Discovery of a Quasi-Periodic Oscillation in the Ultraluminous X-ray Source IC 342 X-1: XMM-Newton Results

We report the discovery of a quasi-periodic oscillation (QPO) at 642 mHz in an {\it XMM-Newton} observation of the ultraluminous X-ray source (ULX) IC 342 X-1. The QPO has a centroid at $\nu_{QPO} = 642 \pm 20$ mHz, a coherence factor of $Q = 11.6$, and an amplitude (rms) of 4.1\% with significance of $3.6\sigma$. The energy dependence study shows that the QPO is stronger in the energy range 0.3 - 5.0 keV. A subsequent observation (6 days later) does not show any signature of the QPO in the power density spectrum. The broadband energy spectra (0.3 - 40.0 keV) obtained by quasi-simultaneous observations of {\it XMM-Newton} and {\it NuSTAR} can be well described by an absorbed {\it diskbb} plus {\it cutoffpl} model. The best fitted spectral parameters are power-law index ($\Gamma$) $\sim$ 1.1, cutoff energy ($E_c$) $\sim$ 7.9 keV and disc temperature ($kT_{in}$) $\sim$ 0.33 keV, where the QPO is detected. The unabsorbed bolometric luminosity is $\sim$ 5.34$\times$ 10$^{39}$ erg~s$^{-1}$. Comparing with the well known X-ray binary GRS 1915+105, our results are consistent with the mass of the compact object in IC 342 X-1 being in the range $\sim 20 - 65 ~ M_\odot$. We discuss the possible implications of our results.Comment: 7 pages, 3 figures (2 colour), in press (MNRAS

Self-Microemulsifying System

Oral route is preferred for drug administration; however according to the recent scenario 40% of new drug candidates have poor water solubility and low bioavailability. One of the biggest challenges in drug delivery science is to improve low oral bioavailability problem which is associated with the hydrophobic drugs due to their unprecedented potential as a drug deliver with the broad range of application. Self-emulsifying systems have been proved as highly useful technological innovations to vanquish such bioavailability problem by virtue of their diminutive globule size, higher solubilization tendency for hydrophobic drugs, robust formulation advantages, and easy to scale up. Self-microemulsifying systems are isotropic mixers of oil, surfactant, drug and co-emulsifier or solubilizer, which spontaneously form transparent micro-emulsions with oil droplets ranging between 100 and 250 nm. Micro emulsified drug can be easily absorbed through the lymphatic pathway and it bypasses the hepatic first-pass effect. Self-microemulsifying system is a thermodynamically stable system and overcomes the drawback of layering of emulsions after sitting for a long period of time. The present literature gives exhaustive information on the formulation design and characterization of self-microemulsifying systems

DEVELOPMENT AND VALIDATION OF REVERSED PHASE HPLC-PDA METHOD FOR THE QUANTIFICATION OF CHRYSIN IN SOLID LIPID NANOPARTICLES

Objective: The main aim of the present study was to develop and validate a simple, precise and accurate Reversed-Phase HPLC-PDA method for the quantitative determination of Chrysin in solid lipid nanoparticles (SLNs). Methods: The RP-HPLC-PDA system equipped with a C-18 reversed-phase column (250 × 4.6 mm, particle size 5 μm) was employed in the present study. HPLC grade methanol and water in 85:15 (v/v) ratio was selected as the mobile phase at flow rate of 1 ml/min under an ambient column oven temperature. The detection wavelength was kept at 268 nm. Validation of developed method was performed according to the ICH guidelines. Results: The developed reversed-phase HPLC-PDA method was found to be linear in the concentration range of 0.2-10 µg/ml with a correlation coefficient of 0.999. The method was also observed to be precise with % relative standard deviation (RSD) below 2%. The limit of detection and limit of quantification of this method were found to be 0.05µg/ml and 0.14µg/ml, respectively. The percent recovery of the developed method was estimated to more than 99%. Conclusion: The developed HPLC method can be utilized for the determination of Chrysin with a high degree of accuracy, precision, robustness, specificity in solid lipid nanoparticles in the presence of excipients

No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation

Ensuring fairness in Recommendation Systems (RSs) across demographic groups is critical due to the increased integration of RSs in applications such as personalized healthcare, finance, and e-commerce. Graph-based RSs play a crucial role in capturing intricate higher-order interactions among entities. However, integrating these graph models into the Federated Learning (FL) paradigm with fairness constraints poses formidable challenges as this requires access to the entire interaction graph and sensitive user information (such as gender, age, etc.) at the central server. This paper addresses the pervasive issue of inherent bias within RSs for different demographic groups without compromising the privacy of sensitive user attributes in FL environment with the graph-based model. To address the group bias, we propose F2PGNN (Fair Federated Personalized Graph Neural Network), a novel framework that leverages the power of Personalized Graph Neural Network (GNN) coupled with fairness considerations. Additionally, we use differential privacy techniques to fortify privacy protection. Experimental evaluation on three publicly available datasets showcases the efficacy of F2PGNN in mitigating group unfairness by 47% - 99% compared to the state-of-the-art while preserving privacy and maintaining the utility. The results validate the significance of our framework in achieving equitable and personalized recommendations using GNN within the FL landscape.Comment: To appear as a full paper in AAAI 202