Gitek Bestill

Gitek Bestill is a system where the merchant will order bread from the bakers, who will then process these orders. The system has a calendar where all the placed orders can be seen, and running campaigns can be displayed. Shrinkage can be registered and a list can be viewed with the shrinkage of the different products. The bakers can add, delete and change products. A search for orders can be done by both merchants and bakers. Gitek Bestill has been developed in HTML, CSS, PHP, JavaScript/jQuery and MySQL.Gitek Bestill er et system for brødbestilling foretatt av kjøpmenn i Coop, og bakere som tar i mot disse bestillingene. Systemet har en kalenderoversikt hvor man ser plasserte ordre, og aktuelle kampanjer. Svinn på brød kan også registreres og man ser liste over brødene med svinn. Bakere har mulighet for å legge til, slette og endre produkter. Søk etter ordre finnes for både kjøpmenn og bakere. Gitek Bestill er utviklet i HTML, CSS, PHP, Javascript/ jQuery og MySQL.Gitek A

Undervisning for dybdelæring med bruk av modeller i naturfag

Denne masteravhandlingen undersøker hvordan naturfaglærere reflekterer over hvordan modeller kan brukes for å legge til rette for dybdelæring i naturfag. Med bakgrunn i denne tematikken er følgende problemstilling utgangspunktet for studien: «Hvordan reflekterer naturfaglærere over bruk av modeller i sammenheng med dybdelæring?». Formålet med oppgaven er å utvikle en forståelse for hvordan naturfaglærere ønsker å anvende modeller i undervisning, og på hvilke måter tilnærmingen kan legge til rette for at elevene utvikler en dypere forståelse eller ikke. På bakgrunn av oppgavens problemområde undersøker også denne studien hvordan naturfaglærere forstår begrepet dybdelæring. Problemstillingen ble undersøkt ved hjelp av kvalitativ forskningsmetode. Det ble gjennomført semi-strukturerte intervjuer av fire naturfaglærere på ungdomsskolen. Videre ble datamaterialet analysert ved bruk av induktiv tematisk analyse ettersom empirien dannet grunnlaget for tema, koder og kategorier. Resultatene drøftes opp mot relevant teori omkring dybdelæring og bruk av modeller i naturfag. Mine funn viser en varierende forståelse av begrepet dybdelæring som påvirker hvordan undervisning for dybdelæring praktiseres. Evne til å se sammenhenger og evne til å anvende kunnskaper i nye situasjoner, anerkjennes av samtlige lærere. Motivasjon trekkes også frem som en viktig faktor for læringsutbyttet. Ulikhetene viser seg riktignok i forhold til hvorvidt enkeltfaglig dybde vektlegges. Som et resultat av dette indikerer denne studien at enkelte lærere benytter modeller først og fremst som et verktøy for å beskrive, presentere og forklare naturfaglig innhold, mens andre utforsker modellenes epistemiske funksjoner i større grad. I lys av det teoretiske rammeverket drøfter jeg hvorvidt undervisningstilnærmingene kan bidra til å utvikle elevenes dybdeforståelse.This master's thesis examines how science teachers reflect on how models can be used to facilitate in-depth learning in science. Based on this topic, the following research question for the study is: "How do science teachers reflect on the use of models in the context of in-depth learning?". The purpose of the study is to develop an understanding of how science teachers want to use models in teaching, and in what ways their approach can facilitate students developing a deeper understanding. Based on the topic of the thesis, this study also examines how the science teachers understand the concept of deep learning. The research question was investigated using a qualitative research design. Semi-structured interviews were conducted on four science teachers in the secondary school. Furthermore, the data was analyzed using inductive thematic analysis as the empirical data formed the basis for themes, codes and categories. The results are discussed in relation to relevant theory about deep learning and the use of models in science. My findings show a varying understanding of in-depth learning which affects how teaching for deep learning is practiced. The ability to see connections and the ability to apply knowledge in new situations is recognized by all teachers. Motivation is also highlighted as an important factor for the learning outcomes. Admittedly, the differences occur in relation to whether depth is emphasized in specific subjects. As a result, this study indicates that some teachers primarily use models as a tool to describe, present and explain science content. Others explore the models' epistemic functions to a greater extent. In light of the theoretical framework, I discuss whether the teaching approaches can contribute to developing students' in-depth understanding

ADHD symptoms in neurometabolic diseases: Underlying mechanisms and clinical implications

Neurometabolic diseases (NMDs) are typically caused by genetic abnormalities affecting enzyme functions, which in turn interfere with normal development and activity of the nervous system. Although the individual disorders are rare, NMDs are collectively relatively common and often lead to lifelong difficulties and high societal costs. Neuropsychiatric manifestations, including ADHD symptoms, are prominent in many NMDs, also when the primary biochemical defect originates in cells and tissues outside the nervous system. ADHD symptoms have been described in phenylketonuria, tyrosinemias, alkaptonuria, succinic semialdehyde dehydrogenase deficiency, X-linked ichthyosis, maple syrup urine disease, and several mitochondrial disorders, but are probably present in many other NMDs and may pose diagnostic and therapeutic challenges. Here we review current literature linking NMDs with ADHD symptoms. We cite emerging evidence that many NMDs converge on common neurochemical mechanisms that interfere with monoamine neurotransmitter synthesis, transport, metabolism, or receptor functions, mechanisms that are also considered central in ADHD pathophysiology and treatment. Finally, we discuss the therapeutic implications of these findings and propose a path forward to increase our understanding of these relationships.publishedVersio

Association Between Proportion of Nuclei With High Chromatin Entropy and Prognosis in Gynecological Cancers

Background: Nuclear texture analysis measuring differences in chromatin structure has provided prognostic biomarkers in several cancers. There is a need for improved cell-by-cell chromatin analysis to detect nuclei with highly disorganized chromatin. The purpose of this study was to develop a method for detecting nuclei with high chromatin entropy and to evaluate the association between the presence of such deviating nuclei and prognosis. Methods: A new texture-based biomarker that characterizes each cancer based on the proportion of high–chromatin entropy nuclei (<25% vs ≥25%) was developed on a discovery set of 175 uterine sarcomas. The prognostic impact of this biomarker was evaluated on a validation set of 179 uterine sarcomas, as well as on independent validation sets of 246 early-stage ovarian carcinomas and 791 endometrial carcinomas. More than 1 million images of nuclei stained for DNA were included in the study. All statistical tests were two-sided. Results: An increased proportion of high–chromatin entropy nuclei was associated with poor clinical outcome. The biomarker predicted five-year overall survival for uterine sarcoma patients with a hazard ratio (HR) of 2.02 (95% confidence interval [CI] = 1.43 to 2.84), time to recurrence for ovarian cancer patients (HR = 2.91, 95% CI = 1.74 to 4.88), and cancer-specific survival for endometrial cancer patients (HR = 3.74, 95% CI = 2.24 to 6.24). Chromatin entropy was an independent prognostic marker in multivariable analyses with clinicopathological parameters (HR = 1.81, 95% CI = 1.21 to 2.70, for sarcoma; HR = 1.71, 95% CI = 1.01 to 2.90, for ovarian cancer; and HR = 2.03, 95% CI = 1.19 to 3.45, for endometrial cancer). Conclusions: A novel method detected high–chromatin entropy nuclei, and an increased proportion of such nuclei was associated with poor prognosis. Chromatin entropy supplemented existing prognostic markers in multivariable analyses of three gynecological cancer cohorts.publishedVersio

Involvement of the 14-3-3 gene family in autism spectrum disorder and schizophrenia: Genetics, transcriptomics and functional analyses

The 14-3-3 protein family are molecular chaperones involved in several biological functions and neurological diseases. We previously pinpointed YWHAZ (encoding 14-3-3ζ) as a candidate gene for autism spectrum disorder (ASD) through a whole-exome sequencing study, which identified a frameshift variant within the gene (c.659-660insT, p.L220Ffs*18). Here, we explored the contribution of the seven human 14-3-3 family members in ASD and other psychiatric disorders by investigating the: (i) functional impact of the 14-3-3ζ mutation p.L220Ffs*18 by assessing solubility, target binding and dimerization; (ii) contribution of common risk variants in 14-3-3 genes to ASD and additional psychiatric disorders; (iii) burden of rare variants in ASD and schizophrenia; and iv) 14-3-3 gene expression using ASD and schizophrenia transcriptomic data. We found that the mutant 14-3-3ζ protein had decreased solubility and lost its ability to form heterodimers and bind to its target tyrosine hydroxylase. Gene-based analyses using publicly available datasets revealed that common variants in YWHAE contribute to schizophrenia (p = 6.6 × 10-7), whereas ultra-rare variants were found enriched in ASD across the 14-3-3 genes (p = 0.017) and in schizophrenia for YWHAZ (meta-p = 0.017). Furthermore, expression of 14-3-3 genes was altered in post-mortem brains of ASD and schizophrenia patients. Our study supports a role for the 14-3-3 family in ASD and schizophrenia

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Background Improved markers of prognosis are needed to stratify patients with early-stage colorectal cancer to refine selection of adjuvant therapy. The aim of the present study was to develop a biomarker of patient outcome after primary colorectal cancer resection by directly analysing scanned conventional haematoxylin and eosin stained sections using deep learning. Methods More than 12 000 000 image tiles from patients with a distinctly good or poor disease outcome from four cohorts were used to train a total of ten convolutional neural networks, purpose-built for classifying supersized heterogeneous images. A prognostic biomarker integrating the ten networks was determined using patients with a non-distinct outcome. The marker was tested on 920 patients with slides prepared in the UK, and then independently validated according to a predefined protocol in 1122 patients treated with single-agent capecitabine using slides prepared in Norway. All cohorts included only patients with resectable tumours, and a formalin-fixed, paraffin-embedded tumour tissue block available for analysis. The primary outcome was cancer-specific survival. Findings 828 patients from four cohorts had a distinct outcome and were used as a training cohort to obtain clear ground truth. 1645 patients had a non-distinct outcome and were used for tuning. The biomarker provided a hazard ratio for poor versus good prognosis of 3·84 (95% CI 2·72–5·43; p<0·0001) in the primary analysis of the validation cohort, and 3·04 (2·07–4·47; p<0·0001) after adjusting for established prognostic markers significant in univariable analyses of the same cohort, which were pN stage, pT stage, lymphatic invasion, and venous vascular invasion. Interpretation A clinically useful prognostic marker was developed using deep learning allied to digital scanning of conventional haematoxylin and eosin stained tumour tissue sections. The assay has been extensively evaluated in large, independent patient populations, correlates with and outperforms established molecular and morphological prognostic markers, and gives consistent results across tumour and nodal stage. The biomarker stratified stage II and III patients into sufficiently distinct prognostic groups that potentially could be used to guide selection of adjuvant treatment by avoiding therapy in very low risk groups and identifying patients who would benefit from more intensive treatment regimes

Exome chip analyses in adult attention deficit hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable childhood-onset neuropsychiatric condition, often persisting into adulthood. The genetic architecture of ADHD, particularly in adults, is largely unknown. We performed an exome-wide scan of adult ADHD using the Illumina Human Exome Bead Chip, which interrogates over 250 000 common and rare variants. Participants were recruited by the International Multicenter persistent ADHD CollaboraTion (IMpACT). Statistical analyses were divided into 3 steps: (1) gene-level analysis of rare variants (minor allele frequency (MAF)<1%); (2) single marker association tests of common variants (MAFgreater than or equal to1%), with replication of the top signals; and (3) pathway analyses. In total, 9365 individuals (1846 cases and 7519 controls) were examined. Replication of the most associated common variants was attempted in 9847 individuals (2077 cases and 7770 controls) using fixed-effects inverse variance meta-analysis. With a Bonferroni-corrected significance level of 1.82E−06, our analyses of rare coding variants revealed four study-wide significant loci: 6q22.1 locus (P=4.46E−08), where NT5DC1 and COL10A1 reside; the SEC23IP locus (P=6.47E−07); the PSD locus (P=7.58E−08) and ZCCHC4 locus (P=1.79E−06). No genome-wide significant association was observed among the common variants. The strongest signal was noted at rs9325032 in PPP2R2B (odds ratio=0.81, P=1.61E−05). Taken together, our data add to the growing evidence of general signal transduction molecules (NT5DC1, PSD, SEC23IP and ZCCHC4) having an important role in the etiology of ADHD. Although the biological implications of these findings need to be further explored, they highlight the possible role of cellular communication as a potential core component in the development of both adult and childhood forms of ADHD

Chromatin organisation and cancer prognosis: a pan-cancer study

Background: Chromatin organisation affects gene expression and regional mutation frequencies and contributes to carcinogenesis. Aberrant organisation of DNA has been correlated with cancer prognosis in analyses of the chromatin component of tumour cell nuclei using image texture analysis. As yet, the methodology has not been sufficiently validated to permit its clinical application. We aimed to define and validate a novel prognostic biomarker for the automatic detection of heterogeneous chromatin organisation. Methods Machine learning algorithms analysed the chromatin organisation in 461 000 images of tumour cell nuclei stained for DNA from 390 patients (discovery cohort) treated for stage I or II colorectal cancer at the Aker University Hospital (Oslo, Norway). The resulting marker of chromatin heterogeneity, termed Nucleotyping, was subsequently independently validated in six patient cohorts: 442 patients with stage I or II colorectal cancer in the Gloucester Colorectal Cancer Study (UK); 391 patients with stage II colorectal cancer in the QUASAR 2 trial; 246 patients with stage I ovarian carcinoma; 354 patients with uterine sarcoma; 307 patients with prostate carcinoma; and 791 patients with endometrial carcinoma. The primary outcome was cancer-specific survival. Findings: In all patient cohorts, patients with chromatin heterogeneous tumours had worse cancer-specific survival than patients with chromatin homogeneous tumours (univariable analysis hazard ratio [HR] 1·7, 95% CI 1·2–2·5, in the discovery cohort; 1·8, 1·0–3·0, in the Gloucester validation cohort; 2·2, 1·1–4·5, in the QUASAR 2 validation cohort; 3·1, 1·9–5·0, in the ovarian carcinoma cohort; 2·5, 1·8–3·4, in the uterine sarcoma cohort; 2·3, 1·2–4·6, in the prostate carcinoma cohort; and 4·3, 2·8–6·8, in the endometrial carcinoma cohort). After adjusting for established prognostic patient characteristics in multivariable analyses, Nucleotyping was prognostic in all cohorts except for the prostate carcinoma cohort (HR 1·7, 95% CI 1·1–2·5, in the discovery cohort; 1·9, 1·1–3·2, in the Gloucester validation cohort; 2·6, 1·2–5·6, in the QUASAR 2 cohort; 1·8, 1·1–3·0, for ovarian carcinoma; 1·6, 1·0–2·4, for uterine sarcoma; 1·43, 0·68–2·99, for prostate carcinoma; and 1·9, 1·1–3·1, for endometrial carcinoma). Chromatin heterogeneity was a significant predictor of cancer-specific survival in microsatellite unstable (HR 2·9, 95% CI 1·0–8·4) and microsatellite stable (1·8, 1·2–2·7) stage II colorectal cancer, but microsatellite instability was not a significant predictor of outcome in chromatin homogeneous (1·3, 0·7–2·4) or chromatin heterogeneous (0·8, 0·3–2·0) stage II colorectal cancer. Interpretation: The consistent prognostic prediction of Nucleotyping in different biological and technical circumstances suggests that the marker of chromatin heterogeneity can be reliably assessed in routine clinical practice and could be used to objectively assist decision making in a range of clinical settings. An immediate application would be to identify high-risk patients with stage II colorectal cancer who might have greater absolute benefit from adjuvant chemotherapy. Clinical trials are warranted to evaluate the survival benefit and cost-effectiveness of using Nucleotyping to guide treatment decisions in multiple clinical settings

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Prognostics from adaptive spatial entropy in early ovarian cancer cell nuclei

Providing a robust and reliable estimation of a patient's prognosis is necessary to make a qualified selection of the appropriate treatment for that patient. Digital image analysis of cancer cell nuclei is useful to make such estimation. In particular, texture analysis of the DNA organisation of nuclei has through a substantial number of studies proved to provide quantitative information of prognostic relevance. Most previous studies have used the first, second or higher order statistics to estimate the prognosis, i.e. applied statistical texture analysis. We will in this study take a different approach where we attempt to exploit the internal structure of DNA-specific stained nuclei. In our novel approach, we apply a novel, refined adaptive segmentation method to extract small dark and bright structures within the nuclei, and estimate the spatial entropy of the dark or bright structures of each nucleus based on the area of the segmented objects. Finally, we will use the spatial entropies to obtain some very few, but powerful novel adaptive texture features by adaptively estimating the discrimination value of each spatial entropy using the combined knowledge of all relevant spatial entropies of all nuclei across a number patients. We have analysed our novel approach on a dataset containing 134 patients with early ovarian cancer when using a proper evaluation method based on statistical bootstrapping. The results are very promising. Our method performs significantly better than the previously most promising method based on texture analysis. Moreover, it performs consistently at least about equally well as all other approaches based on image analysis. Combining the best feature of our novel approach with a single other feature, we also obtain the best performance among all approaches based on image analysis. If selecting a subset of the dataset based on a set of predefined criteria unrelated to digital image analysis, our novel approach attains a correct classification rate of 84 \%. This facilitate to a two-step recognition system. Again, our novel approach is consistently better, perhaps also significantly better, than all other approaches based on image analysis. In conclusion, our novel approach seems to hold a promise of reliable estimation of the prognosis, which is necessary to make a qualified selection of the appropriate adjuvant treatment. Due to a very low dimensionality and the use of proper performance estimation, we expect that our approach will generalise well on an independent validation dataset. Moreover, because of the combination of high adaptivity in all stages of our approach and an addressed concern for the overfitting problem, we expect relatively good generalisation beyond the case under study. Nevertheless, caution must be called for, and new proper tests must as always be performed in the case of generalisations