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Covering: September 1964 to June 2023 Bacteria and fungi living in symbiosis with insects have been studied over the last sixty years and found to be important sources of bioactive natural products. Not only classic producers of secondary metabolites such as Streptomyces and other members of the phylum Actinobacteria but also numerous bacteria from the phyla Proteobacteria and Firmicutes and an impressive array of fungi (usually pathogenic) serve as the source of a structurally diverse number of small molecules with important biological activities including antimicrobial, cytotoxic, antiparasitic and specific enzyme inhibitors. The insect niche is often the exclusive provider of microbes producing unique types of biologically active compounds such as gerumycins, pederin, dinactin, and formicamycins. However, numerous insects still have not been described taxonomically, and in most cases, the study of their microbiota is completely unexplored. In this review, we present a comprehensive survey of 553 natural products produced by microorganisms isolated from insects by collating and classifying all the data according to the type of compound (rather than the insect or microbial source). The analysis of the correlations among the metadata related to insects, microbial partners, and their produced compounds provides valuable insights into the intricate dynamics between insects and their symbionts as well as the impact of their metabolites on these relationships. Herein, we focus on the chemical structure, biosynthesis, and biological activities of the most relevant compounds. [ABSTRACT FROM AUTHOR]

SUMMARY: Metallic well casings strongly impact electromagnetic fields due to their high electrical conductivity. We can take advantage of their presence and increase the sensitivity of EM signals to deeper subsurface structure by driving electric currents down through existing casings, or using the metal as a wave guide for EM telemetry. Interpretation of such well-casing-enhanced measurements requires accurate simulation of these setups. The Method of Moments (MoM) can be used for modelling well casings without having to discretize them finely as part of the subsurface model. Extending the MoM for EM sources directly connected to well casings is straightforward in principle. However, we find that the accuracy of MoM results for such configurations depends strongly on details of the computational model definition, such as the exact position of the connection point and the discretization of the source wire. We explain those important details and provide strategies for the most accurate MoM modelling of electric currents injected into well casings. [ABSTRACT FROM AUTHOR]

Janus particles have been considered suitable for biomedicine owing to their asymmetric structure and unique properties. Although Janus particles have been applied in biosensing for dual-mode sensing, there are almost no reports for the detection of multiple indicators. In fact, many patients require different diagnoses, such as the examination of hepatogenic diseases in diabetics. Here, a Janus particle based on SiO2 was synthesized using a Pickering emulsion method. A novel strategy for detecting glucose and alpha-fetoprotein (AFP) based on different principles using this Janus particle was then constructed as a detection platform. Composed of adjustable dendritic silica loaded with gold nanoclusters (Au NCs) and glucose oxidase (GOx) and spherical SiO2 coupled with AFP antibody, this Janus fluorescent probe achieved the double detection of glucose and AFP. With the protection of dendritic silica, the enzyme temperature stability was enhanced. Moreover, the low limit of detection for glucose (0.5 μM in PBS and 2.5 μM in serum) and AFP (0.5 ng mL−1) illustrated the feasibility of the application of the Janus material in integrated detection. This work not only supported the use of a Janus fluorescent probe as a detection platform toward glucose and AFP but also showed the potential of Janus particles in integrated detection in the future. [ABSTRACT FROM AUTHOR]

Estimation of silver eel production, Anguilla anguilla L., is fundamental for the management of eel stocks. In the hydropower‐regulated rivers Shannon and Erne, Ireland, production is calculated using catch data from a conservation trap and transport programme. However, in both rivers gaps in silver eel catch datasets tend to occur, which can lead to biases in production estimates. Generalised additive models (GAMs) were used to model daily catch in these rivers based on a variety of environmental variables. Final models for the Shannon and Erne explained 83.7% and 78.8% of deviance in daily catch, respectively. A second model on the Erne included catch from a nearby fishing site in an attempt to increase explanatory power and explained 91.7% of deviance. Although model accuracy was increased, reliance on catch from another site may limit the applicability of the model. Model predictions were combined with estimates of fishing efficiency to predict production for the Shannon (36,210 kg; 0.85 kg/ha) and Erne (66,899–67,047 kg; 2.55–2.56 kg/ha). These values represented a 9.3% and 2.8%–3.0% increase on production estimated from incomplete catch records alone for the Shannon and Erne, respectively. [ABSTRACT FROM AUTHOR]

The latent budget model is a mixture model for compositional data sets in which the entries, a contingency table, may be either realizations from a product multinomial distribution or distribution free. Based on this model, the latent budget analysis considers the interactions of two variables; the explanatory (row) and the response (column) variables. The package lba uses expectation-maximization and active constraints method (ACM) to carry out, respectively, the maximum likelihood and the least squares estimation of the model parameters. It contains three main functions, lba which performs the analysis, goodnessfit for model selection and goodness of fit and the plotting functions plotcorr and plotlba used as a help in the interpretation of the results. [ABSTRACT FROM AUTHOR]

The article discusses the kidnapping/trial of 3 Ohio citizens by 16 Virginia slavecatchers on the banks of the Ohio River and taken to Parkersburg (now West Virginia) to stand trial, based on reports of "Marietta Intelligencer," July 24, 1845 issue, and "The Marietta Register," July 26, 1866 issue.

Introduction. The Centers for Disease Control and Prevention (CDC)'s National Tuberculosis Surveillance System (NTSS) is the national repository of tuberculosis (TB) data in the United States. Jurisdictions report to NTSS through the Report of Verified Case of Tuberculosis (RVCT) form that transitioned to a web-based system in 2009. Materials and Methods. To improve RVCT data quality, CDC conducted a quality assurance (QA) needs assessment to develop QA strategies. These include QA components (case detection, data accuracy, completeness, timeliness, data security, and confidentiality); sample tools such as National TB Indicators Project (NTIP) to identify TB case reporting discrepancies; comprehensive training course; resource guide and toolkit. Results and Discussion. During July-September 2011, 73 staff from 34 (57%) of 60 reporting jurisdictions participated in QA training. Participants stated usefulness of sharing jurisdictions' QA methods; 66 (93%) wrote that the QA tools will be effective for their activities. Several jurisdictions reported implementation of QA tools pertinent to their programs. Data showed >8% increase in NTSS and NTIP enrollment through Secure Access Management Services, which monitors system usage, from August 2011- February 2012. Conclusions. Despite challenges imposed by web-based surveillance systems, QA strategies can be developed with innovation and collaboration. These strategies can also be used by other disease programs to ensure high data quality. [ABSTRACT FROM AUTHOR]

The article presents a sketch of the pioneers of the White Oak Settlement in Marietta, Ohio given by James Lawton at the celebration of the Centennial Fourth of July in Barlow.

To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. The average gamma pass rates using the 3%, 3 mm and 2%, 2 mm criteria were above 97 and 91%, respectively, for all volumes of interests considered. All DVH points calculated on sCT differed less than  ±2.5% from the corresponding points on CT. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast for integration in an MR-guided radiotherapy workflow. [ABSTRACT FROM AUTHOR]

This study investigates the dosimetric impact of using population-based Hounsfield units (HUs) and ICRU-based HUs as a function of the number of tissue segments for bulk density assignment toward MRI-based treatment planning in the abdomen. To avoid potential geometric differences between CT and MR images, CT images rather than MR images were chosen to simulate an MRI-only planning scenario. A retrospective study was performed utilizing 18 patients that had previously undergone stereotactic body radiation therapy for liver or pancreas cancer. HU values in the CT datasets were collected for various tissue types, and compared with the HUs derived from ICRU report 46. Doses were recalculated using the fluence obtained from clinical plans and with (1) homogeneous assignment, (2) ICRU-based HU assignment and (3) population-based HU assignment using three, four, five, nine or ten tissue segments. Dose-volume metrics for targets and organs-at-risk for all scenarios were compared with those obtained using the clinical CT. For the planning target volume (PTV) D99.9%, the mean differences from clinical CT plans were  −2.1%  ±  3.9%, −0.6%  ±  0.3% and  −0.1%  ±  0.3% for homogeneity, ICRU-HUs and population-HUs using ten tissue segments, respectively. The population-HU method resulted in better dosimetric accuracy compared to the ICRU-HU method (p-value  <  0.05). The dosimetric accuracy of homogeneity plans was comparable to that of both ICRU-HU and population-HU plans when targets were far from the lungs but deteriorated when targets were close to the lungs. As the number of tissue segments decreased, the dosimetric accuracy for PTV D99.9% reduced for the population-HU method, from  −0.1% for ten tissue segments to  −0.4% for three tissue segments, while no such dependence was observed for the ICRU-HU method. Hence, to generate a clinically acceptable plan when using MRI to synthesize CT in the abdomen for treatment planning, it might be sufficient for electron density assignment with either the population-HU or ICRU-HU method to only use three tissue segments. [ABSTRACT FROM AUTHOR]

A magnetic resonance (MR)-only radiotherapy workflow can reduce cost, radiation exposure and uncertainties introduced by CT-MRI registration. A crucial prerequisite is generating the so called pseudo-CT (pCT) images for accurate dose calculation and planning. Many pCT generation methods have been proposed in the scope of photon radiotherapy. This work aims at verifying for the first time whether a commercially available photon-oriented pCT generation method can be employed for accurate intensity-modulated proton therapy (IMPT) dose calculation. A retrospective study was conducted on ten prostate cancer patients. For pCT generation from MR images, a commercial solution for creating bulk-assigned pCTs, called MR for Attenuation Correction (MRCAT), was employed. The assigned pseudo-Hounsfield Unit (HU) values were adapted to yield an increased agreement to the reference CT in terms of proton range. Internal air cavities were copied from the CT to minimise inter-scan differences. CT- and MRCAT-based dose calculations for opposing beam IMPT plans were compared by gamma analysis and evaluation of clinically relevant target and organ at risk dose volume histogram (DVH) parameters. The proton range in beam’s eye view (BEV) was compared using single field uniform dose (SFUD) plans. On average, a mm) gamma pass rate of 98.4% was obtained using a dose threshold after adaptation of the pseudo-HU values. Mean differences between CT- and MRCAT-based dose in the DVH parameters were below 1 Gy (). The median proton range difference was mm, with on average 96% of all BEV dose profiles showing a range agreement better than 3 mm. Results suggest that accurate MR-based proton dose calculation using an automatic commercial bulk-assignment pCT generation method, originally designed for photon radiotherapy, is feasible following adaptation of the assigned pseudo-HU values. [ABSTRACT FROM AUTHOR]

An MR-only radiotherapy planning (RTP) workflow would reduce the cost, radiation exposure and uncertainties introduced by CT-MRI registrations. In the case of prostate treatment, one of the remaining challenges currently holding back the implementation of an RTP workflow is the MR-based localisation of intraprostatic gold fiducial markers (FMs), which is crucial for accurate patient positioning. Currently, MR-based FM localisation is clinically performed manually. This is sub-optimal, as manual interaction increases the workload. Attempts to perform automatic FM detection often rely on being able to detect signal voids induced by the FMs in magnitude images. However, signal voids may not always be sufficiently specific, hampering accurate and robust automatic FM localisation. Here, we present an approach that aims at automatic MR-based FM localisation. This method is based on template matching using a library of simulated complex-valued templates, and exploiting the behaviour of the complex MR signal in the vicinity of the FM. Clinical evaluation was performed on seventeen prostate cancer patients undergoing external beam radiotherapy treatment. Automatic MR-based FM localisation was compared to manual MR-based and semi-automatic CT-based localisation (the current gold standard) in terms of detection rate and the spatial accuracy and precision of localisation. The proposed method correctly detected all three FMs in 15/17 patients. The spatial accuracy (mean) and precision (STD) were 0.9 mm and 0.5 mm respectively, which is below the voxel size of mm3 and comparable to MR-based manual localisation. FM localisation failed (3/51 FMs) in the presence of bleeding or calcifications in the direct vicinity of the FM. The method was found to be spatially accurate and precise, which is essential for clinical use. To overcome any missed detection, we envision the use of the proposed method along with verification by an observer. This will result in a semi-automatic workflow facilitating the introduction of an MR-only workflow. [ABSTRACT FROM AUTHOR]

To fully exploit the advantages of magnetic resonance imaging (MRI) for radiotherapy (RT) treatment planning, a method is required to overcome the problem of lacking electron density information. We aim to establish and evaluate a new method for computed tomography (CT) data generation based on MRI and image registration. The thereby generated CT data is used for dose accumulation. We developed a process flow based on an initial pair of rigidly co-registered CT and T2-weighted MR image representing the same anatomical situation. Deformable image registration using anatomical landmarks is performed between the initial MRI data and daily MR images. The resulting transformation is applied to the initial CT, thus fractional CT data is generated. Furthermore, the dose for a photon intensity modulated RT (IMRT) or intensity modulated proton therapy (IMPT) plan is calculated on the generated fractional CT and accumulated on the initial CT via inverse transformation. The method is evaluated by the use of phantom CT and MRI data. Quantitative validation is performed by evaluation of the mean absolute error (MAE) between the measured and the generated CT. The effect on dose accumulation is examined by means of dose-volume parameters. One patient case is presented to demonstrate the applicability of the method introduced here. Overall, CT data derivation lead to MAEs with a median of 37.0 HU ranging from 29.9 to 66.6 HU for all investigated tissues. The accuracy of image registration showed to be limited in the case of unexpected air cavities and at tissue boundaries. The comparisons of dose distributions based on measured and generated CT data agree well with the published literature. Differences in dose volume parameters kept within 1.6% and 3.2% for photon and proton RT, respectively. The method presented here is particularly suited for application in adaptive RT in current clinical routine, since only minor additional technical equipment is required. [ABSTRACT FROM AUTHOR]