Prediction of heat capacities of solid inorganic salts from group contributions

A group contribution technique is proposed to predict the coefficients in the heat capacity correlation, C{sub p} = a + bT + c/T{sup 2} + dT{sup 2}, for solid inorganic salts. The results from this work are compared with fits to experimental data from the literature. It is shown to give good predictions for both simple and complex solid inorganic salts. Literature heat capacities for a large number (664) of solid inorganic salts covering a broad range of cations (129), anions (17) and ligands (2) have been used in regressions to obtain group contributions for the parameters in the heat capacity temperature function. A mean error of 3.18% is found when predicted values are compared with literature values for heat capacity at 298{degrees} K. Estimates of the error standard deviation from the regression for each additivity constant are also determined

Identification of G1-Regulated Genes in Normally Cycling Human Cells

BACKGROUND: Obtaining synchronous cell populations is essential for cell-cycle studies. Methods such as serum withdrawal or use of drugs which block cells at specific points in the cell cycle alter cellular events upon re-entry into the cell cycle. Regulatory events occurring in early G1 phase of a new cell cycle could have been overlooked. METHODOLOGY AND FINDINGS: We used a robotic mitotic shake-off apparatus to select cells in late mitosis for genome-wide gene expression studies. Two separate microarray experiments were conducted, one which involved isolation of RNA hourly for several hours from synchronous cell populations, and one experiment which examined gene activity every 15 minutes from late telophase of mitosis into G1 phase. To verify synchrony of the cell populations under study, we utilized methods including BrdU uptake, FACS, and microarray analyses of histone gene activity. We also examined stress response gene activity. Our analysis enabled identification of 200 early G1-regulated genes, many of which currently have unknown functions. We also confirmed the expression of a set of genes candidates (fos, atf3 and tceb) by qPCR to further validate the newly identified genes. CONCLUSION AND SIGNIFICANCE: Genome-scale expression analyses of the first two hours of G1 in naturally cycling cells enabled the discovery of a unique set of G1-regulated genes, many of which currently have unknown functions, in cells progressing normally through the cell division cycle. This group of genes may contain future targets for drug development and treatment of human disease

Impact of non-axillary sentinel node biopsy on staging and treatment of breast cancer patients

The purpose of this study was to evaluate the occurrence of lymphatic drainage to non-axillary sentinel nodes and to determine the implications of this phenomenon. A total of 549 breast cancer patients underwent lymphoscintigraphy after intratumoural injection of 99mTc-nanocolloid. The sentinel node was intraoperatively identified with the aid of intratumoural administered patent blue dye and a gamma-ray detection probe. Histopathological examination of sentinel nodes included step-sectioning at six levels and immunohistochemical staining. A sentinel node outside level I or II of the axilla was found in 149 patients (27%): internal mammary sentinel nodes in 86 patients, other non-axillary sentinel nodes in 44 and both internal mammary and other non-axillary sentinel nodes in nineteen patients. The intra-operative identification rate was 80%. Internal mammary metastases were found in seventeen patients and metastases in other non-axillary sentinel nodes in ten patients. Staging improved in 13% of patients with non-axillary sentinel lymph nodes and their treatment strategy was changed in 17%. A small proportion of clinically node negative breast cancer patients can be staged more precisely by biopsy of sentinel nodes outside level I and II of the axilla, resulting in additional decision criteria for postoperative regional or systemic therapy

Systematic quantification of gene interactions by phenotypic array analysis

A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations. From a genome screen for hydroxyurea (HU) chemical-genetic interactions, 298 haploid deletion strains were selected for further analysis. The strength of interactions was quantified using a wide range of HU concentrations affecting reference strain growth. The selectivity of interaction was determined by comparison with drugs targeting other cellular processes. Bio-modules were defined as gene clusters with shared strength and selectivity of interaction profiles. The functions and connectivity of modules involved in processes such as DNA repair, protein secretion and metabolic control were inferred from their respective gene composition. The work provides an example of, and a general experimental framework for, quantitative analysis of gene interaction networks that buffer cell growth

Antineoplastic Drugs as a Potential Risk Factor in Occupational Settings: Mechanisms of Action at the Cell Level, Genotoxic Effects, and Their Detection Using Different Biomarkers

U članku je prikazana osnovna podjela antineoplastičnih lijekova prema mehanizmima djelovanja na razini stanice. Objašnjeni su mehanizmi genotoksičnosti najvažnijih vrsta lijekova koji se primjenjuju u okviru uobičajenih protokola za liječenje zloćudnih novotvorina. Navedena je važeća klasifi kacija antineoplastika prema kancerogenom potencijalu, podaci o mutagenom potencijalu te je prikazana njihova podjela u skladu s anatomsko-terapijsko-kemijskim sustavom klasifi kacije. Sustavno su prikazani najvažniji rezultati svjetskih i hrvatskih istraživanja na populacijama radnika izloženih antineoplasticima, provedenih u razdoblju 1980.-2009. s pomoću četiri najčešće primjenjivane metode: analize izmjena sestrinskih kromatida, analize kromosomskih aberacija, mikronukleus-testa i komet-testa. Objašnjena su osnovna načela navedenih metoda te raspravljene njihove prednosti i nedostaci. Biološki pokazatelji daju važne podatke o individualnoj osjetljivosti profesionalno izloženih ispitanika koji mogu poslužiti unaprjeđenju postojećih uvjeta rada i upravljanju rizicima pri izloženosti genotoksičnim agensima. Na osnovi prednosti i nedostataka citogenetičkih metoda zaključeno je da je mikronukleus-test, koji podjednako uspješno dokazuje klastogene i aneugene učinke, jedna od najboljih metoda dostupnih za otkrivanje štetnih djelovanja antineoplastičnih lijekova koji su u aktivnoj primjeni.This article brings an overview of the mechanisms of action of antineoplastic drugs used in the clinical setting. It also describes the genotoxic potentials of the most important classes of antineoplastic drugs involved in standard chemotherapy protocols. Classifi cation of antineoplastic drugs according to the IARC monographs on the evaluation of carcinogenic risks to humans is accompanied by data on their mutagenicity and the most recent updates in the Anatomical Therapeutic Chemical (ATC) Classifi cation System. We report the main fi ndings of biomonitoring studies that were conducted in exposed healthcare workers all over the world between 1980 and 2009 using four biomarkers: sister chromatid exchanges, chromosome aberrations, micronuclei. and the comet assay. The methods are briefl y explained and their advantages and disadvantages discussed. Biomarkers provide important information on individual genome sensitivity, which eventually might help to improve current working practices and to manage the risks related with exposure to genotoxic agents. Taking into consideration all known advantages and drawbacks of the existing cytogenetic methods, the micronucleus assay, which is able to detect both clastogenic and aneugenic action, is the most suitable biomarker for assessing harmful effects of antineoplastic drugs currently used in health care

Contextual definition generation

This paper explores the concept of dynamically generating definitions using a deep-learning model. We do this by creating a dataset that contains definition entries and contexts associated with each definition. We then fine-tune a GPT-2 based model on the dataset to allow the model to generate contextual definitions. We evaluate our model with human raters by generating definitions using two context types: short-form (the word used in a sentence) and long-form (the word used in a sentence along with the prior and following sentences). Results indicate that the model performed significantly better when generating definitions using short-form contexts. Additionally, we evaluate our model against human-generated definitions. The results show promise for the model, showing that the model was able to match human-level fluency. However, while it was able to reach human-level accuracy in some instances, it failed in others

WikiMorph: Learning to Decompose Words into Morphological Structures

This paper presents WikiMorph, a tool that automatically breaks down words into morphemes, etymological compounds (morphemes from root languages), and generates contextual definitions for each component. It comes in two flavors: a dataset and a deep-learning-based model. The dataset was extracted from Wiktionary and contains over 450k entries. We then used this dataset to train a GPT-2 model to generalize and decompose any word into morphemes and their definitions. We find that the model accurately generates complex breakdowns when given a high-quality initial definition