No statistical learning advantage in children over adults: Evidence from behaviour and neural entrainment.

Explicit recognition measures of statistical learning (SL) suggest that children and adults have similar linguistic SL abilities. However, explicit tasks recruit additional cognitive processes that are not directly relevant for SL and may thus underestimate children\u27s true SL capacities. In contrast, implicit tasks and neural measures of SL should be less influenced by explicit, higher-level cognitive abilities and thus may be better suited to capturing developmental differences in SL. Here, we assessed SL to six minutes of an artificial language in English-speaking children (n = 56, 24 females, M = 9.98 years) and adults (n = 44; 31 females, M = 22.97 years), using explicit and implicit behavioural measures and an EEG measure of neural entrainment. With few exceptions, children and adults showed largely similar performance on the behavioural explicit and implicit tasks, replicating prior work. Children and adults also demonstrated robust neural entrainment to both words and syllables, with a similar time course of word-level entrainment, reflecting learning of the hidden word structure. These results demonstrate that children and adults have similar linguistic SL abilities, even when learning is assessed through implicit performance-based and neural measures

Ca2+-Dependent Transcriptional Repressors KCNIP and Regulation of Prognosis Genes in Glioblastoma

Glioblastomas (GBMs) are the most aggressive and lethal primary astrocytic tumors in adults, with very poor prognosis. Recurrence in GBM is attributed to glioblastoma stem-like cells (GSLCs). The behavior of the tumor, including proliferation, progression, invasion, and significant resistance to therapies, is a consequence of the self-renewing properties of the GSLCs, and their high resistance to chemotherapies have been attributed to their capacity to enter quiescence. Thus, targeting GSLCs may constitute one of the possible therapeutic challenges to significantly improve anti-cancer treatment regimens for GBM. Ca2+ signaling is an important regulator of tumorigenesis in GBM, and the transition from proliferation to quiescence involves the modification of the kinetics of Ca2+ influx through store-operated channels due to an increased capacity of the mitochondria of quiescent GSLC to capture Ca2+. Therefore, the identification of new therapeutic targets requires the analysis of the calcium-regulated elements at transcriptional levels. In this review, we focus onto the direct regulation of gene expression by KCNIP proteins (KCNIP1–4). These proteins constitute the class E of Ca2+ sensor family with four EF-hand Ca2+-binding motifs and control gene transcription directly by binding, via a Ca2+-dependent mechanism, to specific DNA sites on target genes, called downstream regulatory element (DRE). The presence of putative DRE sites on genes associated with unfavorable outcome for GBM patients suggests that KCNIP proteins may contribute to the alteration of the expression of these prognosis genes. Indeed, in GBM, KCNIP2 expression appears to be significantly linked to the overall survival of patients. In this review, we summarize the current knowledge regarding the quiescent GSLCs with respect to Ca2+ signaling and discuss how Ca2+via KCNIP proteins may affect prognosis genes expression in GBM. This original mechanism may constitute the basis of the development of new therapeutic strategies

Cell signalling: Control of free calcium

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A graphical method for performance mapping of machines and milling tools

Optimal design of the machining setup in terms of installed machines, cutting tools and process parameters is of paramount importance for every manufacturing company. In most of the metal cutting companies, all choices related to machine eligibility and cutting parameters selection typically come from heuristic approaches and follow supplier indications or base on the skill of experienced machine operators. More advanced solutions, such as model-based and virtual approaches, are adopted less frequently mainly due to the lack of these techniques in grasping the underlying knowledge successfully. Aim of this work is to introduce a synthetic graphical representation of machining centers and cutting tools capabilities, to provide an accessible way to evaluate the feasibility and close-to-limit conditions of the cutting process. Taking inspiration from previous scientific works from the measurement engineering field, a set of 2D and 3D graphs are presented to map machine, tools and process capabilities, as well as their obtainable manufacturing performances and expectable tool life. This approach synthesizes the nominal data coming from different sources (catalogues, database, tool model geometries etc.) and the real cutting tools parameters used during the production phase. Some examples are provided to show the potential of this graphical evaluation in supporting process planning and decision-making and in formalizing the machining setup knowledge. Further developments are devoted to extend the method to other manufacturing processes, including hybrid processes. At the same time, an in-process data gathering software will be integrated for building a solid database that can be used by an autonomous multi-technological process selector, as well as by a pre-process condition advisor in an Industry 4.0 oriented way

Prehospital use of magnesium sulfate as neuroprotection in acute stroke

Background Magnesium sulfate is neuroprotective in preclinical models of stroke and has shown signals of potential efficacy with an acceptable safety profile when delivered early after stroke onset in humans. Delayed initiation of neuroprotective agents has hindered earlier phase 3 trials of neuroprotective agents. Methods: We randomly assigned patients with suspected stroke to receive either intravenous magnesium sulfate or placebo, beginning within 2 hours after symptom onset. A loading dose was initiated by paramedics before the patient arrived at the hospital, and a 24-hour maintenance infusion was started on the patient's arrival at the hospital. The primary outcome was the degree of disability at 90 days, as measured by scores on the modified Rankin scale (range, 0 to 6, with higher scores indicating greater disability). Results: Among the 1700 enrolled patients (857 in the magnesium group and 843 in the placebo group), the mean (±SD) age was 69±13 years, 42.6% were women, and the mean pretreatment score on the Los Angeles Motor Scale of stroke severity (range, 0 to 10, with higher scores indicating greater motor deficits) was 3.7±1.3. The final diagnosis of the qualifying event was cerebral ischemia in 73.3% of patients, intracranial hemorrhage in 22.8%, and a stroke-mimicking condition in 3.9%. The median interval between the time the patient was last known to be free of stroke symptoms and the start of the study-drug infusion was 45 minutes (interquartile range, 35 to 62), and 74.3% of patients received the study-drug infusion within the first hour after symptom onset. There was no significant shift in the distribution of 90-day disability outcomes on the global modified Rankin scale between patients in the magnesium group and those in the placebo group (P=0.28 by the Cochran–Mantel–Haenszel test); mean scores at 90 days did not differ between the magnesium group and the placebo group (2.7 in each group, P=1.00). No significant between-group differences were noted with respect to mortality (15.4% in the magnesium group and 15.5% in the placebo group, P=0.95) or all serious adverse events. Conclusions: Prehospital initiation of magnesium sulfate therapy was safe and allowed the start of therapy within 2 hours after the onset of stroke symptoms, but it did not improve disability outcomes at 90 days. (Funded by the National Institute of Neurological Disorders and Stroke; FAST-MAG ClinicalTrials.gov number, NCT00059332.)

Variables with time-varying effects and the Cox model: Some statistical concepts illustrated with a prognostic factor study in breast cancer

International audienceBACKGROUND: The Cox model relies on the proportional hazards (PH) assumption, implying that the factors investigated have a constant impact on the hazard - or risk - over time. We emphasize the importance of this assumption and the misleading conclusions that can be inferred if it is violated; this is particularly essential in the presence of long follow-ups. METHODS: We illustrate our discussion by analyzing prognostic factors of metastases in 979 women treated for breast cancer with surgery. Age, tumour size and grade, lymph node involvement, peritumoral vascular invasion (PVI), status of hormone receptors (HRec), Her2, and Mib1 were considered. RESULTS: Median follow-up was 14 years; 264 women developed metastases. The conventional Cox model suggested that all factors but HRec, Her2, and Mib1 status were strong prognostic factors of metastases. Additional tests indicated that the PH assumption was not satisfied for some variables of the model. Tumour grade had a significant time-varying effect, but although its effect diminished over time, it remained strong. Interestingly, while the conventional Cox model did not show any significant effect of the HRec status, tests provided strong evidence that this variable had a non-constant effect over time. Negative HRec status increased the risk of metastases early but became protective thereafter. This reversal of effect may explain non-significant hazard ratios provided by previous conventional Cox analyses in studies with long follow-ups. CONCLUSIONS: Investigating time-varying effects should be an integral part of Cox survival analyses. Detecting and accounting for time-varying effects provide insights on some specific time patterns, and on valuable biological information that could be missed otherwise

A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis

Patients with newly diagnosed multiple myeloma (NDMM) with high-risk disease are in need of new treatment strategies to improve the outcomes. Multiple clinical, cytogenetic, or gene expression features have been used to identify high-risk patients, each of which has significant weaknesses. Inclusion of molecular features into risk stratification could resolve the current challenges. In a genome-wide analysis of the largest set of molecular and clinical data established to date from NDMM, as part of the Myeloma Genome Project, we have defined DNA drivers of aggressive clinical behavior. Whole-genome and exome data from 1273 NDMM patients identified genetic factors that contribute significantly to progression free survival (PFS) and overall survival (OS) (cumulative R2 = 18.4% and 25.2%, respectively). Integrating DNA drivers and clinical data into a Cox model using 784 patients with ISS, age, PFS, OS, and genomic data, the model has a cumlative R2 of 34.3% for PFS and 46.5% for OS. A high-risk subgroup was defined by recursive partitioning using either a) bi-allelic TP53 inactivation or b) amplification (≥4 copies) of CKS1B (1q21) on the background of International Staging System III, comprising 6.1% of the population (median PFS = 15.4 months; OS = 20.7 months) that was validated in an independent dataset. Double-Hit patients have a dire prognosis despite modern therapies and should be considered for novel therapeutic approaches