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Article: Dietary Acrylamide: A Detailed Review on Formation, Detection, Mitigation, and Its Health Impacts.

Govindaraju, Indira / Sana, Maidin / Chakraborty, Ishita / Rahman, Md Hafizur / Biswas, Rajib / Mazumder, Nirmal

2024 Volume 13, Issue 4

Abstract: In today's fast-paced world, people increasingly rely on a variety of processed foods due to their busy lifestyles. The enhanced flavors, vibrant colors, and ease of accessibility at reasonable prices have made ready-to-eat foods the easiest and simplest ...

Abstract	In today's fast-paced world, people increasingly rely on a variety of processed foods due to their busy lifestyles. The enhanced flavors, vibrant colors, and ease of accessibility at reasonable prices have made ready-to-eat foods the easiest and simplest choice to satiate hunger, especially those that undergo thermal processing. However, these foods often contain an unsaturated amide called 'Acrylamide', known by its chemical name 2-propenamide, which is a contaminant formed when a carbohydrate- or protein-rich food product is thermally processed at more than 120 °C through methods like frying, baking, or roasting. Consuming foods with elevated levels of acrylamide can induce harmful toxicity such as neurotoxicity, hepatoxicity, cardiovascular toxicity, reproductive toxicity, and prenatal and postnatal toxicity. This review delves into the major pathways and factors influencing acrylamide formation in food, discusses its adverse effects on human health, and explores recent techniques for the detection and mitigation of acrylamide in food. This review could be of interest to a wide audience in the food industry that manufactures processed foods. A multi-faceted strategy is necessary to identify and resolve the factors responsible for the browning of food, ensure safety standards, and preserve essential food quality traits.
Language	English
Publishing date	2024-02-12
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods13040556
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Article: Hydrocarbons recovery through catalytic pyrolysis of compostable and recyclable waste plastics using a novel desk-top staged reactor

Bhoi, Prakashbhai R. / Rahman, Md Hafizur

Environmental technology & innovation. 2022 Aug., v. 27

2022

Abstract: The overall objective of this study is to develop a novel two-staged fixed-bed reactor to evaluate the catalytic pyrolysis of recyclable (HDPE) and compostable waste plastics and to enhance the selectivity of gasoline range hydrocarbons in pyrolysis ... ...

Abstract	The overall objective of this study is to develop a novel two-staged fixed-bed reactor to evaluate the catalytic pyrolysis of recyclable (HDPE) and compostable waste plastics and to enhance the selectivity of gasoline range hydrocarbons in pyrolysis liquid. In this research, both used and unused compostable bioplastics, recyclable high-density polyethylene (HDPE) grocery bags, and unused HDPE were used as a feedstock Additionally, a mixture of recyclable plastic and compostable bioplastic with 1:1 ratio was pyrolyzed at 500 °C, and the pyrolysis oil products were compared with commercial gasoline. Zeolite Socony Mobil-5 (ZSM-5) was used as a catalyst in all the catalytic pyrolysis experiments, which produced wax-free shorter chain hydrocarbons similar to commercial gasoline. The addition of recyclable HDPE in compostable plastics improved the selectivity of gasoline range hydrocarbon (C6−C12) by 10% and increased the calorific value of pyrolysis liquids by 7.5% (43 MJ/kg), which is equivalent to commercial gasoline. Moreover, both compostable and recyclable plastics produced aromatic and aliphatic hydrocarbons that have potential applications in lubricants, additives, and petrochemical industries.
Keywords	bioplastics ; catalysts ; environmental technology ; feedstocks ; gasoline ; polyethylene ; pyrolysis ; pyrolysis oils ; zeolites
Language	English
Dates of publication	2022-08
Publishing place	Elsevier B.V.
Document type	Article
ISSN	2352-1864
DOI	10.1016/j.eti.2022.102453
Database	NAL-Catalogue (AGRICOLA)

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Article: An insight on sources and biodegradation of bioplastics: a review.

Pooja, Nag / Chakraborty, Ishita / Rahman, Md Hafizur / Mazumder, Nirmal

3 Biotech

2023 Volume 13, Issue 7, Page(s) 220

Abstract: Durability and affordability are two main reasons for the widespread consumption of plastic in the world. However, the inability of these materials to undergo degradation has become a significant threat to the environment and human health To address this ...

Abstract	Durability and affordability are two main reasons for the widespread consumption of plastic in the world. However, the inability of these materials to undergo degradation has become a significant threat to the environment and human health To address this issue, bioplastics have emerged as a promising alternative. Bioplastics are obtained from renewable and sustainable biomass and have a lower carbon footprint and emit fewer greenhouse gases than petroleum-based plastics. The use of these bioplastics sourced from renewable biomass can also reduce the dependency on fossil fuels, which are limited in availability. This review provides an elaborate comparison of biodegradation rates of potential bioplastics in soil from various sources such as biomass, microorganisms, and monomers. These bioplastics show great potential as a replacement for conventional plastics due to their biodegradable and diverse properties.
Language	English
Publishing date	2023-05-31
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	2600522-0
ISSN	2190-5738 ; 2190-572X
ISSN (online)	2190-5738
ISSN	2190-572X
DOI	10.1007/s13205-023-03638-4
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Article: Did the COVID‐19 lockdown in Delhi and Kolkata improve the ambient air quality of the two cities?

Datta, Arindam / Rahman, Md. Hafizur / Suresh, R.

Journal of environmental quality. 2021 Mar., v. 50, no. 2

2021

Abstract: To control the spread of COVID‐19, the government of India imposed a nationwide lockdown on all nonessential activities from 22 Mar. to 3 May 2020. Daily ambient particulate matter ≤10 μm in diameter (PM₁₀), particulate matter ≤2.5 μm in diameter (PM₂.₅), ...

Abstract	To control the spread of COVID‐19, the government of India imposed a nationwide lockdown on all nonessential activities from 22 Mar. to 3 May 2020. Daily ambient particulate matter ≤10 μm in diameter (PM₁₀), particulate matter ≤2.5 μm in diameter (PM₂.₅), NO, NO₂, and O₃ concentrations in Delhi and Kolkata from 1 March to 3 May in both 2019 and 2020 were collected from different monitoring stations along with meteorological data to study the impact of the COVID‐19 lockdown on ambient pollutant concentrations. In 2020, average ambient concentrations of PM₁₀ and PM₂.₅ were significantly decreased (Delhi: 59 and 43%, respectively; Kolkata: 49 and 50%, respectively) during the lockdown period compared with the same period during 2019 in both cities. Average ambient O₃ concentration in Delhi was significantly lower in 2020 (38.5 μg m–³) compared with 2019 (44.7 μg m–³) during the study period. However, average ambient O₃ concentration was significantly higher during 2020 (46.9 μg m–³) compared with 2019 (31.4 μg m–³) in Kolkata. Effect size analysis of different predictive variables reveals that the lockdown period explains maximum variation in ambient concentrations of PM₁₀ and PM₂.₅ during 2020 in both cities. However, maximum variation in ambient O₃ concentrations in both cities was explained primarily by spatial variation rather than by the lockdown period. This study suggests that major policy implementation related to the transport and industrial sectors that aims to address the ambient air pollution problem in India may reduce the ambient particulate matter concentrations, although it may not have a significant effect on other ambient air pollutants such as O₃ in major Indian cities.
Keywords	COVID-19 infection ; air ; air pollution ; air quality ; issues and policy ; meteorological data ; particulates ; India
Language	English
Dates of publication	2021-03
Size	p. 485-493.
Publishing place	John Wiley & Sons, Ltd
Document type	Article
Note	JOURNAL ARTICLE
ZDB-ID	120525-0
ISSN	1537-2537 ; 0047-2425
ISSN (online)	1537-2537
ISSN	0047-2425
DOI	10.1002/jeq2.20192
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Article ; Online: Intimate partner violence and excess fertility among women of reproductive age in Malawi.

Dadabhai, Sufia / Quaynor, Laura / Bandala-Jacques, Antonio / Seyama, Linly / Rahman, Md Hafizur / Phiri, Richard / Decker, Michele R / Taha, Taha E

PloS one

2024 Volume 19, Issue 1, Page(s) e0297959

Abstract: Purpose: Gender inequity and adverse health outcomes continue to be of concern among women in sub-Saharan Africa. We determined prevalence of intimate partner violence and excess fertility (having more children than desired) in reproductive age women in ...

Abstract	Purpose: Gender inequity and adverse health outcomes continue to be of concern among women in sub-Saharan Africa. We determined prevalence of intimate partner violence and excess fertility (having more children than desired) in reproductive age women in Malawi. We also explored factors associated with these outcomes and with spousal fertility intentions. Patients and methods: In a cross-sectional study, a total of 360 women and 410 men were recruited using multi-stage sampling from communities in a peri-urban setting in Blantyre District, Southern Malawi in 2021. Women and men were separately interviewed by trained study workers using a structured questionnaire. In addition to descriptive analyses, we used univariate and multivariate logistic regression models to assess associations of risk factors with the outcomes of intimate partner violence and excess fertility. Results: Among women, lifetime prevalence of intimate partner violence was 23.1%, and excess fertility was experienced by 25.6%. Intimate partner violence was associated with male partners alcohol consumption (adjusted odds ratio 2.13; P = 0.019). Women were more likely to report excess fertility if they were older (adjusted odds ratio 2.0, P<0.001, for a 5-year increase). Alcohol consumption by the male partner (adjusted odds ratio 2.14; P = 0.025) and women being able to refuse sex with their male partner (adjusted odds ratio 0.50; P = 0.036) were associated with discordant fertility preferences. Conclusions: Intimate partner violence, excess fertility, and social and health inequities continue to be prevalent in Malawi. These data suggest the underlying proximal and distal factors associated with these adverse outcomes such as alcohol consumption may be addressed through education, couple interactive communication, and community dialogue. To ensure sustainability and effectiveness, strong leadership involvement, both governmental and non-governmental, is needed.
MeSH term(s)	Child ; Humans ; Male ; Female ; Cross-Sectional Studies ; Malawi/epidemiology ; Intimate Partner Violence ; Risk Factors ; Surveys and Questionnaires ; Fertility ; Prevalence ; Sexual Partners
Language	English
Publishing date	2024-01-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0297959
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Article ; Online: Artificial Intelligence-Driven Image Analysis of Bacterial Cells and Biofilms.

Ragi, Shankarachary / Rahman, Md Hafizur / Duckworth, Jamison / Jawaharraj, Kalimuthu / Chundi, Parvathi / Gadhamshetty, Venkataramana

IEEE/ACM transactions on computational biology and bioinformatics

2023 Volume 20, Issue 1, Page(s) 174–184

Abstract: The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate ... ...

Abstract	The current study explores an artificial intelligence framework for measuring the structural features from microscopy images of the bacterial biofilms. Desulfovibrio alaskensis G20 (DA-G20) grown on mild steel surfaces is used as a model for sulfate reducing bacteria that are implicated in microbiologically influenced corrosion problems. Our goal is to automate the process of extracting the geometrical properties of the DA-G20 cells from the scanning electron microscopy (SEM) images, which is otherwise a laborious and costly process. These geometric properties are a biofilm phenotype that allow us to understand how the biofilm structurally adapts to the surface properties of the underlying metals, which can lead to better corrosion prevention solutions. We adapt two deep learning models: (a) a deep convolutional neural network (DCNN) model to achieve semantic segmentation of the cells, (d) a mask region-convolutional neural network (Mask R-CNN) model to achieve instance segmentation of the cells. These models are then integrated with moment invariants approach to measure the geometric characteristics of the segmented cells. Our numerical studies confirm that the Mask-RCNN and DCNN methods are 227x and 70x faster respectively, compared to the traditional method of manual identification and measurement of the cell geometric properties by the domain experts.
MeSH term(s)	Artificial Intelligence ; Biofilms ; Desulfovibrio ; Bacteria/genetics ; Steel/chemistry
Chemical Substances	Steel (12597-69-2)
Language	English
Publishing date	2023-02-03
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2021.3138304
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Article ; Online: Verbal and Social Autopsy of Adult Deaths and Adult Care-Seeking Pattern in Mozambique, 2019-2020.

Rahman, Md Hafizur / Macicame, Ivalda / Wilson, Emily / Nhachungue, Sheila / Amouzou, Agbessi

The American journal of tropical medicine and hygiene

2023 Volume 108, Issue 5_Suppl, Page(s) 17–28

Abstract: In sub-Saharan Africa, recent data about causes of adult death and care-seeking during illnesses are limited. This analysis examines adult deaths using verbal and social autopsy data from a nationally and provincially representative sample registration ... ...

Abstract	In sub-Saharan Africa, recent data about causes of adult death and care-seeking during illnesses are limited. This analysis examines adult deaths using verbal and social autopsy data from a nationally and provincially representative sample registration system in Mozambique. Causes of death among those 18 years and older were assigned using the InSilicoVA algorithm, and underlying social causes were examined using the pathway to survival model. Care-seeking was analyzed in different groups to determine if care was sought from formal providers (doctor, nurse/midwife, and trained community health worker) or other providers (traditional provider, family member, and pharmacist), using χ2 tests and multinomial regression models. Among the 4,040 adult deaths reported during 2019-2020, the major causes were HIV (17%), cancer (13%), injury (10%), cardiovascular diseases (9%), pneumonia (7%), tuberculosis (5%), and maternal causes (3%). Formal care-seeking was more likely among adults who had primary or higher level education (relative risk ratio [RRR]: 1.6, P < 0.001; RRR: 1.7, P < 0.01), were married (RRR: 1.3, P < 0.01), and had highest household wealth (RRR: 3.1, P < 0.001). Formal care-seeking was less likely among adults who were male (RRR: 0.7, P < 0.001), had social capital (RRR: 0.7, P < 0.05), or resided in the southern region (RRR: 0.4, P < 0.001). Information about adult causes of death is useful for formulating policy and for developing, monitoring, and evaluating programs to improve adult health in Mozambique. Care-seeking-related information helps identify barriers for seeking care from formal health providers while emphasizing the need for generating local resources and strengthening outreach health systems service delivery.
MeSH term(s)	Adult ; Humans ; Male ; Female ; Mozambique/epidemiology ; Autopsy ; Patient Acceptance of Health Care ; Family ; Cardiovascular Diseases ; Cause of Death
Language	English
Publishing date	2023-04-10
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2942-7
ISSN	1476-1645 ; 0002-9637
ISSN (online)	1476-1645
ISSN	0002-9637
DOI	10.4269/ajtmh.22-0548
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Article ; Online: In-situ particle analysis with heterogeneous background: a machine learning approach.

Alam, Adeeb Ibne / Rahman, Md Hafizur / Zia, Akhter / Lowry, Nate / Chakraborty, Prabuddha / Hassan, Md Rafiul / Khoda, Bashir

Scientific reports

2024 Volume 14, Issue 1, Page(s) 10609

Abstract: We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size ... ...

Abstract	We propose a novel framework that combines state-of-the-art deep learning approaches with pre- and post-processing algorithms for particle detection in complex/heterogeneous backgrounds common in the manufacturing domain. Traditional methods, like size analyzers and those based on dilution, image processing, or deep learning, typically excel with homogeneous backgrounds. Yet, they often fall short in accurately detecting particles against the intricate and varied backgrounds characteristic of heterogeneous particle-substrate (HPS) interfaces in manufacturing. To address this, we've developed a flexible framework designed to detect particles in diverse environments and input types. Our modular framework hinges on model selection and AI-guided particle detection as its core, with preprocessing and postprocessing as integral components, creating a four-step process. This system is versatile, allowing for various preprocessing, AI model selections, and post-processing strategies. We demonstrate this with an entrainment-based particle delivery method, transferring various particles onto substrates that mimic the HPS interface. By altering particle and substrate properties (e.g., material type, size, roughness, shape) and process parameters (e.g., capillary number) during particle entrainment, we capture images under different ambient lighting conditions, introducing a range of HPS background complexities. In the preprocessing phase, we apply image enhancement and sharpening techniques to improve detection accuracy. Specifically, image enhancement adjusts the dynamic range and histogram, while sharpening increases contrast by combining the high pass filter output with the base image. We introduce an image classifier model (based on the type of heterogeneity), employing Transfer Learning with MobileNet as a Model Selector, to identify the most appropriate AI model (i.e., YOLO model) for analyzing each specific image, thereby enhancing detection accuracy across particle-substrate variations. Following image classification based on heterogeneity, the relevant YOLO model is employed for particle identification, with a distinct YOLO model generated for each heterogeneity type, improving overall classification performance. In the post-processing phase, domain knowledge is used to minimize false positives. Our analysis indicates that the AI-guided framework maintains consistent precision and recall across various HPS conditions, with the harmonic mean of these metrics comparable to those of individual AI model outcomes. This tool shows potential for advancing in-situ process monitoring across multiple manufacturing operations, including high-density powder-based 3D printing, powder metallurgy, extreme environment coatings, particle categorization, and semiconductor manufacturing.
Language	English
Publishing date	2024-05-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-59558-7
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Article: Real-time face mask position recognition system based on MobileNet model.

Rahman, Md Hafizur / Jannat, Mir Kanon Ara / Islam, Md Shafiqul / Grossi, Giuliano / Bursic, Sathya / Aktaruzzaman, Md

Smart health (Amsterdam, Netherlands)

2023 Volume 28, Page(s) 100382

Abstract: COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its ... ...

Abstract	COVID-19 is a highly contagious disease that was first identified in 2019, and has since taken more than six million lives world wide till date, while also causing considerable economic, social, cultural and political turmoil. As a way to limit its spread, the World Health Organization and medical experts have advised properly wearing face masks, social distancing and hand sanitization, besides vaccination. However, people wear masks sometimes uncovering their mouths and/or noses consciously or unconsciously, thereby lessening the effectiveness of the protection they provide. A system capable of automatic recognition of face mask position could alert and ensure that an individual is wearing a mask properly before entering a crowded public area and putting themselves and others at risk. We first develop and publicly release a dataset of face mask images, which are collected from 391 individuals of different age groups and gender. Then, we study six different architectures of pre-trained deep learning models, and finally propose a model developed by fine tuning the pre-trained state of the art MobileNet model. We evaluate the performance (accuracy, F1-score, and Cohen's Kappa) of this model on the proposed dataset and MaskedFace-Net, a publicly available synthetic dataset created by image editing. Its performance is also compared to other existing methods. The proposed MobileNet is found as the best model providing an accuracy, F1-score, and Cohen's Kappa of 99.23%, 99.22%, and 99.19%, respectively for face mask position recognition. It outperforms the accuracy of the best existing model by about 2%. Finally, an automatic face mask position recognition system has been developed, which can recognize if an individual is wearing a mask correctly or incorrectly. The proposed model performs very well with no drop in recognition accuracy from real images captured by a camera.
Language	English
Publishing date	2023-01-31
Publishing country	Netherlands
Document type	Journal Article
ISSN	2352-6483
ISSN	2352-6483
DOI	10.1016/j.smhl.2023.100382
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Article ; Online: Effect of sucrose, trehalose, maltose and xylose on rheology, water mobility and microstructure of gluten-free model dough based on high hydrostatic pressure treated starches.

Rahman, Md Hafizur / Sun, Hong-Nan / Zhang, Miao / Mu, Tai-Hua / Khan, Nasir Mehmood

International journal of biological macromolecules

2023 Volume 231, Page(s) 123184

Abstract: Due to functional and physicochemical properties, starch in its native state has limited range of applications. Simultaneously, information on effects of different sugars and their interactions with modified starch on gluten-free model dough is also ... ...

Abstract	Due to functional and physicochemical properties, starch in its native state has limited range of applications. Simultaneously, information on effects of different sugars and their interactions with modified starch on gluten-free model dough is also limited. To better overcome these restrictions, the effects of sucrose, trehalose, maltose and xylose on rheology, water mobility and microstructure of gluten-free dough prepared with high hydrostatic pressure (HHP) treated maize (MS), potato (PS) and sweet potato starch (SS) were investigated. MS, PS and SS dough with trehalose exhibited a lower degree of dependence of G' on frequency sweep (z'), higher strength (K) and relative elastic part of maximum creep compliance (J
MeSH term(s)	Maltose ; Trehalose ; Xylose ; Sucrose ; Water/chemistry ; Hydrostatic Pressure ; Starch/chemistry ; Rheology ; Glutens/chemistry ; Flour
Chemical Substances	Maltose (69-79-4) ; Trehalose (B8WCK70T7I) ; Xylose (A1TA934AKO) ; Sucrose (57-50-1) ; Water (059QF0KO0R) ; Starch (9005-25-8) ; Glutens (8002-80-0)
Language	English
Publishing date	2023-01-09
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	282732-3
ISSN	1879-0003 ; 0141-8130
ISSN (online)	1879-0003
ISSN	0141-8130
DOI	10.1016/j.ijbiomac.2023.123184
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