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Article ; Online: Seasonal variations of some soil nutrients in a natural and an agricultural olive grove in Adana, Turkey.

Koçak, Burak

2022 Volume 194, Issue 4, Page(s) 246

Abstract: The bioavailability and cycling of nutrients in soil are two of the most important factors for healthy plant growth and development in natural and agricultural ecosystems. Seasonal variations of some soil macronutrient (phosphorus and potassium) and ... ...

Abstract	The bioavailability and cycling of nutrients in soil are two of the most important factors for healthy plant growth and development in natural and agricultural ecosystems. Seasonal variations of some soil macronutrient (phosphorus and potassium) and micronutrient (copper, manganese, and zinc) contents were investigated in a natural olive (Olea europaea L.) grove (NO) and an agricultural olive gene garden (OGG) located in Adana, Turkey. Soils were sampled at 0-10 cm and at 10-20 cm depth in the months of November, February, May, and August between 2013 and 2015. Soil phosphorus, potassium, copper, manganese, and zinc contents were in the range between 0.37 and 8.65 mg kg
MeSH term(s)	Ecosystem ; Environmental Monitoring ; Nutrients ; Olea ; Phosphorus/analysis ; Seasons ; Soil ; Turkey
Chemical Substances	Soil ; Phosphorus (27YLU75U4W)
Language	English
Publishing date	2022-03-04
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	782621-7
ISSN	1573-2959 ; 0167-6369
ISSN (online)	1573-2959
ISSN	0167-6369
DOI	10.1007/s10661-022-09903-y
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Article ; Online: Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics.

Koçak, Burak

Diagnostic and interventional radiology (Ankara, Turkey)

2022 Volume 28, Issue 5, Page(s) 450–462

Abstract: Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility might ... ...

Abstract	Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology research to deal with large and complex imaging data sets. Nowadays, ML tools have become easily accessible to anyone. Such a low threshold to accessibility might lead to inappropriate usage and misinterpretation, without a clear intention. Therefore, ensuring methodological rigor is of paramount importance. Getting closer to the real-world clinical implementation of AI, a basic understanding of the main concepts should be a must for every radiology professional. In this respect, simplified explanations of the key concepts along with pitfalls and recommendations would be helpful for general radiology community to develop and improve their AI mindset. In this work, twenty-two key issues are reviewed within three categories: pre-modeling, modeling, and post-modeling. Firstly, the concept is shortly defined for each issue. Then, related common pitfalls and best practices are provided. Specifically, the issues included in this paper were validity of scientific question, unrepresentative samples, sample size, missing data, quality of reference standard, batch effect, reliability of features, feature scaling, multi-collinearity, class imbalance, data and target leakage, high-dimensional data, optimization, overfitting, generalization, performance metrics, clinical utility, comparison with conventional statistical and clinical methods, interpretability and explainability, randomness, transparent reporting, and sharing data.
MeSH term(s)	Artificial Intelligence ; Humans ; Machine Learning ; Radiology/methods ; Reproducibility of Results
Language	English
Publishing date	2022-10-10
Publishing country	Turkey
Document type	Journal Article ; Review
ZDB-ID	2184145-7
ISSN	1305-3612 ; 1305-3612
ISSN (online)	1305-3612
ISSN	1305-3612
DOI	10.5152/dir.2022.211297
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Article ; Online: Artificial intelligence to predict task activation from resting state fMRI.

Kocak, Burak

European radiology

2021 Volume 31, Issue 7, Page(s) 5251–5252

Language	English
Publishing date	2021-05-07
Publishing country	Germany
Document type	Editorial
ZDB-ID	1085366-2
ISSN	1432-1084 ; 0938-7994 ; 1613-3749
ISSN (online)	1432-1084
ISSN	0938-7994 ; 1613-3749
DOI	10.1007/s00330-021-07975-x
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Article: Seasonal variations of some soil nutrients in a natural and an agricultural olive grove in Adana, Turkey

Koçak, Burak

Environmental monitoring and assessment. 2022 Apr., v. 194, no. 4

2022

Abstract	The bioavailability and cycling of nutrients in soil are two of the most important factors for healthy plant growth and development in natural and agricultural ecosystems. Seasonal variations of some soil macronutrient (phosphorus and potassium) and micronutrient (copper, manganese, and zinc) contents were investigated in a natural olive (Olea europaea L.) grove (NO) and an agricultural olive gene garden (OGG) located in Adana, Turkey. Soils were sampled at 0–10 cm and at 10–20 cm depth in the months of November, February, May, and August between 2013 and 2015. Soil phosphorus, potassium, copper, manganese, and zinc contents were in the range between 0.37 and 8.65 mg kg⁻¹, 181.81 and 1030.67 mg kg⁻¹, 1.41 and 2.74 mg kg⁻¹, 13.88 and 51.06 mg kg⁻¹, and 0.39 and 2.27 mg kg⁻¹, respectively. All soil nutrients significantly decreased as soil depth increased in all sampling times (P < 0.05). In general, significant seasonal effects were observed in all soil nutrients at 0–10 cm depth that was more variable than at 10–20 cm depth. Soil phosphorus negatively and positively correlated with soil potassium in NO and in OGG at 0–10 cm depth, respectively (P < 0.05). Soil zinc was negatively and positively correlated with soil phosphorus in NO and in OGG at 10–20 cm depth, respectively (P < 0.05). In conclusion, soil depth might be a more important factor than seasonality on the vertical distribution of soil nutrients in olive groves. In addition, correlations between soil nutrients in this study should be taken into consideration for the optimum management of agricultural practices in biological olive groves.
Keywords	Olea europaea ; bioavailability ; copper ; gardens ; genes ; growth and development ; manganese ; olives ; phosphorus ; plant growth ; potassium ; soil depth ; soil nutrients ; spatial distribution ; zinc
Language	English
Dates of publication	2022-04
Size	p. 246.
Publishing place	Springer International Publishing
Document type	Article
ZDB-ID	782621-7
ISSN	1573-2959 ; 0167-6369
ISSN (online)	1573-2959
ISSN	0167-6369
DOI	10.1007/s10661-022-09903-y
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Article: Response of Soil Microbial Respiration to Spirotetramat Insecticide Under Different Soil Field Capacities

Koçak, Burak

Water, air, and soil pollution. 2022 Sept., v. 233, no. 9

2022

Abstract: Due to the repeated applications of pesticides, the amount of the pesticides and their products by decomposition may accumulate in the soil ecosystems which are affected by abiotic and biotic factors. Soil microorganisms are the important players that ... ...

Abstract	Due to the repeated applications of pesticides, the amount of the pesticides and their products by decomposition may accumulate in the soil ecosystems which are affected by abiotic and biotic factors. Soil microorganisms are the important players that are able to decompose and utilize these chemical waste materials as energy sources and regulate them in the cycling in the soil environment. One of the important insecticides for the control of insect pests including aphids is spirotetramat which can provide protection to plant roots from the attack of insects when it was sprayed on the crops. However, effects of high concentrations of spirotetramat on soil microbial respiration under different soil water contents are unknown. Recommended field dose (RFD) and its 5 (RFD × 5) and 10 (RFD × 10) folds of spirotetramat were mixed with a clay soil; these mixtures were humidified at 50% (50FC), 75% (75FC), and 100% (100FC) of field capacity and then incubated at 28 °C for 21 days. At the end of the incubation period, (1) in general, soil microbial respiration was significantly increased as soil moisture increased in all treatments (50FC < 75FC < 100FC, P < 0.05); (2) all concentrations of spirotetramat significantly decreased the microbial respiration under 100FC (P < 0.05); (3) only RFD × 5 significantly reduced this activity under 75FC (P < 0.05); (4) no significant differences between control and treatments were found under 50FC. In conclusion, high concentrations of spirotetramat insecticide had low toxic effects on soil microbial respiration while the soil moisture regulated the toxicity effects of this insecticide.
Keywords	air ; clay soils ; edaphic factors ; energy ; field capacity ; insecticides ; insects ; soil pollution ; soil water ; toxicity
Language	English
Dates of publication	2022-09
Size	p. 361.
Publishing place	Springer International Publishing
Document type	Article
ZDB-ID	120499-3
ISSN	1573-2932 ; 0049-6979 ; 0043-1168
ISSN (online)	1573-2932
ISSN	0049-6979 ; 0043-1168
DOI	10.1007/s11270-022-05850-z
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

Koçak, Burak / Keleş, Ali / Köse, Fadime

Diagnostic and interventional radiology (Ankara, Turkey)

2024

Abstract: Purpose: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions.: Methods: The primary source of ... ...

Abstract	Purpose: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. Methods: The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. Results: Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines ( Conclusion: The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. Clinical significance: This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.
Language	English
Publishing date	2024-02-20
Publishing country	Turkey
Document type	Journal Article
ZDB-ID	2184145-7
ISSN	1305-3612 ; 1305-3612
ISSN (online)	1305-3612
ISSN	1305-3612
DOI	10.4274/dir.2024.232604
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Article ; Online: Should Calcineurin Inhibitors/Sirolimus Be Ceased Completely In Posterior Reversible Encephalopathy Syndrome?

Karataş, Cihan / Akyollu, Başak / Arpalı, Emre / Kocak, Burak

Transplantation proceedings

2024 Volume 56, Issue 1, Page(s) 93–96

Abstract: Background: To investigate the relationship between immunosuppressive treatments and posterior reversible encephalopathy syndrome (PRES) in transplant patients.: Methods: We presented a retrospective study of 4 cases of PRES in transplant patients. ... ...

Abstract	Background: To investigate the relationship between immunosuppressive treatments and posterior reversible encephalopathy syndrome (PRES) in transplant patients. Methods: We presented a retrospective study of 4 cases of PRES in transplant patients. Patient records were reviewed to identify potential risk factors, clinical presentations, radiological findings, and immunosuppressive treatments used. Results: Our analysis revealed a potential association between immunosuppressive treatments and the development of PRES in transplant patients. Specifically, we found that adjusting or switching immunosuppressive treatments can improve outcomes and prevent the recurrence of PRES. Conclusion: Our findings highlight the importance of recognizing PRES as a potential complication of immunosuppressive treatments in transplant patients. Early detection and management, including a review of immunosuppressive treatments, may improve patient outcomes and prevent further complications.
MeSH term(s)	Humans ; Calcineurin Inhibitors/adverse effects ; Immunosuppressive Agents/adverse effects ; Posterior Leukoencephalopathy Syndrome/chemically induced ; Posterior Leukoencephalopathy Syndrome/diagnostic imaging ; Retrospective Studies ; Sirolimus
Chemical Substances	Calcineurin Inhibitors ; Immunosuppressive Agents ; Sirolimus (W36ZG6FT64)
Language	English
Publishing date	2024-01-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	82046-5
ISSN	1873-2623 ; 0041-1345
ISSN (online)	1873-2623
ISSN	0041-1345
DOI	10.1016/j.transproceed.2023.11.012
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Article: How Did Soil Depth and Sampling Time Influence on Soil Organic Carbon, Soil Nitrogen, and Soil Biological Properties in a Mediterranean Olive Grove?

Koçak, Burak / Darıcı, Cengiz

Communications in soil science and plant analysis. 2022 Jan. 02, v. 53, no. 1

2022

Abstract: The objective of this study was to determine the effects of sampling time and depth on some soil properties in a natural olive grove. Soil samples were collected at 0–10 cm (S0) and 10–20 cm (S1) depths in February, May, August, and November between 2013 ...

Abstract	The objective of this study was to determine the effects of sampling time and depth on some soil properties in a natural olive grove. Soil samples were collected at 0–10 cm (S0) and 10–20 cm (S1) depths in February, May, August, and November between 2013 and 2015 in Cukurova University Campus, Adana, Turkey. The soil properties determined were soil organic carbon (SOC), total nitrogen (TN), C/N, soil C mineralization and rate (Cmin) under laboratory conditions, aerobic bacteria and fungi counts. Effect of soil depth on these properties was more significant than sampling time. SOC, TN, soil aerobic bacteria and fungi counts, and Cmin were generally decreased as soil depth increased. Carbon mineralizations in S0 and S1 were lowest in May 2014 and highest in May 2015. In general, there were no significant differences between depths and sampling times in soil bacteria counts and C/N while fungi counts in S0 were significantly higher in S1 in every sampling time (P < .05). SOC was significantly correlated with all soil properties in S1 and positively correlated with TN in both depths (P < .05). In conclusion, heterogeneity of soil properties was more comprehensible in depth than in sampling times.
Keywords	mineralization ; nitrogen ; olives ; plant analysis ; soil depth ; soil heterogeneity ; soil organic carbon ; total nitrogen
Language	English
Dates of publication	2022-0102
Size	p. 30-44.
Publishing place	Taylor & Francis
Document type	Article
ZDB-ID	419718-5
ISSN	1532-2416 ; 0010-3624
ISSN (online)	1532-2416
ISSN	0010-3624
DOI	10.1080/00103624.2021.1971697
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Multiple sclerosis versus cerebral small vessel disease in MRI: a practical approach using qualitative and quantitative signal intensity differences in white matter lesions.

Yuzkan, Sabahattin / Balsak, Serdar / Cinkir, Ufuk / Kocak, Burak

Acta radiologica (Stockholm, Sweden : 1987)

2023 Volume 65, Issue 1, Page(s) 106–114

Abstract: Background: Multiple sclerosis (MS) and cerebral small vessel disease (CSVD) are relatively common radiological entities that occasionally necessitate differential diagnosis.: Purpose: To investigate the differences in magnetic resonance imaging (MRI) ...

Abstract	Background: Multiple sclerosis (MS) and cerebral small vessel disease (CSVD) are relatively common radiological entities that occasionally necessitate differential diagnosis. Purpose: To investigate the differences in magnetic resonance imaging (MRI) signal intensity (SI) between MS and CSVD related white matter lesions. Material and methods: On 1.5-T and 3-T MRI scanners, 50 patients with MS (380 lesions) and 50 patients with CSVD (395 lesions) were retrospectively evaluated. Visual inspection was used to conduct qualitative analysis on diffusion-weighted imaging (DWI)_b1000 to determine relative signal intensity. The thalamus served as the reference for quantitative analysis based on SI ratio (SIR). The statistical analysis utilized univariable and multivariable methods. There were analyses of patient and lesion datasets. On a dataset restricted by age (30-50 years), additional evaluations, including unsupervised fuzzy c-means clustering, were performed. Results: Using both quantitative and qualitative features, the optimal model achieved a 100% accuracy, sensitivity, and specificity with an area under the curve (AUC) of 1 in patient-wise analysis. With an AUC of 0.984, the best model achieved a 94% accuracy, sensitivity, and specificity when using only quantitative features. The model's accuracy, sensitivity, and specificity were 91.9%, 84.6%, and 95.8%, respectively, when using the age-restricted dataset. Independent predictors were T2_SIR_max (optimal cutoff=2.1) and DWI_b1000_SIR_mean (optimal cutoff=1.1). Clustering also performed well with an accuracy, sensitivity, and specificity of 86.5%, 70.6%, and 100%, respectively, in the age-restricted dataset. Conclusion: SI characteristics derived from DWI_b1000 and T2-weighted-based MRI demonstrate excellent performance in differentiating white matter lesions caused by MS and CSVD.
MeSH term(s)	Humans ; Adult ; Middle Aged ; Multiple Sclerosis/diagnostic imaging ; White Matter/diagnostic imaging ; White Matter/pathology ; Retrospective Studies ; Magnetic Resonance Imaging/methods ; Diffusion Magnetic Resonance Imaging/methods ; Cerebral Small Vessel Diseases/diagnostic imaging ; Sensitivity and Specificity
Language	English
Publishing date	2023-03-02
Publishing country	England
Document type	Journal Article
ZDB-ID	105-3
ISSN	1600-0455 ; 0284-1851 ; 0349-652X
ISSN (online)	1600-0455
ISSN	0284-1851 ; 0349-652X
DOI	10.1177/02841851231155608
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Article ; Online: Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM.

Kocak, Burak / Keles, Ali / Akinci D'Antonoli, Tugba

European radiology

2023 Volume 34, Issue 4, Page(s) 2805–2815

Abstract: Objective: To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations.: Methods: Google Scholar, Web of ... ...

Abstract	Objective: To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations. Methods: Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed. Results: A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively. Conclusion: Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists. Clinical relevance statement: Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities. Key points: • Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously.
MeSH term(s)	Humans ; Checklist ; Artificial Intelligence ; Diagnostic Imaging ; Radiography
Language	English
Publishing date	2023-09-22
Publishing country	Germany
Document type	Systematic Review ; Journal Article
ZDB-ID	1085366-2
ISSN	1432-1084 ; 0938-7994 ; 1613-3749
ISSN (online)	1432-1084
ISSN	0938-7994 ; 1613-3749
DOI	10.1007/s00330-023-10243-9
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