Diagnostic classification of flash drought events reveals distinct classes of forcings and impacts

Recent years have seen growing appreciation that rapidly intensifying “flash droughts” are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility-based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the Contiguous United States (CONUS) for the period 1979-2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture “flash droughts” are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways.

Diagnostic Classification Model for Forced-Choice Items and Noncognitive Tests

The forced-choice (FC) item formats used for noncognitive tests typically develop a set of response options that measure different traits and instruct respondents to make judgments among these options in terms of their preference to control the response biases that are commonly observed in normative tests. Diagnostic classification models (DCMs) can provide information regarding the mastery status of test takers on latent discrete variables and are more commonly used for cognitive tests employed in educational settings than for noncognitive tests. The purpose of this study is to develop a new class of DCM for FC items under the higher-order DCM framework to meet the practical demands of simultaneously controlling for response biases and providing diagnostic classification information. By conducting a series of simulations and calibrating the model parameters with a Bayesian estimation, the study shows that, in general, the model parameters can be recovered satisfactorily with the use of long tests and large samples. More attributes improve the precision of the second-order latent trait estimation in a long test, but decrease the classification accuracy and the estimation quality of the structural parameters. When statements are allowed to load on two distinct attributes in paired comparison items, the specific-attribute condition produces better a parameter estimation than the overlap-attribute condition. Finally, an empirical analysis related to work-motivation measures is presented to demonstrate the applications and implications of the new model.

Del trastorno del diálogo tónico a la inestabilidad psicomotriz: Taxonomía diagnóstica

The goal of this work is to deepen the relationship between hyperactivity and tonic dialogue, by considering the body as a common element of communication even if their psychomotor manifestations and meanings are different during development. While tonic dialogue is vital for the child in the constitution of the attachment bond, psychomotor instability is the expression of a difficulty in relating to reality. In the clinic it is difficult to place instability in international diagnostic classifications, due to the multi-problematic nature and variability of expressions of it. Research confirms the consequences of a disorder of tonic dialogue, constituting a model on which future behaviors will be organized. The authors hypothesize that each stage of the body communication, if lived with difficulty, constitutes a matrix on which diversified expressions of instability will be structured. The intent, therefore, is to collect theoretical-clinical material in order to subsequently make a diagnostic classification of psychomotor instability. El objetivo de este trabajo es profundizar en la relación entre hiperactividad y diálogo tónico al considerar el cuerpo como elemento común de comunicación, aunque sus manifestaciones psicomotoras y significados sean diferentes durante el desarrollo. Mientras que el diálogo tónico es vital para que el niño establezca el vínculo de apego, la inestabilidad psicomotora es la expresión de una dificultad para relacionarse con la realidad. La complejidad y variabilidad de los cuadros clínicos dificulta su ubicación en las clasificaciones diagnósticas internacionales. La investigación luego confirma las consecuencias del trastorno del diálogo tónico, constituyendo un modelo sobre el que se organizarán los comportamientos futuros. Los autores plantean la hipótesis de que cada etapa del diálogo tónico, si se vive con dificultad, constituye una matriz sobre la que se estructurarán diversas expresiones de inestabilidad. La intención es recopilar material teórico-clínico para posteriormente realizar una clasificación diagnóstica de la inestabilidad psicomotora.

Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials

Imaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.

Test-level and Item-level Model Fit Comparison of General vs. Specific Diagnostic Classification Models: A Case of True DCM

The current study compared the model fit indices, skill mastery probabilities, and classification accuracy of six Diagnostic Classification Models (DCMs): a general model (G-DINA) against five specific models (LLM, RRUM, ACDM, DINA, and DINO). To do so, the response data to the grammar and vocabulary sections of a General English Achievement Test, designed specifically for cognitive diagnostic purposes from scratch, was analyzed. The results of the test-level-model fit values obtained strong evidence in supporting the G-DINA and LLM models possessing the best model fit. In addition, the ACDM and RRUM were almost very identical to that of the G-DINA. The value indices of the DINO and DINA models were very close to each other but larger than those of the G-DINA and LLM. The model fit was also investigated at the item level, and the results revealed that model selection should be performed at the item level rather than the test level, and most of the specific models might perform well for the test. The findings of this study suggested that the relationships among the attributes of grammar and vocabulary are not ‘either-or’ compensatory or non-compensatory but a combination of both.

Mutational Landscape in Myeloproliferative Neoplasms: Implications on Prognosis and Clinical Management

Mutations are a critical piece in understanding how myeloproliferative neoplasms (MPNs) occur, specifically the pathobiology of JAK/STAT activation. Mutations play such an important role, in fact, that they are a key part of the diagnostic classification for these diseases. Furthermore, the mutational landscape of MPNs affects both the prognosis and the biology of disease progression. Current research in the field is focused on understanding how and why these mutations occur, as well as how to attack them to address disease at the time of progression or even before disease progression has occurred.