vasa is required for GURKEN accumulation in the oocyte, and is involved in oocyte differentiation and germline cyst development

Skip to Next Section The Drosophila gene vasa is required for pole plasm assembly and function, and also for completion of oogenesis. To investigate the role of vasa in oocyte development, we generated a new null mutation of vasa, which deletes the entire coding region. Analysis of vasa-null ovaries revealed that the gene is involved in the growth of germline cysts. In vasa-null ovaries, germaria are atrophied, and contain far fewer developing cysts than do wild-type germaria; a phenotype similar to, but less severe than, that of a null nanos allele. The null mutant also revealed roles for vasa in oocyte differentiation, anterior-posterior egg chamber patterning, and dorsal-ventral follicle patterning, in addition to its better-characterized functions in posterior embryonic patterning and pole cell specification. The anterior-posterior and dorsal-ventral patterning phenotypes resemble those observed in gurken mutants. vasa-null oocytes fail to efficiently accumulate many localized RNAs, such as Bicaudal-D, orb, oskar, and nanos, but still accumulate gurken RNA. However, GRK accumulation in the oocyte is severely reduced in the absence of vasa function, suggesting a function for VASA in activating gurken translation in wild-type ovaries

Neurological Soft Signs in Individuals with Pathological Gambling

Increased neurological soft signs (NSSs) have been found in a number of neuropsychiatric syndromes, including chemical addiction. The present study examined NSSs related to perceptual-motor and visuospatial processing in a behavioral addiction viz., pathological gambling (PG). As compared to mentally healthy individuals, pathological gamblers displayed significantly poorer ability to copy two- and three-dimensional figures, to recognize objects against a background noise, and to orient in space on a road-map test. Results indicated that PG is associated with subtle cerebral cortical abnormalities. Further prospective clinical research is needed to address the NSSs' origin and chronology (e.g., predate or follow the development of PG) as well as their response to therapeutic interventions and/or their ability to predict such a response

Predicting post-trauma stress symptoms from pre-trauma psychophysiologic reactivity, personality traits and measures of psychopathology

Background: Most individuals exposed to a traumatic event do not develop post-traumatic stress disorder (PTSD), although many individuals may experience sub-clinical levels of post-traumatic stress symptoms (PTSS). There are notable individual differences in the presence and severity of PTSS among individuals who report seemingly comparable traumatic events. Individual differences in PTSS following exposure to traumatic events could be influenced by pre-trauma vulnerabilities for developing PTSS/PTSD. Methods Pre-trauma psychological, psychophysiological and personality variables were prospectively assessed for their predictive relationships with post-traumatic stress symptoms (PTSS). Police and firefighter trainees were tested at the start of their professional training (i.e., pre-trauma; n = 211) and again several months after exposure to a potentially traumatic event (i.e., post-trauma, n = 99). Pre-trauma assessments included diagnostic interviews, psychological and personality measures and two psychophysiological assessment procedures. The psychophysiological assessments measured psychophysiologic reactivity to loud tones and the acquisition and extinction of a conditioned fear response. Post-trauma assessment included a measure of psychophysiologic reactivity during recollection of the traumatic event using a script-driven imagery task. Results: Logistic stepwise regression identified the combination of lower IQ, higher depression score and poorer extinction of forehead (corrugator) electromyogram responses as pre-trauma predictors of higher PTSS. The combination of lower IQ and increased skin conductance (SC) reactivity to loud tones were identified as pre-trauma predictors of higher post-trauma psychophysiologic reactivity during recollection of the traumatic event. A univariate relationship was also observed between pre-trauma heart rate (HR) reactivity to fear cues during conditioning and post-trauma psychophysiologic reactivity. Conclusion: The current study contributes to a very limited literature reporting results from truly prospective examinations of pre-trauma physiologic, psychologic, and demographic predictors of PTSS. Findings that combinations of lower estimated IQ, greater depression symptoms, a larger differential corrugator EMG response during extinction and larger SC responses to loud tones significantly predicted higher PTSS suggests that the process(es) underlying these traits contribute to the pathogenesis of subjective and physiological PTSS. Due to the low levels of PTSS severity and relatively restricted ranges of outcome scores due to the healthy nature of the participants, results may underestimate actual predictive relationships

KATANA - a charge-sensitive triggering system for the Sπ\piRIT experiment

KATANA - the Krakow Array for Triggering with Amplitude discrimiNAtion - has been built and used as a trigger and veto detector for the S$\pi$RIT TPC at RIKEN. Its construction allows operating in magnetic field and providing fast response for ionizing particles, giving the approximate forward multiplicity and charge information. Depending on this information, trigger and veto signals are generated. The article presents performance of the detector and details of its construction. A simple phenomenological parametrization of the number of emitted scintillation photons in plastic scintillator is proposed. The effect of the light output deterioration in the plastic scintillator due to the in-beam irradiation is discussed.Comment: 14 pages, 11 figure

Parameterizing neural networks for disease classification

Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency

KRATTA, a triple telescope array for charged reaction products

KRATTA, a new, low threshold, broad energy range triple telescope array has been built to measure the energy, emission angles and isotopic composition of light charged reaction products. It has been equipped with fully digital chains of electronics. The array performed very well during the ASY-EOS experiment, conducted in May 2011 at GSI. The structure and performance of the array are presented using the first experimental results

Leveraging Explainable Artificial Intelligence to Optimize Clinical Decision Support

OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches. METHODS: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert\u27s historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. RESULTS: The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues. CONCLUSION: We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues

Cas9-triggered chain ablation of cas9 as a gene drive brake

With the advent of clustered, regularly interspaced, short palindromic repeats (CRISPR)–CRISPR-associated protein 9 (Cas9) technology, researchers can construct gene drives that can bias the inheritance of edited alleles to alter entire populations. As demonstrated with the mutagenic chain reaction in Drosophila4, the CRISPR-Cas9 system can propagate genomic modification together with the genome-editing machinery itself. Although gene drives might have the potential to control insect-borne diseases and agricultural pests, substantial concerns have been raised over unanticipated ecological consequences as a result of drive use. Here we report the development of a potential Cas9-based gene drive 'brake' that remains inert in a wild-type genome but is activated by Cas9 to both cleave the genomic cas9 sequence and to convert an incoming cas9 allele into a brake. This means that the propagation of the brake is favored in a cas9-carrying population