A review of camera trapping for conservation behaviour research

An understanding of animal behaviour is important if conservation initiatives are to be effective. However, quantifying the behaviour of wild animals presents significant challenges. Remote-sensing camera traps are becoming increasingly popular survey instruments that have been used to non-invasively study a variety of animal behaviours, yielding key insights into behavioural repertoires. They are well suited to ethological studies and provide considerable opportunities for generating conservation-relevant behavioural data if novel and robust methodological and analytical solutions can be developed. This paper reviews the current state of camera-trap-based ethological studies, describes new and emerging directions in camera-based conservation behaviour, and highlights a number of limitations and considerations of particular relevance for camera-based studies. Three promising areas of study are discussed: (1) documenting anthropogenic impacts on behaviour; (2) incorporating behavioural responses into management planning and (3) using behavioural indicators such as giving up densities and daily activity patterns. We emphasize the importance of reporting methodological details, utilizing emerging camera trap metadata standards and central data repositories for facilitating reproducibility, comparison and synthesis across studies. Behavioural studies using camera traps are in their infancy; the full potential of the technology is as yet unrealized. Researchers are encouraged to embrace conservation-driven hypotheses in order to meet future challenges and improve the efficacy of conservation and management processes

De novo protein design:How do we expand into the universe of possible protein structures?

Protein scientists are paving the way to a new phase in protein design and engineering. Approaches and methods are being developed that could allow the design of proteins beyond the confines of natural protein structures. This possibility of designing entirely new proteins opens new questions: What do we build? How do we build into protein-structure space where there are few, if any, natural structures to guide us? To what uses can the resulting proteins be put? And, what, if anything, does this pursuit tell us about how natural proteins fold, function and evolve? We describe the origins of this emerging area of fully de novo protein design, how it could be developed, where it might lead, and what challenges lie ahead

Computational design of water-soluble α-helical barrels

The design of protein sequences that fold into prescribed de novo structures is challenging. General solutions to this problem require geometric descriptions of protein folds and methods to fit sequences to these. The α-helical coiled coils present a promising class of protein for this and offer considerable scope for exploring hitherto unseen structures. For α-helical barrels, which have more than four helices and accessible central channels, many of the possible structures remain unobserved. Here, we combine geometrical considerations, knowledge-based scoring, and atomistic modeling to facilitate the design of new channel-containing α-helical barrels. X-ray crystal structures of the resulting designs match predicted in silico models. Furthermore, the observed channels are chemically defined and have diameters related to oligomer state, which present routes to design protein function

Gender violence in schools: taking the ‘girls-as-victims’ discourse forward

This paper draws attention to the gendered nature of violence in schools. Recent recognition that schools can be violent places has tended to ignore the fact that many such acts originate in unequal and antagonistic gender relations, which are tolerated and ‘normalised’ by everyday school structures and processes. After examining some key concepts and definitions, we provide a brief overview of the scope and various manifestations of gender violence in schools, noting that most research to date has focused on girls as victims of gender violence within a heterosexual context and ignores other forms such as homophobic and girl violence. We then move on to look at a few interventions designed to address gender violence in schools in the developing world and end by highlighting the need for more research and improved understanding of the problem and how it can be addressed

Navigating the structural landscape of de Novo α-helical bundles

The association of amphipathic α helices in water leads to α-helical-bundle protein structures. However, the driving force for thisthe hydrophobic effectis not specific and does not define the number or the orientation of helices in the associated state. Rather, this is achieved through deeper sequence-to-structure relationships, which are increasingly being discerned. For example, for one structurally extreme but nevertheless ubiquitous class of bundlethe α-helical coiled coilsrelationships have been established that discriminate between all-parallel dimers, trimers, and tetramers. Association states above this are known, as are antiparallel and mixed arrangements of the helices. However, these alternative states are less well understood. Here, we describe a synthetic-peptide system that switches between parallel hexamers and various up–down–up–down tetramers in response to single-amino-acid changes and solution conditions. The main accessible states of each peptide variant are characterized fully in solution and, in most cases, to high resolution with X-ray crystal structures. Analysis and inspection of these structures helps rationalize the different states formed. This navigation of the structural landscape of α-helical coiled coils above the dimers and trimers that dominate in nature has allowed us to design rationally a well-defined and hyperstable antiparallel coiled-coil tetramer (apCC-Tet). This robust de novo protein provides another scaffold for further structural and functional designs in protein engineering and synthetic biology

Speeds and arrival times of solar transients approximated by self-similar expanding circular fronts

The NASA STEREO mission opened up the possibility to forecast the arrival times, speeds and directions of solar transients from outside the Sun-Earth line. In particular, we are interested in predicting potentially geo-effective Interplanetary Coronal Mass Ejections (ICMEs) from observations of density structures at large observation angles from the Sun (with the STEREO Heliospheric Imager instrument). We contribute to this endeavor by deriving analytical formulas concerning a geometric correction for the ICME speed and arrival time for the technique introduced by Davies et al. (2012, ApJ, in press) called Self-Similar Expansion Fitting (SSEF). This model assumes that a circle propagates outward, along a plane specified by a position angle (e.g. the ecliptic), with constant angular half width (lambda). This is an extension to earlier, more simple models: Fixed-Phi-Fitting (lambda = 0 degree) and Harmonic Mean Fitting (lambda = 90 degree). This approach has the advantage that it is possible to assess clearly, in contrast to previous models, if a particular location in the heliosphere, such as a planet or spacecraft, might be expected to be hit by the ICME front. Our correction formulas are especially significant for glancing hits, where small differences in the direction greatly influence the expected speeds (up to 100-200 km/s) and arrival times (up to two days later than the apex). For very wide ICMEs (2 lambda > 120 degree), the geometric correction becomes very similar to the one derived by M\"ostl et al. (2011, ApJ, 741, id. 34) for the Harmonic Mean model. These analytic expressions can also be used for empirical or analytical models to predict the 1 AU arrival time of an ICME by correcting for effects of hits by the flank rather than the apex, if the width and direction of the ICME in a plane are known and a circular geometry of the ICME front is assumed.Comment: 15 pages, 5 figures, accepted for publication in "Solar Physics

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Density of states, Potts zeros, and Fisher zeros of the Q-state Potts model for continuous Q

The Q-state Potts model can be extended to noninteger and even complex Q in the FK representation. In the FK representation the partition function,Z(Q,a), is a polynomial in Q and v=a-1(a=e^-T) and the coefficients of this polynomial,Phi(b,c), are the number of graphs on the lattice consisting of b bonds and c connected clusters. We introduce the random-cluster transfer matrix to compute Phi exactly on finite square lattices. Given the FK representation of the partition function we begin by studying the critical Potts model Z_{CP}=Z(Q,a_c), where a_c=1+sqrt{Q}. We find a set of zeros in the complex w=sqrt{Q} plane that map to the Beraha numbers for real positive Q. We also identify tilde{Q}_c(L), the value of Q for a lattice of width L above which the locus of zeros in the complex p=v/sqrt{Q} plane lies on the unit circle. We find that 1/tilde{Q}_c->0 as 1/L->0. We then study zeros of the AF Potts model in the complex Q plane and determine Q_c(a), the largest value of Q for a fixed value of a below which there is AF order. We find excellent agreement with Q_c=(1-a)(a+3). We also investigate the locus of zeros of the FM Potts model in the complex Q plane and confirm that Q_c=(a-1)^2. We show that the edge singularity in the complex Q plane approaches Q_c as Q_c(L)~Q_c+AL^-y_q, and determine the scaling exponent y_q. Finally, by finite size scaling of the Fisher zeros near the AF critical point we determine the thermal exponent y_t as a function of Q in the range 2<Q<3. We find that y_t is a smooth function of Q and is well fit by y_t=(1+Au+Bu^2)/(C+Du) where u=u(Q). For Q=3 we find y_t~0.6; however if we include lattices up to L=12 we find y_t~0.50.Comment: to appear in Physical Review

Differential sensing with arrays of de novo designed peptide assemblies

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings

Integrating Positive and Clinical Psychology: Viewing Human Functioning as Continua from Positive to Negative Can Benefit Clinical Assessment, Interventions and Understandings of Resilience

In this review we argue in favour of further integration between the disciplines of positive and clinical psychology. We argue that most of the constructs studied by both positive and clinical psychology exist on continua ranging from positive to negative (e.g., gratitude to ingratitude, anxiety to calmness) and so it is meaningless to speak of one or other field studying the “positive” or the “negative”. However, we highlight historical and cultural factors which have led positive and clinical psychologies to focus on different constructs; thus the difference between the fields is more due to the constructs of study rather than their being inherently “positive” or “negative”. We argue that there is much benefit to clinical psychology of considering positive psychology constructs because; (a) constructs studied by positive psychology researchers can independently predict wellbeing when accounting for traditional clinical factors, both cross-sectionally and prospectively, (2) the constructs studied by positive psychologists can interact with risk factors to predict outcomes, thereby conferring resilience, (3) interventions that aim to increase movement towards the positive pole of well-being can be used encourage movement away from the negative pole, either in isolation or alongside traditional clinical interventions, and (4) research from positive psychology can support clinical psychology as it seeks to adapt therapies developed in Western nations to other cultures