Evaluation of environmental impacts of cotton polo shirt production in Bangladesh using life cycle assessment.

The textile industry is widely considered as one of the most pollutant-releasing sectors globally. As the world is moving toward sustainability, it is essential to thoroughly assess how a textile product affects the environment. The aim of this study is to investigate the environmental impact of textile manufacturing in Bangladesh using life cycle assessment (LCA) method. A cradle-to-gate LCA was conducted to produce 1000 units of cotton polo shirts following ISO 14040 standards. LCA was also conducted for each final product at every manufacturing stage of cotton polo shirt, including the production of 1000 kg of cotton fibers, 1000 kg of yarn, 1000 kg of grey fabrics, 1000 kg of dyed-finished fabrics, and finally, assembling of 1000 pieces of polo shirts. Inventory analysis revealed that for producing 1000 pieces of polo shirts, 0.12 hector of land, 363.89 kg of cotton fiber, 324.84 kg of yarn, 320.45 kg of knitted fabric, and 299.5 kg of dyed-finished fabrics were required. The study also found that 1550.9 kWh of electricity, 15.47 L of diesel, and 72.54 m3 of natural gas were needed to produce 1000 pieces of polo shirts. The potential environmental impacts were categorized into 12 different types based on the CML 2001 method and calculated using openLCA 2.0 software. The outcomes of LCA revealed considerable environmental impacts in different categories during manufacturing of cotton polo shirts in Bangladesh. For example, the global warming potential (GWP) associated with the manufacturing of 1000 pieces of polo shirts was 1345.97 kg CO2-eq. Among several production stages of polo shirts, the highest contributor of GWP was dyeing section (38.36 %), followed by cotton fiber production (29.32 %) and yarn manufacturing (18.92 %). The obtained data also revealed that for cultivating cotton fibers, manufacturing of yarn and grey fabrics, and finally dyeing-finishing of same quantity fabric (1000 kg), the GWP were 1084.41 kg CO2-eq, 783.67 kg CO2-eq, 145.88 kg CO2-eq, 1723.88 kg CO2-eq, 314.94 kg CO2-eq, respectively. The outcome of the impact assessment will be crucial for decision-making when it comes to taking remedial actions to lessen negative environmental consequences for the sustainable development of textile industry in Bangladesh.

SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction.

Motivation: Protein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ( ϕ and ψ ) prediction is a key sub-problem in predicting protein structures. However, reliable determination of backbone torsion angles using conventional experimental methods is slow and expensive. Therefore, considerable effort is being put into developing computational methods for predicting backbone angles. Results: We present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention-based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, TEST2020-HQ, CAMEO and CASP. The experimental results suggest that our proposed self-attention-based network, together with transfer learning, has achieved notable improvements over the best alternate methods. Availability and implementation: SAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle. Supplementary information: Supplementary data are available at Bioinformatics Advances online.