Minimizing Control for Credit Assignment with Strong Feedback

The success of deep learning ignited interest in whether the brain learns hierarchical representations using gradient-based learning. However, current biologically plausible methods for gradient-based credit assignment in deep neural networks need infinitesimally small feedback signals, which is problematic in biologically realistic noisy environments and at odds with experimental evidence in neuroscience showing that top-down feedback can significantly influence neural activity. Building upon deep feedback control (DFC), a recently proposed credit assignment method, we combine strong feedback influences on neural activity with gradient-based learning and show that this naturally leads to a novel view on neural network optimization. Instead of gradually changing the network weights towards configurations with low output loss, weight updates gradually minimize the amount of feedback required from a controller that drives the network to the supervised output label. Moreover, we show that the use of strong feedback in DFC allows learning forward and feedback connections simultaneously, using learning rules fully local in space and time. We complement our theoretical results with experiments on standard computer-vision benchmarks, showing competitive performance to backpropagation as well as robustness to noise. Overall, our work presents a fundamentally novel view of learning as control minimization, while sidestepping biologically unrealistic assumptions

Uncertainty estimation under model misspecification in neural network regression

Although neural networks are powerful function approximators, the underlying modelling assumptions ultimately define the likelihood and thus the model class they are parameterizing. In classification, these assumptions are minimal as the commonly employed softmax is capable of representing any discrete distribution over a finite set of outcomes. In regression, however, restrictive assumptions on the type of continuous distribution to be realized are typically placed, like the dominant choice of training via mean-squared error and its underlying Gaussianity assumption. Recently, modelling advances allow to be agnostic to the type of continuous distribution to be modelled, granting regression the flexibility of classification models. While past studies stress the benefit of such flexible regression models in terms of performance, here we study the effect of the model choice on uncertainty estimation. We highlight that under model misspecification, aleatoric uncertainty is not properly captured, and that a Bayesian treatment of a misspecified model leads to unreliable epistemic uncertainty estimates. Overall, our study provides an overview on how modelling choices in regression may influence uncertainty estimation and thus any downstream decision making process

Continual Learning in Recurrent Neural Networks with Hypernetworks

The last decade has seen a surge of interest in continual learning (CL), and a variety of methods have been developed to alleviate catastrophic forgetting. However, most prior work has focused on tasks with static data, while CL on sequential data has remained largely unexplored. Here we address this gap in two ways. First, we evaluate the performance of established CL methods when applied to recurrent neural networks (RNNs). We primarily focus on elastic weight consolidation, which is limited by a stability-plasticity trade-off, and explore the particularities of this trade-off when using sequential data. We show that high working memory requirements, but not necessarily sequence length, lead to an increased need for stability at the cost of decreased performance on subsequent tasks. Second, to overcome this limitation we employ a recent method based on hypernetworks and apply it to RNNs to address catastrophic forgetting on sequential data. By generating the weights of a main RNN in a task-dependent manner, our approach disentangles stability and plasticity, and outperforms alternative methods in a range of experiments. Overall, our work provides several key insights on the differences between CL in feedforward networks and in RNNs, while offering a novel solution to effectively tackle CL on sequential data.Comment: 13 pages and 4 figures in the main text; 20 pages and 2 figures in the supplementary material

Dietary Fat Patterns and Outcomes in Acute Pancreatitis in Spain

Background/Objective: Evidence from basic and clinical studies suggests that unsaturated fatty acids (UFAs) might be relevant mediators of the development of complications in acute pancreatitis (AP). Objective: The aim of this study was to analyze outcomes in patients with AP from regions in Spain with different patterns of dietary fat intake. Materials and Methods: A retrospective analysis was performed with data from 1,655 patients with AP from a Spanish prospective cohort study and regional nutritional data from a Spanish cross-sectional study. Nutritional data considered in the study concern the total lipid consumption, detailing total saturated fatty acids, UFAs and monounsaturated fatty acids (MUFAs) consumption derived from regional data and not from the patient prospective cohort. Two multivariable analysis models were used: (1) a model with the Charlson comorbidity index, sex, alcoholic etiology, and recurrent AP; (2) a model that included these variables plus obesity. Results: In multivariable analysis, patients from regions with high UFA intake had a significantly increased frequency of local complications, persistent organ failure (POF), mortality, and moderate-to-severe disease in the model without obesity and a higher frequency of POF in the model with obesity. Patients from regions with high MUFA intake had significantly more local complications and moderate-to-severe disease; this significance remained for moderate-to-severe disease when obesity was added to the model. Conclusions: Differences in dietary fat patterns could be associated with different outcomes in AP, and dietary fat patterns may be a pre-morbid factor that determines the severity of AP. UFAs, and particulary MUFAs, may influence the pathogenesis of the severity of AP

A novel targeted RNA-Seq panel identifies a subset of adult patients with acute lymphoblastic leukemia with BCR-ABL1-like characteristics

BCR-ABL1-like B-cell precursor acute lymphoblastic leukemia (BCP-ALL) remains poorly characterized in adults. We sought to establish the frequency and outcome of adolescent and adult BCR-ABL1-like ALL using a novel RNA-Seq signature in a series of patients with BCP-ALL. To this end, we developed and tested an RNA-Seq custom panel of 42 genes related to a BCR-ABL1-like signature in a cohort of 100 patients with BCP-ALL and treated with risk-adapted ALL trials. Mutations related to BCR-ABL1-like ALL were studied in a panel of 33 genes by next-generation sequencing (NGS). Also, CRLF2 overexpression and IKZF1/CDKN2A/B deletions were analyzed. Twenty out of 79 patients (12-84 years) were classified as BCR-ABL1-like (25%) based on heatmap clustering, with significant overexpression of ENAM, IGJ, and CRLF2 (P ≤ 0.001). The BCR-ABL1-like subgroup accounted for 29% of 15-60-year-old patients, with the following molecular characteristics: CRLF2 overexpression (75% of cases), IKZF1 deletions (64%), CDKN2A/B deletions (57%), and JAK2 mutations (57%). Among patients with postinduction negative minimal residual disease, those with the BCR-ABL1-like ALL signature had a higher rate of relapse and lower complete response duration than non-BCR-ABL1-like patients (P = 0.007). Thus, we have identified a new molecular signature of BCR-ABL1-like ALL that correlates with adverse prognosis in adult patients with ALL

A novel targeted RNA-Seq panel identifies a subset of adult patients with acute lymphoblastic leukemia with BCR-ABL1-like characteristics

BCR-ABL1-like B-cell precursor acute lymphoblastic leukemia (BCP-ALL) remains poorly characterized in adults. We sought to establish the frequency and outcome of adolescent and adult BCR-ABL1-like ALL using a novel RNA-Seq signature in a series of patients with BCP-ALL. To this end, we developed and tested an RNA-Seq custom panel of 42 genes related to a BCR-ABL1-like signature in a cohort of 100 patients with BCP-ALL and treated with risk-adapted ALL trials. Mutations related to BCR-ABL1-like ALL were studied in a panel of 33 genes by next-generation sequencing (NGS). Also, CRLF2 overexpression and IKZF1/CDKN2A/B deletions were analyzed. Twenty out of 79 patients (12-84 years) were classified as BCR-ABL1-like (25%) based on heatmap clustering, with significant overexpression of ENAM, IGJ, and CRLF2 (P <= 0.001). The BCR-ABL1-like subgroup accounted for 29% of 15-60-year-old patients, with the following molecular characteristics: CRLF2 overexpression (75% of cases), IKZF1 deletions (64%), CDKN2A/B deletions (57%), and JAK2 mutations (57%). Among patients with postinduction negative minimal residual disease, those with the BCR-ABL1-like ALL signature had a higher rate of relapse and lower complete response duration than non-BCR-ABL1-like patients (P = 0.007). Thus, we have identified a new molecular signature of BCR-ABL1-like ALL that correlates with adverse prognosis in adult patients with ALL

Prognostic heterogeneity of adult B-cell precursor acute lymphoblastic leukaemia patients with t(1;19)(q23;p13)/TCF3-PBX1 treated with measurable residual disease-oriented protocols

The prognosis of t(1;19)(q23;p13)/transcription factor 3-pre-B-cell leukaemia homeobox 1 (TCF3-PBX1) in adolescent and adult patients with acute lymphoblastic leukaemia (ALL) treated with measurable residual disease (MRD)-oriented trials remains controversial. In the present study, we analysed the outcome of adolescent and adult patients with t(1;19)(q23;p13) enrolled in paediatric-inspired trials. The patients with TCF3-PBX1 showed similar MRD clearance and did not have different survival compared with other B-cell precursor ALL patients. However, patients with TCF3-PBX1 had a significantly higher cumulative incidence of relapse, especially among patients aged ≥35 years carrying additional cytogenetic alterations. These patients might benefit from additional/intensified therapy (e.g. immunotherapy in first complete remission with or without subsequent haematopoietic stem cell transplantation). 40 __ $u https://creativecommons.org/licenses/by-nc-nd/4.0

DNA Methylation Profiles and Their Relationship with Cytogenetic Status in Adult Acute Myeloid Leukemia

Background: Aberrant promoter DNA methylation has been shown to play a role in acute myeloid leukemia (AML) pathophysiology. However, further studies to discuss the prognostic value and the relationship of the epigenetic signatures with defined genomic rearrangements in acute myeloid leukemia are required. Methodology/Principal Findings: We carried out high-throughput methylation profiling on 116 de novo AML cases and we validated the significant biomarkers in an independent cohort of 244 AML cases. Methylation signatures were associated with the presence of a specific cytogenetic status. In normal karyotype cases, aberrant methylation of the promoter of DBC1 was validated as a predictor of the disease-free and overall survival. Furthermore, DBC1 expression was significantly silenced in the aberrantly methylated samples. Patients with chromosome rearrangements showed distinct methylation signatures. To establish the role of fusion proteins in the epigenetic profiles, 20 additional samples of human hematopoietic stem/ progenitor cells (HSPC) transduced with common fusion genes were studied and compared with patient samples carrying the same rearrangements. The presence of MLL rearrangements in HSPC induced the methylation profile observed in the MLL-positive primary samples. In contrast, fusion genes such as AML1/ETO or CBFB/MYH11 failed to reproduce the epigenetic signature observed in the patients. Conclusions/Significance: Our study provides a comprehensive epigenetic profiling of AML, identifies new clinical markers for cases with a normal karyotype, and reveals relevant biological information related to the role of fusion proteins on the methylation signatur

Mannose-binding lectin-deficient genotypes as a risk factor of pneumococcal meningitis in infants

OBJECTIVES: The objective of this study was to evaluate to evaluate the role of mannose-binding-lectin deficient genotypes in pneumococcal meningitis (PM) in children. METHODS: We performed a 16-year retrospective study (January 2001 to March 2016) including patients ≤ 18 years with PM. Variables including attack rate of pneumococcal serotype (high or low invasive capacity) and MBL2 genotypes associated with low serum MBL levels were recorded. RESULTS: Forty-eight patients were included in the study. Median age was 18.5 months and 17/48 episodes (35.4%) occurred in children ≤ 12 months old. Serotypes with high-invasive disease potential were identified in 15/48 episodes (31.2%). MBL2 deficient genotypes accounted for 18.8% (9/48). Children ≤ 12 months old had a 7-fold risk (95% CI: 1.6-29.9; p 12 months old. A sub-analysis of patients by age group revealed significant proportions of carriers of MBL2 deficient genotypes among those ≤ 12 months old with PM caused by opportunistic serotypes (54.5%), admitted to the PICU (Pediatric Intensive Care Unit) (46.7%) and of White ethnicity (35.7%). These proportions were significantly higher than in older children (all p<0.05). CONCLUSIONS: Our results suggest that differences in MBL2 genotype in children ≤12 months old affects susceptibility to PM, and it may have an important role in the episodes caused by non-high invasive disease potential serotypes