The financial performance of socially responsible investment

In this paper, I investigate performance of socially responsible investment (SRI) in the US market, in comparison with conventional investment (CI), in terms of funds, portfolios and indexes. A prominent finding is that expenses charged by SRI funds do not evidently result in underperformance of SRI funds, since they are lower than those of CI funds. More importantly, based on various performance estimates for the whole period (April 2007 –March 2013) and two sub periods (April 2007-March 2010 and April 2010-March 2013), no statistically significant difference in performance between SRI and CI is found. The finding indicates that adoption of socially responsible screens does not inevitably scarify investment performance. However, economically, SRI funds seem to underperform characteristics- matched CI funds, while SRI is likely to outperform CI at the fund portfolio and index levels. It may be weak evidence for assumptions that imposing social criteria reduces diversification efficiency and thereby adversely affects performance, but when a SRI portfolio overcomes the shortage by investing in an enough number of assets, it has a better performance than a matched conventional portfolio. If the assumptions are true, socially responsible investors are encouraged to diversify their portfolios by investing in various SRI funds, rather than only focusing on a single fund. Consequently, socially responsible investors are able to do both well and good simultaneously. Moreover, there is no clear evidence that incorporating social screens into investment decisions contributes to better performance in the economic downturn or financial crisis, as the outperformance of SRI funds for the financial crisis period is attributable to their different investment characteristics. Indeed, compared with matched conventional funds, SRI funds have more exposures to small cap, value oriented and low momentum stocks

Preparation and evaluation of ofloxacin-loaded palmitic acid solid lipid nanoparticles

The purpose of this study was to use solid lipid nanoparticles (SLN) to improve the pharmacological activity of ofloxacin. Ofloxacin-loaded SLN were prepared using palmitic acid as lipid matrix and poly vinyl alcohol (PVA) as emulsifier by a hot homogenization and ultrasonication method. The physicochemical characteristics of SLN were investigated by optical microscope, scanning electron microscopy, and photon correlation spectroscopy. Pharmacokinetics was studied after oral administration in mice. In vitro antibacterial activity and in vivo antibacterial efficacy of the SLN were investigated using minimal inhibitory concentrations (MIC) and a mouse protection model. The results demonstrated that the encapsulation efficiency, loading capacity, diameter, polydispersivity index, and zeta potential of the nanoparticles were 41.36% ± 1.50%, 4.40% ± 0.16%, 156.33 ± 7.51 nm, 0.26 ± 0.04, and −22.70 ± 1.40 mv, respectively. The SLN showed sustained release and enhanced antibacterial activity in vitro. Pharmacokinetic results demonstrated that SLN increased the bioavailability of ofloxacin by 2.27-fold, and extended the mean residence time of the drug from 10.50 to 43.44 hours. Single oral administrations of ofloxacin-loaded nanoparticles at 3 drug doses, 5 mg/kg, 10 mg/kg, and 20 mg/kg, all produced higher survival rates of lethal infected mice compared with native ofloxacin. These results indicate that SLN might be a promising delivery system to enhance the pharmacological activity of ofloxacin

Acute toxicity study of tilmicosin-loaded hydrogenated castor oil-solid lipid nanoparticles

<p>Abstract</p> <p>Background</p> <p>Our previous studies demonstrated that tilmicosin-loaded hydrogenated castor oil solid lipid nanoparticles (Til-HCO-SLN) are a promising formulation for enhanced pharmacological activity and therapeutic efficacy in veterinary use. The purpose of this work was to evaluate the acute toxicity of Til-HCO-SLN.</p> <p>Methods</p> <p>Two nanoparticle doses were used for the study in ICR mice. The low dose (766 mg/kg.bw) with tilmicosin 7.5 times of the clinic dosage and below the median lethal dose (LD₅₀) was subcutaneously administered twice on the first and 7th day. The single high dose (5 g/kg.bw) was the practical upper limit in an acute toxicity study and was administered subcutaneously on the first day. Blank HCO-SLN, native tilmicosin, and saline solution were included as controls. After medication, animals were monitored over 14 days, and then necropsied. Signs of toxicity were evaluated via mortality, symptoms of treatment effect, gross and microscopic pathology, and hematologic and biochemical parameters.</p> <p>Results</p> <p>After administration of native tilmicosin, all mice died within 2 h in the high dose group, in the low dose group 3 died after the first and 2 died after the second injections. The surviving mice in the tilmicosin low dose group showed hypoactivity, accelerated breath, gloomy spirit and lethargy. In contrast, all mice in Til-HCO-SLN and blank HCO-SLN groups survived at both low and high doses. The high nanoparticle dose induced transient clinical symptoms of treatment effect such as transient reversible action retardation, anorexy and gloomy spirit, increased spleen and liver coefficients and decreased heart coefficients, microscopic pathological changes of liver, spleen and heart, and minor changes in hematologic and biochemical parameters, but no adverse effects were observed in the nanoparticle low dose group.</p> <p>Conclusions</p> <p>The results revealed that the LD₅₀of Til-HCO-SLN and blank HCO-SLN exceeded 5 g/kg.bw and thus the nanoparticles are considered low toxic according to the toxicity categories of chemicals. Moreover, HCO-SLN significantly decreased the toxicity of tilmicosin. Normal clinic dosage of Til-HCO-SLN is safe as evaluated by acute toxicity.</p

Rapid and sustainable fabrication of antibacterial chitosan/PVA–SiO2 nanofiber air filters by needleless electrospinning

With the growing demand for air purification, the large-scale deployment of filtration materials is of increasing importance. However, conventional nanofiber membranes derived from synthetic polymers often generate non-degradable waste upon disposal, which can lead to secondary environmental pollution. In this study, chitosan and polyvinyl alcohol were used as the primary raw materials to fabricate environmentally friendly nanofiber membranes via needleless electrospinning. Under optimized processing conditions (60 kV, 20 min), the resulting membranes exhibited excellent filtration performance, achieving a quality factor of 0.059 41 Pa−1, high filtration efficiencies of 96.94% for PM2.5 and 99.34% for PM10, and a pressure drop of only 15.7 Pa. Moreover, the membranes demonstrated complete (100%) antibacterial efficacy against Staphylococcus aureus and Escherichia coli within 16 h under our experimental conditions. This work presents a rapid, sustainable, and scalable strategy for producing high-performance air filtration membranes that combine efficient particulate removal, low air resistance, and antibacterial properties, thereby offering a promising solution to reduce secondary pollution from spent filters

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%

Clinical and genomic characterization of carbapenem-resistant Enterobacterales bloodstream infections in patients with hematologic malignancies

BackgroundCarbapenem-resistant Enterobacterales (CRE) bloodstream infections (BSIs) pose a significant risk to patients with hematologic malignancies, yet the distinct features and outcomes of these infections are not thoroughly understood.MethodsThis retrospective study examined the characteristics and clinical outcomes of patients with Enterobacterales BSIs at the Hematology Department of Fujian Medical University Union Hospital from 2018 to 2022. Whole-genome sequencing was conducted on 45 consecutive CRE BSI isolates during this period.ResultsA total of 301 patients with Enterobacterales BSIs were included, with 65 (21.6%) cases of CRE and 236 (78.4%) cases of carbapenem-susceptible Enterobacterales (CSE). CRE infections accounted for 16.9% to 26.9% of all Enterobacterales BSIs, and carbapenem-resistant Klebsiella pneumoniae (CRKP) was the predominant strain. The most frequent sequence type (ST) and carbapenemase among CRKP were ST11 (68.6%) and blaKPC-2 (80.0%), respectively. Perianal infections, multiple infection foci, and a history of multiple hospitalizations, ICU stays, and prior CRE infections were identified as risk factors for CRE BSIs. Patients in the CRE group experienced significantly higher proportions of infection-related septic shock (43.1% vs. 19.9%, P &lt; 0.0003) and 30-day all-cause mortality (56.9% vs. 24.6%, P &lt; 0.0001) compared to those in the CSE group. Patient’s age and disease subtypes, strain subtypes, and antimicrobial treatment regimens significantly influenced survival in patients with CRE BSIs.ConclusionsCRE BSIs are a frequent complication in patients with hematological malignancies undergoing treatment and are associated with poor survival rates. A comprehensive understanding of risk factors and ongoing surveillance of prevalent strains are essential for the effective management of these infections

Experimental demonstration of reconstructing quantum states with generative models

Quantum state tomography, a process that reconstructs a quantum state from measurements on an ensemble of identically prepared copies, plays a crucial role in benchmarking quantum devices. However, brute-force approaches to quantum state tomography would become impractical for large systems, as the required resources scale exponentially with the system size. Here, we explore a machine learning approach and report an experimental demonstration of reconstructing quantum states based on neural network generative models with an array of programmable superconducting transmon qubits. In particular, we experimentally prepare the Greenberger-Horne-Zeilinger states and random states up to five qubits and demonstrate that the machine learning approach can efficiently reconstruct these states with the number of required experimental samples scaling linearly with system size. Our results experimentally showcase the intriguing potential for exploiting machine learning techniques in validating and characterizing complex quantum devices, offering a valuable guide for the future development of quantum technologies

Deep quantum neural networks equipped with backpropagation on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figure