AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. More specifically, both of the proposed frameworks utilize AlpaCare as base LLM which employs both few-shot in-context learning and instruction tuning techniques to extract PICO-related terms from the clinical trial reports. We applied these approaches to the widely used coarse-grained datasets such as EBM-NLP, EBM-COMET and fine-grained datasets such as EBM-NLPrev and EBM-NLPh. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at https://github.com/shrimonmuke0202/AlpaPICO.git.

Sorption study of long-lived 94Nb on laterite: Effects of physicochemical parameters on sorption.

Zr-Nb alloy is used as the pressure tube in pressurized heavy water reactors (PHWR). Prolonged neutron irradiation of the pressure tubes leads to the formation of a long-lived radioisotope 94Nb. Thus, the discharged pressure tubes possess huge 94Nb activity which persists for a prolonged period.If these discharged pressure tubes come in contact with ground water, 94Nb isotope may leach and migrate and this can lead to a long-term radiological impact in the environment.In the present study, we have explored the capability of laterite as a filler material for the containment and retarding the migration of 94Nb. In this regard, detailed characterization of the laterite soil was carried out using energy dispersive X-ray fluorescence (EDXRF), X-ray diffraction (XRD), fourier transform infra-red spectrometry (FTIR), total cation exchange capacity determination, zeta potential measurement and thermogravimetric analysis (TGA). The sorption study of 94Nb on laterite was carried and the effects of different physico-chemical parameters like pH, ionic strength, temperature and equilibration time were evaluated. Ionic strength, temperature and time dependent sorption studies assist to explore the probable sorption mechanism of 94Nb on laterite, which helps in understanding the migration behaviour of 94Nb in natural aquatic environment. This study suggests that laterite is a promising material in containment of 94Nb isotope owing to its good cation exchange behaviour in the acidic medium and ability to form surface complex in the neutral medium.

Thioflavin-T-Incorporated Cerium-ATP Coordination Polymer Nanoparticles: A Promising System for Detection of Uranyl Ion (UO22+) in Aqueous Medium.

Lanthanide-based coordination polymer nanoparticles (Ln-CPNs) are studied for analytical and biomedical sensing, mainly utilizing the luminescent properties of lanthanides. However, the inclusion of fluorescent guest molecules within the nanoparticles can lead to a stimuli-responsive system with modulated photo-physical properties that can be useful for detecting analytes. In this work, we have synthesized cerium(III) coordination polymer nanoparticles using adenosine triphosphate (ATP) molecules as the ligands. The porous polymeric structure enables CPNs for reversible inclusion and release of thioflavin-T (ThT), which is a weakly emissive dye in the free state but becomes highly emissive when incorporated into the Ce-ATP CPNs. Modulation of the photo-physical properties of ThT-incorporated CPNs in response to the radiotoxic and environmentally alarming uranyl ion (UO22+) has been used for its detection in aqueous medium in the range of 0-20 µM by fluorimetry with an LOD of 80 ng mL-1 (0.34 µM) in deionized water without interference from the most commonly occurring metal ions. Furthermore, the sensing platform has been successfully utilized for UO22+ determination in seawater sample without any pre-treatment and adjustment of pH.

Performance of the nitrogen reduction reaction on metal bound g-C6N6: a combined approach of machine learning and DFT.

Developing a cost-effective and environmentally benign substitute for the energy-intensive Haber-Bosch process for the production of ammonia is a global challenge. The electrocatalytic nitrogen reduction reaction (NRR) under ambient conditions through the six proton-electron process has attracted significant interest. Herein, a series of transition-metal (TM) based single atom catalysts (SAC) embedded on carbon nitride (C6N6) have been chosen to explore the NRR activity. The promising metals have been primarily screened through density functional theory (DFT) by calculating their adsorption energies on C6N6 - energies for dinitrogen binding and the barriers at the rate determining step. Based on these criteria, amongst the 18 metal centers, Ta based C6N6 emerges as a good candidate for the reduction of nitrogen to NH3. On the other hand, for the Machine Learning (ML) regression models, the covalent radius and the d-band center of the TM have been identified as the most correlated descriptors for predicting the adsorption energy of nitrogen on the active metal center. Besides, probabilistic modeling using the soft voting technique in the classification model allows us to predict the most efficient single atom catalyst. Despite the realistic bottleneck of having only a limited number of TMs to choose from, this technique effectively predicts the best catalyst from a modest dataset. With the highest probabilistic score, Ta based C6N6 dominates over the other catalysts in a good agreement with DFT findings. This letter manifests the effectiveness of the soft voting technique in an ensemble-based classification model.

Effect of different physico-chemical factors on sorption of Pa(V) on iron oxides.

The effects of different physico-chemical parameters on sorption of protactinium (Pa) on three different iron oxides (goethite, hematite and magnetite) were studied. The sorption of Pa(V) on all the three iron oxides was low at pH 1, increased to ~ 90% at neutral pH and then decreased slightly above pH 9. Presence of humic acid increases the sorption of Pa(V) on iron oxides in acidic medium whereas sorption is reduced in basic medium. The classical methods like ionic strength, time and temperature dependent sorption studies have been used for the elucidation of the sorption mechanism of Pa(V) on iron oxides. Physisorption in acidic pH and chemisorption in basic pH are the most probable mechanisms. The study demonstrates that classical method alone can be used for the sorption mechanism investigations in the circumstances where it is difficult to have spectroscopic evidences.

Sorption of Nb(V) on pyrolusite (ß-MnO2): Effect of pH, humic acid, ionic strength, equilibration time and temperature.

The sorption of Nb(V) on pyrolusite has been studied and the effect of pH, ionic strength, humic acid, temperature and equilibration time were also investigated in a series of batch equilibrium experiments. The sorption was found to be affected by solution pH, ionic strength and humic acid. The sorption was high in neutral/near neutral pH (~96 %) but lower sorption was observed both in acidic (~55 %) and basic (~85 %) media. Sorption was decreased in acidic pH with increase of ionic strength and reverse effect was seen in basic pH although the effect is less prominent. Presence of humic acid causes enhancement of sorption in acidic pH whereas sorption declined in basic pH. The sorption process is endothermic in acid medium and exothermic in basic medium. In acid medium the sorption is entropy driven process. Kinetics of the sorption study was found to follow pseudo first order in acidic pH whereas pseudo second order in basic pH.

Interaction of niobium with iron-oxide colloids and the role of humic acid.

In this work, we report the sorption of Nb on iron oxides and the effect of humic acid. Iron oxides viz. goethite, hematite and magnetite were chemically synthesised and characterised by X-ray diffraction, particle size, surface area and zeta potential measurement. The sorption of Nb on all the three iron oxides was low (∼40%) at pH 1, increased to âˆ¼ 90% at pH 8 and decreased marginally above pH 8. The effect of humic acid on the sorption was very small. Thermodynamic parameters viz. activation energy, enthalpy, entropy, free energy and sticking probability were calculated to understand the mechanism of the sorption process. Although the enthalpy was positive, the free energy change was negative i.e. the sorption was entropy driven process. The sorption followed pseudo-second-order kinetics and sticking probability model indicated that the process was chemisorption. This study is important to understand the probable migration of 94Nb (half life 20300 y) during underground storage of radioactive waste.