A new approach is proposed for lossless raster image compression employing interpolative coding. A new multifunction prediction scheme is presented first. Then, interpolative coding, which has not been applied frequently for image compression, is explained briefly. Its simplification is introduced in regard to the original approach. It is determined that the JPEG LS predictor reduces the information entropy slightly better than the multi-functional approach. Furthermore, the interpolative coding was moderately more efficient than the most frequently used arithmetic coding. Finally, our compression pipeline is compared against JPEG LS, JPEG 2000 in the lossless mode, and PNG using 24 standard grayscale benchmark images. JPEG LS turned out to be the most efficient, followed by JPEG 2000, while our approach using simplified interpolative coding was moderately better than PNG. The implementation of the proposed encoder is extremely simple and can be performed in less than 60 lines of programming code for the coder and 60 lines for the decoder, which is demonstrated in the given pseudocodes.

• Klíčová slova
• JPEG 2000 lossless, JPEG LS, PNG, algorithm, computer science, interpolative coding, predictions,
• Publikační typ
• časopisecké články MeSH

OBJECTIVES: A growing body of research has incorporated the Social Vulnerability Index (SVI) into an expanded understanding of the social determinants of health. Although each component of SVI and its association with individual-level mental health conditions have been well discussed, variation in mentally unhealthy days (MUDs) at a county level is still unexplored. To systematically examine the geographically varying relationships between SVI and MUDs across the US counties, our study adopted two different methods: 1) aspatial regression modeling (ordinary least square [OLS]); and 2) locally calibrated spatial regression (geographically weighted regression [GWR]). STUDY DESIGN: This study used a cross-sectional statistical design and geospatial data manipulation/analysis techniques. Analytical unit is each of the 3109 counties in the continental USA. METHODS: We tested the model performance of two different methods and suggest using both methods to reduce potential issues (e.g., Simpson's paradox) when researchers apply aspatial analysis to spatially coded data sets. We applied GWR after checking the spatial dependence of residuals and non-stationary issues in OLS. GWR split a single OLS equation into 3109 equations for each county. RESULTS: Among 15 SVI variables, a combination of eight variables showed the best model performance. Notably, unemployment, person with a disability, and single-parent households with children aged under 18 years especially impacted the variation of MUDs in OLS. GWR showed better model performance than OLS and specified each county's varying relationships between subcomponents of SVI and MUDs. For example, GWR specified that 69.3% (2157 of 3109) of counties showed positive relationships between single-parent households and MUDs across the USA. Higher positive relationships were concentrated in Michigan, Kansas, Texas, and Louisiana. CONCLUSIONS: Our findings could contribute to the literature regarding social determinants of community mental health by specifying spatially varying relationships between SVI and MUDs across US counties. Regarding policy implementation, in counties containing more social and physical minorities (e.g., single-parent households and disabled population), policymakers should attend to these groups of people and increase intervention programs to reduce potential or current mental health illness. The results of GWR could help policymakers determine the specific counties that need more support to reduce regional mental health disparities.

• Klíčová slova
• Geographically weighted regression, Mentally unhealthy days (MUDs), Social Vulnerability Index, Spatial modeling,
• MeSH
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• prostorová analýza MeSH
• prostorová regrese * MeSH
• průřezové studie MeSH
• sociální zranitelnost * MeSH
• Check Tag
• dítě MeSH
• lidé MeSH
• mladiství MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Michigan MeSH

BACKGROUND: Current experimental data on RNA interactions remain limited, particularly for non-coding RNAs, many of which have only recently been discovered and operate within complex regulatory networks. Researchers often rely on in-silico interaction detection algorithms, such as TargetScan, which are based on biochemical sequence alignment. However, these algorithms have limited performance. RNA-seq expression data can provide valuable insights into regulatory networks, especially for understudied interactions such as circRNA-miRNA-mRNA. By integrating RNA-seq data with prior interaction networks obtained experimentally or through in-silico predictions, researchers can discover novel interactions, validate existing ones, and improve interaction prediction accuracy. RESULTS: This paper introduces Pi-GMIFS, an extension of the generalized monotone incremental forward stagewise (GMIFS) regression algorithm that incorporates prior knowledge. The algorithm first estimates prior response values through a prior-only regression, interpolates between these prior values and the original data, and then applies the GMIFS method. Our experimental results on circRNA-miRNA-mRNA regulatory interaction networks demonstrate that Pi-GMIFS consistently enhances precision and recall in RNA interaction prediction by leveraging implicit information from bulk RNA-seq expression data, outperforming the initial prior knowledge. CONCLUSION: Pi-GMIFS is a robust algorithm for inferring acyclic interaction networks when the variable ordering is known. Its effectiveness was confirmed through extensive experimental validation. We proved that RNA-seq data of a representative size help infer previously unknown interactions available in TarBase v9 and improve the quality of circRNA disease annotation.

• Klíčová slova
• Bayesian network, Circular RNA, Functional annotation, Penalized regression, Structure inference,
• MeSH
• algoritmy MeSH
• genové regulační sítě MeSH
• kruhová RNA * genetika   metabolismus   MeSH
• lidé MeSH
• lineární modely MeSH
• messenger RNA * genetika   metabolismus   MeSH
• mikro RNA * genetika   metabolismus   MeSH
• sekvenční analýza RNA metody   MeSH
• sekvenování transkriptomu * metody   MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• kruhová RNA * MeSH
• messenger RNA * MeSH
• mikro RNA * MeSH

This work presents new calculations of conversion coefficients (CCs) from total air kerma to dose-equivalent quantitiesH'(0.07),H'(3),H*(10),Hp(0.07),Hp(3), andHp(10) for area and personal dosimetry for mono-energetic photons in the energy range from 2 keV to 50 MeV, assuming secondary charged particle equilibrium. Calculations using the Monte Carlo N-Particle® (MCNP) code were performed for a large number of photon energies with the aim of preventing errors resulting from possible improper interpolation between currently available sparsely spaced CC values, when an average CC value over a photon fluence spectrum needs to be determined. The CC values were compared with the values published in the ISO 4037-3:2019 standard. A very close agreement was achieved for the majority of CCs. Larger discrepancies were found for some CCs, often for low photon energies or large angles of radiation incidence, which were taken from older publications or when CC values were interpolated or extrapolated. Furthermore, some differences were found in the MeV energy range, which are significant for dosimeter calibrations, e.g. the presented values of CC toH*(10) for the main photon energies of137Cs and60Co radionuclides are both lower by 2.8%. Finally, it was found that the values of CCs toHp(0.07;E, α)slabgiven in ISO 4037-3:2019 were not taken correctly from the source publication. In conclusion, the CC values given in ISO 4037-3:2019 should be updated in view of the results obtained.

• Klíčová slova
• ISO 4037 standard, MCNP, Monte Carlo, area dosimetry, conversion coefficients, personal dosimetry, total air kerma,
• MeSH
• dávka záření * MeSH
• fotony * MeSH
• lidé MeSH
• metoda Monte Carlo MeSH
• radiometrie * metody   MeSH
• vzduch MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH