TY  - JOUR
AU  - Miloradović, Zorana
AU  - Kovačević, Jovana
AU  - Miočionović, Jelena
AU  - Đekić, Ilija
AU  - Kljajević, Nemanja
AU  - Smigić, Nada
PY  - 2024
UR  - https://www.cell.com/heliyon/abstract/S2405-8440(24)03473-X
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38187278
UR  - https://imagine.imgge.bg.ac.rs/handle/123456789/2343
AB  - The objective of this study was to identify the requirements needed for selling dairy products through e-commerce, as well as current gaps and challenges that exist for small scale dairy processors (SSDPs), and need to be addressed in order to comply with those requirements. A mixed method research design was used for training needs assessment. Qualitative (in-depth interview with 7 online platform representatives (OPRs)) and quantitative approach (survey questionnaire with 58 SSDPs) were conducted. Interview transcripts were coded and codes were grouped into seven themes. Hierarchical cluster analysis was applied to 146 answers from 58 SSDPs. They were divided into 4 clusters. Mean sums of responses between clusters were compared by Mann-Whitney U test. OPRs suggested that SSDPs should be provided with tools and resources to help them achieve food safety and quality targets, as well as practical knowledge and skills. They reported that it is crucial to find a solution for the cold chain transportation, for maintaining consistent product quality. Survey results showed that SSDPs use kitchen equipment (79.3%) and kitchen cleaning products (81.0%) for dairy processing. In total, 43.1% process raw milk and only 24.1% have product label on the package. Only members of cluster 3 and 4 sell their products online (73.7% and 90.0%, respectively), mostly using their own social media platforms (57.9% and 60.0%, respectively), transporting products to end buyers by themselves in hand refrigerators (47.4% and 70.0%, respectively). By analyzing the differences among clusters of SSDPs, trainings can be tailored to the characteristics and knowledge gaps of each group.
PB  - CellPress
T2  - Heliyon
T2  - HeliyonHeliyon
T1  - E-commerce readiness and training needs of small-scale dairy processors in Serbia: Understanding barriers and knowledge gaps
IS  - 6
VL  - 10
DO  - 10.1016/j.heliyon.2024.e27442
ER  - 

@article{
author = "Miloradović, Zorana and Kovačević, Jovana and Miočionović, Jelena and Đekić, Ilija and Kljajević, Nemanja and Smigić, Nada",
year = "2024",
abstract = "The objective of this study was to identify the requirements needed for selling dairy products through e-commerce, as well as current gaps and challenges that exist for small scale dairy processors (SSDPs), and need to be addressed in order to comply with those requirements. A mixed method research design was used for training needs assessment. Qualitative (in-depth interview with 7 online platform representatives (OPRs)) and quantitative approach (survey questionnaire with 58 SSDPs) were conducted. Interview transcripts were coded and codes were grouped into seven themes. Hierarchical cluster analysis was applied to 146 answers from 58 SSDPs. They were divided into 4 clusters. Mean sums of responses between clusters were compared by Mann-Whitney U test. OPRs suggested that SSDPs should be provided with tools and resources to help them achieve food safety and quality targets, as well as practical knowledge and skills. They reported that it is crucial to find a solution for the cold chain transportation, for maintaining consistent product quality. Survey results showed that SSDPs use kitchen equipment (79.3%) and kitchen cleaning products (81.0%) for dairy processing. In total, 43.1% process raw milk and only 24.1% have product label on the package. Only members of cluster 3 and 4 sell their products online (73.7% and 90.0%, respectively), mostly using their own social media platforms (57.9% and 60.0%, respectively), transporting products to end buyers by themselves in hand refrigerators (47.4% and 70.0%, respectively). By analyzing the differences among clusters of SSDPs, trainings can be tailored to the characteristics and knowledge gaps of each group.",
publisher = "CellPress",
journal = "Heliyon, HeliyonHeliyon",
title = "E-commerce readiness and training needs of small-scale dairy processors in Serbia: Understanding barriers and knowledge gaps",
number = "6",
volume = "10",
doi = "10.1016/j.heliyon.2024.e27442"
}

Miloradović, Z., Kovačević, J., Miočionović, J., Đekić, I., Kljajević, N.,& Smigić, N.. (2024). E-commerce readiness and training needs of small-scale dairy processors in Serbia: Understanding barriers and knowledge gaps. in Heliyon
CellPress., 10(6).
https://doi.org/10.1016/j.heliyon.2024.e27442

Miloradović Z, Kovačević J, Miočionović J, Đekić I, Kljajević N, Smigić N. E-commerce readiness and training needs of small-scale dairy processors in Serbia: Understanding barriers and knowledge gaps. in Heliyon. 2024;10(6).
doi:10.1016/j.heliyon.2024.e27442 .

Miloradović, Zorana, Kovačević, Jovana, Miočionović, Jelena, Đekić, Ilija, Kljajević, Nemanja, Smigić, Nada, "E-commerce readiness and training needs of small-scale dairy processors in Serbia: Understanding barriers and knowledge gaps" in Heliyon, 10, no. 6 (2024),
https://doi.org/10.1016/j.heliyon.2024.e27442 . .

TY  - CONF
AU  - Trivanović, Drenka
AU  - Kovačević, Jovana
AU  - Arsić, Aleksandra
AU  - Vujačić, Marko
AU  - Bogosavljević, Nikola
AU  - Okić Djordjević, Ivana
AU  - Živanović, Milena
AU  - Mojsilović, Slavko
AU  - Maljković, Mirjana
AU  - Jauković, Aleksandra
PY  - 2023
UR  - https://belbi.bg.ac.rs/
UR  - https://imagine.imgge.bg.ac.rs/handle/123456789/1995
AB  - As adipose tissue (AT) undergoes metabolic reprogramming with age, we investigated
skeletal site-specific and age-dependent lipid profile of bone marrow adipose tissue (BMAT).
Acetabular and femoral BMAT, and gluteofemoral subcutaneous adipose tissue (gfSAT)
were obtained from matched osteoarthritis patients. Patients were classified into two
groups: younger (≤ 60 years) and aged (>60 years) adults. BMAT and gfSAT were explored
by using thin layer/gas chromatography coupled with cellular and molecular assays.
Data were interpreted and visualized by applying linear discriminant analysis (LDA) and
hierarchical clustering of fatty acid (FA) composition. Statistics was estimated by nonparametric
tests and Spearman’s rank correlation.
Analyses of total lipids revealed significantly reduced triglyceride content in femoral
(fBMAT) than in acetabular BMAT (aBMAT) and gfSAT. Frequencies of spontaneously
released saturated palmitic (C16:0) and stearic acids (C18:0) were higher in fBMAT than
in aBMAT and gfSAT (p=0.036 and p=0.046, n=8). Cluster heatmap and LDA showed that
fBMAT differed to acetabular and gfSAT, while acetabular and gfSAT were more similar in
FA profiles. FA profiles of AT depots varied with patient’s age. Contribution of palmitic acid
was increased in aged group in all AT depots, while stearic acid declined in aged group in
BMAT compartments only. fBMAT cellularity declined with age (r=-0.675, n=14, p=0.037).
Additionally, the presence of CD45-CD31-CD34+CD24+ adipogenic progenitor (stem) cells
was increased in fBMAT (0.46±0.03%) when compared to aBMAT (0.21±0.01%) depot.
Femoral mesenchymal stem cells displayed pronounced adipogenesis comparing to their
acetabular counterparts.
Our findings suggest that specific lipid profile of fBMAT imposes adipogenic commitment
of stem cells within this skeletal site.
PB  - Belgrade : Institute of molecular genetics and genetic engineering
C3  - 4th Belgrade Bioinformatics Conference
T1  - Fatty Acid Data Analysis Unravels Skeletal Site and Age-Specific Features of Human Bone Marrow Adiposity
EP  - 54
SP  - 54
VL  - 4
UR  - https://hdl.handle.net/21.15107/rcub_imagine_1995
ER  - 

@conference{
author = "Trivanović, Drenka and Kovačević, Jovana and Arsić, Aleksandra and Vujačić, Marko and Bogosavljević, Nikola and Okić Djordjević, Ivana and Živanović, Milena and Mojsilović, Slavko and Maljković, Mirjana and Jauković, Aleksandra",
year = "2023",
abstract = "As adipose tissue (AT) undergoes metabolic reprogramming with age, we investigated
skeletal site-specific and age-dependent lipid profile of bone marrow adipose tissue (BMAT).
Acetabular and femoral BMAT, and gluteofemoral subcutaneous adipose tissue (gfSAT)
were obtained from matched osteoarthritis patients. Patients were classified into two
groups: younger (≤ 60 years) and aged (>60 years) adults. BMAT and gfSAT were explored
by using thin layer/gas chromatography coupled with cellular and molecular assays.
Data were interpreted and visualized by applying linear discriminant analysis (LDA) and
hierarchical clustering of fatty acid (FA) composition. Statistics was estimated by nonparametric
tests and Spearman’s rank correlation.
Analyses of total lipids revealed significantly reduced triglyceride content in femoral
(fBMAT) than in acetabular BMAT (aBMAT) and gfSAT. Frequencies of spontaneously
released saturated palmitic (C16:0) and stearic acids (C18:0) were higher in fBMAT than
in aBMAT and gfSAT (p=0.036 and p=0.046, n=8). Cluster heatmap and LDA showed that
fBMAT differed to acetabular and gfSAT, while acetabular and gfSAT were more similar in
FA profiles. FA profiles of AT depots varied with patient’s age. Contribution of palmitic acid
was increased in aged group in all AT depots, while stearic acid declined in aged group in
BMAT compartments only. fBMAT cellularity declined with age (r=-0.675, n=14, p=0.037).
Additionally, the presence of CD45-CD31-CD34+CD24+ adipogenic progenitor (stem) cells
was increased in fBMAT (0.46±0.03%) when compared to aBMAT (0.21±0.01%) depot.
Femoral mesenchymal stem cells displayed pronounced adipogenesis comparing to their
acetabular counterparts.
Our findings suggest that specific lipid profile of fBMAT imposes adipogenic commitment
of stem cells within this skeletal site.",
publisher = "Belgrade : Institute of molecular genetics and genetic engineering",
journal = "4th Belgrade Bioinformatics Conference",
title = "Fatty Acid Data Analysis Unravels Skeletal Site and Age-Specific Features of Human Bone Marrow Adiposity",
pages = "54-54",
volume = "4",
url = "https://hdl.handle.net/21.15107/rcub_imagine_1995"
}

Trivanović, D., Kovačević, J., Arsić, A., Vujačić, M., Bogosavljević, N., Okić Djordjević, I., Živanović, M., Mojsilović, S., Maljković, M.,& Jauković, A.. (2023). Fatty Acid Data Analysis Unravels Skeletal Site and Age-Specific Features of Human Bone Marrow Adiposity. in 4th Belgrade Bioinformatics Conference
Belgrade : Institute of molecular genetics and genetic engineering., 4, 54-54.
https://hdl.handle.net/21.15107/rcub_imagine_1995

Trivanović D, Kovačević J, Arsić A, Vujačić M, Bogosavljević N, Okić Djordjević I, Živanović M, Mojsilović S, Maljković M, Jauković A. Fatty Acid Data Analysis Unravels Skeletal Site and Age-Specific Features of Human Bone Marrow Adiposity. in 4th Belgrade Bioinformatics Conference. 2023;4:54-54.
https://hdl.handle.net/21.15107/rcub_imagine_1995 .

Trivanović, Drenka, Kovačević, Jovana, Arsić, Aleksandra, Vujačić, Marko, Bogosavljević, Nikola, Okić Djordjević, Ivana, Živanović, Milena, Mojsilović, Slavko, Maljković, Mirjana, Jauković, Aleksandra, "Fatty Acid Data Analysis Unravels Skeletal Site and Age-Specific Features of Human Bone Marrow Adiposity" in 4th Belgrade Bioinformatics Conference, 4 (2023):54-54,
https://hdl.handle.net/21.15107/rcub_imagine_1995 .

TY  - CONF
AU  - Ćirić, Nevena
AU  - Kovačević, Jovana
PY  - 2023
UR  - https://belbi.bg.ac.rs/
UR  - https://imagine.imgge.bg.ac.rs/handle/123456789/2018
AB  - Intrinsically disordered proteins and regions are widely distributed within most proteomes.
Recent studies show that they are associated with many essential biological processes
and a broad range of human diseases. Given the prevalence of disordered proteins and
the growing acknowledgement of their functional relevance, considerable effort has been
made by the bioinformatics community to provide computational tools to predict protein
disorder. To date, based on various characteristics of protein disorder, along with variety of
diverse computational approaches, numerous disorder predictors have been developed.
Over the past decade several review papers examining intrinsic disorder predictors have
been published. All these papers have played a significant role in stimulating and greatly
facilitating the development of this actively growing field by pinpointing the potential
room for improvement. Inspired by these, in this work we aim to integrate the relevant
information regarding the existing intrinsic disorder predictors from the corresponding
research papers in a novel review, including latest prediction tools. In addition, for each
disorder predictor, we examined the possibility of their retraining using different datasets.
Here, we present an overview of 23 protein disorder prediction methods, including the
thorough analysis of their advantages and weaknesses which derive from their different
computational approaches. Regarding this, we precisely describe the methodology used
for building the models and categorize them by different classification schemes. The
performance of these models is presented by their scores from the most recent CAID
competition. Additional contribution of this work is the models’ retraining availability
analysis. We describe in detail the predictors’ implementation source code (if available) and
propose a way around to overcome the obstacles with retraining procedure (if possible).
This insight might be very useful, since older models were trained on significantly
smaller datasets compared to the newer ones, due to the increase in the number of
experimentally annotated disorder proteins with time. With respect to this, we discuss
in detail the possibility of retraining the models on a different (bigger, novel) dataset in
order to perform full-scale comparison of their expression power in delineating disorder
in proteins.
PB  - Belgrade : Institute of molecular genetics and genetic engineering
C3  - 4th Belgrade Bioinformatics Conference
T1  - Methodology, performance and retrainability survey of intrinsic disorder predictors
EP  - 78
SP  - 78
VL  - 4
UR  - https://hdl.handle.net/21.15107/rcub_imagine_2018
ER  - 

@conference{
author = "Ćirić, Nevena and Kovačević, Jovana",
year = "2023",
abstract = "Intrinsically disordered proteins and regions are widely distributed within most proteomes.
Recent studies show that they are associated with many essential biological processes
and a broad range of human diseases. Given the prevalence of disordered proteins and
the growing acknowledgement of their functional relevance, considerable effort has been
made by the bioinformatics community to provide computational tools to predict protein
disorder. To date, based on various characteristics of protein disorder, along with variety of
diverse computational approaches, numerous disorder predictors have been developed.
Over the past decade several review papers examining intrinsic disorder predictors have
been published. All these papers have played a significant role in stimulating and greatly
facilitating the development of this actively growing field by pinpointing the potential
room for improvement. Inspired by these, in this work we aim to integrate the relevant
information regarding the existing intrinsic disorder predictors from the corresponding
research papers in a novel review, including latest prediction tools. In addition, for each
disorder predictor, we examined the possibility of their retraining using different datasets.
Here, we present an overview of 23 protein disorder prediction methods, including the
thorough analysis of their advantages and weaknesses which derive from their different
computational approaches. Regarding this, we precisely describe the methodology used
for building the models and categorize them by different classification schemes. The
performance of these models is presented by their scores from the most recent CAID
competition. Additional contribution of this work is the models’ retraining availability
analysis. We describe in detail the predictors’ implementation source code (if available) and
propose a way around to overcome the obstacles with retraining procedure (if possible).
This insight might be very useful, since older models were trained on significantly
smaller datasets compared to the newer ones, due to the increase in the number of
experimentally annotated disorder proteins with time. With respect to this, we discuss
in detail the possibility of retraining the models on a different (bigger, novel) dataset in
order to perform full-scale comparison of their expression power in delineating disorder
in proteins.",
publisher = "Belgrade : Institute of molecular genetics and genetic engineering",
journal = "4th Belgrade Bioinformatics Conference",
title = "Methodology, performance and retrainability survey of intrinsic disorder predictors",
pages = "78-78",
volume = "4",
url = "https://hdl.handle.net/21.15107/rcub_imagine_2018"
}

Ćirić, N.,& Kovačević, J.. (2023). Methodology, performance and retrainability survey of intrinsic disorder predictors. in 4th Belgrade Bioinformatics Conference
Belgrade : Institute of molecular genetics and genetic engineering., 4, 78-78.
https://hdl.handle.net/21.15107/rcub_imagine_2018

Ćirić N, Kovačević J. Methodology, performance and retrainability survey of intrinsic disorder predictors. in 4th Belgrade Bioinformatics Conference. 2023;4:78-78.
https://hdl.handle.net/21.15107/rcub_imagine_2018 .

Ćirić, Nevena, Kovačević, Jovana, "Methodology, performance and retrainability survey of intrinsic disorder predictors" in 4th Belgrade Bioinformatics Conference, 4 (2023):78-78,
https://hdl.handle.net/21.15107/rcub_imagine_2018 .

TY  - CONF
AU  - Zečević, Anđelka
AU  - Kovačević, Jovana
AU  - Davidović, Radoslav
PY  - 2023
UR  - https://belbi.bg.ac.rs/
UR  - https://imagine.imgge.bg.ac.rs/handle/123456789/1975
AB  - Information aggregation from various gen, disease, and gen-disease databases such
as DisGeNet, COSMIC, HumsaVar, Orphanet, ClinVar, HPO, and Diseases into a unique
database would enable researchers to analyze and compare valuable domain findings
in a more convenient and systematic way. However, the aggregation poses numerous
challenges due to non-uniform information annotation across the databases. In this work,
we address the problem of mapping a disease name, when needed, into a standardized
disease code (DOID) based on Natural Language Processing text representation
techniques. We examine the benefits and limitations of using off-the-shelf embeddings
such as Med2vec, and language models such as BioBERT, UmlsBERT, and PubMedBERT
in retrieval scenarios with respect to standard full-text search. In addition to qualitative
improvements, we elaborate on the technical requirements and computational
complexities that come with the embracement of language models and semantic search.
PB  - Belgrade : Institute of molecular genetics and genetic engineering
C3  - 4th Belgrade Bioinformatics Conference
T1  - Mapping of Disease Names to Disease Codes based on Natural Language Processing Techniques
EP  - 37
SP  - 37
VL  - 4
UR  - https://hdl.handle.net/21.15107/rcub_imagine_1975
ER  - 

@conference{
author = "Zečević, Anđelka and Kovačević, Jovana and Davidović, Radoslav",
year = "2023",
abstract = "Information aggregation from various gen, disease, and gen-disease databases such
as DisGeNet, COSMIC, HumsaVar, Orphanet, ClinVar, HPO, and Diseases into a unique
database would enable researchers to analyze and compare valuable domain findings
in a more convenient and systematic way. However, the aggregation poses numerous
challenges due to non-uniform information annotation across the databases. In this work,
we address the problem of mapping a disease name, when needed, into a standardized
disease code (DOID) based on Natural Language Processing text representation
techniques. We examine the benefits and limitations of using off-the-shelf embeddings
such as Med2vec, and language models such as BioBERT, UmlsBERT, and PubMedBERT
in retrieval scenarios with respect to standard full-text search. In addition to qualitative
improvements, we elaborate on the technical requirements and computational
complexities that come with the embracement of language models and semantic search.",
publisher = "Belgrade : Institute of molecular genetics and genetic engineering",
journal = "4th Belgrade Bioinformatics Conference",
title = "Mapping of Disease Names to Disease Codes based on Natural Language Processing Techniques",
pages = "37-37",
volume = "4",
url = "https://hdl.handle.net/21.15107/rcub_imagine_1975"
}

Zečević, A., Kovačević, J.,& Davidović, R.. (2023). Mapping of Disease Names to Disease Codes based on Natural Language Processing Techniques. in 4th Belgrade Bioinformatics Conference
Belgrade : Institute of molecular genetics and genetic engineering., 4, 37-37.
https://hdl.handle.net/21.15107/rcub_imagine_1975

Zečević A, Kovačević J, Davidović R. Mapping of Disease Names to Disease Codes based on Natural Language Processing Techniques. in 4th Belgrade Bioinformatics Conference. 2023;4:37-37.
https://hdl.handle.net/21.15107/rcub_imagine_1975 .

Zečević, Anđelka, Kovačević, Jovana, Davidović, Radoslav, "Mapping of Disease Names to Disease Codes based on Natural Language Processing Techniques" in 4th Belgrade Bioinformatics Conference, 4 (2023):37-37,
https://hdl.handle.net/21.15107/rcub_imagine_1975 .