The role of machine learning in identification of early gestational diabetes mellitus prediction models

Introduction: Gestational diabetes mellitus(GDM) is a condition in which carbohydrate intolerance develops during pregnancy. The estimated prevalence of GDM ranges from less than 1% to 28% and is commonly diagnosed between 24 and 28 weeks of gestation. Untreated GDM represents a severe threat to the affected women and their offspring. Machine learning (ML) is a computer science discipline focused on algorithms that improve automatically through experience to make predictions or decisions without being explicitly programmed to do so

The influence of mechanical properties of workpiece material on the main cutting force in face milling

The paper presents the research into cutting forces in face milling of three different materials: steel Č 4732 (EN42CrMo4), nodular cast iron NL500 (EN-GJS-500-7) and silumine AlSi10Mg (EN AC-AlSi10Mg). Obtained results show that hardness and tensile strength values of workpiece material have a significant influence on the main cutting force, and thereby on the cutting energy in machining

Utjecaj mehaničkih karakteristika materijala obratka na glavnu silu rezanja pri čeonom glodanju

The paper presents the research into cutting forces in face milling of three different materials: steel Č 4732 (EN42CrMo4), nodular cast iron NL500 (EN-GJS-500-7) and silumine AlSi10Mg (EN AC-AlSi10Mg). Obtained results show that hardness and tensile strength values of workpiece material have a significant influence on the main cutting force, and thereby on the cutting energy in machining.U radu su prikazana istraživanja sila rezanja pri čeonom glodanju za tri različita materijala: čelik Č 4732 (EN42CrMo4), nodularni lijev NL500 (EN-GJS-500-7) i silumin AlSi10Mg (EN AC-AlSi10Mg). Dobiveni rezultati pokazuju da vrijednosti tvrdoće i vlačne čvrstoće materijala obratka imaju veliki utjecaj na glavnu silu rezanja, a time i na ukupno utrošenu energiju rezanja pri obradi

Prostorno-vremenska interpolacija klimatskih elemenata primenom geostatistike i mašinskog učenja

High resolution daily maps for climate elements are a valuable source of information and serve as an input for climatology, meteorology, agriculture, hydrology, ecology, and many other research areas and disciplines. Spatio-temporal interpolation methods are o en used for creation of daily maps for climate elements. In this research, already existing spatio-temporal geostatistical interpolation methods and newly developed spatio-temporal interpolation methods based on machine learning algorithms are applied to and evaluated on climate element case studies. A spatio-temporal regression kriging model for global land areas for mean daily temperature is simpli ed by using only a geometric temperature trend, digital elevation model, and topographic wetness index (without MODIS LST) as covariates and adapted for Croatian territories for the year 2008 in this dissertation. e leave-one-out and 5-fold cross-validation show that the accuracy of the model a er adaptation is 97.8% in R2 and 1.2 ◦C in RMSE, which is an improvement of 3.4% in R2 and 0.7 ◦C in RMSE. e adapted daily mean temperature model also outperforms previously developed models for Croatia and shows similar or be er accuracy in comparison with models for other local areas. e results show that the spatio-temporal regression kriging model for global land areas can be adapted to local areas using a national weather station network, thus providing more accurate daily mean temperature maps at a 1 km spatial resolution. e proposed adapted geostatistical model for Croatia still provides larger prediction errors in mountainous regions making it convenient for application in agricultural areas that are at lower altitudes. A di erent approach to spatial or spatio-temporal interpolation of climate elements is to use machine learning algorithms together with spatial covariates. A novel Random Forest Spatial Interpolation (RFSI) methodology for spatial or spatio-temporal interpolation is proposed and evaluated in this dissertation. e RFSI methodology is based on the Random Forest algorithm that uses innovative spatial predictors: observations at n nearest locations and distances to them. e RFSI methodology is applied and evaluated in three case studies. In the rst, a synthetic (simulated) case study, the accuracy of RFSI is compared with the accuracy of ordinary kriging, Random Forest for spatial prediction (RFsp), inverse distance weighting, nearest neighbour, and trend surface mapping interpolation methods. In this case study, RFSI outperforms nearest neighbour and trend surface mapping and has similar accuracy as RFsp and inverse distance weighting. RFSI is outperformed by ordinary kriging because this case study is created by geostatistical simulation and consequentially ordinary kriging is an optimal interpolation method in this case. In the following two real-world case studies, a daily precipitation for Catalonia for the 2016–2018 period and a daily mean temperature for Croatia for the year 2008, the accuracy of RFSI is compared with the accuracy of spatio-temporal regression kriging, inverse distance weighting, standard Random Forest and RFsp using a nested vii UNIVERSITY OF BELGRADE Faculty of Civil Engineering Department of Geodesy and Geoinformaticsk-fold leave-location-out cross-validation and RFSI outperformed all of them. RFSI is recommended for the interpolation of complex variables due to Random Forest’s ability to model non-linear relations between covariates and target variables. RFSI can be used for spatial or spatio-temporal interpolation of any environmental variable. Next, a MeteoSerbia1km dataset — a rst gridded dataset for daily climate elements (maximum, minimum, and mean temperature, mean sea level pressure, and total precipitation) at a 1 km spatial resolution for Serbian territories for the 2000–2019 period — is created using RFSI methodology for spatio-temporal interpolation. Additionally, monthly and annual summaries and daily, monthly, and annual long term means maps of the climate elements are generated by aggregating the daily MeteoSerbia1km maps. e nested 5-fold leave-location-out cross-validation is used to access the accuracy of the MeteoSerbia1km daily dataset. e accuracy is high for daily temperature variables and sea level pressure and lower for daily precipitation which was expected due to its complexity. MeteoSerbia1km daily maps are further compared with the 10-km E-OBS daily maps and show high correlation with them except for daily precipitation. e automation of the RFSI methodology is implemented within the R package meteo, in the form of four new R functions for creation, prediction, tuning, and cross-validation processes of RFSI model.Gridovani podaci dnevnih klimatskih elemenata visoke rezolucije predstavljaju znacajan izvor in- ˇ formacija koje se koriste kao ulazni podaci za analize u klimatologiji, meteorologiji, poljoprivredi, hidrologiji, ekologiji i ostalim istraziva ˇ ckim oblastima i disciplinama. Prostorno-vremenske inter- ˇ polacione metode cesto se koriste za kreiranje gridovanih dnevnih klimatskih elemenata. Glob- ˇ alni model prostorno-vremenskog regresionog kriginga za srednje dnevne temperature iznad povrsi ˇ Zemlje je pojednostavljen koristeci samo geometrijski temperaturni trend, digitalni model terena ´ i topografski indeks vlaznosti (bez MODIS LST snimaka) kao prediktore i kalibrisan za podru ˇ cje ˇ Hrvatske koristeci podatke iz 2008 godine u ovoj disertaciji. Na osnovu prostorne kros-validacije, ´ tacnost kalibrisanog modela iznosi R ˇ 2=97.8% i RMSE=1.2 ◦C, sto je pobolj ˇ sanje od 3.4% i 0.7 ˇ ◦C u odnosu na globalni model. Prilagodeni model srednjih dnevnih temperatura nadmasuje ostale ve ˇ c´ razvijene modele za podrucje Hrvatske u pogledu ta ˇ cnosti i ima sli ˇ cnu ili ve ˇ cu ta ´ cnost u odnosu na ˇ modele za druga lokalna podrucja ili dr ˇ zave. Rezultati pokazuju da se globalni model prostorno- ˇ vremenskog regresionog kriginga moze prilagoditi lokalnim podru ˇ cjima koriste ˇ ci mre ´ zu nacional- ˇ nih meteoroloskih stanica i tako proizvesti gridovane podatke srednjih dnevnih temperatura ve ˇ ce ´ tacnosti sa prostornom rezolucijom od 1 km. Kalibrisani model za podru ˇ cje Hrvatske jo ˇ s uvek ima ˇ manju tacnost u planinskim predelima, ˇ sto ga ˇ cini pogodnim za primenu u poljoprivrednim po- ˇ drucjima koja su na ni ˇ zim nadmorskim visinama. ˇ Algoritmi masinskog u ˇ cenja kombinovani sa inovativnim prostornim prediktorima predstavljaju ˇ novi oblik modela za prostornu ili prostorno-vremensku interpolaciju, koji mogu da se koriste i za interpolaciju klimatskih elemenata. U ovoj disertaciji je predstavljena i testirana inovativna Random Forest Spatial Interpolation (RFSI) metodologija za prostornu ili prostorno-vremensku interpolaciju. RFSI metodologija je bazirana na Random Forest algoritmu masinskog u ˇ cenja koji koristi inovativne ˇ prostorne prediktore: opazanja na ˇ n najblizih lokacija i rastojanja do njih. RFSI metodologija je ˇ primenjena i testirana na tri studije slucaja. U prvoj sinteti ˇ ckoj studiji, koja predstavlja simulirani ˇ set podataka, tacnost RFSI metodologije je pore ˇ dena sa tacno ˇ sˇcu obi ´ cnog kriging-a, ˇ Random Forest for spatial prediction (RFsp) metode, metode inverznih distanci (eng. inverse distance weighting), najblizeg suseda (eng. ˇ nearest neighbour) i mapiranja povrsi trenda (eng. ˇ trend surface mapping). U ovom slucaju, RFSI je pokazao ve ˇ cu ta ´ cnost u pore ˇ denju sa metodama najblizeg suseda i mapiranja ˇ povrsi trenda i sli ˇ cnu ta ˇ cnost kao RFsp i metoda inverznih distanci. Obi ˇ cni kriging je o ˇ cekivano dao ˇ bolje rezultate od RFSI metodologije iz razloga sto je simulirani set podataka kreiran geostatisti ˇ ckom ˇ simulacijom i samim tim obicni kriging predstavlja optimalnu metodu interpolacije u ovom slu ˇ caju. ˇ U ostale dve studije slucaja, koje se odnose na dnevne koli ˇ cine padavina za podru ˇ cje Katalonije ˇ za 2016–2018 period i srednje dnevne temperature za podrucje Hrvatske za 2008 godinu, ta ˇ cnost ˇ ix UNIVERZITET U BEOGRADU Gradevinski fakultet Odsek za geodeziju i geoinformatikuRFSI metodologije je poredena sa tacno ˇ sˇcu prostorno-vremenskog regresionog kriginga, metode in- ´ verznih distanci, standardnom Random Forest i RFsp metodom koristeci ugnje ´ zdenu prostornu kros- ˇ validaciju. RFSI metodologija je pokazala najbolje rezultate u ovim studijama. RFSI metodologija se preporucuje za interpolaciju slo ˇ zenih parametara zbog osobine ˇ Random Forest algoritma da moze da ˇ modelira nelinearne veze izmedu prediktora i modeliranog parametra. RFSI metodologija se takode moze koristiti za prostornu ili prostorno-vremensku interpolaciju bilo kog drugog parametra ˇ zivotne ˇ sredine. Koristeci RFSI metodologiju za prostorno-vremensku interpolaciju, kreiran je MeteoSerbia1km ´ set podataka koji predstavlja prvi set gridovanih dnevnih klimatskih elemenata (maksimalne, minimalne i srednje temperature, atmosferskog pritiska na nivou mora i kolicine padavina) sa pros- ˇ tornom rezolucijom od 1 km za podrucje Srbije za period 2000–2019. Agregacijom dnevnih gri- ˇ dovanih podataka dodatno su kreirani gridovani podaci mesecnih i godi ˇ snjih proseka (ukupne ˇ kolicine za padavine) i gridovani podaci dnevnih, mese ˇ cnih i godi ˇ snjih dugoro ˇ cnih proseka kli- ˇ matskih elemenata. Tacnost dnevnih MeteoSerbia1km gridovanih podaka je ocenjena pomo ˇ cu ´ ugnjezdene prostorne kros-validacije. Ta ˇ cnost dnevnih temperatura i atmosferskog pritiska na ˇ nivou mora je visoka, dok je tacnost dnevnih padavina o ˇ cekivano ne ˇ sto manja zbog slo ˇ zenosti samih ˇ padavina. Dnevni MeteoSerbia1km gridovani podaci su takode poredeni sa E-OBS setom dnevnih gridovanih podataka sa prostornom rezolucijom od 10 km i pokazuju visok stepen korelacije, osim za padavine. RFSI metodolgija je automatizovana i implementirana u okviru R paketa meteo, kroz cetiri ˇ nove R funkcije za procese kreiranja, predikcije, kalibrisanja i kros-validacije RFSI modela

Prognostic significans of molecular markers, clinical and pathological parameters in patients operated for early rectal cancer

Karcinom rektuma zbog visoke incidence i trenda njenog rasta u budućnosti, predstavlja veliki javnozdravstveni problem. Hirurško lečenje je i dalje primarno u tretmanu ove bolesti ali se dobri rezultati mogu očekivati samo multidisciplinarnim pristupom odnosno kombinovanom, miltimodalnom terapijom. U tom cilju nameće se potreba za pronalaženjem prognostičkih biomarkera uz pomoć kojih se mogu predvideti bolesnici sa visokim rizikom za recidiv odnosno kandidati za intenzivnije postoperativno praćenje i agresivnije terapijske modalitete. Cilj ove studije je da se zajedno sa kliničko-patološkim parametrima ispita prognostički potencijal vaskularnog endotelijalnog faktora rasta (VEGF), receptora epidermalnog faktora rasta (EGFR) i CD44v6 kod pacijenata sa T3N0 karcinomom rektuma. Materijal i metode. Ovo je studija preseka koja obuhvata 163 bolesnika sa T3N0 karcinomom rektuma, koji su kurativno operisani na III odeljenju, Klinike za digestivnu hirurgiju – Prve hirurške klinike, Kliničkog centra Srbije u periodu 2003-2013 godina. VEGF, EGFR i CD44v6 ekspresija je ispitivana imunohistohemijski. Kao parametri od interesa definisani su: pojava lokalnog recidiva, distalnih metastaza i preživljavanje bolesnika. Reziltati.U studiji je bilo 102 bolesnika muškog i 61 bolesnik ženskog pola. Prosečna starost bila je 62 godine (31-88 godina) a postoperativno su praćeni u proseku 81 mesec (4-177 meseci). Kod 6 bolesnika je dijagrostikovan lokalni a kod 31 distalni recidiv bolesti. Kod bolesnika sa pozitivnom VEGF i CD44v6 ekspresijom preživljavanje je bilo lošije u odnosu na bolesnike sa negativnom VEGF i CD44v6 ekspresijom. Kliničkopatološki parametri (mucinozni tip adenocarcinoma, vaskularna invazija, invazija limfatika i perineuralna invazija, gradus tumora, način rasta tumora, uznapredovali T3 stadijumi, intraoperativna perforacija tumora) takođe značajno utiču na prognozu bolesti. Zaključak: Povišena VEGF i CD44v6 ekspresija kod T3N0 karcinoma rektuma zajedno sa standardnim histopatološkim karakteristikama tumora može dati dovoljno informacija za definisanje pacijenata sa visokim rizikom za pojavu recidiva bolesti i lošijom prognozom.Rectal carcinoma still presents major health problem. Besides the need for individualized and meticulous preoperative staging there is sometimes a problem in choosing the optimal mode of treatment. Today’s problem presents a group of rectal cancer patients in T3N0M0 stage. These patients may not benefit from aggressive neoadjuvant and adjuvant approach. Nevertheless, we still have around 20% of patients in this group who develop distant or local relapse of the disease. There is a need for predictive and prognostic markers that could help us determine the subgroup of patients with high risk of relapse. The aim of this study is to determine the prognostic potential of VEGF, EGFR,CD44v6 and clinicopathological parameters in patients with T3N0 rectal carcinoma in the absence of neoadjuvant treatment. Methods. This was retrospective analysis of 163 selected T3N0 rectal cancer patients, operated on the Department for Colorectal Surgery of the Clinic for Digestive Surgery-First Surgical Clinic, Clinical Centre of Serbia. VEGF, EGFR and CD44v6 expression was immunohistochemically assessed. Parameters of interest were: Local recurrence, distant metastases, disease free survival, disease specific and overall survival. Results.There were 102 men and 61 women. The median age was 62 years (range, 31-88 years). Median follow-up interval was 81 months (range, 4-177 months). During the follow-up period 6 patients developed local recurrence, in 31 patients we discovered distant metastases. Disease free survival, disease specific and ovareall survival were lower in VEGF and CD44v6 positive tumors. Clinicopathological parameters (mucinous type of adenocarcinoma, vascular invasion, lymphatic invasion, perineural invasion, tumor diferentiation, tumor growth, advanced T3 stages, intraoperative tumor perforation) also significantly affect the prognosis of the disease. Conclusion. Elevated VEGF and CD44v6 expression in T3N0 rectal carcinoma together with the standard histopathological characteristics, can provide enough information to define patients with high risk for recurrence and poor prognosis

Random Forest Spatial Interpolation

For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made

Characterization of nanostructured spinel NiFe2O4 obtained by soft mechanochemical synthesis

Powdery nickel ferrite, NiFe2O4 has been obtained by soft mechanochemical synthesis in a planetary ball mill. Ni(OH)2 and Fe(OH)3 are used as initial compounds. This mixture was mechanically activated for 25h, uniaxial pressed and sintered at 1100°C for 2h. The phase composition of the sintered sample was analyzed by X-ray diffraction (XRD), energy dispersive spectrometer (EDS) and Raman spectroscopy. Morphologies were examined by scanning electron microscopy (SEM). The electrical DC/resistivity/conductivity at different temperatures was measured using a Source Meter Keithley 2410. An Impedance/Gain-Phase Analyzer (HP-4194) was used to measure the impedance spectra (100Hz - 10MHz) at different temperatures. [Projekat Ministarstva nauke Republike Srbije, br. III 45003 i br. III 45015

Women\u27s beach handball game statistics: Differences and predictive power for winning and losing teams

The objectives of the present study were: (i) to compare beach handball game-related statistics by match outcome (winning and losing teams), and (ii) to identify characteristics that discriminate performances in the match. The game-related statistics of the 72 women’s matches played in the VIII Women’s Beach Handball World Championship (2018) were analysed. The game-related statistics were taken from the official Web page. A validation of the data showed their reliability to be very good (the inter-observer mean reliability was α=0.82 and the intra-observer mean was α=0.86). For the differences between winning/losing teams a parametric (unpaired t-test) or non-parametric (Mann-Whitney U test) test was applied depending on whether the variable met or did not meet normality, respectively. A stepwise discriminant analysis was then performed to determine the variables that predicted performance (victory or defeat). Five variables showed differences between the winning and losing teams: total points (p<.001; ES=1.09), technical faults (p<.001; ES=‑0.96), the number of players with either negative (p<.001; ES=‑0.86) or positive (p<.001; ES=1.05) valuations and overall valuation (p<.001; ES=1.29). The predictive model correctly classified 80.6% of the matches using two variables (Wilks’s λ=0.618; canonical correlation index=0.618): overall valuation and GK shots

Influence of maternal dexamethasone treatment on morphometric characteristics of pituitary GH cells and body weight in near-term rat fetuses.

Growth hormone (GH) and glucocorticoids have a powerful influence on controlling fetal growth, differentiation and maturation of numerous tissues. In the present study, the effect of maternal dexamethasone (Dx) treatment on GH cells and body weight in 19- and 21-day-old rat fetuses was investigated using immunocytochemical and morphometric methods. Pregnant female rats received daily injections of 1.0-0.5-0.5 mg Dx/kg b.w. on days 16-18 of pregnancy (experimental group), while the control group received an equal volume of saline. Dx treatment of pregnant rats enhanced immunostaining intensity and significantly increased (

Mikrobiološka svojstva zemljišta pod povrćem na lokalitetu Bačko Gradište

Modern systems of vegetable production combine yield stability and quality with effective protection of the environment. Since microorganisms are the biological component of soil, their population size biodiversity reflects the level of biological activity of soil, i.e., they may serve as useful indicators for soil characterization. Dominance of certain groups of microorganisms affects the processes of soil synthesis and decomposition and it determines the quality of soil and its applicability for the production of safe food. Analyses conducted as several sites in the location of Bačko Gradište (BAG Food Processing Company) have shown that the microbiological activity of the soils was high. The total numbers of bacteria and ammonifiers ranged up to x108, of Azotobacter up to x103, free N-fixing bacteria up to x106, of fungi up to x104, and of actinomycetes up to x1085. To achieve an optimum level of vegetable production, simultaneously enforcing the values of the natural ecosystem, it is necessary to monitor microorganism status in the soil and work on the development of effective microbial strains.Na osnovu zastupljenosti pojedinih grupa mikroorganizama, enzimatske aktivnosti i biodiverziteta kao pokazatelja biogenosti, može se proceniti plodnost i zdravstveno stanje zemljišta. Dominantnost pojedinih grupa mikroorganizama usmerava procese sinteze, razgradnje i određuje kvalitet zemljišta za proizvodnju zdravstveno ispravne hrane. Termin zdravo zemljište je ekološka oznaka kojom se naglašava kvalitet, a ne samo količina prinosa u proizvodnji ratarskih i povrtarskih biljaka. Na osnovu rezultata ispitivanih lokaliteta Bačko Gradište, ("BAG"), može se zaključiti da je mikrobiološka aktivnost zemljišta visoka. Naime, zastupljenost ukupnog broja bakterija i amonifikatora kreće se čak do x108, Azotobacter-a do x103, oligonitrofila do x106, brojnost gljiva x104 a aktinomiceta do x105