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Background There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives us insight into a gene's functionality by informing us how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, we analyzed if we could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results We performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. Our results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the percentage of correctly classified genes to their correct Gene Ontology Slim term for each ontology reaches over 80% with high accuracy (reflected in F-measures over 0.80) of the classification rules produced. Conclusions We confirmed that in classifying genes to their correct Gene Ontology Slim term, the inclusion of neighbour information from those genes is beneficial. Knowing the location of a gene and the Gene Ontology Slim information from neighbouring genes gives us insight into that gene's functionality. This benefit is seen by just including information from a gene's two-nearest neighbouring genes.

Elucidating genetic networks provides the foundation for the development of new treatments or cures for diseased pathways, and determining novel gene functionality is critical for bringing a better understanding on how an organism functions as a whole. In this dissertation, I developed a methodology that correctly locates genes that may be involved in genetic networks with a given gene based on its location over 50% of the time or based on its description over 43% of the time. I also developed a methodology that makes it easier to predict how a gene product behaves in a cellular context by suggesting the correct Gene Ontology term over 80% of the time. The designed software provides researchers with a way to focus their search for coregulated genes which will lead to better microarray chip design and limits the list of possible functions of a gene product. This ultimately saves the researcher time and money.

A novel methodological approach for identifying clusters of similar medical incidents by analyzing large databases of incident reports is described. The discovery of similar events allows the identification of patterns and trends, and makes possible the prediction of future events and the establishment of barriers and best practices. Two techniques from the fields of information science and artificial intelligence have been integrated—namely, case based reasoning and information retrieval—and very good clustering accuracies have been achieved on a test data set of incident reports from transfusion medicine. This work suggests that clustering should integrate the features of an incident captured in traditional form based records together with the detailed information found in the narrative included in event reports.

Lymphatic spread determines treatment decisions in prostate cancer (PCa) patients. 68Ga-PSMA-PET/CT can be performed, although cost remains high and availability is limited. Therefore, computed tomography (CT) continues to be the most used modality for PCa staging. We assessed if convolutional neural networks (CNNs) can be trained to determine 68Ga-PSMA-PET/CT-lymph node status from CT alone. In 549 patients with 68Ga-PSMA PET/CT imaging, 2616 lymph nodes were segmented. Using PET as a reference standard, three CNNs were trained. Training sets balanced for infiltration status, lymph node location and additionally, masked images, were used for training. CNNs were evaluated using a separate test set and performance was compared to radiologists’ assessments and random forest classifiers. Heatmaps maps were used to identify the performance determining image regions. The CNNs performed with an Area-Under-the-Curve of 0.95 (status balanced) and 0.86 (location balanced, masked), compared to an AUC of 0.81 of experienced radiologists. Interestingly, CNNs used anatomical surroundings to increase their performance, “learning” the infiltration probabilities of anatomical locations. In conclusion, CNNs have the potential to build a well performing CT-based biomarker for lymph node metastases in PCa, with different types of class balancing strongly affecting CNN performance.

BackgroundBayesian penalized likelihood reconstruction for PET (e.g., GE Q.Clear) aims at improving convergence of lesion activity while ensuring sufficient signal-to-noise ratio (SNR). This study evaluated reconstructed spatial resolution, maximum/peak contrast recovery (CRmax/CRpeak) and SNR of Q.Clear compared to time-of-flight (TOF) OSEM with and without point spread function (PSF) modeling.MethodsThe NEMA IEC Body phantom was scanned five times (3 min scan duration, 30 min between scans, background, 1.5–3.9 kBq/ml F18) with a GE Discovery MI PET/CT (3-ring detector) with spheres filled with 8-, 4-, or 2-fold the background activity concentration (SBR 8:1, 4:1, 2:1). Reconstruction included Q.Clear (beta, 150/300/450), “PSF+TOF4/16” (iterations, 4; subsets, 16; in-plane filter, 2.0 mm), “OSEM+TOF4/16” (identical parameters), “PSF+TOF2/17” (2 it, 17 ss, 2.0 mm filter), “OSEM+TOF2/17” (identical), “PSF+TOF4/8” (4 it, 8 ss, 6.4 mm), and “OSEM+TOF2/8” (2 it, 8 ss, 6.4 mm). Spatial resolution was derived from 3D sphere activity profiles. RC as (sphere activity concentration [AC]/true AC). SNR as (background mean AC/background AC standard deviation).ResultsSpatial resolution of Q.Clear150 was significantly better than all conventional algorithms at SBR 8:1 and 4:1 (Wilcoxon, each p < 0.05). At SBR 4:1 and 2:1, the spatial resolution of Q.Clear300/450 was similar or inferior to PSF+TOF4/16 and OSEM+TOF4/16. Small sphere CRpeak generally underestimated true AC, and it was similar for Q.Clear150/300/450 as with PSF+TOF4/16 or PSF+TOF2/17 (i.e., relative differences < 10%). Q.Clear provided similar or higher CRpeak as OSEM+TOF4/16 and OSEM+TOF2/17 resulting in a consistently better tradeoff between CRpeak and SNR with Q.Clear. Compared to PSF+TOF4/8/OSEM+TOF2/8, Q.Clear150/300/450 showed lower SNR but higher CRpeak.ConclusionsQ.Clear consistently improved reconstructed spatial resolution at high and medium SBR compared to PSF+TOF and OSEM+TOF, but only with beta = 150. However, this is at the cost of inferior SNR with Q.Clear150 compared to Q.Clear300/450 and PSF+TOF4/16/PSF+TOF2/17 while CRpeak for the small spheres did not improve considerably. This suggests that Q.Clear300/450 may be advantageous for the 3-ring detector configuration because the tradeoff between CR and SNR with Q.Clear300/450 was superior to PSF+TOF4/16, OSEM+TOF4/16, and OSEM+TOF2/17. However, it requires validation by systematic evaluation in patients at different activity and acquisition protocols.

In recent decades, the incidence of neuroendocrine tumors (NETs) has steadily increased. Due to the slow-growing nature of these tumors and the lack of early symptoms, most cases are diagnosed at advanced stages, when curative treatment options are no longer available. Prognosis and survival of patients with NETs are determined by the location of the primary lesion, biochemical functional status, differentiation, initial staging, and response to treatment. Somatostatin analogue (SSA) therapy has been a mainstay of antisecretory therapy in functioning neuroendocrine tumors, which cause various clinical symptoms depending on hormonal hypersecretion. Beyond symptomatic management, recent research demonstrates that SSAs exert antiproliferative effects and inhibit tumor growth via the somatostatin receptor 2 (SSTR2). Both the PROMID (placebo-controlled, prospective, randomized study in patients with metastatic neuroendocrine midgut tumors) and the CLARINET (controlled study of lanreotide antiproliferative response in neuroendocrine tumors) trial showed a statistically significant prolongation of time to progression/progression-free survival (TTP/PFS) upon SSA treatment, compared to placebo. Moreover, the combination of SSA with peptide receptor radionuclide therapy (PRRT) in small intestinal NETs has proven efficacy in the phase 3 neuroendocrine tumours therapy (NETTER 1) trial. PRRT is currently being tested for enteropancreatic NETs versus everolimus in the COMPETE trial, and the potential of SSTR-antagonists in PRRT is now being evaluated in early phase I/II clinical trials. This review provides a synopsis on the pharmacological development of SSAs and their use as antisecretory drugs. Moreover, this review highlights the clinical evidence of SSAs in monotherapy, and in combination with other treatment modalities, as applied to the antiproliferative management of neuroendocrine tumors with special attention to recent high-quality phase III trials.

Legume-supported cropping systems affect environmental, production, and economic impacts. In Europe, legume production is still marginal with grain legumes covering less than 3% of arable land. A transition towards legume-supported systems could contribute to a higher level of protein self-sufficiency and lower environmental impacts of agriculture. Suitable approaches for designing legume-supported cropping systems are required that go beyond the production of prescriptive solutions. We applied the DEED framework with scientists and advisors in 17 study areas in nine European countries, enabling us to describe, explain, explore, and redesign cropping systems. The results of 31 rotation comparisons showed that legume integration decreased N fertilizer use and nitrous oxide emissions (N 2 O) in more than 90% of the comparisons with reductions ranging from 6 to 142 kg N ha −1 and from 1 to 6 kg N 2 O ha −1 , respectively. In over 75% of the 24 arable cropping system comparisons, rotations with legumes had lower nitrate leaching and higher protein yield per hectare. The assessment of above-ground biodiversity showed no considerable difference between crop rotations with and without legumes in most comparisons. Energy yields were lower in legume-supported systems in more than 90% of all comparisons. Feasibility and adaptation needs of legume systems were discussed in joint workshops and economic criteria were highlighted as particularly important, reflecting findings from the rotation comparisons in which 63% of the arable systems with legumes had lower standard gross margins. The DEED framework enabled us to keep close contact with the engaged research-farmer networks. Here, we demonstrate that redesigning legume-supported cropping systems through a process of close stakeholder interactions provides benefits compared to traditional methods and that a large-scale application in diverse study areas is feasible and needed to support the transition to legume-supported farming in Europe.

BackgroundThis study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [123I]FP-CIT in different settings.MethodsThe first subject sample comprised 645 subjects from the Parkinson’s Progression Marker Initiative (PPMI), 207 healthy controls (HC), and 438 Parkinson’s disease (PD) patients. The second sample comprised 372 patients from clinical routine patient care, 186 with non-neurodegenerative parkinsonian syndrome (PS) and 186 with neurodegenerative PS. Single-photon emission computed tomography (SPECT) images of the clinical sample were reconstructed with two different reconstruction algorithms (filtered backprojection, iterative ordered subsets expectation maximization (OSEM) reconstruction with resolution recovery). The putaminal specific binding ratio (SBR) was computed using an anatomical region of interest (ROI) predefined in standard (MNI) space in the Automated Anatomic Labeling (AAL) atlas or using hottest voxels (HV) analysis in large predefined ROIs. SBR values were transformed to z-scores using mean and standard deviation of the SBR in a normal database of varying sizes (n = 5, 10, 15,…, 50) randomly selected from the HC subjects (PPMI sample) or the patients with non-neurodegenerative PS (clinical sample). Accuracy, sensitivity, and specificity for identifying patients with PD or neurodegenerative PS were determined as performance measures using a predefined fixed cutoff on the z-score. This was repeated for 10,000 randomly selected normal databases, separately for each size of the normal database. Mean and 5th percentile of the performance measures over the 10,000 realizations were computed. Accuracy, sensitivity, and specificity when using the whole set of HC or non-neurodegenerative PS subjects as normal database were used as benchmark.ResultsMean loss of accuracy of the putamen SBR z-score was below 1% when the normal database included at least 15 subjects, independent of subject sample (PPMI or clinical), reconstruction method (filtered backprojection or OSEM), and ROI method (AAL or HV). However, the variability of the accuracy of the putamen SBR z-score decreased monotonically with increasing size of normal database and was still considerable at size 15. In order to achieve less than 5% “maximum” loss of accuracy (defined by the 5th percentile) in all settings required at least 25 to 30 subjects in the normal database. Reduction of mean and “maximum” loss of accuracy of the putamen SBR z-score by further increasing the size of the normal database was very small beyond size 40.ConclusionsThe results of this study suggest that 25 to 30 is the minimum size of the normal database to reliably achieve good performance of semi-quantitative analysis in dopamine transporter (DAT) SPECT, independent of the algorithm used for image reconstruction and the ROI method used to estimate the putaminal SBR.

In recent years there has been a growing interest in metastasis directed radiotherapy (MDRT) in hormone-sensitive oligometastatic prostate cancer. The role of MDRT in castration resistant prostate cancer (CRPC) patients remains controversial. Our study retrospectively compared MDRT to standard of care (SOC) in oligometastatic/oligoprogressive CRPC patients staged by PSMA PET CT. Patients either received SOC or MDRT with continuation of androgen deprivation therapy (ADT). Investigated endpoints contained biochemical progression-free survival (bPFS), overall survival (OS) and freedom from second line therapy (FFSLT). In this retrospective monocenter study, all patients with PSMA PET between January 2014 and July 2018 were screened. 55 oligometastatic/oligoprogressive CRPC patients were identified and further analysed, 34 received MDRT and 21 SOC. Baseline characteristics were similarly distributed between groups. Kaplan–Meier estimates suggested a trend for improved bPFS ( p  = 0.10) and improved OS ( p  = 0.01) by MDRT. Additionally, FFSLT was significantly prolonged in MDRT patients ( p  = 0.006). Multivariate cox regression analyses revealed MDRT as the only parameter that was significantly associated with bPFS (hazard ratio 0.36, p  = 0.048) and OS (hazard ratio 0.14, p  = 0.006). No high-grade radiation induced toxicities were observed. In our study MDRT was a well-tolerated treatment option with low toxicity in oligometastatic/oligoprogressive CRPC patients, resulting in improved OS and freedom from second-line therapy and a potential improvement of bPFS compared to SOC.