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We present time-resolved Gd-Gd electron paramagnetic resonance (TiGGER) at 240 GHz for tracking inter-residue distances during a protein’s mechanical cycle in the solution state. TiGGER makes use of Gd-sTPATCN as spin labels, whose favorable qualities include a spin-7/2 EPR-active center, short linker, narrow intrinsic linewidth, and virtually no anisotropy at high fields (8.6 T) when compared to nitroxide spin labels. Using TiGGER, we determined that upon light activation, the C-terminus and N-terminus of AsLOV2 separate in less than 1 s and relax back to equilibrium with a time constant of approximately 60 s. TiGGER revealed that the light-activated long-range mechanical motion is slowed in the Q513A variant of AsLOV2 and is correlated to the similarly slowed relaxation of the optically excited chromophore as described in recent literature. TiGGER has the potential to valuably complement existing methods for the study of triggered functional dynamics in proteins.

Esta obra compuesta por dos libros es un esfuerzo colectivo que sitúa la formación de las clases medias en el centro de las historias de América Latina de los dos últi-mos siglos. Esta contribución interdisciplinaria a las historias globales de las clases medias, las historias de América Latina y los estudios interseccionales cuenta con la participación de académicos de diferentes lugares de la región. Además, invita a un público más amplio a interesarse en la importancia de las clases medias para entender la modernidad, la democracia, el neoliberalismo y la decolonialidad. Al incluir investigaciones producidas por y en varios países del continente, incorpora tendencias de la historia social, los estudios culturales y la teoría del discurso. Así mismo, sitúa categorías analíticas como raza y género en el centro de la formación de clases. La obra aspira a iniciar una conversación crítica y global sobre las formas en que el estudio de las clases medias proporciona relecturas cruciales sobre cómo ha sido entendida históricamente América Latina como región.

A key goal of all TKA alignment strategies is to achieve joint balance. This study aims to compare the alignments achieved by preoperatively planning to a novel distracted joint gap protocol to common alignment strategies as well as to the alignment of a healthy non-arthritic population.A retrospective study comprised of 145 knees was performed. A long-leg supine CT scan, weightbearing AP knee X-ray and two distracted knee X-rays (one each in extension and flexion, making use of an ankle weight to open the joint) were taken pre-operatively. This imaging was used to perform segmentation, landmarking and 3D-to-2D registration. The medial and lateral joint gaps were determined in extension and flexion.The mean weightbearing, KA planned and distracted joint planned HKA were 4.7° (±5.9°) varus, 0.3° (±3.2°) varus, and 2.2° (±3.5°) varus. This compares to a healthy adult HKA of 1.3° (±2.3°) varus. A patient level comparison between the planned KA and distracted joint HKA found that the coronal angles of the two alignments are within 3° of each other for 64% patients, within 3-5° for 26% of patients and greater than 5° for the remaining 10% of patients.Of those compared, the planned distracted HKA was the closest to the constitutional varus HKA of a healthy population. Patient level analysis highlighted the fundamental differences between the planned KA and joint distracted alignments. By considering both hard and soft tissue, the planned joint distracted alignment allows for a more holistic foundation for pre-operative surgical planning for a given patient.

Neural activations during auditory oddball tasks may be endophenotypes for psychosis and bipolar disorder. The authors investigated oddball neural deviations that discriminate multiple diagnostic groups across the schizophrenia-bipolar spectrum (schizophrenia, schizoaffective disorder, psychotic bipolar disorder, and nonpsychotic bipolar disorder) and clarified their relationship to clinical and cognitive features.Auditory oddball responses to standard and target tones from 64 sensor EEG recordings were compared across patients with psychosis (total N=597; schizophrenia, N=225; schizoaffective disorder, N=201; bipolar disorder with psychosis, N=171), patients with bipolar disorder without psychosis (N=66), and healthy comparison subjects (N=415) from the second iteration of the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP2) study. EEG activity was analyzed in voltage and in the time-frequency domain (low, beta, and gamma bands). Event-related potentials (ERPs) were compared with those from an independent sample collected during the first iteration of B-SNIP (B-SNIP1; healthy subjects, N=211; psychosis group, N=526) to establish the repeatability of complex oddball ERPs across multiple psychosis syndromes (r values0.94 between B-SNIP1 and B-SNIP2).Twenty-six EEG features differentiated the groups; they were used in discriminant and correlational analyses. EEG variables from the N100, P300, and low-frequency ranges separated the groups along a diagnostic continuum from healthy to bipolar disorder with psychosis/bipolar disorder without psychosis to schizoaffective disorder/schizophrenia and were strongly related to general cognitive function (r=0.91). P50 responses to standard trials and early beta/gamma frequency responses separated the bipolar disorder without psychosis group from the bipolar disorder with psychosis group. P200, N200, and late beta/gamma frequency responses separated the two bipolar disorder groups from the other groups.Neural deviations during auditory processing are related to psychosis history and bipolar disorder. There is a powerful transdiagnostic relationship between severity of these neural deviations and general cognitive performance. These results have implications for understanding the neurobiology of clinical syndromes across the schizophrenia-bipolar spectrum that may have an impact on future biomarker research.

A series of Cu(II) complexes with cyclam-based ligands containing two

The synthesis and photophysical evaluation of a series of hydrophilic sulphonated and pH responsive luminescent europium(III) probes are reported. The Eu emission intensities and lifetimes increase very significantly on lowering pH, leading to a ‘switching on’ of Eu luminescence in more acidic media. By varying the nature of the substituents at an integral aniline nitrogen atom located in the sensitising chromophore, the pKa value of the complex can be tuned to between 4.3 and 6.3 for a set of five Eu(III) complexes. These systems are designed to allow conjugation to targeting vectors permitting, for example, the monitoring of receptor internalisation in cells. This approach can be used to tag proteins selectively and allows the monitoring of uptake into acidic organelles, enabling the creation of time-resolved internalisation assays to follow pH change in real time, both in vitro and in cellulo.

Educational psychology usually focuses on explaining phenomena. As a result, researchers seldom explore how well their models predict the outcomes they care about using best-practice approaches to predictive statistics. In this paper, we focus less on explanation and more on prediction, showing how both are important for advancing the field. We apply predictive models to the role of teachers on student engagement: the thoughts, attitudes, and behaviours that translate motivation into progress. We integrate the suggestions from four prominent motivational theories, and aim to identify those most critical behaviours for predicting changes in students’ engagement in physical education. Students (N = 1,324 all from Year 7, 52% girls) from 17 low socio-economic status schools rated their teacher’s demonstration of 71 behaviours in the middle of the school year. We also assessed students’ engagement at the beginning and end of the year. We trained elastic-net regression models on 70% of the data and then assessed their predictive validity on the held-out data (30%). The models showed that teacher behaviours predicted 4.39% of the variance in students’ change in engagement. Some behaviours that were most consistently associated with a positive change in engagement were being good role models (β = 0.046), taking interest in students’ lives outside of class (β = 0.033), and allowing students to make choices (β = 0.029). The influential behaviours did not neatly fit within any single motivational theory. These findings support arguments for integrating different theoretical approaches, and suggest practitioners may want to consider multiple theories when designing interventions. More generally, we argue that researchers in educational psychology should more frequently test how well their models not just explain, but predict the outcomes they care about.

We propose techniques for fault diagnosis in discrete-event systems modelled by labelled Petri nets, where fault events are modelled as unobservable transitions. The proposed approach combines an offline and an online algorithm. The offline algorithm constructs a diagnoser in the form of sets of inequalities that capture the legal, normal and faulty behaviour. To implement the offline algorithm, we adopt the Fourier–Motzkin method for elimination of variables from these sets of inequalities. Upon observing an event, the diagnoser is used to determine whether a fault occurred or might have occurred. The occurrence of a fault can be verified by checking the observed sequence against the sets of inequalities. This approach has the advantage that the tradeoff between the size of the diagnoser and the time for computing the diagnosis is achieved. In addition, fault diagnosis in both bounded and unbounded Petri nets can be addressed.

Kalman filters are widely used for estimating the state of a system based on noisy or inaccurate sensor readings, for example in the control and navigation of vehicles or robots. However, numerical instability or modelling errors may lead to divergence of the filter, leading to erroneous estimations. Establishing robustness against such issues can be challenging. We propose novel formal verification techniques and software to perform a rigorous quantitative analysis of the effectiveness of Kalman filters. We present a general framework for modelling Kalman filter implementations operating on linear discrete-time stochastic systems, and techniques to systematically construct a Markov model of the filter's operation using truncation and discretisation of the stochastic noise model. Numerical stability and divergence properties are then verified using probabilistic model checking. We evaluate the scalability and accuracy of our approach on two distinct probabilistic kinematic models and four Kalman filter implementations.

Current clinical phenomenological diagnosis in psychiatry neither captures biologically homologous disease entities nor allows for individualized treatment prescriptions based on neurobiology. In this report, we studied two large samples of cases with schizophrenia, schizoaffective, and bipolar I disorder with psychosis, presentations with clinical features of hallucinations, delusions, thought disorder, affective, or negative symptoms. A biomarker approach to subtyping psychosis cases (called psychosis Biotypes) captured neurobiological homology that was missed by conventional clinical diagnoses. Two samples (called “B-SNIP1” with 711 psychosis and 274 healthy persons, and the “replication sample” with 717 psychosis and 198 healthy persons) showed that 44 individual biomarkers, drawn from general cognition (BACS), motor inhibitory (stop signal), saccadic system (pro- and anti-saccades), and auditory EEG/ERP (paired-stimuli and oddball) tasks of psychosis-relevant brain functions were replicable (r’s from .96–.99) and temporally stable (r’s from .76–.95). Using numerical taxonomy (k-means clustering) with nine groups of integrated biomarker characteristics (called bio-factors) yielded three Biotypes that were virtually identical between the two samples and showed highly similar case assignments to subgroups based on cross-validations (88.5%–89%). Biotypes-1 and -2 shared poor cognition. Biotype-1 was further characterized by low neural response magnitudes, while Biotype-2 was further characterized by overactive neural responses and poor sensory motor inhibition. Biotype-3 was nearly normal on all bio-factors. Construct validation of Biotype EEG/ERP neurophysiology using measures of intrinsic neural activity and auditory steady state stimulation highlighted the robustness of these outcomes. Psychosis Biotypes may yield meaningful neurobiological targets for treatments and etiological investigations.