing an organization. The session was one in a series on Grand Challenges of the Sustainability Transition organized by the Sustainability Science Program at Harvard University with the generous support of the Italy’s Ministry for Environment, Land and Sea. This particular session was held in cooperation with the new Mega Program on Climate Change, Agriculture and Food Security being developed by the Consultative Group on International Agricultural Research (CGIAR) and the Earth System Science Partnership. This summary report of the session is our synthesis of the main points and arguments that emerged from the discussions. It does not represent a consensus document, since no effort was made at the Session to arrive at a single consensus view. Rather, we report here on what we heard to be the major themes discussed at the session. Any errors or misrepresentations remain solely our responsibility

Effect van zomerklimaat bij Cymbidium

Abstract NL Bij Cymbidium kan in sommige zomers de uitgroei van de bloemtak vertragen of zelfs stil gaan staan, waardoor het gewenste oogsttijdstip niet gehaald wordt. Onderzoek gefinancierd door het Productschap Tuinbouw en uitgevoerd door Wageningen UR Glastuinbouw heeft laten zien dat dit het gevolg is van een te hoge temperatuur. Maximaal 26 oC gaf een betere takstrekking, vroegere productie en nauwelijks bloemschade. Er was bovendien een trend naar meer totaal geoogst gewicht, meer scheuten in het 2e teeltjaar, veelal betere kwaliteit en/of productie en bij de cultivar ‘Esther’ was de houdbaarheid op de vaas beter. Maximaal 26 oC met hoge RV en maximaal licht toe laten gaf bij ‘Esther’ betere resultaten dan maximaal 26 oC met lage RV en een normaal gekrijtte kas, maar bij Earlysue ‘Paddy’ was er geen meerwaarde. Een hoge RV gaf bij gelijkblijvende hoge temperatuur weinig verbetering in de takstrekking en nog steeds veel bloemschade. De bloemtakken lijken vooral in een jong stadium gevoelig voor een te hoge temperatuur. Abstract English During the summer, elongation of the Cymbidium flower stem can be delayed or even stopped, which delays harvest. Research at Wageningen UR Greenhouse Horticulture (funded by the Horticulture Board) found that this delay is caused by too high temperature. A maximum of 26 oC gave better stem elongation, earlier production, no flower damage, more total harvested weight, and more shoots in the subsequent growing season, than the control without a maximum temperature. Quality and production often improved and the vase-life of ‘Esther’ was longer. A maximum of 26 oC with high humidity and maximal light gave better results for ‘Esther’ than a maximum of 26 oC with low humidity and normal light levels. For Earlysue ‘Paddy’ there was no advantage of high humidity and maximal light levels. A high RV without a maximum temperature gave no improvement in stem elongation and flower damage still occurred. Young flowering stems seem to be more sensitive to high temperature than older stems

The community ecology perspective of omics data

The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract

Epistemic and social scripts in computer-supported collaborative learning

Collaborative learning in computer-supported learning environments typically means that learners work on tasks together, discussing their individual perspectives via text-based media or videoconferencing, and consequently acquire knowledge. Collaborative learning, however, is often sub-optimal with respect to how learners work on the concepts that are supposed to be learned and how learners interact with each other. One possibility to improve collaborative learning environments is to conceptualize epistemic scripts, which specify how learners work on a given task, and social scripts, which structure how learners interact with each other. In this contribution, two studies will be reported that investigated the effects of epistemic and social scripts in a text-based computer-supported learning environment and in a videoconferencing learning environment in order to foster the individual acquisition of knowledge. In each study the factors ‘epistemic script’ and ‘social script’ have been independently varied in a 2×2-factorial design. 182 university students of Educational Science participated in these two studies. Results of both studies show that social scripts can be substantially beneficial with respect to the individual acquisition of knowledge, whereas epistemic scripts apparently do not to lead to the expected effects

Stability in Mother-Child Interactions From Infancy Through Adolescence

Objective. This study examined homotypic stability in mother-child interactions, applying similar rating scales of mother-child interactions at 1 and 4.5 years, and heterotypic stability from 1 to 13 years and 4.5 to 13 years, using conceptually similar but not identical rating scales at age 13. Design. The authors coded videotaped mother-child interactions in 202 families when children were 1, 4.5, and 13 years of age during age-appropriate and developmentally salient structured tasks for relationship quality. Results. Multiple regression analyses controlled for the effects of child birth order and gender as well as maternal age and education. Maternal and dyadic, but not child, interaction qualities at 1 year significantly predicted similar or equivalent constructs at 4.5 and 13 years. Heterotypic stability from 1 to 13 years was partially or fully mediated by the same constructs at 4.5 years. Conclusions. Maternal behaviors showed a pattern of homotypic and heterotypic stability, whereas dyadic behaviors were somewhat less stable. Child behaviors showed evidence of homotypic and heterotypic instability

Geometric evaluations of CT and MRI based deep learning segmentation for brain OARs in radiotherapy.

Objective. Deep-learning auto-contouring (DL-AC) promises standardisation of organ-at-risk (OAR) contouring, enhancing quality and improving efficiency in radiotherapy. No commercial models exist for OAR contouring based on brain magnetic resonance imaging (MRI). We trained and evaluated computed tomography (CT) and MRI OAR autosegmentation models in RayStation. To ascertain clinical usability, we investigated the geometric impact of contour editing before training on model quality. Approach. Retrospective glioma cases were randomly selected for training (n = 32, 47) and validation (n = 9, 10) for MRI and CT, respectively. Clinical contours were edited using international consensus (gold standard) based on MRI and CT. MRI models were trained (i) using the original clinical contours based on planning CT and rigidly registered T1-weighted gadolinium-enhanced MRI (MRIu), (ii) as (i), further edited based on CT anatomy, to meet international consensus guidelines (MRIeCT), and (iii) as (i), further edited based on MRI anatomy (MRIeMRI). CT models were trained using: (iv) original clinical contours (CTu) and (v) clinical contours edited based on CT anatomy (CTeCT). Auto-contours were geometrically compared to gold standard validation contours (CTeCT or MRIeMRI) using Dice Similarity Coefficient, sensitivity, and mean distance to agreement. Models' performances were compared using paired Student's t-testing. Main results. The edited autosegmentation models successfully generated more segmentations than the unedited models. Paired t-testing showed editing pituitary, orbits, optic nerves, lenses, and optic chiasm on MRI before training significantly improved at least one geometry metric. MRI-based DL-AC performed worse than CT-based in delineating the lacrimal gland, whereas the CT-based performed worse in delineating the optic chiasm. No significant differences were found between the CTeCT and CTu except for optic chiasm. Significance. T1w-MRI DL-AC could segment all brain OARs except the lacrimal glands, which cannot be easily visualized on T1w-MRI. Editing contours on MRI before model training improved geometric performance. MRI DL-AC in RT may improve consistency, quality and efficiency but requires careful editing of training contours

Single Spin Measurement using Single Electron Transistors to Probe Two Electron Systems

We present a method for measuring single spins embedded in a solid by probing two electron systems with a single electron transistor (SET). Restrictions imposed by the Pauli Principle on allowed two electron states mean that the spin state of such systems has a profound impact on the orbital states (positions) of the electrons, a parameter which SET's are extremely well suited to measure. We focus on a particular system capable of being fabricated with current technology: a Te double donor in Si adjacent to a Si/SiO2 interface and lying directly beneath the SET island electrode, and we outline a measurement strategy capable of resolving single electron and nuclear spins in this system. We discuss the limitations of the measurement imposed by spin scattering arising from fluctuations emanating from the SET and from lattice phonons. We conclude that measurement of single spins, a necessary requirement for several proposed quantum computer architectures, is feasible in Si using this strategy.Comment: 22 Pages, 8 Figures; revised version contains updated references and small textual changes. Submitted to Phys. Rev.

Mixtures modeling identifies chemical inducers versus repressors of toxicity associated with wildfire smoke

Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological responses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures