We propose that a dietary reduction of Lipopolysaccharide (LPS) could reduce risk and slow the progression of cognitive decline and Alzheimer’s disease. LPS is a component of the highly inflammatory cell membrane of Gram-negative bacteria. The highest dietary sources of LPS are processed meat and dairy products, which can sharply raise plasma LPS within one to two hours. The LPS in these foods is packaged into chylomicrons, which enter the bloodstream. Smaller amounts of LPS from colonic microbiota do not account for the sharp postprandial increase of plasma LPS. Once in the bloodstream, LPS can trigger the release of inflammatory cytokines within three hours. LPS can increase cytokines via LPS binding protein and cluster of differentiation-14, triggering toll-like receptor-4, and activating nuclear factor kappa-B. Nuclear factor kappa-B can increase cytokine levels, including interleukin-1-beta, interleukin-6, and tumor necrosis factor-alpha. LPS and inflammatory cytokines are higher in Alzheimer’s disease, both in plasma and in the central nervous system. These cytokines can enter the brain via active transport and induce microglial activation and inflammatory damage in the brain, including loss of synapses and neurons. Elevated brain inflammation from dietary LPS can contribute to cognitive impairment and increased risk of dementia.

Load balancing, energy efficiency and fault tolerance are among the most important data dissemination issues in Wireless Sensor Networks (WSNs). In order to successfully cope with the mentioned issues, two main approaches (namely, Data-centric Storage and Distributed Data Storage) have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the mentioned problems, the proposed solutions typically focus on one issue at a time. In this paper, we integrate the Data-centric Storage (DCS) features into Distributed Data Storage (DDS) mechanisms and present a novel approach, denoted as Collaborative Memory and Energy Management (CoMEM), to overcome both problems and bring memory and energy efficiency to the data loss mechanism of WSNs. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms existing approaches in various WSN scenarios.

Brain pathological changes impair cognition early in disease etiology. There is an urgent need for understanding aging-linked mechanisms of early memory loss to develop therapeutic strategies and prevent the development of cognitive impairment. Tusc2, also named Fus1, is a mitochondrial-resident protein regulating Ca2+ fluxes to and from mitochondria impacting overall health. We reported that Tusc2-/- female mice develop chronic inflammation and age prematurely, causing age- and gender-dependent spatial memory deficits at 5 m.o. Therefore, we investigated Tusc2-dependent mechanisms of memory impairment in mice, comparing changes in resident and brain-infiltrating immune cells. Interestingly, Tusc2-/- female mice demonstrated proinflammatory increase in astrocytes, IFN-γ expression in CD4+T cells and Granzyme-B in CD8+T cells. We also found fewer FOXP3+ T-regulatory cells and Ly49G+ NK and Ly49G+ NKT cells in female Tusc2-/- brain, suggesting a weakened anti-inflammatory response. Moreover, Tusc2-/- hippocampi exhibited Tusc2- and sex-specific protein changes associated with brain plasticity, including mTOR activation, and Calbindin and CamKII dysregulation affecting intracellular Ca2+ dynamics. Overall, data suggest that dysregulation of Ca2+-dependent processes and proinflammatory brain microenvironment heighten in Tusc2-/- mice could underlie cognitive impairment. Thus, strategies to modulate the brain’s Tusc2-, Ca2+, and mitochondria-dependent pathways should be explored to improve cognitive health.

Children with hearing loss have been found to have significantly more behavioral and emotional challenges than their typically hearing peers, though these outcomes are variable at the individual level. Working memory deficits have been found to relate to executive functioning and overall emotion regulation, leading to behavior challenges. Language development is essential for development of social relationships and communicating one’s needs - this may lead to distress when children cannot communicate effectively. Based on prior findings in children with hearing loss and their typically hearing peers, working memory and language skills were hypothesized to be related to parent and teacher report of socioemotional functioning. Participants were 35 children with hearing loss (66% female, M = 5.17 years old, SD = ±1.97) whose language, working memory, and socioemotional functioning were evaluated during the course of treatment and educational planning. Bivariate analyses indicated that working memory was related to a number of socioemotional domains (e.g., functional communication, atypicality, withdrawal), as were language scores (e.g., social skills, inattention). The direction of these associations was such that stronger working memory and language skills were related to more regulated socioemotional functioning. A call to action of the current study includes more education with regard to profiles and presentations of children with hearing loss, and an early focus on socioemotional learning to foster the development of regulatory skills.

Global environmental sustainability is a crucial issue, and Zambia, like many other countries, has particular potential and challenges in this regard. Environmental sustainability has been and is today a priority on the global development agenda due to serious issues emerging from extensive interference with human-related activities. Maintaining environmental sustainability is essential for preserving the nation's natural resources, fostering economic growth, and guaranteeing the welfare of its citizens. Even if there are many significant challenges, coordinated initiatives incorporating international cooperation, sustainable practices, and community engagement can open the door to a more sustainable future. While the ongoing discussion about environmental sustainability takes very diverse forms and concentrate on various aspects of the problem, this paper aims to discuss climate change, habitat and biodiversity, land degradation and agricultural constraints in the Zambian context. Using desktop approach, the paper will utilize reports, academic journals and policy documents in an attempt to define a common starting point from which to gain an understanding of the magnitude of the issue.

This paper illustrates an experimental activity for the closed-loop position control of an actuator made using shape memory alloy (SMA) wire. A solution with the self-sensing effect was implemented to miniaturize the systems, i.e., without external sensors. A proportional control algorithm was initially used, demonstrating the idea's feasibility: the wire can behave simultaneously as an actuator and sensor. An experimental investigation was subsequently conducted for the optimization of the developed actuator. As for the material, a Flexinol wire, Ni-Ti alloy, with a diameter of 0.150 mm and a length of 200 mm, was used. Preliminarily, a characterization of the SMA wire at constant and variable loads was carried out: the characteristics detected are elongation vs. electric current and elongation vs. electrical resistance. The control system is PC-based with a data acquisition card (DAQ). A drive board has been designed and built to read the wire's electrical resistance and power it by pulse width modulation (PWM). A notable result is that the actuator works with good precision and in dynamic conditions, even when it is called to support a load up to 65% different from that for which the electrical resistance-length correlation has previously been experimentally obtained, on which the control is based. This opens up the possibility of using the actuator in a counteracting configuration with a spring, which makes hardware implementation and control management simple.

Background: In the field of brain-computer interfaces, neural decoding plays a crucial role in translating neural signals into meaningful physical actions. These signals are transmitted to processors for decoding via wired or wireless channels, often encountering data loss, commonly referred to as "packet loss." Despite its importance, the impact of different types and degrees of packet loss on neural decoding has yet to be comprehensively studied. Understanding this impact is critical for advancing neural signal processing and data filtering. Methods: This paper addresses this gap by constructing four distinct packet loss models, simulating congestion packet loss, distributed packet loss, and burst packet loss scenarios. Using macaque superior arm-movement decoding experiments, we evaluate the effects of these packet loss types on decoding performance. Results: Our findings reveal that continuous burst packet loss significantly degrades decoding performance at the same packet loss probability, and decoding parameters that are sensitive to data continuity are more heavily impacted by packet loss. Conclusions: This study provides an analysis of the relationship between packet loss and neural decoding results, offering valuable insights for future strategies aimed at preventing and handling packet loss in neural signal acquisition.

Recurrent Pregnancy Loss (RPL) affects 1-2% of women, and its triggering factors are unclear. Several studies have shown the vaginal, endometrial, and gut microbiota may play a role in RPL. A decrease in the quantity of Lactobacillus crispatus in local microbiota has been associated with an increase in local (vaginal and endometrial) inflammatory response and immune cell activation that leads to pregnancy loss. The inflammatory response may be triggered by gram-negative bacteria, lipopolysaccharides (LPS), viral infections, mycosis, or atypia (tumor growth). Bacterial structures and metabolites produced by microbiota could be involved in immune cell modulation and may be responsible for immune cell activation and molecular mimicry. Gut microbiota metabolic products may increase the amount of circulating pro-inflammatory lymphocytes, which, in turn, will migrate into vaginal or endometrial tissues. Local proinflammatory Th1 and Th17 subpopulations along with a decrease in Treg and tolerogenic NK cells are accountable for the increase in pregnancy loss. Local microbiota may modulate the local inflammatory response increasing pregnancy success. Analyzing local and gut microbiota may be necessary to characterize some RPL patients. Although oral supplementation of probiotics has not been shown to modify vaginal or endometrial microbiota, the metabolites produced by it may benefit patients. Lactobacillus crispatus transplantation into the vagina may enhance the required immune tolerogenic response to achieve a normal pregnancy. The effect of hormone stimulation and progesterone to maintain early pregnancy on microbiota has not been adequately studied, and more research is needed in this area. Well-designed clinical trials are needed to ascertain the benefit of microbiota modulation in RPL.

In recent years, due to the detrimental impact of traditional gas-liquid refrigerants on the environment, the pursuit of solid-state refrigeration technology based on elastocaloric effect, as a promising sustainable alternative, has been promoted. However, the traditional trial-and-error method is inefficient and difficult to meet the material application requirements. In this work, machine learning (ML) was employed to accelerate the development of NiTi-based shape memory alloy (SMA) with excellent elastocaloric effect. By means of active learning, we identified 9 novel NiTi-based SMAs in four iterations, the phase transformation-induced entropy change (ΔS) of which are as high as over 90 J/kg·K-1. Moreover, the model exhibits good interpretability, allowing us to understand the relevance between the target and features from the aspect of mechanism.

Background: People with Down syndrome (DS) have a trisomy of chromosome 21 and marked deficits in verbal memory but relatively preserved visuospatial perception. Verbal memories are related to semantic knowledge. Little is known about the organization of semantic knowledge in people with DS, which was the aim of this study. Aims: The extent of false memory in people with DS was examined. Possible differences in the number of false memories induced by nominal and verbal themes were examined. Methods and Procedures: In the study phase, ten word lists with semantically related associates were presented. In the recognition phase, participants made judgments on whether each word had been represented previously as semantically related lures, old words, and semantically unrelated new words. Outcomes and Results: Differences in the processing of nominal and verbal theme-related lures were observed among people with DS. When processing nominal theme-related lures, people with DS showed delayed false positives. When processing verbal theme-related lures, people with DS showed no differences compared to controls. Similar patterns have been observed in people with DS when processing nominal theme-related associates and verbal theme-related associates. People with DS have delayed recognition of associates. When processing unrelated words, they showed significantly higher false positives than controls for both nominal and verbal eliciting themes. Further analyses of the themes revealed differences in false positives across syntactic categories among groups. Conclusions and Implications: People with DS showed a delayed recognition of associates and deviance in recognizing unrelated words in the nominal and verbal theme categories. People with DS were delayed in terms of nouns, but deviated with verbs in associated and unrelated words. Additionally, people with DS showed distinct patterns in processing five nominal lures and three verbal lures, suggesting that they formed false memories differently based on distinct syntactic categories. Thus, it was concluded that people with DS develop a deviant semantic structure. Category-based interventions for people with DS should be implemented in order to improve their semantic knowledge through lexical connections.

During the last few years, the world witnessed one of the worst calamities to humankind- the COVID-19 pandemic. We also witnessed the development of vaccines at an unprecedented pace against the SARS-CoV-2- the causative agent. India was among the hard-hit countries by COVID-19, developed its indigenous vaccine and implemented vaccines developed in other countries as well. Initially, the antibody level and the neutralizing antibody titer against the pathogen were considered as the correlates of immune protection from the disease. However, the long-term protection from the pathogen was provided by the immune memory cells viz. T and B memory cells and their ability to efficiently make recall responses. In this regard, globally the research studies focused mainly on the mRNA-based vaccines. The studies on immune memory response, particularly- the B cell memory response bestowed by the vaccines given to Indians is relatively obscure. We studied the level of RBD specific memory B cells in the peripheral circulation of the study participants and the ability of B cells to secrete antigen-specific antibodies among Indians vaccinated with Covaxin (BBV152), Covishield (AZD1222), Corbevax (BECOV2D) and Sputnik Light (Gam-COVID-Vac) as well as unvaccinated individuals. Our study revealed that Corbevax and Sputnik Light conferred better antibody-secreting cell (ASC) responses.

This paper presents the novel development of a shape memory polymer foam based on polymer-polymer nanocomposites. Herein, polylactide (PLA)/biosourced polyamide (PA) foams are fabricated by in-situ fibrillation of polymer blends and subsequent supercritical CO2 foaming technique. In this system, PLA serves as a shape memory polymer to endow this foam with a shape memory effect (SME), and in-situ generated PA nanofibers are employed to reinforce the PLA cell walls and provide an additional permanent phase. Concentration of PA, 5 wt.%, is chosen to form an entangled nanofibrillar network. Foams of PLA/PA nanoblends with the same content of constituents are fabricated to reveal the effect of minor phase morphology on the cell structure and shape memory behavior of polymer foams. Profiting from the reinforcing effect of PA nanofibers, the PLA/PA nanocomposite foam exhibits smaller foam cells, a narrower cell size distribution and a comparable cell concentration than the PLA/PA nanoblend foam. In addition, PA nanofibers, unlike PA nanodroplets, favor the shape fixation ratio and recovery ratio and shorten the shape recovery time.

In this work, we consider a one-dimensional laminated beam in the case of non-equal wave speeds with only one infinite memory on the effective rotation angle. In this case, we establish the general decay result for the energy of solution without any kind of internal or boundary control. The main result is obtained by applying the method used in Guesmia et al. (Electron. J. Differential Equations 193: 1-45, 2012) and the second-order energy.

This study addresses the implementation of the International Financial Reporting Standard 9 (IFRS 9) in the European Union as of January 1, 2018, replacing the International Accounting Standard 39 (IAS 39) to introduce a new model for recognizing Loan Loss Provisions (LLP) based on Expected Credit Loss (ECL). This model responds to criticisms of the former Incurred Credit Loss (ICL) system for its inability to reflect credit losses in a timely manner, potentially exacerbating the effects of financial crises (Bushman & Williams, 2015; Gebhardt & Novotny-Farkas, 2011). The study focuses on the effects of adopting the ECL model on the level of Loan Loss Allowances (LLA) in loans, own equity, and the Common Equity Tier 1 (CET1) ratio across 13 Portuguese commercial banks. A mean comparison test is used to evaluate scenarios before and after the application of the ECL model, highlighting the importance of regulator actions and the adequacy of loss recognition policies, including the effects of Regulation (EU) 2017/2395 of December 12. The results obtained demonstrate significant negative impacts on the net values of loans, own equity, and the CET1 ratio upon adopting the IFRS 9 ECL model, due to the widespread increase in LLAs.

Increasing concerns on animal welfare in commercial pig industry include aggression between pigs as it affects their health and growth. Early detection of aggressive behaviors is essential for optimizing their living environment. A major challenge for detection is that these behaviors are observed occasionally in normal conditions. Under this circumstance, a limited amount of aggressive behavior data will lead to class imbalance issue, making it difficult to develop an effective classification model for the detection of aggressive behaviors. To overcome this problem, we approach the aggressive behavior detection problem as a case of anomaly detection rather than classification and propose a model based on anomaly detection, which can better handle unbalanced class distribution and effectively detect the infrequent, aggressive episodes of pigs. The model consists of a convolutional neural network (CNN) and a variational long short-term memory (LSTM) autoencoder. Additionally, we adopted a training method similar to weakly supervised anomaly detection and included a few aggressive behavior data in the training set for prior learning. To effectively utilize the aggressive behavior data, we created Reconstruction Loss Inversion, a novel objective function, to train the autoencoder-based model to increase the reconstruction error of aggressive behaviors by inverting the loss function. Our anomaly detection approach significantly outperforms traditional classification-based methods, effectively identifying aggressive behaviors in a natural farming environment. This method offers a robust solution for detecting aggressive animal behaviors and contributes to improving their welfare.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

In this paper, a new type of self-excited thermomechanical oscillator containing an oscillating shape memory alloy (SMA) filament with two symmetrically arranged spheres is investigated. The self-excitation of the oscillations is due to a heater of constant temperature, which causes periodic contractions of the filament when it approaches it. The contracted filament moves away from the heater a distance sufficient to cool it. Under the action of the weight of the spheres, the cooled filament re-approaches the heater, causing the above processes to repeat periodically. On the basis of experimental studies, approximating functions of the heater's heat field distribution are derived. A dynamic model of the oscillator has been created, in which the minor and major hysteresis in the SMA alloy and the distribution of the heat field around the heater have been taken into account. By numerical solutions of the differential equations, the laws of motion of the spheres are obtained. The displacements of the spheres in two perpendicular directions were measured using an experimental system. The obtained experimental results validate the proposed dynamic model and its assumptions with a high degree of confidence. Conclusions are drawn about the stochastic nature of the oscillations due to the hysteresis properties of the SMA and the temperature variation of the natural frequency of the oscillating system.

The Internet of Things (IoT) is a concept based on the integration of various devices and methods of communication to exchange information in order to contribute to the improvement of everyday life, business and environment. This paper focuses on the establishment of a modern Internet of Things platform to ensure secure collection, processing and storage of data, under unexpected events. This platform establishes reliable connectivity which enhance system uptime; the key to success when implementing an IoT strategy. For this purpose, we proposed a hybrid approach combining different connectivity technologies to ensure the availability and smooth operation of the system and the prevention of data loss in unforeseen cases.

Periodontitis is a chronic inflammatory disease which results in the destruction of the tooth’ s supporting tissues and the alveolar bone resorption. The complement becomes a major link between infection and inflammatory pathology including periodontitis. Gingipians as important virulence factors of P. gingivalis have the activity of C5 convertase, could cleave C5 into fully functional C5a to activate C5aR. The above process could be blocked by the C5aR antagonist (PMX-53) to suppress local periodontal inflammation, and then achieves the purpose of treatment of periodontitis. Nanoparticles incorporated within gelatin are promising carrier system for drug delivery in recent years. This study aimed to investigate whether gelatin nanoparticles loaded with PMX-53 prevents alveolar bone resorption in miniature swines model of periodontitis. Four miniature swines were placed ligatures around the maxillary and mandibular fourth premolar and first molar on both sides for seven weeks to induce periodontitis. Then, animals were assigned randomly to four groups: minocycline-treated group, gelatin with PMX53-treated group, gelatin-treated group and a sham control group. They were treated with 1ml related drugs respectively, into gingival sulcusl for 4 times at one-week intervals. We showed that local treatments with gelatin nanoparticles loaded with PMX-53 could inhibit alveolar bone loss of periodontitis. Our study revealed that gelatin nanoparticles loaded with PMX-53 prevented alveolar bone resorption miniature swines model of periodontitis. In addition, provided proof-of-concept for local targeting of gelatin nanoparticles loaded with PMX-53 as a powerful candidate for the treatment of periodontitis.

This literature review paper delves into the synergies between Climate-Smart Agriculture (CSA) and the effective management of food losses. With the escalating challenges posed by climate change and the pressing need to address food security concerns, the intersection of these two critical domains becomes paramount for sustainable agricultural practices. The review explores existing research on the implementation of climate-smart agricultural techniques and their impact on mitigating food losses throughout the agricultural value chain. The paper synthesizes findings related to climate-resilient crop varieties, precision farming, water-use efficiency, and sustainable soil management, highlighting their contributions to reducing post-harvest losses and enhancing overall food security. In addition to summarizing current knowledge, this paper identifies gaps in the literature and proposes avenues for future research. This review serves as a foundation for researchers, policymakers, and practitioners seeking to contribute to the advancement of climate-smart agriculture and the reduction of food losses, paving the way for a more sustainable and resilient global food system.

The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a reflection is developed on the dual attention network that applies the learning equations and coordinates subnetworks toward purposeful behaviors. Speculation is made on possible developments of functionalities that could provide additional skills and at the same time shed light on competencies typical of ``natural intelligence".

To better understand the shape memory materials and self-healing materials, a new series of liquid-crystalline shape memory polyurethane (LC-SMPU) composites, named SMPU-OOBAm, were successfully prepared by incorporating 4-octyldecyloxybenzoic acid (OOBA) into the PEG-based SMPU. The effect of OOBA on the structure, morphology and properties has been carefully investigated. The results demonstrate that SMPU-OOBAm have liquid crystalline properties, triple-shape memory properties and self-healing properties. The incorporated OOBA promotes the crystallizability of both soft and hard segments of SMPU, and the crystallization rate of the hard segment of SMPU decreases when the OOBA-content increases. Additionally, the SMPU-OOBAm forms a two-phase separated structure (SMPU phase and OOBA phase), and it shows two-step modulus changes upon heating. Therefore, the SMPU-OOBAm shows triple-shape memory behavior, and the shape recovery ratio decreases with an increase in the OOBA content. Finally, SMPU-OOBAm shows self-healing properties. The new mechanism can be ascribed to the heating-induced “bleeding” of OOBA in the liquid crystalline state and the subsequent re-crystallization upon cooling. This successful combination of liquid crystalline properties, triple-shape memory properties and self-healing properties make the SMPU-OOBAm composites with many promise applications in smart optical devices, smart electronic devices and smart sensors.

Can the type of implant rehabilitation influence peri-implant bone loss in case of full-arch mandibular prosthesis? The purpose of the study was to assess, by orthopantomograms (OPG), bone loss around implants in different types of implant-supported prosthetic rehabilitations and evaluate potential risk factors associated with the number and location of implants that may have an association with bone defect. A radiographic study was conducted on 22317 OPGs in the time range from 2010 to 2024. All OPGs with implant-supported prosthetic mandibular rehabilitations were included in the study. A total of 155 OPGs were evaluated, with peri-implant bone loss identified in 64 (41.3%). Distal implants (furthest from the center) across various positioning patterns were most susceptible to bone loss, with positions 3.6 and 4.6 demonstrating the most frequent occurrences (25 and 26 cases, respectively). χ² test revealed significant associations between both implant positioning pattern (p < 0.001) and number of implants (p < 0.001) with peri-implant bone loss. Also by updating the sample of OPGs, increased susceptibility to bone resorption was found for implants placed distal to the mental foramen compared to mesial ones in full-arch implant-supported fixed prostheses. Prospective clinical studies will then be useful to investigate this finding further.

Proper Post-Activation Performance Enhancement helps to improve athletic performance. This study aimed to investigate the effects of two different velocity loss (VL), 10%VL and 20%VL, on post-activation performance enhancement (PAPE) in 20m sprint performance among sprint athletes. Twenty-four male sprint athletes (100m sprint time: 10.96±0.15 s) were recruited. A randomized crossover experimental design was used for traditional group (TG), 10%VL, and 20%VL interventions. Sprint tests were conducted at the 4th, 8th, 12th, and 16th minutes after the intervention. Two-way repeated measures ANOVA revealed an interaction effect between group and time on 20m sprint performance (F=2.817, p=0.037, partial η²=0.585). Simple effect analyses showed significant differences compared to baseline at the 4th minute for the 20%VL group (P<0.05). Cohen's d values indicated improvement in 10m sprint times at the 8th minute of the rest interval for all three intervention groups (TG: ES=-0.270, 10%VL: ES=-0.038, 20%VL: ES=-0.279). Improvement in 20m sprint times was observed at the 4th minute for the 20%VL group (ES=-0.296) and at the 16th minute for the 10%VL group (ES=-0.276). Compared to traditional PAPE schemes based on 1RM, PAPE schemes based on velocity loss (20%VL) can better induce PAPE effects in sprint athletes.

This paper proposes a novel facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depthwise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while also capturing the dependencies between different facial regions. Additionally, a novel loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets show that the proposed LFNSB model performs excellently in facial expression recognition tasks. It achieves high recognition accuracy while significantly reducing the number of parameters, thus substantially lowering model complexity.

Benveniste’s experiments – known in the lay press as the “water memory” phenomenon – are generally considered to be a closed case. However, the amount of data generated by twenty years of well-conducted experiments prevents closing the file so simply. An issue, which has been little highlighted so far, merits to be emphasized. Indeed, if Benveniste failed to persuade his peers of the value of his experiments, it was mainly because of a stumbling block, namely the difficulty of convincingly proving the causal relationship between the supposed cause (“informed water”) and the experimental outcomes in different biological models. To progress in the understanding of this phenomenon, we abandon the idea of any role of water in these experiments (“water memory” and its avatars). In other words, we assume that “control” and “test” conditions to be evaluated were physically identical and differed only by their respective designations. We show in this article how simple probabilistic considerations allow to build a model that accounts for all aspects of Benveniste’s experiments. In this model based on probability amplitudes, constructive and destructive interferences emerge – or not – according to the experimental context. The recording of a statistical regularity shapes the intertwined whole constituted by the probability amplitudes of the states of the experimenter’s cognitive structures and of the experimental system. This model provides an alternative explanation to Benveniste’s experiments where water plays no role and where the place of the experimenter is central.

Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

Cytomegalovirus infection contributes to 10-30% congenital hearing loss in children. Vertebrate peripheral auditory organs include the outer, middle and inner ear. Their development is regulated by multiple signaling pathways. However, most ear diseases due to viral infections are due to congenital infections and reactivation, and affect healthy adults to a lesser extent. This may be due to the fact that viral infections affect signaling pathways that are important for the development of peripheral hearing organs. Therefore, an in-depth understanding of the relationship between viral infections and the signaling pathways involved in the development of peripheral hearing organs is important for the prevention and treatment of ear diseases. In this review, we summaries the effects of viruses on signaling pathways and signaling molecules in development of peripheral auditory organ, with a focus on Human cytomegalovirus as an example.

This paper will argue that memory from early childhood underlies many practices and beliefs that we commonly refer to as “religious”. The consciousness of young children does not yet have certain features that characterizes adult consciousness. This paper will concentrate on four of these: (I) a reality that is recognizable; (II) a sense of temporal duration; (III) a sense of self; (IV) an experience of the world that is deeply affected by our acquaintance with (a) language. Absence of these features presumably characterizes the consciousness of infants. It also often characterizes mystical experiences. The paper will argue that the human tendency to engage in so-called religious practices and beliefs makes most sense on the assumption that adults somehow preserve the memory of their state of being in childhood.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to a deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated to the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air-glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and the sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, and the diameter of the nested tube is d0=29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28×10-4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72×10-2 dB/m. To our knowledge, this is the first research to achieve an anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band erbium laser therapy instrument.

This paper aims to address the exponential stability and stabilization problems for a class of delayed nonlinear Markov jump systems under randomly occurring Denial-of-Service (DoS) attack and packet loss. Firstly, the stochastic characteristics of DoS attack and packet loss are depicted by the attack success rate and packet loss rate. Secondly, a Period Observation Window (POW) method and a hybrid-input strategy are proposed to compensate for the impact of DoS attack and packet loss on the system. Thirdly, A Dynamic Event-triggered Mechanism (DETM) is introduced to save more network resources and ensure the security and reliability of systems. Then, by constructing a general common Lyapunov functional and combining with the DETM and other inequality analysis techniques, the less conservative stability and stabilization criteria for the underlying systems are derived. In the end, the effectiveness of our result is verified through a numerical example.

This study introduces a novel method for early prediction of Kimchi cabbage (Brassica rapa subsp. pekinensis (Lour.) Hanelt) height, utilizing drone imagery and a long short-term memory (LSTM) model. The research was conducted on a testbed at the National Institute of Agricultural Sciences (NAS) in South Korea, encompassing two distinct soil types (loam and sandy loam) to investigate their impact on growth. High-resolution drone images were captured throughout the growing season to generate a canopy height model (CHM) for estimating plant height at various stages. Missing height data were interpolated using a logistic growth curve, and an LSTM model was trained on this data to predict the final height of Kimchi cabbage at harvest. Three LSTM models were developed using time-series data collected at 29, 36, and 44 days after planting (DAP). The model trained on data from DAP 44 demonstrated the highest accuracy with a coefficient of determination (R²) of 0.83, a mean absolute error (MAE) of 2.48 cm, and a root mean square error (RMSE) of 3.26 cm, outperforming models trained on earlier data. Color-coded maps were generated to visualize the spatial distribution of predicted Kimchi cabbage heights, revealing variations in growth patterns across the testbed and confirming the model's potential for site-specific management. Considering the trade-off between accuracy and prediction timing, the model trained on DAP 36 data (MAE = 2.77 cm) was deemed optimal for informing cultivation management decisions. This research demonstrates the feasibility and effectiveness of integrating drone imagery, logistic growth curves, and LSTM models for early and accurate prediction of Kimchi cabbage height. The proposed technology enables data-driven decision-making for farmers, facilitating timely interventions based on predicted growth patterns. This could lead to improved crop yields, resource optimization, and a more sustainable agricultural future. Future research will focus on refining the model's accuracy and exploring its applicability to other crops, further expanding the potential of precision agriculture technologies.

In order to solve the problem of large eddy current loss and high temperature rise caused by large amount of permanent magnet, a new type of combined magnetic pole permanent magnet motor is proposed in this paper. The sinusoidal distribution subdomain model of non-uniform air gap rotor is established by using Laplace equation, and the analytical expression of eddy current loss of rotor in uniform air-gap and non-uniform air-gap is derived. The effect of rotor eccentricity on eddy current loss is obtained. According to the characteristics of distributed winding of non-uniform air gap combined pole permanent magnet motor, equivalent treatment is performed to obtain the equivalent thermal conductivity, so as to establish equivalent thermal network model of motor, calculate the temperature value of each component of the motor, and verify the correctness of the thermal network model through the method of magnetothermal bidirectional coupling. Finally, an experimental platform is set up to carry out temperature rise experiments on the two prototypes respectively. Experimental results show that the non-uniform air-gap rotor structure can effectively reduce the rotor eddy current loss and motor temperature rise, and also verify the accuracy of the analytical model calculation results.

This paper examines the use of multisensory virtual reality (VR) as a novel approach in psychological therapy for autobiographical memory retrieval with benefits for cognitive enhancement, stress reduction etc. The research in this paper was done under Romanian Arut Research Grant no. 27/09/10/2023 ”Intelligent VR system for treating autobiographical/episodic memory deficits".  By simulating realistic multisensory experiences, VR technology offers an immersive environment that surpasses traditional therapeutic methods in effectiveness. Previous studies demonstrate improved outcomes in treating various psychological conditions (affective disorders and PTSD, specific phobias and age-related cognitive decline). Technological advancements in VR, such as olfactory integration cand contribute to the realism and therapeutic potential of these environments, the integration of various physical stimuli with VR holds promising potential psychological therapies and highlighting the need for further interdisciplinary research. In this pilot study we tested the efficacy of a new system for triggering autobiographical memory retrieval. For this we used images combined with odors in a congruent manner and offering participants the chance to interact with the VR environment by using 2 virtual hands. We evaluated the efficacy of this system using qualitative methods with emphasis on the evaluation of the emotions associated with memory recollection and the ease of triggering memories. All participants in our pilot study experienced intense emotions related to childhood or adolescence and the pleasant feelings they had during the experiment persisted even after it ended. This is an advancement to what exists currently and original research elements for our paper.

This paper analyzes and compares models for predicting average magnet losses in interior permanent-magnet motors with fractional-slot concentrated windings due to harmonics in the armature reaction (assuming sinusoidal phase currents). Particularly, loss models adopting different formulations and solutions to the Helmholtz equation to solve for the eddy currents are compared to a simpler model relying on an assumed eddy-current distribution. Boundaries in terms of magnet dimensions and angular frequency are identified (numerically and using an identified approximate analytical expression) to aid the machine designer whether the more simple loss model is applicable or not. The assumption of a uniform flux-density variation (used in the loss models) is also investigated for the case of V-shaped and straight interior permanent magnets. Finally, predicted volumetric loss densities are exemplified for combinations of slot and pole numbers common in automotive applications.

Recurrent pregnancy loss (RPL) affects around 2 % of women of reproductive age. Primary RPL is defined by ≥2 pregnancy losses and no normal birth delivery. In secondary RPL, the losses are after a normal pregnancy and delivery. Most cases have no clear aetiology, although primary cases are the most complex. Several gene polymorphisms have been associated with RPL. Some polymorphisms are increased in women of Asian or Caucasian race. In admixed RPL populations, the information on possible genetic links is scarce and contradictory. The study aimed to assess the frequency of two SNP in different enzymes involved in medical conditions observed during pregnancy. Microsomal epoxy hydrolase (mEPH) is involved in detoxifying xenobiotics. It is present in ovaries, and it is hormonally regulated. The endothelial nitric oxide synthase forms nitric oxide from arginine and is involved in vascular tone. Two SNPs, rs1051740 and rs1799983, were assessed. The study included 26 controls and 63 primary RPL patients. Significant differences were observed in the frequencies of both SNP (p<0.05). The frequency of mutated alleles was higher in patients. Double mutated homozygotes were encountered only in RLP patients (p<0.05). Genetic polymorphisms rs1051740 and rs1799983 may be involved in primary RPL in the Venezuelan admix population. Genetic studies could provide crucial information on the aetiology of primary RPL.

The objective of this study was to evaluate the genetic effects involved on pepper fruit post-harvest water loss, and correlated to morphology traits. Fruits of eight landraces of C. baccatum, and their 28 hybrids, were evaluated in a randomized complete block design. Analysis of variance, diallel analysis, phenotypic and genotypic correlation were performed to compare fruit water loss (WL), width (FW), length (FL), total soluble solids (TSS), dry matter content (DMC), pericarp thickness (PT), cuticle thickness (CT), and exocarp thickness (ET). WL varied from 14 to 68% during storage. Non-additive effects played more important role than additive, including CT, EP and TSS. Effective strategies to reduce WL should be achieved by repeated backcrosses followed by selection in segregating populations (SP). Therefore, the selection of hybrids should follow high specific combining ability traits. Selecting fruits with higher CT and lower TSS values are indirectly selecting fruits with lower WL. Varieties with low WL can be breed using landraces 04 and 58 as parents or by the hybrid 04 x 44 in open lines in SP. Brazilian C. baccatum landraces are a source of genetic variability, and the SP emerging from crossing lines with reduced WL should develop new ways for conventional breeding.

We discuss a topological method of storing and retrieving information from flash memory. We first present a sensing method where the threshold voltage level, as represented by a sensing time, is extracted in one sensing cycle. The sense time distribution from one set of flash cells, e.g. one physical row, is then processed in software to decode the digital state of each cell. The decoding method uses topological constraints but no rigid or predetermined voltage thresholds to digitize the distribution. The software defined nature of the topologically defined flash (TDF) allows greater flexibility for allocating cells to arbitrary number of digital states.

The information on the land use and soil conservation practice based on year 2006, 2010 and 2014, hence offering an opportunity to model the impacts of land use change on erosion, deposition and surface water runoff. Limitation in the use of hydrological models had been their inability to handle the large amount of input data that describe the heterogeneity of the natural system. In this study, a procedure that takes into account soil conservation practice based on the land use change, the response of soil erosion and sediment export from the George Town Conurbation catchment area, and average annual sediment yields were estimated for each grid cell of the watershed to identify the critical erosion areas of rural and urban planning proposes. Average annual sediment yield and data on a grid basis estimated using Universal Soil Loss Equation (USLE) and an emerging technology represented by Geographic Information System (GIS) used as a tool to produce a map for erosion rate. The changing of the land use from forest to agriculture and then to an urban area is a challenging task to research on land use demand for population, and environmental impact assessment is important for the planning of natural resources management, allowing research the modification of land use properly and implement more sustainable for long term management strategies. The challenge is to formulate strategies that would promote an integrated approach to the land use planning at an appropriate level as to address the issues that arose. Modelling for creating urban growth boundary for the George Town Conurbation must have to be controlled surface runoff and soil loss and sediment export from land use of the George Town Conurbation catchment.

The relationship of Amyotrophic Lateral Sclerosis, Parkinson Disease and other age-related neurodegenerative diseases associated with mitochondrial dysfunction has led to study of the mitochondrial fission gene Drp1 in Drosophila. Drp1 can interact with mitochondrial proteins including parkin and the Drosophila Bcl-2 proteins. Mutations in Drp1 can lead to mitochondrial dysfunction and neuronal loss. Ddc-Gal4 was exploited to direct the expression of Drp1 and the inhibition of Drp1 in neurons. Here, the inhibition of Drp1 seems to compromise locomotor function throughout life but does not alter longevity. The consequences of Drp1 overexpression can be suppressed through overexpression of pro-survival Bcl-2 gene Buffy or by inhibition of the pro-cell death Bcl-2 homologue Debcl. The expression of Buffy suppresses the poor climbing induced by loss of Drp1 function. Interestingly, the loss of parkin function is rescued by the directed inhibition of Drp1. Alteration of the expression of Drp1 acts to phenocopy neurodegenerative disease phenotypes in Drosophila, while overexpression of Buffy can counteract these phenotypes to improve overall health. The pro-survival effect of Buffy can rescue the phenotypic effect due to the Drp1-induced apoptosis. The diminished health due to either overexpression or inhibition of Drp1 has produced a novel model to investigate mechanisms underlying neurodegenerative disease.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 70% for known items and 62% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.

Probiotics, known for regulating gut microbiota, may aid those with overweight or obesity, but their mechanisms require more research. This study involved 75 overweight or obese young adults, randomly assigned to either a Bifidobacterium brevis BBr60 group or a placebo group. Both groups received diet guidance and took either BBr60 (1×1010 CFU/day) or a placebo for 12 weeks. Re-searchers analyzed body composition, serum glucose, lipids, liver and kidney function, comprehensive metabolome, and intestinal homeostasis before and after the intervention. After 12 weeks, BBr60 significantly reduced weight and BMI compared to pre-treatment levels and outperformed the placebo. The BBr60 group also showed improved blood biochemistry, with notably lower fasting blood glucose (FBG) levels than the placebo group (p < 0.05). Additionally, BBr60 influenced vital serum and fecal metabolites related to three amino acid metabolic pathways and regulated the bacteria Dialister, Klebsiella, and Bacteroides, which correlated strongly with serum metabolites. These findings indicate that BBr60 can safely and effectively regulating BMI, body weight, serum glucose, lipids, and liver function markers, which may involve BBr60's impact on key gut bacteria, which influence metabolites related to the valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and alanine, aspartate, and glutamate metabolism.

Neuron degeneration is the most common old age complications worldwide. Alzheimer's disease (AD) is one of the most common neurodegenerative condition with limited drug options and significant adverse effects. Hence, there is a growing interest in exploring natural products for their potential pharmacological activities against AD. This study investigates the efficacy of Verbesina encelioides flower extract (VFE) in alleviating neurobehavioral damage induced by Scopolamine in rat mimicking AD model. GC-MS analysis showed that V. encelioides flower extract is rich in carotenoids like spirilloxanthin. Various behavioural models were used to assess the rats' learning and memory capabilities. Additionally, biochemical tests were conducted on the AD rat models to evaluate oxidative stress in the brain, including superoxide dismutase (SOD), nitric oxide (NO), reduced glutathione (GSH), and anti-acetylcholinesterase (AChE) enzyme assays. Findings from this study suggest that the VFE significantly improved the rats' cognitive aptitude and alleviated them from oxidative stress by reducing free radical generation. Based on the results obtained from GC-MS analysis, in silico interaction analysis of gingkolide A and spirilloxanthin with acetylcholinesterase was carried out. In summary, this study suggests that V. enceloides flower extract improves learning abilities and mitigates cognitive deficits caused by scopolamine. Thus V. enceloides could be potentially seen as natural therapeutic intervention against neurodegenerative disorders likes Alzheimer’s disease and dementia.

Background: Assessing the impact of bone health clinical management in breast cancer (BC) patients receiving adjuvant endocrine therapy and designing a personalized clinical pathway to reduce bone loss, in an Italian research hospital. Methods: The primary endpoint was to assess (through the process improvement organizational method) the clinical pathway that post-surgical BC patients prescribed with endocrine therapy undergo to prevent bone loss. The secondary endpoint was to design a personalized clinical pathway for a prompt implementation of guidelines, to assess and possibly prescribe antiresorptive therapy. Results: During the first year of the new diagnostic therapeutic assistance pathway execution, an 60% increase in Dual-Energy X-ray Absorptiometry evaluations within 30 days and a 39.5% increase in antiresorptive therapy prescription within 90 days (since prescription of endocrine therapy) were shown, thus increasing patients’ compliance. Conclusion: Case managers and bone health specialists in this context can improve patients’ adherence to therapies and bone health, helping physicians to expand their collaboration.

Recent research has suggested that working memory training interventions may benefit children with Developmental Language Disorder (DLD). The current study investigated a short and engaging adaptive working memory intervention that targeted executive skills and aimed to improve both language comprehension and working memory abilities in children with DLD. Forty-seven 6- to 10-year-old children with DLD were randomly allocated to an executive working memory training intervention (n=24) or an active control group (n=23). A pre-test/intervention/post-test/9-month-follow-up design was used. Outcome measures included assessments of language (to evaluate far transfer of the training) and working memory (to evaluate near transfer of the training). Hierarchical multiple regression analyses controlling for pre-intervention performance and age found group to be a significant predictor of sentence comprehension and of performance on six untrained working memory measures at post-intervention and 9-month follow-up. Children in the intervention group showed significantly higher language comprehension and working memory scores at both time points than children in the active control group. The intervention programme showed potential to improve working memory and language comprehension in children with DLD and demonstrated several advantages: it involved short sessions over a short period; caused little disruption in the school day; and was enjoyed by children.

The global food system has three recognized challenges: (a)increasing the availability of food consumption, (b) reducing food loss, and (c) reducing food waste. The increasing demand for food consumption, increasing quantity of food loss, and corresponding increase in food waste are resulting in serious health, aesthetic, social, economic, and environmental problems due to a lack of appropriate planning and management. Despite its importance, there is no clear, concise, and comprehensive definition of food consumption, loss, and waste. Generally, food consumption, food loss, and food waste are dealt with separately. This article presents a logically constructed ontological framework of food consumption, loss, and waste. It gives equal importance to all three aspects of global food management. The systematic ontological framework is general, and the analysis can be applied to any country. The framework deconstructs the combinatorial complexity of the problem and explicates the pathways to manage the consumption, loss, and waste. The ontological framework encapsulates 19*8*7*4*5*6 = 25,536 possible components of the challenge. A critical analysis based on available data using the framework will help to develop strategies to deal with the problem. It can help to discover the gaps and to find ways to bridge the gaps. It is novel to conceptualize food consumption, loss, and waste together.

When training deep neural networks tasks, the most popular choices are cross-entropy loss. On the other hand, in general speaking, a decent loss function can take on shapes that are considerably more flexible and ought to be adapted for different activities and datasets. In most of the classification tasks, generally if the true class is not correctly recognized by the network (top1), that class is placed among the five classes with the highest probability (top5). This shows that the network does not necessarily recognize the correct class with a low probability, but a class similar to it (such as 3 vs. 8 in mnist) assigns a higher probability and this causes a mistake in that task. Accordingly, we proposed a loss function deals with the error of class that the neural network incorrectly recognized as correct, in addition to the correct class error. We call our proposed loss as False Positive Loss (FPL), with the intention of viewing and designing loss functions not only through the utilization of true class but also through the utilization of the value of false positive classes. One of the core properties of our proposed loss is full adaptability, which makes False Positive Loss be fully capable of getting reformulated by using other widely used loss functions formulas based on the task or the need of the users. Extensive experimental results demonstrate that our suggested loss function outperforms other well-known losses on a variety of tasks and datasets. As can be observed, the performance of our False Positive Loss is superior to that of the cross-entropy loss when it comes to tasks involving 2D picture classification. We have compared our loss with cross entropy as the most common classification loss function on some models (such as ResNet-18, ResNet-50 and Efficientnet-V2) through classification known as a basic computer vision task. with both random or pre-trained initial weights. As a result, in some cases the models with our loss outperform the same tasks with cross entropy from the viewpoint of some metric (i.e. accuracy and FP). For example, the resnet-50 on cifar-10 dataset with random initialization indicated a top1 accuracy of 94.93 with cross entropy and 95.25 with our loss, while for top5 accuracy the results are 99.86 and 99.87, respectively.

Recent evidence has highlighted the critical role of memory cells in maintaining lifelong food allergies, thereby identifying these cells as therapeutic targets. IgG⁺ memory B cells replenish pools of IgE-secreting cells upon allergen exposure, which contract thereafter due to the short lifespan of tightly regulated IgE-expressing cells. Advances in the detection and highly dimensional analysis of allergen-specific B and T cells from allergic patients have provided insight on their phenotype and function. The newly identified Th2A and Tfh13 populations represent a leap in our understanding of allergen-specific T cell phenotypes, though how these populations contribute to IgE memory responses remains poorly understood. Within, we discuss the mechanisms by which memory B and T cells are activated, integrating knowledge from human systems and fundamental research. We then focus on memory reactivation; specifically, on the pathways of secondary IgE responses. Throughout, we identify areas of future research which will help identify immunotargets for a transformative therapy for food allergy.

Together with display and input, storage and processing are requisite to computing. While storage technologies have improved tremendously in capacity and speed over the years, the basic principles enabling information storage into digital 1 and 0 remains the same: induction of phase change in the storage substrate. But recently, there has been much research into structural phase change material (SPCM) and exploration of its possible use in various types of memory storage applications. Despite unconventional use of structural change between amorphous and crystalline state as well as that between crystal structures for encoding information, key barriers for its widespread use remains access speed, capacity to cost ratio, and fidelity of storage. Hence, given the performance requirement of SPCM for memory applications, what are the material and design considerations that feed into translating a promising application into a practical reality? Given the important role of kinetic and thermal energy in structural organization of a phase change material, precise characterization of structural change in the material with external physical factors such as heat, voltage and current, is critical for storage material design. Next comes the precision at which individual memory cells for storing single bits of information could be defined reproducibly and at high fidelity using SPCM. In congruent with memory cell definition lies the equally important aspect of constraining the field characteristics used in modulating the phase state of the memory medium. Specifically, while heat is useful for mediating the “melting” of a crystalline material into its amorphous state, heat conduction is less useful for transferring the “switch command” from the effector to the memory material. More importantly, choice of structural phase change material for memory applications likely revolves around those where individual memory cells could be defined in a cross array format, which is amenable to high density information storage. Durability and fidelity of information storage are additional factors of design that favours selection of SPCM with phase change occurring at narrow operating windows without hysteresis over extended cycling. But, the most important requirement is speed of access. To this end, energetic cost of phase transitions might affect operation of the phase change memory at the system level: for example, usage of large current for high energy transition step may impact on device durability. Ultimately, there is a fundamental limit on the number of reproducible phase transitions in a SPCM before fidelity of information storage is no longer guaranteed. Hence, what are the drivers for uptake of phase change memories in consumer devices? Performance gains must be realized in access speed, storage capacity, form factor, and fidelity of information storage for practical application of structural phase change memories.

Previous studies reported a reduction of the perceived effort and an improvement of the perfor-mances of healthy subjects and patients when a motor task was combined with artistic images with respect to non-artistic images. This phenomenon, called Michelangelo effect, could contribute to the efficacy of art therapy in neurorehabilitation. In this study, the possible occurrence of this effect was tested in a cognitive task by asking to 15 healthy subjects and 17 patients with stroke to solve a digital version of the classical memory card game. Three different types of images were used in a randomized order: French cards, artistic portraits, and photos of famous people (to compensate the possible effects of face recognition). Healthy subjects were involved to test the usability and the load demanding of the developed system, reporting no statistically significant differences among the three sessions (p > 0.05). Conversely, patients had a better performance in terms of time (p = 0.014) and number of trials (p = 0.007) needed to complete the task in presence of artistic stimuli, accompanied by a reduction of the perceived effort (p = 0.033). Furthermore, artistic stimuli, with respect to the other two types of images, seemed more associated to visuo-spatial control than to linguistic functions.

This work investigated the isothermal holding time dependence of the densification, microstructure, weight loss and tensile properties of Fe-Mn-Si powder compacts. Elemental Fe, Mn and Si powder mixtures with a nominal composition of Fe-28Mn-3Si (in weight percent) were ball milled for 5h and subsequently pressed under a uniaxial pressure of 400 MPa. The compacted Fe-Mn-Si powder mixtures were sintered at 1200 ℃ for 0, 1, 2 and 3 h, respectively. In general, the density, weight loss and tensile properties increased with the increase of isothermal holding time. A significant increase in density, weight loss and tensile properties occurred in the compacts isothermally holding for 1 h, as compared to those with no isothermal holding. However, further extension of isothermal holding time (2 and 3 h) only played a limited role in promoting the density and tensile properties. The weight loss of the sintered compacts was mianly caused by the sublimation of Mn in Mn depletion region on the surface layer of the sintered Fe-Mn-Si compacts. The length of the Mn depletion region increased as isothermal holding time increased. A single α-Fe phase was detected on the surface of all the sintered compacts, and the locations beyond the Mn depletion region were comprised of a dual dominant γ-austenite and minor ε-martensite.

The research paper provides a mathematical framework for understanding phase shift in wave phenomena, bridging theoretical foundations with real-world applications. It emphasizes the importance of phase shift in physics and engineering, particularly in fields like telecommunications and acoustics. Key equations are introduced to explain phase angle, time delay, frequency, and wavelength relationships. The study also introduces the concept of time distortion due to a 1° phase shift, crucial for precise time measurements in precision instruments. The research also addresses infinitesimal wave energy loss related to phase shift, enriching our understanding of wave behavior, and impacting scientific and engineering disciplines.

Hearing loss in infancy leads to preventable speech, language, and cognitive developmental delay [1, 2]. Sensorineural hearing loss (SNHL) is caused by damages, problems, or issues related to the inner ear such as the cochlea with or without the auditory nerve; cranial nerve VIII, involvement. There are three anatomic areas which include the outer ear: composed of the auricle and external auditory canal and the middle ear: which includes the tympanic membrane, ossicles, and the middle ear space, the inner ear: composed of the cochlea, semi-circular canals, and internal auditory canals. The unique anatomical shape of the auricle catches the incoming sound waves to send them down the external auditory canal. Hearing risk assessment should be part of all health visits while regular hearing screening checks are done for all children from 4 to 21 years [1, 2]. Assessment of hearing loss includes history, physical examination and specific hearing assessment tests.

Many aging adults experience some form of hearing problems that may arise from auditory peripheral damage. However, it has been increasingly acknowledged that hearing loss is not only a dysfunction of the auditory periphery but results from changes within the entire auditory system, from periphery to cortex. Damage to the auditory periphery is associated with an increase in neural activity at various stages throughout the auditory pathway. Here, we review neurophysiological evidence of hyperactivity, auditory perceptual difficulties that may result from hyperactivity, and outline open conceptual and methodological questions related to the study of hyperactivity. We suggest that hyperactivity alters all aspects of hearing – including spectral, temporal, spatial hearing – and, in turn, impairs speech comprehension when background sound is present. By focusing on the perceptual consequences of hyperactivity and the potential challenges of investigating hyperactivity in humans, we hope to bring animal and human electrophysiologists closer together to better understand hearing problems in older adulthood.

This paper explores indigenous perspectives on the understanding and management of cultural heritage within the Mapungubwe and Makgabeng Cultural landscapes in South Africa. State-based management system paid little attention to indigeneity resulting in contested interpretation of the archaeological past. This by-passing of indigenous input is a product of colonial and apartheid policies that glorified Western practices at the expense of black African heritage stakeholders. World heritage mechanisms are also skewed in favor of modern practice which is overly designed to be sensitive to tangible aspects of the past than the intangible elements that make up much of indigenous African heritage. We used the archaeology of Mapungubwe and Makgabeng Cultural landscapes as a test case for the impact of indigenous voices on heritage, but the results have wider applicability. We employed semi-structured interviews and focus group discussions to gather qualitative data that we calibrated with our lived experiences as products of Tsonga-speakers from this broader landscape. The results expose that indigenous people use physical manifestation of sites, ethnographic inquiry, names, oral history, spirit mediums, landscape memory, genealogy, and praise poetry to identify, interpret, evaluate, and manage their cultural heritage. Additionally, indigenous perspectives which rely on collective memory, ritual practices, and indigenous knowledge systems have huge potential for enriching our understanding of the archaeological heritage. We propose a more inclusive archaeology and heritage management practice that involve indigenous groups in the identification, interpretation, grading, and presentation of the African past.

This study examines the relationship between semantic memory and social cognition in schizophrenia, addressing how these cognitive domains intersect. Semantic memory, which includes general world knowledge and word meanings, was evaluated using verbal fluency tasks and the Camels and Cactus Test. Social cognition, essential for social interaction, was assessed through emotion recognition (Faces Test) and Theory of Mind (Hinting Task). Participants included 50 individuals with schizophrenia and 30 controls. The schizophrenia group showed significantly lower performance on both semantic memory and social cognition tasks. Notably, strong correlations were found between the Camels and Cactus Test and social cognition measures, suggesting that social cognition deficits in schizophrenia may be linked to semantic memory impairments. Regression analyses highlighted that the Camels and Cactus Test significantly predicted social cognition performance, independent of symptomatology. These findings underscore the interconnectedness of semantic memory and social cognition in schizophrenia, suggesting that semantic memory deficits, particularly in non-categorical associations, play a important role in social cognitive impairments. This study provides new insights into the cognitive underpinnings of schizophrenia, emphasizing the need for further research to explore these relationships and their implications for cognitive models and therapeutic interventions.

Understanding the complex relationship between sleep and memory is a major challenge in neuroscience. Many studies on memory consolidation in humans suggest that sleep triggers offline memory processes, resulting in less forgetting of declarative memory and performance stabilization in non-declarative memory. However, issues related to non-optimal experimental designs, task characteristics and measurements, and inappropriate data analysis practices can significantly affect the interpretation of the effect of sleep on memory. In this article, we discuss these issues and suggest constructive solutions to address them. We believe that implementing these solutions in future sleep and memory research will significantly advance this field by improving the understanding of the specific role of sleep in memory consolidation.

To live in the information society means to be surrounded by billions of electronic devices full of sensors that constantly acquire data. This enormous amount of data must be processed and classified. A solution commonly adopted is to send these data to server farms to be remotely elaborated. The drawback is a huge battery drain due to high amount of information that must be exchanged. To compensate this problem data must be processed locally, near the sensor itself. But this solution requires huge computational capabilities. While microprocessors, even mobile ones, nowadays have enough computational power, their performance are severely limited by the Memory Wall problem. Memories are too slow, so microprocessors cannot fetch enough data from them, greatly limiting their performance. A solution is the Processing-In-Memory (PIM) approach. New memories are designed that are able to elaborate data inside them eliminating the Memory Wall problem. In this work we present an example of such system, using as a case of study the Bitmap Indexing algorithm. Such algorithm is used to classify data coming from many sources in parallel. We propose an hardware accelerator designed around the Processing-In-Memory approach, that is capable of implementing this algorithm and that can also be reconfigured to do other tasks or to work as standard memory. The architecture has been synthesized using CMOS technology. The results that we have obtained highlights that, not only it is possible to process and classify huge amount of data locally, but also that it is possible to obtain this result with a very low power consumption.

Sinonasal carcinomas are aggressive neoplasms that present a high morbidity and mortality rate with unfavorable prognosis. This group of tumors exhibits morphological and genetic diversity. Genetic and epigenetic alterations in these neoplasms are currently targets for diagnosis and treatment. The most common type of cancer originated in the sinonasal tract are sinonasal squa-mous cell carcinomas (SNSCC), which presents different histological patterns and variable his-tological aggressiveness. A significant number of alterations have been reported in sinonasal tu-mors, including deficiencies in the SwItch/Sucrose non-fermentable (SWI/SNF) chromatin remod-eling complex. In the sinonasal tract, deficiencies of the subunits SMARCB1/INI1, SMAR-CA4/BRG1 and SMARCA2 have been noted in carcinomas, adenocarcinomas, and soft tissue tu-mors with distinctive high-grade morphology and fatal prognosis. Objective: To identify the status of the SWI/SNF complex using immunohistochemistry in sinonasal squamous cell carci-nomas and its association with morphology and survival. Methods: A total of 1582 SNSCC cases were analyzed, selecting those with high-grade malignancy morphology, which were then evalu-ated immunohistochemically for SMARCB1 and SMARCA4 proteins. Their expression was compared with the biological behavior and survival of the patients. Results: Among the SNSCC, 47% corresponded to the non-keratinizing squamous cell carcinoma (NKSCC) type with high-grade characteristics, 40% were keratinizing squamous cell carcinomas (KSCC), 9% were SMARCB1-deficient carcinomas, and 4% were SMARCA4-deficient carcinomas. Mosaic expres-sion for SMARCB1 (NKSCC 33%, KSCC 21.9%) and SMARCA4 (NKSCC 14.6%, KSCC 12.2%) was identified, showing an impact on tumor size and progression. Conclusions: We identified that the mosaic expression of INI1 in NKSCC exhibited a worse prognosis compared to intact carcinomas and was similar to SMARCB1-deficent carcinoma.

In recent years, many molecular and environmental factors have been studied to understand how synaptic plasticity is modulated. Sleep, as an evolutionary conserved biological function, has shown to be a critical player for the consolidation and filtering of synaptic circuitry underlying memory traces. Although sleep disturbances do not alter normal memory consolidation, they may reflect fundamental circuit malfunctions that can play a significant role in exacerbating diseases, such as autism and Alzheimer’s disease. Very recently, scientists sought to answer part of this enigma and they identified p75 neurotrophic receptor (p75NTR) as a critical player in mediating impairments in hippocampal-dependent associative plasticity upon sleep deprivation. This paper will review the role of the p75NTR, critically discuss the impact and implications of this research as the bridge for sleep research and neurological diseases.

The shape memory alloys belong to the smart materials thanks to their thermomechanical proprieties' reply to thermal or to mechanical loading. These materials can change shape, stiffness, displacement, natural frequency, and many mechanical characteristics in response to stress or to heat such as conduction, convection or radiation. However, heating by convection or conduction are the most useful and studied methods unlike radiation. Therefore, this paper aims to study the radiation effect on the shape memory alloy behavior

The immune response is crucial in Pregnancy Loss (RPL), which affects 1-2% of women. In RPL, peripheral blood lymphocytes and local immune cells are activated with a concomitant decrease in the leukocyte tolerogenic response in the uterus and decidua. IL-17 plays an essential role in T cell activation, weakening tolerogenic responses of the T cells. The report aims to analyze the IL-17 genetic polymorphism, circulating IL-17 lymphocyte populations, the reaction upon lymphocyte priming, the amount of cytokine in serum, and the number of T-reg cells in a group of 50 RPL and 50 normal women from the admixed Venezuelan population. No differences were observed in the genetic polymorphisms rs2275913 and rs763780. A significant decrease (P&lt;0.001) was observed in RPL patients compared to controls in the IL-17+ cell populations CD3+, CD8+, CD56+, and CD4CD25 FoxP3, but not in the CD4IL17+ cells. The response upon cell priming was significantly decreased (P&lt;0.001) in all subpopulations of RPL as compared to controls. It is concluded that despite the increase in IL-17A in the serum of patients, the number of circulating IL-17 cells and the lymphocyte response to stimulation was significantly decreased (p&lt;0.001). No differences in the genetic polymorphism compared to controls suggest that biological factors influence IL-17 levels in RPL patients, which must be carefully studied.

The comparison of group means in latent variable models plays a vital role in empirical research in the social sciences. The present article discusses extensions of invariance alignment and Haberman linking concerning the choice of linking functions for comparisons of many groups. Robust linking functions are proposed for invariance alignment and robust Haberman linking that are particularly suited to item response data under partial invariance. In a simulation study, it is shown that both linking approaches have comparable performance, and in some conditions, the newly proposed robust Haberman linking outperforms invariance alignment.

This paper presents a case study of Fulani herdsmen in Nigeria, whose traditional ethnoveteri-nary practices risk being lost as the country transitions to more intensive and enclosed livestock practices. We use a planetary health framing to make visible the value of indigenous practices that are less damaging to the environment, animal welfare and human health. Through ethnographic observation, focus group discussions (FGD) and key stakeholder interviews, we show that the Fulani use a complex system of herbal medicines and traditional herding practices to maintain herd health, manage and treat animal disease when it arises. However, their traditions often sit uncomfortably with commercial farming practices. As traditional Fulani grazing lands are eroded, dispossessed Fulani take employment from businessmen farmers but both parties’ inex-perience with shed hygiene, artificial feed and less environmentally resilient crossbreeds leads to increased incidence of infectious disease. This in turn drives higher use of antibiotics. There is a ‘causal chain’ of underlying drivers that leads, through poorer environmental, animal and hu-man health, to increased use of antibiotics. The antibiotic resistance that emerges from this chain threatens human health now and into the future. Through a planetary health framing we advocate for more respect for Fulani herdsmen and their traditional ethnoveterinary practices as an alter-native to increasing antibiotic use.

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

Kynurenic acid (KYNA) is an endogenous tryptophan (Trp) metabolite known to possess neuroprotective property. KYNA plays critical roles in nociception, neurodegeneration, and neuroinflammation. A lower level of KYNA is observed in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases or psychiatric disorders such as depression and autism spectrum disorders, whereas a higher level of KYNA is associated with the pathogenesis of schizophrenia. Little is known about the optimal concentration for neuroprotection and the threshold for neurotoxicity. In this study the effects of KYNA on memory functions were investigated by passive avoidance test in mice. Six different doses of KYNA were administered intracerebroventricularly to previously trained CFLP mice and they were observed following 24 hours. High doses of KYNA (i.e., 20-40 μg/2 μl) significantly decreased the avoidance latency, whereas a low dose of KYNA (0.5 μg/2 μl) significantly elevated it compared with controls, suggesting that the low dose of KYNA enhanced memory function. Furthermore, six different receptor blockers were applied to reveal the mechanisms underlying the memory enhancement induced by KYNA. The series of tests revealed the possible involvement of the serotonergic, dopaminergic, α and β adrenergic, and opiate systems in the nootropic effect. The study confirmed that a low dose of KYNA improved a memory component of cognitive domain, which was mediated by, at least in part, four systems of neurotransmission in an animal model of learning and memory.

The role of innate immunity in neutralization of viral infections (including COVID-19) and forming long-lasting and specific immune memory is considered. It is assumed that antiviral protection is generated by the mechanism of RNA interference (RNAi) and is based on the presence of specific viral patterns in the DNA library of the host cells.

We consider a random dynamical system arising in a model of associative memory. This system can be seen as a small (stochastic and deterministic) perturbation of a determinstic system having two weak attractors which are destroyed after the perturbation. We show, with a computer aided proof, that the system has a kind of chaotic itineracy. Typical orbits are globally chaotic, while they spend relatively long time visiting attractor's ruins.

Minimizing optical losses in resonant cavities is crucial for improving photonic device performance. This study focuses on the development of a simulation tool to analyze scattering losses in Fabry-Pérot interferometers (FPI), offering precise modeling of waveguide dynamics and contributing to accurate loss predictions across various platforms. Optical cavities often suffer from scattering losses due to surface roughness and material defects. Our approach integrates theoretical models and simulations to quantify these losses, utilizing the FPI as a model system. We identified upper and lower reflectivity thresholds, beyond which accurate measurement of losses becomes unreliable. For reflectivity below a certain threshold, measurement errors arise, while excessively high reflectivity can reduce fringe visibility and introduce detector sensitivity issues. Simulations were used to validate the model's ability to predict reflectivity and attenuation in waveguides with varying loss levels. The software’s flexibility to adjust transmission parameters for different cavity configurations enhances its utility for a broad range of photonic systems. Our study offers a novel methodology for optical loss analysis, with practical applications in optimizing photonic devices. By providing a reliable tool for precise loss measurement, this work supports advancements in optical technologies, enabling the design of more efficient, high-performance devices across various applications.

Ecological changes evoke many felt losses and types of grief. These affect sustainability efforts in profound ways. Scholarship on the topic is growing, but the relationship between general grief research and ecological grief has received surprisingly little attention. This interdisciplinary article applies theories of grief, loss, and bereavement to ecological grief. Special attention is given to research about “non-death loss” and other broad frameworks about grief. Dynamics related to both local and global ecological grief are discussed. Kinds of potential losses arising out of ecological issues are clarified with the frameworks of tangible/intangible loss, ambiguous loss, nonfinite loss, and shattered assumptions. Various possible types of ecological grief are illuminated by discussing the frameworks of chronic sorrow and anticipatory grief/mourning. Earlier scholarship about disenfranchised ecological grief is augmented by further distinctions about various forms it may take. The difficulties in defining complicated or prolonged grief in ecological context are discussed, and four types of “complicated ecological grief” are explored. Based on the findings, three special forms of ecological loss and grief are named and discussed: transitional loss and grief, lifeworld loss, and shattered dreams. The implications of the results for ecological grief scholarship, counselling and coping are briefly discussed. The results can be used by psychological and healthcare professionals and researchers, but also by members of the public who wish to reflect on their eco-emotions.

Hearing loss stands as the most prevalent sensory deficit among humans, posing a significant global health challenge. Projections indicate that by 2050, approximately 10% of the world's population will grapple with disabling hearing impairment. While approximately half of these cases have a genetic etiology, traditional interventions such as hearing aids and cochlear implants do not completely restore normal hearing. The absence of biological treatment has prompted significant efforts in recent years, with a strong focus on gene therapy to address hereditary hearing loss. Although several studies have exhibited promising recovery of common forms of genetic deafness in mouse models, existing challenges must be overcome to make gene therapy applicable in the near future. Herein, we summarize the primary gene therapy strategies employed over past years, provide an overview of the recent achievements in the preclinical studies for genetic hearing loss, and outline the current key obstacles to cochlear gene therapy.

This study proposes a method for image matching between infrared(IR)-RGB images using a deep learning network to estimate the deformation field. We propose a deformation field generator (DFG) that estimates the deformation field of the transformation matrix to match each pixel or IR image to the RGB image. DFG is a network that receives IR and RGB images as input; the output size is two channels and has the sample resolution as the input image. By warping the IR image through a grid-sampler that warps the image according to the value of the deformation field, we can obtain a warped IR image that matches the RGB image. Additionally, to check whether the warped IR image matched the RGB image, the masking images detecting the segmentation of objects were photographed in two images. Without directly comparing IR and RGB images, we proposed mask loss that warps the IR mask image through the deformation field and grid sampler and then compares the warped IR mask image with the RGB mask image. Mask loss solves the spatial similarity comparison problem with multi-modality images, such as IR and RGB images, by comparing the mask image with the same modality image as the mask image.

In this era of technology, programming has become more significant than ever before. Python and C++ are both widely used programming languages. Python, the most popular programming language in today’s world, is a high-level object-oriented language whereas C++, the language behind most operating systems, is a low-level object-oriented language. In this paper, we present a comparative study of Python and C++. This paper discusses the introduction to these languages, their memory management techniques, and the reasons behind their program execution speed. Furthermore, we analyzed the execution time and memory used by multiple algorithms in both the languages with best, average, and worst cases. They are also compared with respect to the benefits and issues related to them. Results indicate that C++ is faster than Python in execution speed but Python serves as a better language for beginners due to its simplicity. Moreover, for the best results, the language should be selected according to the type of project.

Memory decline is considered a normal part of aging, while the relationship between melatonin deficiency and cognitive function is complex and not fully understood. The present study investigated the role of melatonin deficiency functioning as a hormone at different ages on working and short-term recognition and spatial memory in rats. An age-related decline in memory function was tested in the Y-maze, the object recognition test, and the radial arm maze. Aging was associated with reduced expression of brain-derived neurotrophic factor (BDNF) throughout the hippocampus, whereas pinealectomy exacerbated this process specifically in the CA3 region of 3- and 14-month-old rats. The region-specific reduced expression of the extracellular signal-regulated kinase (ERK)1/2 and pERK1/2 was observed in young adult rats with pinealectomy. However, in middle-aged rats, the expression of these signaling molecules was either downregulated or upregulated in different hippocampal regions. The reduced ratio of pCREB/CREB in the frontal cortex and hippocampus was associated with memory impairment in young adult and middle-aged melatonin-deficient rats, whereas old rats were unaffected. Our study provides insights into the molecular pathways involved in age-related memory changes associated with melatonin deficiency, highlighting the importance of the BDNF / ERK1/2 / CREB pathway in the hippocampus and suggesting a critical period for intervention.

Our study explores the genetic mechanisms underlying the spotted leaf phenotype in rice, focusing on the rpt5a mutant characterized by persistent reddish-brown leaf spots from seedling stage to maturity, leading to extensive leaf necrosis. Through map-based cloning, we localized the responsible locus to a 330Kb region on chromosome 2 and identified LOC_Os02g56000 (named OsRPT5A) as the causative gene. A point mutation in OsRPT5A, substituting valine for glutamic acid, was identified as the critical factor for the phenotype. Functional complementation and generation of knockout lines in the IR64 background confirmed the central role of OsRPT5A in controlling this trait. Our study also revealed that OsRPT5A is constitutively expressed across various tissues, with its subcellular localization unaffected by mutations. Notably, we observed an abnormal accumulation of reactive oxygen species (ROS) in rpt5a mutants, suggesting a disruption in the ROS system. Complementation studies indicated OsRPT5A's involvement in ROS homeostasis and catalase activity regulation. Moreover, the rpt5a mutant exhibited enhanced resistance to Xanthomonas oryzae pv. oryzae (Xoo), highlighting OsRPT5A's role in rice's pathogen resistance mechanisms. These findings elucidate the genetic basis of the spotted leaf phenotype and emphasize the intricate interplay between plant defense responses and developmental pathways.

Normally, the Nernst voltage calculated from the concentration of the reaction gas in the flow channel is considered to be the ideal voltage (reversible voltage) of the oxyhydrogen fuel cell, but actually it will cause a concentration gradient when the reaction gas flows from the flow channel through the gas diffusion layer to the catalyst layer. The Nernst voltage loss in fuel cells in most of the current literature is thought to be due to the difference in concentration of reaction gas in the flow channel and concentration of reaction gas on the catalyst layer at the time when the high net current density is generated. Based on the Butler-Volmer equation in oxyhydrogen fuel cell, this paper demonstrates that the Nernst voltage loss is caused by the concentration difference of reaction gas in flow channel and on the catalytic layer at the time when equilibrium potential (Galvanic potential) of each electrode is generated.

Background: Young people with intellectual developmental disabilities have a persistent delay in the development of executive functions. Virtual reality (VR) is increasingly being used as a cognitive intervention tool, with significant effectiveness demonstrated in different types of populations. Methods: This pilot study aims to investigate the impact of a cognitive training program utilizing VR on young adults diagnosed with intellectual developmental disability (IDD). The participants (N=15) served as their own control group and were assessed three times: weeks 0, 8 and 16, with a rest period (0-8 weeks) and an intervention period (8-16 weeks). The assessments included measures of cognitive function, provided by E-Prime®. Results: Overall, an improvement in working memory and inhibitory control was found after the intervention, but not in sustained attention. Conclusions: These findings suggest that VR-based cognitive training holds promise as an effective intervention for enhancing cognitive abilities in young adults with intellectual developmental disability.

This paper describes a memory model with 2 levels of information. The lower-level stores source data, is Markov-like and unweighted. Then an upper-level ontology is created from a further 3 phases of aggregating source information, by transposing from an ensemble to a hierarchy at each level. The ontology is useful for search processes and the aggregating process transposes the information from horizontal set-based sequences to more vertical typed-based clusters. The base memory is essentially neutral, where any weighted constraints or preferences should be sent by the calling module. This therefore allows different weight sets to be imposed on the same linking structure. The success of the ontology typing is open to interpretation, but the author would suggest that when clustering text, the result was types based more on use and context, for example, 'linking' with 'structure' or 'provide' with 'web,' for a document describing distributed service-based networks. This allows the system to economise over symbol use, where links to related symbols will be clustered together. The author then conjectures that a third level would be more neural in nature and would include functions or operations to be performed on the data, along with related memory information.

Norepinephrine plays an important role in modulating memory consolidation and evocation through its beta-adrenergic receptors (Adrβ: β1, β2 and β3), which are expressed in the hippocampus (HC) and amygdala (AMY). Here we hypothesized that multisensory stimulation would reverse the memory impairment caused by the inactivation of the Adrβ3 with consequent inhibition of sustained glial-mediated inflammation and glutamatergic depuration. To test this, 21- and 86-day-old Adrβ3KO mice and respective controls underwent to (i) gustative and olfactive stimuli of positive and negative valence associated with (ii) intellectual challenges to reach the food in addition to (iii) objects in hidden places (iv) foraging for 8 weeks followed by (v) analysis of GFAP, Iba-1 and EAAT2 protein expression in the HC and AMY. While this protocol restored the memory impairment when applied to Adrβ3KO animals immediately after weaning, it had no effect when applied to adult animals. In fact, we observed that aging worsens the memory of Adrβ3KO mice. Although no significant expression of GFAP and Iba1 were observed in HC of young and old mice, Adrβ3KO increased EAAT2 expression HC of old mice, while MS didn’t change EAAT2 in young mice but enhanced it expression in older. Relative to AMY of old mice Adrβ3KO increased GFAP expression compared whit WT mice and MS sustained the GFAP expression and increased the EAAT2 expression compared with WT group. These results suggest that a richer and more diverse environment helps to correct memory impairment when applied right after weaning Adrβ3KO animals, also show that the changes in GFAP, Iba1 and EAAT2 expression levels in young and old mice indicate a functional significance in the process of learning and memory and that the control of neuroinflammation in limbic areas mediates this response. They also reinforce the idea that disruption of noradrenergic signaling could be involved in the cognitive impairment observed later in life.

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer.

This paper reports a rational design of temperature-responsive nanofiber meshes with shape-memory effect. The meshes were fabricated by electrospinning a poly(ε-caprolactone) (PCL)-based polyurethane with different contents of soft and hard segments. The effects of PCL diol/hexamethylene diisocyanate (HDI)/1,4-butanediol (BD) molar ratio in terms of the contents of soft and hard segments on the shape-memory properties were investigated. Although the mechanical property improved with increasing hard segment ratio, optimal shape-memory properties were obtained with a PCL/HDI/BD molar ratio of 1:4:3. At a microscopic level, the original nanofibrous structure was easily deformed into a temporary shape, and recovered its original structure when the sample was reheated. A higher recovery rate (>89%) was achieved even when the mesh was deformed up to 400%. Finally, the nanofiber meshes were used to control the alignment of human mesenchymal stem cells (hMSCs). The hMSCs aligned well along the fiber orientation. The proposed nanofibrous meshes with the shape-memory effect have the potential to serve as in vitro platforms for the investigation of cell functions as well as implantable scaffolds for wound-healing applications.

Sudden sensorineural hearing loss (SSNHL) is a rapidly developing acquired idiopathic disorder. Differential expression of microRNAs (miRNAs) have been identified in the acute serum of SSNHL patients. miRNAs are transmitted in both serum and plasma but it is unknown which better reflects changes associated with inner ear disease. Therefore, we sought to compare the serum and plasma miRNA expression levels in adult SSNHL patients. We extracted and reverse transcribed total RNA from serum and plasma, and analyzed the product with quantitative re-al-time PCR. hsa-miR-191-5p was used for normalization, and miRNA expression levels were calculated using the delta Ct method. Paired t-tests comparing serum and plasma samples from 17 SSNHL patients (mean age 51.9 years, Std. deviation 13.9 years) showed no significant differences in miR-128-3p, miR-132-3p, miR-375-3p, miR-590-5p, miR-30a-3p, miR-140-3p, miR-186-5p, and miR-195-5p expression levels. We conclude that, plasma and serum are equally suitable for investigating potential miRNA SSNHL disease markers.

Tinnitus is a symptom often associated with hearing loss (HL) and is in many cases more burdening to the individual than the HL itself. Many approaches have been made to explain the development and chronification of the phantom percept as well as different treatment strategies to lower the tinnitus related burden. In many studies, the variance of the HL data is high and therefore the interpretation of specific data might be difficult. With this retrospective study, we attempt to explain a part of this variance by investigating specifically the effects of non-auditory comorbidity categories on pure-tone audiometric data in a tinnitus patient collective that was homogeneous with respect to auditory comorbidities. We found age dependent as well as number of non-auditory comorbidity (e.g. diabetes mellitus) dependent differences in the mean HL of the tinnitus patients as well as differences in the peak HL frequency relative to the tinnitus frequency. The analysis of the age dependent HL within the different non-auditory comorbidities revealed specific – partially opposed – effects of endocrine, circulatory, muscle-skeletal and digestive disease categories on the hearing thresholds of tinnitus patients. Taken together we argue that in future tinnitus (and non-tinnitus) patient studies also non-auditory comorbidities should be taken into account as possible covariables that might explain the variance found in the auditory threshold development of these patients.

Background: Despite tremendous efforts to reduce neonatal mortality worldwide and in 24 Ethiopia, there is the country is one of the countries with highest neonatal morbidity and health 25 losses. Never the less, the past three decades’ national trend and subnational distribution of 26 neonatal morbidity and mortality and its health loss in Ethiopia, was not well documented. 27 Hence, this study had aimed to analyze the trend and distributions as well as health losses 28 resulting from neonatal morbidity in the past three decades in Ethiopia, 2019. 29 Objective: To assess the national trend and sub national distributions of neonatal morbidity and 30 neonatal mortality in the previous three decades by using Global Burden of Diseases, 2019 data. 31 Methods: The data sources were primary and secondary data related to risk factors, morbidity, 32 disability, deaths, Disability Adjusted Life Years, Years Lived with Disability and Years Life Lost 33 were collected form case studies and case series, household surveys, socio-demographic surveys 34 and surveillances, health facilities surveys, Health facility observations and interviews and 35 published and unpublished literatures. The prevalence, deaths and health losses were calculated 36 by using embedded model. The study period covered from 1990 to 2019. Results were presented 37 buy using texts, tables and graphs. 38 Results: Seventy percent of neonatal disorders had occurred among early neonates aged 0 to 6 39 days. Between 1990 and 2019, the national prevalence of neonatal disorders had increased by 40 47% in to 28,319 (95% UI, 25646-30,361). Bet Similarly, the highest number of neonatal deaths 41 caused by neonatal sepsis had shifted from Southern Nations Nationalities and Peoples region to 42 Oromia region while Harari remains to bear the lowest burden in the last three decades. The 43 highest and the lowest YLL as a result of neonatal disorders had occurred in Oromia 44 [(2,753,038,95% UI, 2,184,142-3,497-926) and Amhara 1,360,847 [(95% UI, 95% UI, 45 1,062,660-1,762,028)] regions respectively. Unlike other regions, Addis Ababa had the highest 46 (52%) prevalence of preterm birth in 2019 47 Conclusion and Recommendation: About seven in ten of neonatal death had occurred among 48 early neonates indicating narrowed occurrence of the event. The highest and the lowest number 49 of neonatal death and health loss had occurred in Oromia and Harari regions respectively. The 50 main causes of death were neonatal sepsis and other infections, neonatal preterm, hemolytic 51 disease and other jaundice and neonatal encephalopathy and trauma. Hence, Contextualized, 52 locally accepted innovative early neonatal care is crucial to avert the event.

Hearing impairment is an immensely diagnosed genetic cause, 5% of the total world population effects with different kind of congenital hearing loss (HL). In third-world countries or countries where consanguineous marriages are more common the frequency rate of genetic disorders are at its zenith. Approximately, the incidence of hearing afflictions is ostensibly 7-8:1000 individuals whereas it is estimated that about 466 million peoples suffer with significant HL, and of theses deaf cases 34 million are children’s up to March, 2020. Several genes and colossal numbers of pathogenic variants cause hearing impairment, which aided in next-generation with recessive, dominant or X-linked inheritance traits. This review highlights on syndromic and non-syndromic HL (SHL and NSHL), and categorized as conductive, sensorineural and mixed HL, which having autosomal dominant and recessive, and X-linked or mitochondrial mode of inheritance. Many hundred genes involved in HL are reported, and their mutation spectrum becomes very wide. Mapping of pathogenic genes in consanguinity family is facilitated to understand the disease history. Review presents the bases of HL and also focused on various genetic factors that cause deafness like the basics of genetic inheritance, and classic and well-characterized inherited factors of it. It also overviews the application of linkage analysis, SNPs genotyping and whole exome sequencing methods, in mapping and identification of new locus, causative genes and their variants in families inherited with HL. Conclusively, this review supports researchers in understanding the location of chromosome, the causative genes and specific locus which causing deafness in humans.

The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex — the smallest quantized object in superconductors — can enable drastic miniaturization to the nanometer scale. In this work, we present numerical modeling of such cells using time-dependent Ginzburg-Landau equations. The cell represents a fluxonic quantum dot containing a small superconducting island, an asymmetric notch for vortex entrance, a guiding track, and a vortex trap. We determine the optimal geometrical parameters for operation at zero magnetic field and the conditions for controllable vortex manipulation by short current pulses. We report ultra-fast vortex motion with velocities more than an order of magnitude faster than those expected for macroscopic superconductors. This phenomenon is attributed to strong interactions with the edges of a mesoscopic island, combined with the nonlinear reduction of flux-flow viscosity due to nonequilibrium effects in the track. Our results show that such cells can be scaled down to sizes comparable to the London penetration depth, ∼100 nm, and can enable ultrafast switching on the picosecond scale with ultra-low energy per operation, ∼10−19 J.

The purpose of this study was to analyze the cross-linguistic influence of previously learned languages and working memory capacities on the vocabulary performance of two different typological languages. The objectives of this study were (1) to compare the working memory capacities of bilingual adults in relation to the vocabulary performance of two different languages never learned by the participants, and (2) to analyze to what extent the typology of previously learned languages influences working memory capaci-ties in relation to the vocabulary performance of French and Nahuatl. A group of 43 Mexican-Spanish college students participated in this experimental study. The participants completed a series of working memory tasks in Nahuatl and French. The results showed that working memory capacities were lower in Nahuatl than in French. Thus, a correlation was found between their first and second language and vo-cabulary performance in French. We can consider the influence of previously learned languages as a significant factor in vocabulary acquisition in accordance with the participants' working memory capacities.

The mathematical structure underlying a sentence is made of two independent processing units in series. The conditional probability density function of sentence length (measured in words) recordable in the extended short–term memory (E–STM) buffer made of CF cells is Gaussian. The number of sentences that authors of Italian and English Literatures have written is compared to that theoretically available to them. The multiplicity factor α describes this comparison. α&gt;1 is more likely than α&lt;1 and often α≫1, therefore the same sentence pattern is reused many times. These texts are easier to read. Very few novels show α&lt;1. In these cases, there are enough diverse sentence patters to convey meaning, but most of them are not used. Texts are more difficult to read, as measured by a universal readability index GU. A mismatch index IM synthetically describes the process. α, GU. and IM increase with year of publication. Future work will concern other literatures – including ancient ones as Greek and Latin – to confirm what, in our opinion, is general feature of human mind.

Unlike animals, persistent dynamic environmental perturbations threaten plants, as they are sessile. Plants keep an inbuilt database of all epigenetic episodes during their lifetime. Present knowledge for exploiting information about how the gene network of plants contributes to epigenetic memory needs to be improved, and extensive in-depth studies are needed. Data from epigenetic memories imparts intelligent behaviour to plants, and these memories can be decoded further to investigate how these plants respond when the same abiotic stress recurs. The epigenetic phenomenon refers to gene expression patterns, including vital factors such as nucleic acids (small ribonucleic acids), histones modelling and methylation of DNA. These changes are cell cycle-associated events that may be transmitted to successive generations. Epigenetic memories due to abiotic stress are necessary for maintaining healthy ecophysiological behaviour of plants and impart the tendency to resist and adapt to challenging environmental conditions. These intelligent memories can be stored and interpreted to prepare for future challenges.

This study estimates the effects of double long memory and structural breaks on the persistence level of six major cryptocurrency markets. We apply the Bai and Perron’s structural break test, Inclán and Tiao’s iterated cumulative sum of squares (ICSS) algorithm, and the fractionally integrated generalized autoregressive conditional heteroscedasticity (FIGARCH) model with different distributions. The results show that long memory and structural breaks characterize the conditional volatility of cryptocurrency markets and confirm our hypothesis that ignoring structural breaks leads to an underestimation of the persistence of volatility modelling. The ARFIMA-FIGARCH model with structural breaks and a skewed Student–t distribution fits the cryptocurrency market’s price dynamics well.

Memristors were proposed in the early ’70s of the XXth century by Leon Chua as a new electrical element linking the charge and the flux. Since that first introduction, these devices have positioned themselves to be considered as possibly fundamental for the new generations of electronic devices. It has to be mentioned that actual memristors have only been recognized to exist as physical elements. In this paper, we apply a modeling framework to generate a model describing experimental measurements performed on a ReRAM. We show how to apply the Dynamic Route Map technique in the general case to obtain an approximation to the differential equation that determines the behaviour of the device.

We investigated the role of the human medio-temporal complex (hMT+) in the memory encoding and storage of a sequence of four coherently moving RDKs by applying repetitive transcranial magnetic stimulation (rTMS) during an early or late phase of the retention interval. Moreover, in a second experiment we also tested whether disrupting the functional integrity of hMT+ during the early phase impaired the precision of the encoded motion directions. Overall, results showed that both recognition accuracy and precision were worse in middle serial positions, suggesting the occurrence of primacy and recency effects. We found that rTMS delivered during the early (but not the late) phase of the retention interval was able to impair not only recognition of RDKs, but also the precision of the retained motion direction. However, such impairment occurred only for RDKs presented in middle positions along the presented sequence, where performance was already closer to chance level. Altogether these findings suggest an involvement of hMT+ in the memory encoding of visual motion direction. Given that both position sequence and rTMS modulated not only recognition but also precision of the stored information, these findings are in support of a model of visual short-term memory with a variable resolution of each stored item, consistent with the assigned amount of memory resources, and that such item-specific memory resolution is supported by the functional integrity of area hMT+.

Soil erosion and sediment transport are quite complex processes as they depend on physical, biological, mechanical, and chemical processes within a particular catchment. Therefore, it is highly essential to better explain engaged physical processes and means of accounting for site-specific conditions, for soil loss and sediment yield estimation. This paper mainly focuses on physical explanations behind soil erosion and common soil erosion models like Universal or Revised Universal Soil Loss Equation(USLE/RUSLE) and Modified Universal Soil Loss Equation(MUSLE). Based on the physical explanations and overall limitations, the MUSLE is selected for the application of sediment yield estimation. The main objective of this paper is to estimate the best exponent of the MUSLE, and to estimate the best combination of the exponent and topographic factor of the MUSLE under hydro-climatic conditions of Ethiopia. For the sake of calibration procedure, the main parameters of the MUSLE which directly affect soil erosion process such as cover, conservation practice, soil erodibility, and topographic factors are estimated based on the past experiences from literature and comparative approaches, whereas the other parameters which do not directly affect the erosion process or which have no any physical meaning (i.e coefficient a and exponent b) are estimated through calibration. It is verified that the best exponent of the MUSLE is 1 irrespective of the topographic factor, which results in the maximum performance of the MUSLE (i.e approximately 100\%). For the best combination of the exponent and topographic factor, the performance of the MUSLE is greater than or equal to 80\% for all four watersheds under our consideration, we expect the same for other watersheds of Ethiopia.

(1) Background: Hepatoblastoma is the most common primary liver cancer in children. Nowadays, poor outcome occurs essentially for patients with distant metastases; (2) Methods: Starting from Mammalian Metabolic Enzyme Database over expression of metabolic enzymes was searched in hepatoblastoma tumors as compared to noncancerous liver tissue in transcriptome dataset GSE131329. With overexpressed enzymes, Elasticnet machine learning tuning was performed according to metastasis status outcome. With significant enzymes a metabolic expression score was computed and integrated in multivariate clinical-biological logistic model; (3) Results: Forty-one overexpressed enzymes discriminate hepatoblastoma tumors and noncancerous liver tissues. Eighteen of them predict metastasis status (AUC 0.90) with 85.7% of sensibility and 92.3% of specificity. Elasticnet machine learning model tuning highlighted major importance of DNMT3B and PFKFB4 expression to predict metastasis. Role of these two enzymes was confirmed by univariate analyses with respective p-values: 0.0058 and 0.0091. Metabolic score computed with combined expression of DNMT3B and PFKFB4 discriminates metastasis status and high-risk CHIC score by univariate analysis (p-value=0.005). Meta.score was found more sensitive than C1/C2 classifier to predict metastasis status (accuracy: 0.72 versus 0.55). Integration of meta.score (DNMT3B, PFKFB4) with epidemiological parameters: gender, age at diagnosis, histological types, and clinical PRETEXT stages in a multivariate model confirmed independent adverse role of meta.score to predict metastasis status (multivariate p-value=0.003, odds ratio: 2.12); (4) Conclusions: Based on metabolic enzyme expression program of hepatoblastoma, we characterized a dual overexpression of PFKFB4 and DNMT3B in samples of patients at risk of metastasis (High risk CHIC stratification). With combined tumor expression of DNMT3B and PFKFB4 a meta.score was computed and this parameter was confirmed as an independent adverse score to predict metastatic status during hepatoblastoma.

Cisplatin is an election chemotherapeutic agent for many cancer treatments. Its cytotoxicity against neoplastic cells is mirrored by that taking place in healthy cells and tissues, resulting in serious adverse events. A very frequent one is ototoxicity, causing hearing loss which may permanently affect quality of life after successful oncologic treatments. Exacerbated oxidative stress is a main cytotoxic mechanism of cisplatin, including ototoxicity. Previous reports have shown antioxidant protection against cisplatin ototoxicity, but there is a lack of comparative studies on the otoprotectant activity and mechanism of antioxidant formulations. In this paper we report that a combination of vitamins A, C, E and Mg++ (ACEMg), previously shown to protect against noise-induced hearing loss, reverses auditory threshold shifts, promotes outer hair cell survival, and attenuates oxidative stress in the cochlea after cisplatin treatment, thus protecting against extreme cisplatin ototoxicity in the rat. The addition of 500 mg N-acetylcysteine (NAC), which administered individually also shows significant attenuation of cisplatin ototoxicity, to the ACEMg formulation, results in degradation of ACEMg otoprotection. Mg++ administered alone, as MgSO4, also prevents cisplatin ototoxicity, but in combination with 500 mg NAC, otoprotection also is greatly degraded. Increasing the dose of NAC to 1000 mg, also results in dramatic loss of otoprotection activity compared with 500 mg NAC. These findings support that single antioxidants or antioxidant combinations, particularly ACEMg in this experimental series, have significant otoprotection efficacy against cisplatin ototoxicity. However, an excess of combined antioxidants and/or elevated doses, above a yet to be defined “antioxidation threshold”, results in unrecoverable redox imbalance with loss of otoprotectant activity.

Biodiversity loss poses a significant threat to ecosystems and human well-being globally, with Nigeria no exception. This article delves into the architectural implications of biodiversity loss in Nigeria, exploring the multifaceted factors contributing to this phenomenon and its ramifications for architectural practice. The discussion encompasses the adverse impacts of deforestation, pollution, and climate change on Nigeria's rich biodiversity and the challenges and opportunities they present for architects. Through examining case studies and examples, the article highlights innovative architectural projects that address biodiversity loss while promoting sustainable design principles. Moreover, it elucidates the importance of interdisciplinary collaboration between architects, biologists, and policymakers in developing effective mitigation and adaptation strategies. Ultimately, this article underscores the urgent need for architects to integrate biodiversity conservation into their practices and advocates for policy interventions that prioritise the preservation of Nigeria's natural heritage.

Quantum cryptography revolutionizes secure information transfer, providing defense against both quantum and classical computational attacks. The primary challenge in extending the reach of quantum communication comes from the exponential decay of signals over long distances. Addressing this challenge, we have developed Quantum-protected Control-based Key Distribution (QCKD), overcoming transmission range limitations by monitoring signal leakages and rendering leaked quantum states significantly non-orthogonal. This paper reports on the experiments with the QCKD, conducted over a 1,707 km fiber line. We demonstrate advancements in critical components of the protocol, showing its remarkable scalability and adaptability for extended distances and multi-user configurations.

Liquidity providers for asset-pair pools with constant value distri- bution, such as Balancer pools, experience some impermanent loss whenever there is price divergence in the tokens’ fiat values. In this paper, we model this impermanent loss for geometric mean invariant pools by deriving a function of two parameters. We analyze our function graphically, showing that unevenly distributed pools with large weighting disparities best mitigate the risk of impermanent loss. We conclude by providing a general protocol for liquidity providers to choose a suitable pool based on their risk tolerance and profit goals.

Burn injury serves as an example of a condition with a robust inflammatory response. The elevation of circulating interleukins (IL)- 1 beta and -6 in children with severe burn injury up-regulate the parathyroid calcium sensing receptor (CaSR) resulting in hypocalcemic hypoparathyroidism with urinary calcium wasting. This effect protects the body from the hypercalcemia resulting from bone resorption liberating calcium into the circulation. Extracellular calcium can exacerbate and prolong the inflammatory response by stimulating mononuclear cell chemokine production as well as the NLRP3 inflammasome of the innate immune system, resulting in increased IL-1 production by monocytes and macrophages. Interestingly, the CaSR response to inflammatory cytokines disappears with age, potentially trapping calcium from bone resorption in the circulation and allowing it to contribute to increased inflammation and possibly increased calcium deposition in small arteries, , such as the coronaries, as conditions with increased chronic inflammation, such as spinal cord injury, osteoarthritis and rheumatoid arthritis have an incidence of cardiovascular disease and coronary artery calcium deposition significantly higher than the unaffected age-matched population.