Substantial Improvement Research Articles

TAG: Teacher-Advice Mechanism With Gaussian Process for Reinforcement Learning.

Reinforcement learning (RL) still suffers from the problem of sample inefficiency and struggles with the exploration issue, particularly in situations with long-delayed rewards, sparse rewards, and deep local optimum. Recently, learning from demonstration (LfD) paradigm was proposed to tackle this problem. However, these methods usually require a large number of demonstrations. In this study, we present a sample efficient teacher-advice mechanism with Gaussian process (TAG) by leveraging a few expert demonstrations. In TAG, a teacher model is built to provide both an advice action and its associated confidence value. Then, a guided policy is formulated to guide the agent in the exploration phase via the defined criteria. Through the TAG mechanism, the agent is capable of exploring the environment more intentionally. Moreover, with the confidence value, the guided policy can guide the agent precisely. Also, due to the strong generalization ability of Gaussian process, the teacher model can utilize the demonstrations more effectively. Therefore, substantial improvement in performance and sample efficiency can be attained. Considerable experiments on sparse reward environments demonstrate that the TAG mechanism can help typical RL algorithms achieve significant performance gains. In addition, the TAG mechanism with soft actor-critic algorithm (TAG-SAC) attains the state-of-the-art performance over other LfD counterparts on several delayed reward and complicated continuous control environments.

Efficient Exploration and Exploitation for Sequential Music Recommendation

Music streaming services heavily rely upon recommender systems to acquire, engage, and retain users. One notable component of these services are playlists, which can be dynamically generated in a sequential manner based on the user’s feedback during a listening session. Online learning to rank approaches have recently been shown effective at leveraging such feedback to learn users’ preferences in the space of song features. Nevertheless, these approaches can suffer from slow convergence as a result of their random exploration component and their session-agnostic exploitation component. To overcome these limitations, we propose a novel online learning to rank approach which efficiently explores the space of candidate recommendation models by restricting itself to the orthogonal complement of the subspace of previous underperforming exploration directions. Moreover, we propose a session-aware exploitation component which leverages the momentum of the current best model during updates. Our thorough evaluation using simulated listening sessions from two large Last.fm datasets demonstrates substantial improvements over state-of-the-art approaches in terms of early-stage performance, which results in an improved user experience during online learning. In addition, we demonstrate that long-term convergence can be further enhanced by adaptively relaxing exploration constraints along the way.

Durability of Effects of Cognitive Remediation on Cognition and Psychosocial Functioning in Schizophrenia: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.

Cognitive remediation provides substantial improvements in cognitive performance and real-world functioning for people living with schizophrenia, but the durability of these benefits needs to be reassessed and better defined. The aims of this study were to provide a comprehensive assessment of the durability of the benefits of cognitive remediation for cognition and functioning in people living with schizophrenia and evaluating potential moderators of effects. A systematic search was conducted in PubMed, Scopus, and PsycINFO, and reference lists of included articles and Google Scholar were inspected. Eligible studies were randomized clinical trials of cognitive remediation in patients diagnosed with schizophrenia spectrum disorders in which follow-up assessments were included. Screening and data extraction were performed by at least two independent reviewers. Cohen's d was used to measure outcomes. Primary outcomes were changes in cognition and functioning from baseline to conclusion of follow-up. Moderators of the durability of effects were assessed. Of 2,840 identified reports, 281 full texts were assessed and 130 reports on 67 studies with 5,334 participants were included. Cognitive remediation produced statistically significant positive effects that persisted at the end of follow-up in global cognition (d=0.23) and in global functioning (d=0.26). Smaller study samples and single-center studies were associated with better cognitive outcomes; longer treatment and follow-up duration, techniques for transferring cognitive gains to the real world, integration with psychiatric rehabilitation, group format of delivery, and more female participants in the sample were associated with better functional outcomes. Cognitive remediation provides durable improvements in cognition and functioning in schizophrenia. This finding corroborates the notion that cognitive remediation should be implemented more widely in clinical and rehabilitation practice.

Effects of Saikosaponin D on Apoptosis, Autophagy, and Morphological Structure of Intestinal Cells of Cajal with Functional Dyspepsia.

Functional dyspepsia (FD) is one of the most common gastrointestinal diseases, with a global prevalence of 10%-30%. However, the specific pathogenesis of FD has not yet been determined. As such, the aim of this study was to investigate the effects of saikosaponin D (SSD) administration on the apoptosis, autophagy, and morphological structure of the intestinal cells of Cajal (ICCs) in FD. A rat model of FD was constructed by stimulating the rat tail with a sponge clamp at one-third of the distal tail length. An autophagy model was constructed for ICCs using glutamate. The apoptosis rate in each group of cells was determined using flow cytometry. The expressions of ghrelin and substance P (SP) were detected using ELISA. The body weight and food intake of male and female rats in the SSD group were consistently higher than those in the model group. The SSD group showed substantial improvement compared with the model group, with no inflammatory cell infiltration and normal gastric mucosal structures. After intervention with SSD, the ultrastructure of the ICCs considerably improved and was clear. Compared with the model group, the expressions of LC3 I/II, ghrelin, and SP proteins in the SSD group were significantly upregulated, and the apoptosis rate was significantly reduced. The administration of SSD improved ICC morphology and structure, inhibited excessive autophagy, and improved FD, a gastrointestinal motility disorder, by regulating ghrelin and SP levels.

Modeling the Impact of Head-Body Rotations on Audio-Visual Spatial Perception for Virtual Reality Applications.

Humans perceive the world by integrating multimodal sensory feedback, including visual and auditory stimuli, which holds true in virtual reality (VR) environments. Proper synchronization of these stimuli is crucial for perceiving a coherent and immersive VR experience. In this work, we focus on the interplay between audio and vision during localization tasks involving natural head-body rotations. We explore the impact of audio-visual offsets and rotation velocities on users' directional localization acuity for various viewing modes. Using psychometric functions, we model perceptual disparities between visual and auditory cues and determine offset detection thresholds. Our findings reveal that target localization accuracy is affected by perceptual audio-visual disparities during head-body rotations, but remains consistent in the absence of stimuli-head relative motion. We then showcase the effectiveness of our approach in predicting and enhancing users' localization accuracy within realistic VR gaming applications. To provide additional support for our findings, we implement a natural VR game wherein we apply a compensatory audio-visual offset derived from our measured psychometric functions. As a result, we demonstrate a substantial improvement of up to 40% in participants' target localization accuracy. We additionally provide guidelines for content creation to ensure coherent and seamless VR experiences.

Generalized Weak Supervision for Neural Information Retrieval

Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing ranking model (called the weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the observed data and significantly outperform the weak labeler. This paper generalizes this idea through an iterative re-labeling process, demonstrating that weakly supervised models can iteratively play the role of weak labeler and significantly improve ranking performance without using manually labeled data. The proposed Generalized Weak Supervision (GWS) solution is generic and orthogonal to the ranking model architecture. This paper offers four implementations of GWS: self-labeling, cross-labeling, joint cross- and self-labeling, and greedy multi-labeling. GWS also benefits from a query importance weighting mechanism based on query performance prediction methods to reduce noise in the generated training data. We further draw a theoretical connection between self-labeling and Expectation-Maximization. Our experiments on four retrieval benchmarks suggest that our implementations of GWS lead to substantial improvements compared to weak supervision if the weak labeler is sufficiently reliable.

Preparation of high-performance materials based on carbon nanofibers and analysis of mechanical properties at room and high temperatures

The shortcomings of conventional building materials, such as inadequate tensile strength and fire resistance, have been significantly exacerbated with the emergence of complex and diverse engineering practices. However, the current research on high-performance building materials is still in its nascent stage. Therefore, our study aims to investigate the potential improvements in the mechanical properties of building materials through the incorporation of carbon fiber and nano silica. Specifically, we focus on assessing the mechanical properties of these modified materials at both room and elevated temperatures. Our experimental results demonstrate a notable enhancement in compressive strength. At room temperature, the H2 group exhibited a 15.1% increase in compressive strength, while the H5 group experienced a 7.3% increase. Notably, the compressive strength of the H7 group reached its highest value at 47.13 MPa. Additionally, the H2 group displayed the highest bending strength at 5.18 MPa. Our electron scanning microscopy analysis revealed the uniform dispersion of carbon fibers within the building materials, without any clumping. This suggests that when the dosage of Carbon Fiber reaches 0.6% of the mass of building materials, it can be uniformly dispersed within the mixture. We have also evaluated the performance of the modified materials under high temperatures. It was observed that the prefabricated building materials group experienced a 65% reduction in compressive strength when subjected to 800 °C. Conversely, the carbon fiber building materials exhibited a compressive strength loss of over 62%. Overall, the carbon fiber building materials outperformed the standard building materials under high temperature conditions, with significantly higher compressive strength. Moreover, the modified materials demonstrated an improvement in the residual splitting strength, ranging from 26% to 37% when exposed to temperatures over 800 °C. These findings indicate a substantial enhancement in the mechanical properties of the proposed modified materials. In conclusion, the incorporation of carbon fiber and nano silica into the building materials resulted in substantial improvements in their mechanical properties. These modified materials hold great potential for practical construction projects, offering higher performance and enhanced durability.

DIML: Deep Interpretable Metric Learning via Structural Matching.

In this paper, we present a new framework named DIML to achieve more interpretable deep metric learning. Unlike traditional deep metric learning method that simply produces a global similarity given two images, DIML computes the overall similarity through the weighted sum of multiple local part-wise similarities, making it easier for human to understand the mechanism of how the model distinguish two images. Specifically, we propose a structural matching strategy that explicitly aligns the spatial embeddings by computing an optimal matching flow between feature maps of the two images. We also devise a multi-scale matching strategy, which considers both global and local similarities and can significantly reduce the computational costs in the application of image retrieval. To handle the view variance in some complicated scenarios, we propose to use cross-correlation as the marginal distribution of the optimal transport to leverage semantic information to locate the important region in the images. Our framework is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods. To extend our DIML to more advanced architectures like vision Transformers (ViTs), we further propose truncated attention rollout and partial similarity to overcome the lack of locality in ViTs. We evaluate our method on three major benchmarks of deep metric learning including CUB200-2011, Cars196, and Stanford Online Products, and achieve substantial improvements over popular metric learning methods with better interpretability.

A High-Speed Floating Point Matrix Multiplier Implemented in Reconfigurable Architecture

Matrix multiplication is a fundamental operation in computational applications across various domains. This paper introduces a novel reconfigurable co-processor that enhances the efficiency of matrix multiplication by concurrently executing addition and multiplication operations upon matrix elements of different sizes. The proposed design aims to reduce computation time and improve efficiency for matrix multiplication equations. Experimental evaluations were conducted on matrices of different sizes to demonstrate the effectiveness of the processor. The results reveal substantial improvements in both time and efficiency when compared to traditional approaches. The reconfigurable transformation processor harnesses parallel processing capabilities, enabling the simultaneous execution of addition and multiplication operations by partitioning input matrices into smaller submatrices and performing parallel computations, thus the processor achieves faster results. Additionally, the design incorporates configurable arithmetic units that dynamically adapt to matrix characteristics, further optimizing performance. The experimental evaluations provide evidence of reduction in computation time and improvement in efficiency. present significant benefits over traditional sequential methods. This makes this co-processor ideally fit for domains that require intensive linear algebra computations such as computer vision, machine learning, and signal processing.

Can Short-Term Online-Monitoring Improve the Current WFD Water Quality Assessment Regime? Systematic Resampling of High-Resolution Data from Four Saxon Catchments

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of integrated guidance on surface water chemical monitoring (e.g., WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires monthly sampling to assess the chemical quality status of a body of surface water. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data are investigated under different sampling scenarios and compared with current, monthly grab sampling data. About 23 million data points were analyzed for this study. Three chemical parameters (dissolved oxygen, nitrate-nitrogen, and chloride concentration) and discharge data were selected from four catchments of different sizes, ranging from 51,391 km2 to 84 km2 (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper, we propose short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general, we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as a proxy for analyzing event-mobilized pollutants, and (III) investigate the related uncertainties and costs of both sampling methods. Results show that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law, as the probability of catching a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, such as dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, such as nitrogen-nitrate or chloride. The analysis of discharge events as a proxy parameter for event-mobilized pollutants proves that the probability of capturing samples during events is significantly increased by STOM.

Efficacy and Safety of Cannabidiol Oil on Chronic Insomnia: The First Randomized, Double-Blind, Placebo-Controlled, Crossover, Pilot Study in Thailand

Objective: Cannabidiol (CBD) is a non-intoxicating extract from Cannabis sativa. The advantages of CBD on sleep-wake cycle and insomnia remain sparse. The present study investigated the impact of a continuous four-week sublingual CBD intake on sleep indicators. Materials and Methods: The present study was a randomized, double-blind, placebo-controlled, crossover pilot study involving 45 chronic insomnia patients. The participants were administered medium-chain triglycerides oil soluble cannabidiol (MCT-CBD) using 1 mg/kg/dose, sublingually for four weeks, then two weeks of washout period, and a subsequent four-week placebo phase. Sleep architecture with N1, N2, N3, and rapid eye movement (REM), sleep quality (PSQI), daytime sleepiness (ESS), and quality of life were investigated. Results: Polysomnography (PSG) revealed that the total sleep time (TST), sleep onset latency (SOL), wake time after sleep onset (WASO), and arousal index were significantly improved compared to the patients who were taking placebo, while sleep structure remained unaltered. Additionally, actigraphy showed a substantial improvement in TST, sleep efficiency (SE), and SOL. The results of PSQI, ESS, and EQ-5D-5L were statistically significant and better than placebo (p<0.05). Conclusion: Continuously sublingual CBD treatment could improve sleep duration, sleep maintenance, sleep induction, quality of life, and daytime sleepiness without altering the sleep architecture in patients with chronic insomnia. No evidence of serious side-effects was found. Keywords: Hemp; CBD; Cannabidiol; Cannabis; Insomnia; Sleep

A novel SCDM algorithm with offset centroid-driven weight adaptation and its application to appearance design of automotive steering wheels

This paper introduces a novel group single-criteria decision-making algorithm that addresses the challenges from personal biases. The algorithm utilizes offset centroid-driven weight adaptation to enhance the fairness and reliability of decisions, providing robust decision-making. The core steps of the new algorithm include splitting the evaluation dataset, calculating the offset centroid, introducing dynamic adjustment coefficients, constructing a comparison matrix, and calculating the weighted mean. To empirically validate the effectiveness and applicability of the proposed algorithm, we conducted a case study involving five appearance design alternatives for automotive steering wheels, and the numerical results demonstrate the algorithm’s substantial improvements on group decision-making outcomes. Remarkably, the algorithm not only facilitates the flourishing of a fair decision-making environment but also can effectively handles biased decision-making scenarios and mitigate the impact of unfairness. By utilizing this algorithm, decision-makers can alleviate individual biases and enable fairer and more reliable decision-making processes. Consequently, this algorithm introduces a novel approach for tackling complex decision problems and exhibits promising prospects for practical applications. Its versatility renders it highly valuable in diverse decision-making processes, empowering decision-makers to achieve fairness and precision in their choices.

Boosting Thermoelectric Performance in Nanocrystalline Ternary Skutterudite Thin Films through Metallic CoTe2 Integration.

Metal-semiconductor nanocomposites have emerged as a viable strategy for concurrently tailoring both thermal and electronic transport properties of established thermoelectric materials, ultimately achieving synergistic performance. In this investigation, a series of nanocomposite thin films were synthesized, embedding metallic cobalt telluride (CoTe2) nanophase within the nanocrystalline ternary skutterudite (Co(Ge1.22Sb0.22)Te1.58 or CGST) matrix. Our approach harnessed composition fluctuation-induced phase separation and in situ growth during thermal annealing to seamlessly integrate the metallic phase. The distinctive band structures of both materials have developed an ohmic-type contact characteristic at the interface, which raised carrier density considerably yet negligibly affected the mobility counterpart, leading to a substantial improvement in electrical conductivity. The intricate balance in transport properties is further influenced by the metallic CoTe2 phase's role in diminishing lattice thermal conductivity. The presence of the metallic phase instigates enhanced phonon scattering at the interface boundaries. Consequently, a 2-fold enhancement in the thermoelectric figure of merit (zT ∼ 1.30) is attained with CGST-7 wt. % CoTe2 nanocomposite film at 655 K compared to that of pristine CGST.

Tribological, corrosion, mechanical, and metallurgical behavior of clay and Al2O3-reinforced aluminum-based composite material

Aluminum-based composite materials have garnered considerable attention in recent years owing to their outstanding mechanical and tribological properties. The integration of clay and Al2O3 into the aluminum matrix has notably elevated its tribological, corrosion, mechanical, and metallurgical characteristics. An examination of the metallurgical properties involved a thorough analysis of the material's microstructure and wettability. Notably, the composite material demonstrated enhanced wettability and a more uniformly distributed reinforcement, leading to improved mechanical attributes when incorporating 5% alumina and 5% clay particles. This composition resulted in heightened strength, improved hardness, and increased fatigue strength. Specifically, the addition of a mixture of ball-milled 5% alumina and 5% clay particles led to substantial improvements, with tensile strength, hardness, and fatigue strength increasing by approximately 40.49%, 44.11%, and 62.15%, respectively. However, this enhancement came at the cost of reduced toughness in aluminum, decreasing by about 46.66% following the addition of the ball-milled mixture. Tribological assessments, involving wear tests under varying loads and speeds, demonstrated that the inclusion of 5% clay and 5% Al2O3 substantially improved wear resistance. Furthermore, corrosion resistance was evaluated, revealing that the composite material exhibited enhanced corrosion resistance attributed to the passivation of the surface. Altogether, these findings underscore the promising potential of aluminum-based composite materials for various applications, particularly when tailored with specific ratios of alumina and clay particles.

MET alterations in advanced non-small cell lung cancer

Precision medicine has helped identify several tumor molecular aberrations to be treated with targeted therapies. These therapies showed substantial improvement in efficacy without excessive toxicity in patients with specific oncogenic drivers with advanced cancers. In metastatic lung cancers, the implementation of broad platforms for molecular tumor sequencing has helped oncology providers identify oncogenic drivers linked with better outcomes when treated upfront with targeted therapies. Mesenchymal-epithelial transition factor (MET) alterations are present in up to 60% of non-small cell lung cancer and are associated with a poor prognosis. Capmatinib and tepotinib are currently the only two approved targeted therapies by the U.S. Food and Drug Administration (FDA) for patients with MET exon 14 skipping mutation. Several agents are being developed to tackle an unmet need in patients with MET alterations. Some of these agents are being used in combination with EGFR targeted therapy to mitigate resistance to EGFR inhibitor. These agents are poised to provide new hope for these patients.

Schistocyte detection in artificial intelligence age.

Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on the timely diagnosis, effective treatment, and positive prognosis of diseases such as thrombocytopenic purpura and hemolytic uremic syndrome. Due to the rapid development of science and technology, laboratory hematology has also advanced. The accuracy and efficiency of tests performed by fully automated hematology analyzers and fully automated morphology analyzers have been considerably improved. In recent years, substantial improvements in computing power and machine learning (ML) algorithm development have dramatically extended the limits of the potential of autonomous machines. The rapid development of machine learning and artificial intelligence (AI) has led to the iteration and upgrade of automated detection of schistocytes. However, along with significantly facilitated operation processes, AI has brought challenges. This review summarizes the progress in laboratory schistocyte detection, the relationship between schistocytes and clinical diseases, and the progress of AI in the detection of schistocytes. In addition, current challenges and possible solutions are discussed, as well as the great potential of AI techniques for schistocyte testing in peripheral blood.

Efficient EEG motion artifact elimination framework for ambulatory epileptic seizure detection application

Motion artifacts are a pervasive challenge in EEG ambulatory monitoring, often obscuring critical neurological signals and impeding accurate seizure detection. In this study, we propose a new approach of outlier based grouping of two level Singular Spectrum Analysis (SSA) decomposition combined with Relative Total Variation (RTV) filter for the effective removal of motion-induced noise from ambulatory EEG data. A two-stage SSA method was employed to decompose single-channel EEG signal, which had been interfered with, into various fre quency bands. The affected sub-band signal was then subjected to an RTV filter to estimate the artifact signal. Subtracting this estimated artifact signal from the contaminated sub-band signal yielded the filtered sub-band signal. Subse quently, the filtered sub-band signal was reintegrated with the other decomposed components from noise-free bands, culminating in the generation of the ultimate denoised EEG signal. Based on the comprehensive set of simulation results, it can be deduced that the algorithm described in the paper outperforms existing methods. It demonstrates superior metrics evaluation in terms of ΔSNR, η , MAE, and PSNR when compared to these alternatives. Our framework sig- nificantly enhances the quality of EEG data by successfully eliminating motion artifacts while preserving crucial brainwave information. To evaluate the prac tical impact of this noise reduction technique, we assess its performance in the context of seizure detection. The results reveal a substantial improvement in the accuracy and reliability of seizure detection algorithms when applied to EEG data preprocessed with proposed method.

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

Synergistic regulation of hydrogen sulfide and nitric oxide on biochemical components, exopolysaccharides, and nitrogen metabolism in nickel stressed rice field cyanobacteria.

The present study examined the regulatory mechanism of hydrogen sulfide (H2S) and nitric oxide (NO) in nickel (Ni) stressed cyanobacteria viz., Nostoc muscorum and Anabaena sp. by analyzing growth, photosynthetic pigments, biochemical components (protein and carbohydrate), exopolysaccharides (EPS), inorganic nitrogen content, and activity of enzymes comprised in nitrogen metabolism and Ni accumulation. The 1µM Ni substantially diminished growth by 18% and 22% in N. muscorum and Anabaena sp. respectively, along with declining the pigment contents (Chl a/Car ratio and phycobiliproteins), and biochemical components. It also exerted negative impacts on inorganic uptake of nitrate and nitrite contents; nitrate reductase and nitrite reductase; and ammonium assimilating enzymes (glutamine synthetase, glutamate synthase, and glutamate dehydrogenase exhibited a reverse trend) activities. Nonetheless, the adverse impact of Ni can be mitigated through the exogenous supplementation of NaHS [sodium hydrosulfide (8µM); H2S donor] and SNP [sodium nitroprusside (10µM); NO donor] which showed substantial improvement on growth, pigments, nitrogen metabolism, and EPS layer and noticeably occurred as a consequence of a substantial reduction in Ni accumulation content which minimized the toxicity effects. The accumulation of Ni on both the cyanobacterial cell surface (EPS layer) are confirmed by the SEM-EDX analysis. Further, the addition of NO scavenger (PTIO; 20µM) and inhibitor of NO (L-NAME; 100µM); and H2S scavenger (HT; 20µM) and H2S inhibitor (PAG; 50µM) reversed the positive responses of H2S and NO and damages were more prominent under Ni stress thereby, suggesting the downstream signaling of H2S on NO-mediated alleviation. Thus, this study concludes the crosstalk mechanism of H2S and NO in the mitigation of Ni-induced toxicity in rice field cyanobacteria.

Men and women undergoing total hip arthroplasty have different clinical presentations before surgery and different outcomes at 1-year follow-up.

The purpose of this study was to investigate the influence of sex on patients undergoing total hip arthroplasty (THA) for hip osteoarthritis (HOA), aiming to assess the clinical and functional outcomes using patient-reported outcome measures (PROMs). A retrospective analysis of patients undergoing THA at Ospedale Galeazzi-Sant'Ambrogio between 2016 and 2022 was conducted. Inclusion criteria encompassed Kellgren-Lawrence grade III or IV HOA, with preoperative and 12-month postoperative PROMs. Enroled patients have been selected from a larger cohort without matching design for confounders. The analyses were performed using R software v4.0.3 (R Core Team) and data distributions were assessed using the Shapiro-Wilk normality test. One hundred ninety patients (72 male and 118 female) who had both preoperative and postoperative PROMs have been analysed from our institutional prosthesis registry (Datareg). Baseline and 12-month post-THA PROMs showed significant improvements overall. VAS score dropped notably from baseline to 3 months postsurgery (7.1 ± 2.1 vs. 0.9 ± 1.7). Functional and mental PROMs, including Harris Hip Score-functional (HHS-F), Harris Hip Score-total (HHS-t), SF-12PSand SF-12MS, exhibited substantial improvements post-THA. Stratifying by sex, males had lower baseline VAS, higher HHS-F, SF-12MSand hip disability and osteoarthritis outcome score-physical function short form (HOOS-PS). At 12 months, males displayed significantly better VAS, HHS-F, SF-12PSand HOOS-PS scores. Complication rates were minimal (1.5%), with stable rates across genders, mostly involving dislocation and periprosthetic fractures. Implant survival at 12 months reached an impressive 99%. THA remains an effective treatment for severe HOA. However, females presented with worse baseline conditions and showed relatively less improvement at 1-year postsurgery compared to males. This difference could be attributed to physiological and psychosocial factors associated with sex, including hormonal changes, muscle mass declineand perception of pain. Longer follow-ups and prospective studies are necessary to validate these findings and facilitate personalised approaches in HOA treatment, emphasising the need for careful consideration of sex-related variables in clinical decision-making for THA patients. Level III.