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Book ; Online: AI Maintenance

Chen, Pin-Yu / Das, Payel

A Robustness Perspective

2023

Abstract: With the advancements in machine learning (ML) methods and compute resources, artificial intelligence (AI) empowered systems are becoming a prevailing technology. However, current AI technology such as deep learning is not flawless. The significantly ... ...

Abstract	With the advancements in machine learning (ML) methods and compute resources, artificial intelligence (AI) empowered systems are becoming a prevailing technology. However, current AI technology such as deep learning is not flawless. The significantly increased model complexity and data scale incur intensified challenges when lacking trustworthiness and transparency, which could create new risks and negative impacts. In this paper, we carve out AI maintenance from the robustness perspective. We start by introducing some highlighted robustness challenges in the AI lifecycle and motivating AI maintenance by making analogies to car maintenance. We then propose an AI model inspection framework to detect and mitigate robustness risks. We also draw inspiration from vehicle autonomy to define the levels of AI robustness automation. Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle, which is an essential milestone toward building sustainable and trustworthy AI ecosystems. Comment: Accepted to IEEE Computer Magazine. To be published in 2023
Keywords	Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computers and Society
Subject code	006
Publishing date	2023-01-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online ; Thesis: Establishment of non-centrosomal microtubule organizing centers in skeletal muscle cells

Das, Payel [Verfasser] / Engel, Felix [Akademischer Betreuer] / Frasch, Manfred [Gutachter]

2024

Author's details	Payel Das ; Gutachter: Manfred Frasch ; Betreuer: Felix Engel
Keywords	Biowissenschaften, Biologie ; Life Science, Biology
Subject code	sg570
Language	English
Publisher	Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Publishing place	Erlangen
Document type	Book ; Online ; Thesis
Database	Digital theses on the web

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Article ; Online: ChatGPT: Literate or intelligent about UN sustainable development goals?

Raman, Raghu / Lathabai, Hiran H / Mandal, Santanu / Das, Payel / Kaur, Tavleen / Nedungadi, Prema

PloS one

2024 Volume 19, Issue 4, Page(s) e0297521

Abstract: Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is ... ...

Abstract	Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests-the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) - suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.
MeSH term(s)	Sustainable Development ; Humans ; Artificial Intelligence ; United Nations ; Goals ; Surveys and Questionnaires ; Literacy ; Intelligence
Language	English
Publishing date	2024-04-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0297521
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Article ; Online: An end-to-end deep learning framework for translating mass spectra to de-novo molecules.

Litsa, Eleni E / Chenthamarakshan, Vijil / Das, Payel / Kavraki, Lydia E

Communications chemistry

2023 Volume 6, Issue 1, Page(s) 132

Abstract: Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a ... ...

Abstract	Elucidating the structure of a chemical compound is a fundamental task in chemistry with applications in multiple domains including drug discovery, precision medicine, and biomarker discovery. The common practice for elucidating the structure of a compound is to obtain a mass spectrum and subsequently retrieve its structure from spectral databases. However, these methods fail for novel molecules that are not present in the reference database. We propose Spec2Mol, a deep learning architecture for molecular structure recommendation given mass spectra alone. Spec2Mol is inspired by the Speech2Text deep learning architectures for translating audio signals into text. Our approach is based on an encoder-decoder architecture. The encoder learns the spectra embeddings, while the decoder, pre-trained on a massive dataset of chemical structures for translating between different molecular representations, reconstructs SMILES sequences of the recommended chemical structures. We have evaluated Spec2Mol by assessing the molecular similarity between the recommended structures and the original structure. Our analysis showed that Spec2Mol is able to identify the presence of key molecular substructures from its mass spectrum, and shows on par performance, when compared to existing fragmentation tree methods particularly when test structure information is not available during training or present in the reference database.
Language	English
Publishing date	2023-06-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2929562-2
ISSN	2399-3669 ; 2399-3669
ISSN (online)	2399-3669
ISSN	2399-3669
DOI	10.1038/s42004-023-00932-3
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Book ; Online: Reprogramming Pretrained Language Models for Protein Sequence Representation Learning

Vinod, Ria / Chen, Pin-Yu / Das, Payel

2023

Abstract: Machine Learning-guided solutions for protein learning tasks have made significant headway in recent years. However, success in scientific discovery tasks is limited by the accessibility of well-defined and labeled in-domain data. To tackle the low-data ... ...

Abstract	Machine Learning-guided solutions for protein learning tasks have made significant headway in recent years. However, success in scientific discovery tasks is limited by the accessibility of well-defined and labeled in-domain data. To tackle the low-data constraint, recent adaptions of deep learning models pretrained on millions of protein sequences have shown promise; however, the construction of such domain-specific large-scale model is computationally expensive. Here, we propose Representation Learning via Dictionary Learning (R2DL), an end-to-end representation learning framework in which we reprogram deep models for alternate-domain tasks that can perform well on protein property prediction with significantly fewer training samples. R2DL reprograms a pretrained English language model to learn the embeddings of protein sequences, by learning a sparse linear mapping between English and protein sequence vocabulary embeddings. Our model can attain better accuracy and significantly improve the data efficiency by up to $10^5$ times over the baselines set by pretrained and standard supervised methods. To this end, we reprogram an off-the-shelf pre-trained English language transformer and benchmark it on a set of protein physicochemical prediction tasks (secondary structure, stability, homology, stability) as well as on a biomedically relevant set of protein function prediction tasks (antimicrobial, toxicity, antibody affinity). Comment: 11 pages, 5 figures
Keywords	Computer Science - Machine Learning ; Computer Science - Computation and Language ; Quantitative Biology - Biomolecules
Subject code	006
Publishing date	2023-01-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The ultimate fate determinants of drug induced cell-death mechanisms in Trypanosomatids.

Das, Payel / Saha, Saradindu / BoseDasgupta, Somdeb

International journal for parasitology. Drugs and drug resistance

2021 Volume 15, Page(s) 81–91

Abstract: Chemotherapy constitutes a major part of modern-day therapy for infectious and chronic diseases. A drug is said to be effective if it can inhibit its target, induce stress, and thereby trigger an array of cell death pathways in the form of programmed ... ...

Abstract	Chemotherapy constitutes a major part of modern-day therapy for infectious and chronic diseases. A drug is said to be effective if it can inhibit its target, induce stress, and thereby trigger an array of cell death pathways in the form of programmed cell death, autophagy, necrosis, etc. Chemotherapy is the only treatment choice against trypanosomatid diseases like Leishmaniasis, Chagas disease, and sleeping sickness. Anti-trypanosomatid drugs can induce various cell death phenotypes depending upon the drug dose and growth stage of the parasites. The mechanisms and pathways triggering cell death in Trypanosomatids serve to help identify potential targets for the development of effective anti-trypanosomatids. Studies show that the key proteins involved in cell death of trypanosomatids are metacaspases, Endonuclease G, Apoptosis-Inducing Factor, cysteine proteases, serine proteases, antioxidant systems, etc. Unlike higher eukaryotes, these organisms either lack the complete set of effectors involved in cell death pathways, or are yet to be deciphered. A detailed summary of the existing knowledge of different drug-induced cell death pathways would help identify the lacuna in each of these pathways and therefore open new avenues for research and thereby new therapeutic targets to explore. The cell death pathway associated complexities in metazoans are absent in trypanosomatids; hence this summary can also help understand the trigger points as well as cross-talk between these pathways. Here we provide an in-depth overview of the existing knowledge of these drug-induced trypanosomatid cell death pathways, describe their associated physiological changes, and suggest potential interconnections amongst them.
MeSH term(s)	Cell Death ; Chagas Disease ; Humans ; Leishmaniasis ; Pharmaceutical Preparations ; Trypanosoma cruzi
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2021-01-25
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2751132-7
ISSN	2211-3207 ; 2211-3207
ISSN (online)	2211-3207
ISSN	2211-3207
DOI	10.1016/j.ijpddr.2021.01.003
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Article ; Online: Machine learning models in the prediction of drug metabolism: challenges and future perspectives.

Litsa, Eleni E / Das, Payel / Kavraki, Lydia E

Expert opinion on drug metabolism & toxicology

2021 Volume 17, Issue 11, Page(s) 1245–1247

MeSH term(s)	Drug Discovery ; Humans ; Machine Learning
Language	English
Publishing date	2021-11-02
Publishing country	England
Document type	Editorial
ZDB-ID	2214462-6
ISSN	1744-7607 ; 1742-5255
ISSN (online)	1744-7607
ISSN	1742-5255
DOI	10.1080/17425255.2021.1998454
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Article ; Online: Resolving the polycistronic aftermath: Essential role of topoisomerase IA in preventing R-loops in Leishmania.

Das, Payel / Hazra, Arnab / Saha, Saradindu / Roy, Sadhana / Mukherjee, Mandrita / Hazra, Saugata / Majumdar, Hemanta K / BoseDasgupta, Somdeb

The Journal of biological chemistry

2024 Volume 300, Issue 4, Page(s) 107162

Abstract: Kinetoplastid parasites are "living bridges" in the evolution from prokaryotes to higher eukaryotes. The near-intronless genome of the kinetoplastid Leishmania exhibits polycistronic transcription which can facilitate R-loop formation. Therefore, to ... ...

Abstract	Kinetoplastid parasites are "living bridges" in the evolution from prokaryotes to higher eukaryotes. The near-intronless genome of the kinetoplastid Leishmania exhibits polycistronic transcription which can facilitate R-loop formation. Therefore, to prevent such DNA-RNA hybrids, Leishmania has retained prokaryotic-like DNA Topoisomerase IA (LdTOPIA) in the course of evolution. LdTOPIA is an essential enzyme that is expressed ubiquitously and is adapted for the compartmentalized eukaryotic form in harboring functional bipartite nuclear localization signals. Although exhibiting greater homology to mycobacterial TOPIA, LdTOPIA could functionally complement the growth lethality of Escherichia coli TOPIA null GyrB ts strain at non-permissive temperatures. Purified LdTOPIA exhibits Mg
MeSH term(s)	DNA Topoisomerases, Type I/metabolism ; DNA Topoisomerases, Type I/genetics ; R-Loop Structures ; Protozoan Proteins/metabolism ; Protozoan Proteins/genetics ; Protozoan Proteins/antagonists & inhibitors ; Protozoan Proteins/chemistry ; Leishmania/enzymology ; Leishmania/genetics ; Animals ; Escherichia coli/genetics ; Escherichia coli/metabolism
Chemical Substances	DNA Topoisomerases, Type I (EC 5.99.1.2) ; Protozoan Proteins
Language	English
Publishing date	2024-03-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2997-x
ISSN	1083-351X ; 0021-9258
ISSN (online)	1083-351X
ISSN	0021-9258
DOI	10.1016/j.jbc.2024.107162
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Article: Enhanced sono-assisted adsorptive uptake of malachite green dye onto magnesium ferrite nanoparticles: Kinetic, isotherm and cost analysis

Das, Payel / Debnath, Parmita / Debnath, Animesh

Environmental nanotechnology, monitoring & management. 2021 Dec., v. 16

2021

Abstract: In this study, the performance of magnesium ferrite nanoparticles (MgF-NPs) as adsorbent was assessed for removal of toxic malachite green (MG) dye from aqueous solution under ultrasonic irradiation. The MgF-NPs exhibited specific surface area of 70.266 ... ...

Abstract	In this study, the performance of magnesium ferrite nanoparticles (MgF-NPs) as adsorbent was assessed for removal of toxic malachite green (MG) dye from aqueous solution under ultrasonic irradiation. The MgF-NPs exhibited specific surface area of 70.266 m²/g and pore volume of 0.311 cc/g with an average pore diameter of 7.354 nm. Optimum solution pH was found to be 8.0 for sono-assisted MG dye adsorption, which is favourable for large scale field application of the proposed method. The adsorption reaction aided by ultrasonication could yield more than 90% MG dye removal within 15 min of reaction due to enhancement of mass transfer. However, instead of ultrasonication when overhead stirring and shaking was used as mixing methods then the MG dye removal efficacy was dropped to 70% and 65%, respectively. The adsorption kinetic strictly followed the pseudo-second-order model and equilibrium isotherm analysis followed the Langmuir isotherm model with maximum monolayer adsorption capacity of 487.60 mg/g. The presence of chloride ion hindered the MG dye removal efficiency from 91% to 67%, whereas the presence of nitrate, sulfate and phosphate ions in the dye solution could not affect the dye removal efficacy significantly. The cost analysis for synthesis of MgF-NPs revealed that the cost involvement of 50.88 USD/Kg of adsorbent which may be considered as economical for large scale industrial application.
Keywords	administrative management ; adsorbents ; adsorption ; aqueous solutions ; chlorides ; cost analysis ; ferrimagnetic materials ; industrial applications ; magnesium ; malachite green ; mass transfer ; models ; nitrates ; pH ; phosphates ; sorption isotherms ; sulfates ; surface area ; toxicity ; ultrasonic treatment
Language	English
Dates of publication	2021-12
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	2821777-9
ISSN	2215-1532
ISSN	2215-1532
DOI	10.1016/j.enmm.2021.100506
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Effect of cosolvents on nano-confined water: a molecular dynamics study.

Das, Payel

Nanoscale

2012 Volume 4, Issue 9, Page(s) 2931–2936

Abstract: We present results from atomistic molecular dynamics simulations to characterize the effects of cosolvents, such as urea and guanidinium (Gdm) salts, on the water confined in hydrophobic carbon nanotubes. We observed complete drying of the nanotube ... ...

Abstract	We present results from atomistic molecular dynamics simulations to characterize the effects of cosolvents, such as urea and guanidinium (Gdm) salts, on the water confined in hydrophobic carbon nanotubes. We observed complete drying of the nanotube interiors of diameter ranging from 8 to 17 Å in urea. In contrast, the water population within nanotube cores smaller than 12 Å remains unaffected in GdmCl solution, whereas larger nanotube interiors become partially dehydrated with prevailing presence of stable Gdm(+)-Gdm(+) dimers. The molecular arrangement and the lifetime inside the nanotube were found to be characteristics of a particular cosolvent. In both urea and GdmCl solutions, preferential cosolvent intrusion resulting in nanotube dehydration is driven by the stronger dispersion interaction of cosolvent than water with the nanotube. The partial drying of the hydrophobic core is attributed to guanidinium's better hydration and weaker self-association propensity compared to urea, as well as to its moderate ion-pairing with strongly hydrated chloride ions. The Gdm(+) induced dehydration varies with the charge density of counter-ions, as the presence of high charge-density sulfate ions impedes penetration of guanidinium, and consequent dehydration of the nanotube. These findings provide important insights into the effect of cosolvents on the nano-confined water in a hydrophobic environment.
MeSH term(s)	Guanidine/chemistry ; Hydrophobic and Hydrophilic Interactions ; Molecular Dynamics Simulation ; Nanotechnology ; Nanotubes, Carbon/chemistry ; Solvents/chemistry ; Urea/chemistry ; Water/chemistry
Chemical Substances	Nanotubes, Carbon ; Solvents ; Water (059QF0KO0R) ; Urea (8W8T17847W) ; Guanidine (JU58VJ6Y3B)
Language	English
Publishing date	2012-04-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2515664-0
ISSN	2040-3372 ; 2040-3364
ISSN (online)	2040-3372
ISSN	2040-3364
DOI	10.1039/c2nr30070b
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