Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.Although childhood-onset psychiatric disorders are often considered as distinct and separate from each other, they frequently co-occur, with partial overlapping symptomatology. Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) commonly co-occur with each other and with other mental disorders, particularly disruptive behavior disorders, oppositional defiant disorder/conduct disorder (ODD/CD). Whether these associated comorbidities represent a spectrum of distinct clinical phenotypes is matter of research. The aim of our study was to describe the clinical phenotypes of youths with ADHD with and without ASD and/or ODD/CD, based on neuropsychological and psychopathological variables. One-hundred fifty-one participants with ADHD were prospectively recruited and assigned to four clinical groups, and assessed by means of parent-reported questionnaires, the child behavior checklist and the behavior rating inventory of executive functions. The ADHD alone group presented a greater impairment in metacognitive executive functions, ADHD+ASD patients presented higher internalizing problems and deficits in Shifting tasks, and ADHD+ODD/CD subjects presented emotional-behavioral dysregulation. Moreover, ADHD+ASD+ODD/CD individuals exhibited greater internalizing and externalizing problems, and specific neuropsychological impairments in the domains of emotional regulation. Our study supports the need to implement the evaluation of the psychopathological and neuropsychological functioning profiles, and to characterize specific endophenotypes for a finely customized establishment of treatment strategies.There is no clear therapeutic algorithm for mucosa-associated lymphoid tissue (MALT) lymphoma beyond Helicobacter pylori eradication and while chemotherapy-based regimens are standard for MALT lymphoma patients in need of systemic treatment, it appears of interest to also investigate chemotherapy-free strategies. We have retrospectively assessed MALT lymphoma patients undergoing upfront systemic treatment, classified either as chemotherapy (=classical cytostatic agents +/- rituximab) or immunotherapy (=immunomodulatory agents or single anti-CD20 antibodies) at the Medical University Vienna 1999-2019. The primary endpoint was progression-free survival (PFS). In total, 159 patients were identified with a median follow-up of 67 months. The majority of patients had extragastric disease (80%), but we also identified 32 patients (20%) with Helicobacter pylori negative or disseminated gastric lymphoma. Regarding the type of first line treatment and outcome, 46% (74/159) received a chemotherapy-based regimen and 54% (85/159) immunotherapy including IMiDs lenalidomide/thalidomide (37%), anti-CD20-anitbodies rituximab/ofatumumab (27%), macrolides clarithromycin/azithromycin (27%) and proteasome inhibitor bortezomib (9%). Median PFS was 76 months (95%CI 50-102), and while the overall response (90% vs. 68%, p less then 0.01) and the complete remission rate (75% vs. 43%, p less then 0.01) was significantly higher for chemotherapy, there was no difference in PFS between chemotherapy (median 81 months, 95%CI 47-116) and immunotherapy (76 months, 95%CI 50-103, p = 0.57), suggesting comparable long-term outcomes. To conclude, our data show higher response rates with chemo- compared to immunotherapy, but this did not translate into a superior PFS. Given the biological background of MALT lymphoma, and the favorable toxicity profile of novel immunomodulatory treatments, this should be further investigated.The chemical composition of bee pollens differs greatly and depends primarily on the botanical origin of the product. Therefore, it is a crucially important task to discriminate pollens of different plant species.  https://www.selleckchem.com/products/6-benzylaminopurine.html  In our work, we aim to determine the applicability of microscopic pollen analysis, spectral colour measurement, sensory, NIR spectroscopy, e-nose and e-tongue methods for the classification of bee pollen of five different botanical origins. Chemometric methods (PCA, LDA) were used to classify bee pollen loads by analysing the statistical pattern of the samples and to determine the independent and combined effects of the above-mentioned methods. The results of the microscopic analysis identified 100% of sunflower, red clover, rapeseed and two polyfloral pollens mainly containing lakeshore bulrush and spiny plumeless thistle. The colour profiles of the samples were different for the five different samples. E-nose and NIR provided 100% classification accuracy, while e-tongue > 94% classification accuracy for the botanical origin identification using LDA. Partial least square regression (PLS) results built to regress on the sensory and spectral colour attributes using the fused data of NIR spectroscopy, e-nose and e-tongue showed higher than 0.8 R2 during the validation except for one attribute, which was much higher compared to the independent models built for instruments.