Activation Process Research Articles

Nanomotion technology: an innovative method to study cell metabolism in Escherichia coli, as a potential indicator for tolerance

Introduction. Antibiotic tolerance corresponds to the bacterial ability to survive a transient exposure to antibiotics and is often associated with treatment failure. Current methods of identifying tolerance based on bacterial growth are time-consuming. This study explores the use of a growth-independent method utilizing nanomotion technology to detect antibiotic-tolerant bacteria. Hypothesis. The nanomotion signal obtained from a nanomechanical sensor measures real-time metabolic activity and cellular processes and could provide valuable information about the tolerance of bacteria to antibiotics that cannot be detected by standard antibiotic susceptibility tests. Aim. The aim of this study is to investigate the potential of nanomotion technology to record antibiotic-tolerant bacteria. Methodology. We generated a slow-growing Escherichia coli strain by manipulating mazF expression levels and confirmed its viability by several standard methods. We subsequently measured its nanomotion and the nanomotion of the WT E. coli in the presence or absence of antibiotics. Supervised machine learning was employed to distinguish slow-growing from exponentially growing bacteria. Observations for bacterial nanomotions were confirmed by standard kill curves. Results. We distinguished slow-growing from exponentially growing bacteria using specific features from the nanomotion signal. Furthermore, the exposition of both growth phenotypes to polymyxin decreased the nanomotion signal indicating cell death. Similarly, when exponentially growing cells were exposed to ampicillin, an antibiotic whose efficacy depends on the growth rate, the nanomotion signal also decreased. In contrast, the nanomotion signal remained unchanged for slow-growing bacteria upon exposure to ampicillin. In addition, antibiotic exposure can cause bacterial elongation, in which the biomass of a cell increases without cell division. By overexpressing sulA, we mimicked antibiotic-induced elongation. Differences in the nanomotion signal were observed when comparing elongating and non-elongating phenotypes. Conclusion. This work shows that nanomotion signals entail information about the reaction to antibiotics that standard MIC-based antibiotic susceptibility tests cannot detect. In the future, nanomotion-based antibiotic tolerance tests could be developed for clinical use in chronic or relapsing infections.

Isolated FeN3 sites anchored hierarchical porous carbon nanoboxes for hydrazine‐assisted rechargeable Zn‐CO2 batteries with ultralow charge voltage

AbstractZn‐CO2 batteries (ZCBs) are promising for CO2 conversion and electric energy release. However, the ZCBs couple the electrochemical CO2 reduction (ECO2R) with the oxygen evolution reaction and competitive hydrogen evolution reaction, which normally causes ultrahigh charge voltage and CO2 conversion efficiency attenuation, thereby resulting in ~90% total power consumption. Herein, isolated FeN3 sites encapsulated in hierarchical porous carbon nanoboxes (Fe‐HPCN, derived from the thermal activation process of ferrocene and polydopamine‐coated cubic ZIF‐8) were proposed for hydrazine‐assisted rechargeable ZCBs based on ECO2R (discharging process: CO2 + 2H+ → CO + H2O) and hydrazine oxidation reaction (HzOR, charging process: N2H4 + 4OH− → N2 + 4H2O + 4e−). The isolated FeN3 endows the HzOR with a lower overpotential and boosts the ECO2R with a 96% CO Faraday efficiency (FECO). Benefitting from the bifunctional ECO2R and HzOR catalytic activities, the homemade hydrazine‐assisted rechargeable ZCBs assembled with the Fe‐HPCN air cathode exhibited an ultralow charge voltage (decreasing by ~1.84 V), excellent CO selectivity (FECO close to 100%), and high 89% energy efficiency. In situ infrared spectroscopy confirmed that Fe‐HPCN can generate rate‐determining *N2 and *CO intermediates during HzOR and ECO2R. This paper proposes FeN3 centers for bifunctional ECO2R/HzOR performance and further presents the pioneering achievements of ECO2R and HzOR for hydrazine‐assisted rechargeable ZCBs.

Neuronal Multi Unit Activity Processing with Metal Oxide Memristive Devices

AbstractIntra‐cortical brain‐machine interfaces (BMIs), able to decode neural activity in real‐time, represent a revolutionary opportunity for treating medical conditions. However, traditional systems focusing on single‐neuron spike detection require high processing rates and power, hindering the up‐scaling for neurons‐population monitoring in clinical application. An intriguing proposition is the memristive integrating sensor (MIS) approach, which uses resistive RAM (RRAM) for threshold‐based neural activity detection. MIS leverages analogue multi‐state switching properties of metal‐oxide RRAM to compress neural inputs by encoding above‐threshold events in resistance displacement, facilitating efficient data down‐sampling in the post‐processing, enabling low‐power, high‐channel systems. Initially tested on spikes and local field potentials, here MIS is adapted to process multi‐unit activity envelope (eMUA)—the envelope of entire spiking activity—which has recently been proposed as crucial input for real‐time neuro‐prosthetic control. Prior necessary modifications to the MIS for effective operation, this adaptation achieved over 95% sensitivity across two types of metal‐oxide devices: Pt/TiOx/Pt and TiN/HfOx/TiN, proving its platform‐agnostic capabilities. Furthermore, towards the integration of MIS with silicon chips, it is shown that it can reduce total system power consumption to below 1 µW, as RRAM encoding stage relaxes the signal preservation and noise requirements that challenge traditional complementary metal‐oxide‐semiconductor (CMOS) front‐ends. This eMUA‐MIS adaptation offers a viable pathway for developing more scalable and efficient BMIs for clinical use.

Machine Learning-Assisted Bayesian Optimization for the Discovery of Effective Additives for Dendrite Suppression in Lithium Metal Batteries.

In the pursuit of enhancing the performance and safety of lithium (Li)-metal batteries, the discovery of effective electrolyte additives to suppress Li dendrites has emerged as a paramount objective. In this study, we employ an inverse design strategy to identify potential additives for dendrite mitigation. Two key mechanisms, namely, the formation of robust solid electrolyte interphase layers and the leveling mechanism, serve as the foundation for our investigation. Our inverse design strategy is guided by molecular properties such as the lowest unoccupied molecular orbital energy and interaction energy upon Li surface adsorption. An active learning process utilizing Bayesian optimization (BO) was utilized to identify potential molecules with ideal properties. Through this screening process, we uncover a collection of 62 molecules with the potential to act as SEI-forming additives, along with 106 molecules for leveling additives, both surpassing the performance of established additives reported in the literature. This work highlights the potential of BO methods in computationally based inverse design of materials for many applications, and the discovered additives could potentially boost the commercialization of Li-metal batteries.

Investigating the average kiloelectron-volt emission of partially observed events in nuclear physics through distance weighted mean based censored control chart.

The average lifespan of particles, a crucial parameter in nuclear physics, is essential for identification purposes. Modern particle detectors excel at recognizing individual radioactive nuclei arrivals and their subsequent decay events. However, challenges arise when matching arrivals with departures, especially when departures are only partially observed. One inefficient approach involves conducting experiments with very low arrival rates to facilitate matching. The kiloelectron-volt E(keV) emission is obtained during this radio active process. This study focuses on the meticulous surveillance of the average keV emission from partially observed events within the domain of nuclear physics. To accomplish this, the methodology employs the statistical approach known as Distance Weighted Mean (DWM), integrated with the application of censored control charts. The utilization of censored control charts allows for the effective management of incomplete data, enabling researchers to make informed decisions despite potential limitations in observation. We propose a DWM based exponentially weighted moving average-cumulative sum (DWM-EC) control chart for monitoring kiloelectron-volt E(keV) data. The proposed charts is developed for Weibull lifetimes under type-I censoring. For the construction of an efficient control charting structure, we employed the conditional median (CM) methods. The goal is to find changes in the mean of Weibull lifetimes with censored data with known and estimated parameter conditions. The performance of the proposed DWM-EC chart is evaluated by the average run length (ARL). Besides a simulation study, a real-life data set on E(keV) related to the alpha decays of 177 Lutetium isotope is also discussed.

Numerical modeling the process of deep slab dehydration and magmatism

This study uses a 2D high-resolution thermo-mechanical coupled model to investigate the dynamic processes of deep plate hydration, dehydration, and subsequent magmatic activity in ocean-continent subduction zones. We reveal the pathways and temporal evolution of water transport to the deep mantle during the subduction process. Plate dehydration plays a critical role in triggering partial melting of the deep mantle and related magmatic activity. Our study shows significant differences in the volumes of melt produced at different depths, with dehydration reactions in deeper regions being weaker compared to shallower ones. It takes a longer time to reach the suitable P-T conditions for hydrous melting in the deep mantle. The results highlight the geophysical significance of water transport in deep subduction zones and its role in magmatic processes, particularly in the formation of magma chambers beneath continental plates.

Self-care behaviours in patients with restless legs syndrome (RLS): development and psychometric testing of the RLS-Self-care Behaviour questionnaire.

Restless legs syndrome (RLS) is a highly prevalent condition that significantly disrupts sleep and causes reduced quality of life. While previous RLS research has mainly focused on the pharmacological treatment, this study presents the first instrument to measure self-care, the RLS-Self-care Behaviour questionnaire (RLS-ScBq). Self-care, defined as an active decision-making process, can empower patients to effectively participate in their own healthcare through awareness, self-control, and self-reliance to cope with their disease. Self-care can in a RLS context include actions such as physical exercise, meditation, and massage. Hence, the aim of this study is to explore the psychometric properties of the RLS-ScBq in patients with RLS. A cross-sectional design, including 788 patients with RLS (65% women, mean age 70.8 years, [standard deviation (SD) =11.4]) was used. Sociodemographics, comorbidities, and RLS-related treatment data, including insomnia symptoms (i.e., Insomnia Severity Index), daytime sleepiness (i.e., Epworth Sleepiness Scale) and RLS symptoms (i.e., RLS-6 scale) were collected. The validity and reliability of the RLS-ScBq were investigated using exploratory factor analysis and Rasch models. The two-factor solution (i.e., physical, and mental actions) showed an explained variance of 32.33% for The Self-care Behaviour Frequency part and 36.28% for The Benefit of Self-care Behaviour part. The internal consistency measured by Cronbach's α was 0.57 and 0.60, and McDonald's ω was 0.60 and 0.67, respectively. No differential item functioning was identified for gender, age, insomnia, daytime sleepiness, or RLS severity. The eight-item RLS-ScBq can serve as a tool enabling healthcare personnel to explore use and benefit of self-care activities in patients with RLS.

Insights into Martian bedform migration: Results from Gale, Jezero and Pasteur craters

AbstractAn attempt is made in this study to advance the understanding of the sand movement on Mars by studying the bedform migration at Gale, Jezero and Pasteur craters. The study on the grain size distribution at Gale Crater using Curiosity rover (MAHLI and APXS) observations reveals that the grains with smaller diameters (~50–150 μ) are more prone to migration and vice‐versa, which gives an idea of the necessary requirements that initiate bedform migration. The chemical analysis of the surface materials at the Gale crater revealed elevated concentrations of P2O5, SO3, Cl and Zn in soil compared to sand and active transportation processes for sand but not soil. The comprehensive chemical makeup of the Martian soil (inactive bedforms) and sand (active bedforms) is characterized by its basaltic nature, with enriched volatile elements such as sulphur, chlorine and zinc, and the presence of minerals like plagioclase, pyroxene and olivine due to the cohesive nature of inactive bedforms. Physical weathering and wind flow velocity play a pivotal role in the formation of different sedimentary bodies, impacting grain size distribution and mineralogy. The effect of dust‐lifting on surface features is studied by analysing Perseverance‐MEDA observations at the Jezero crater to understand the short‐term changes in the bedform. These events are found to involve the redistribution of only a small amount of materials and, thereby, changing surface features on Mars over a short period. To detect the bedform migration in the Pasteur crater, several HiRISE images acquired over different time intervals were used. The changes in the ripple crest (~0.29–1.18 m/Earth year) and dune slip face suggest new grain flow events. In the Pasteur crater, extensive changes in sand deposits near the dunes signify a widespread bedform migration. The stronger north‐westerly and north‐easterly winds dominate these changes. Thus, the bedform migration in the three tropical craters exhibits significant variability driven by localized aeolian processes. This variability is crucial for understanding Mars' geological history, current surface dynamics and eventually, helps in planning future missions.

A highly-efficient peroxymonosulfate activator using a sewage sludge derived biochar supported cobalt oxide: Mechanism and characteristics

The application of biochar derived from sewage sludge (BS) in Fenton-like reactions for contaminant removal has attracted considerable attention. Herein, a BS-supported Co3O4 (BS-Co3O4) catalyst was synthesized using a simple two-step hydrothermal-calcination process to achieve highly efficient activation of peroxymonosulfate (PMS). The introduction of biochar effectively inhibited the aggregation of Co3O4 and reduced cobalt leaching. Compared to Co3O4, the mesoporous BS-Co3O4 exhibited an 8-fold increase in specific surface area (SSA) to 111.92 m2∙g−1, and a 46 %-66.4 % reduction in crystal plane size. The interaction between biochar and cobalt oxide resulted in several notable enhancements in the BS-Co3O4 composite. Specifically, there was an increase in sp2 carbon content, a 42.69 % rise in capacitance, and a 79.99 % reduction in charge transfer resistance. These improvements contributed to enhanced PMS activation performance. The BS-Co3O4 also contained a higher content of Co(II), sp2 carbon, and oxygen vacancies (OV). Co(II) played a crucial role in the redox reaction, accelerating the formation of SO4•− and 1O2 during the PMS activation process. Additionally, OV acted as electron capture centers, further promoting the generation of 1O2. Evidence from reactive species investigations and electron paramagnetic resonance (EPR) tests indicated that SO4•− and 1O2 were the main reactive radicals for eliminating tetracycline (TC). Based on the identification of TC intermediates, two potential degradation pathways were proposed, and a reduction in intermediate toxicity was observed. Experiments involving interference and repetition demonstrated that the BS-Co3O4 catalyst was stable and reusable. This work provides a solution with low metal leaching, high recycling performance, and safety for antibiotic wastewater treatment.

Perylene diimide configuration to construct g-C3N4 and iron phthalocyanine composite catalyst as PMS activator for contaminant degradation

The photocatalysis-assisted peroxymonosulfate (PMS) activation process has shown great potential for organic wastewater treatment. Herein, the composite photocatalyst (FeTAPc/PDI/CN) was obtained by grafting graphitic carbon nitride (CN) and amino iron phthalocyanine (FeTAPc) onto both ends of perylene tetracarboxylic anhydride (PTCDA) through an imidization process. Under simulated solar irradiation, the catalyst exhibited a remarkable degradation performance toward the target pollutant by activated PMS over a wide pH range from 3 to 11. The degradation efficiency of SMX reached 100 % in 7 min with the reaction kinetic constant of 0.845 min−1, which was 13 times higher than that of pure CN. In addition, the removal rate of FeTAPc/PDI/CN-4 for SMX was more than 80 % after 9 cycles. The introduction of phthalocyanine and PDI improved the separation efficiency of photogenerated electron-hole pairs. Furthermore, the active species involved in the SMX degradation were identified as •OH, SO4•-, •O2– and 1O2. The possible degradation pathways were proposed based on the identified intermediates, in which the oxidation of the amino group on the benzene ring was the main degradation pathway. This study is anticipated to result in a favorable method for designing CN-based photocatalysts with high stability and excellent catalytic activity for environmental remediation.

Strong Lewis Electron-Pair Bonding in Vanadium Oxide for Ultra-fast and Long-term Stable Zn-ion Storage

Vanadium-based oxides typically show low electrical conductivity, high repulsion for Zn2+, and severe structure collapse problems, resulting in unsatisfied cathode performance for aqueous Zn-ion batteries (AZIBs). Herein, we propose an advanced structural optimization strategy to address the above issues by constructing strong Lewis electron-pair bonding in vanadium oxide through initial doping Ca and a subsequent in-situ electrochemical activation process. We prepared the precursor of Ca-doped and amorphous carbon-encapsulated V2O3 material (Ca0.17V2O3-x@C) and verified a phase transition into a layered V2O5-typed cathode during the activation process. Importantly, we find the initial-doped Ca and generated abundant oxygen vacancy defects are well restored in the phase-transformed crystal lattice. We reveal that band and bond structures of the phase-transformed cathode are optimized, exhibiting an improved electrical conductivity, optimal Zn2+ binding energy, ultra-low Zn-ion transport barrier, and considerably strong bonds of Ca-O and V-O, thereby realizing enhanced reaction kinetics and stability. The Ca0.17V2O3-x@C exhibits surprisingly high-rate performance (233 mAh g–1 at 40 A g–1) and excellent cycling stability (10,000 cycles with 82% retention at 20 A g–1). This work offers a novel and simple band and bond structure engineering strategy for preparing high-performance phase transformation typed vanadium oxide cathodes for AZIBs.

Integrated bulk and single-cell profiling characterize sphingolipid metabolism in pancreatic cancer

BackgroundAbnormal sphingolipid metabolism (SM) is closely linked to the incidence of cancers. However, the role of SM in pancreatic cancer (PC) remains unclear. This study aims to explore the significance of SM in the prognosis, immune microenvironment, and treatment of PC.MethodsSingle-cell and bulk transcriptome data of PC were acquired via TCGA and GEO databases. SM-related genes (SMRGs) were obtained via MSigDB database. Consensus clustering was utilized to construct SM-related molecular subtypes. LASSO and Cox regression were utilized to build SM-related prognostic signature. ESTIMATE and CIBERSORT algorithms were employed to assess the tumour immune microenvironment. OncoPredict package was used to predict drug sensitivity. CCK-8, scratch, and transwell experiments were performed to analyze the function of ANKRD22 in PC cell line PANC-1 and BxPC-3.ResultsA total of 153 SMRGs were acquired, of which 48 were linked to PC patients’ prognosis. Two SM-related subtypes (SMRGcluster A and B) were identified in PC. SMRGcluster A had a poorer outcome and more active SM process compared to SMRGcluster B. Immune analysis revealed that SMRGcluster B had higher immune and stromal scores and CD8 + T cell abundance, while SMRGcluster A had a higher tumour purity score and M0 macrophages and activated dendritic cell abundance. PC with SMRGcluster B was more susceptible to gemcitabine, paclitaxel, and oxaliplatin. Then SM-related prognostic model (including ANLN, ANKRD22, and DKK1) was built, which had a very good predictive performance. Single-cell analysis revealed that in PC microenvironment, macrophages, epithelial cells, and endothelial cells had relatively higher SM activity. ANKRD22, DKK1, and ANLN have relatively higher expression levels in epithelial cells. Cell subpopulations with high expression of ANKRD22, DKK1, and ANLN had more active SM activity. In vitro experiments showed that ANKRD22 knockdown can inhibit the proliferation, migration, and invasion of PC cells.ConclusionThis study revealed the important significance of SM in PC and identified SM-associated molecular subtypes and prognostic model, which provided novel perspectives on the stratification, prognostic prediction, and precision treatment of PC patients.

디지털 매체 및 인공지능 기반의 과정 중심 음악창작 수업 모형 제안

This study aims to provide specific procedures and core teaching-learning contents for music education in school while suggesting an instructional model for process-centered music creation employing digital media and artificial intelligence. To this end, the researcher reviews the several instructional models for music creation in the field of music education, and identifies the key process for digital media and AI-based music activities. Based on the finding, the researcher introduces a novel instructional model for process-centered music creation based on digital media and AI tools. Furthermore, in the light of the instructional model, the researcher demonstrates how music textbooks can be effectively reorganized using digital media and AI tools. Additionally, the researcher proposes specific educational suggestions for teaching and learning activities and process-centered evaluation, thereby contributing to enhance the quality of music creation process for music education in school.

Welfare ruination, debris and Europeans’ make-shift practices: A research agenda

The mainstream literature on welfare retrenchment, austerity and post-welfare states proposes that since the mid 1970s, welfare states are in a state of ‘permanent austerity’. To offer an alternative to such stories of loss and their problematic nostalgic retelling of recent European history, leaving out its imperial histories, this article proposes to look instead at welfare as the object of ruination. This approach builds on literatures that focus on the experience of austerity and institutional violence to take a step further towards everyday responsive practices. Building on the work of Ann Laura Stoler, studying ruination and debris opens up the possibility to look at (1) the long durée of the colonial heritage of welfare, (2) ruination as an active and violent process and (3) welfare debris and how European dwellers use it and live in it. For Europeans, living amidst ruins and in a time of ruination, then, the question is: how can they build a life with the welfare debris that they are left with? Who is able to navigate the ruins and who is not? For researchers, this perspective can inform a new research agenda: a redirection of attention from welfare as ‘organized provision’ to, instead, how people organize the provisional, improvising with the rubble that remains.

Development of a concept of combined project-production activities planning using digital twins

The object of the study is the project-production activity of project-oriented enterprises in the fields of mechanical engineering, aircraft construction, shipbuilding, instrumentation, and metallurgy. The problem addressed was the informational integration and synchronization of project management processes, implemented by a specific project-oriented enterprise, with the management of its production processes using digital twins. The solution to this problem is aimed at improving the efficiency of project-production management by utilizing digital twins of projects and production in the planning process. The influence of trends on the development of digital technologies was analysed. The need to consider both project and operational activities of enterprises in mechanical engineering, aircraft construction, shipbuilding, instrumentation, and metallurgy as a single project-production activity was identified. It was shown that this approach can be successful when processes and changes in project-production activity can be modelled, forming a rational plan for product release and project execution. The use of digital twins for objects and processes in project-production activities was proposed. The aim and objectives of the study were formulated, focusing on the creation of a concept for planning project-production activities using digital twins for objects and processes in both project and operational activities. The concept defines the structure of the digital environment for project-oriented production, a model of the interpenetration of project and production planning processes, an aggregated critical path method, and simulation modelling for planning project-production activities using digital twins. It was shown that to model project-production activities, models and methods for managing the interaction between the operational and project processes of the company must be applied. It was proposed to use the scientific and practical tools of information interaction theory to manage this interaction. The results of applying the concept in project-oriented companies were demonstrated. The use of tools created based on the proposed concept allowed a reduction in project execution time by 10–15 % and a decrease in production costs by 5–10 % due to effective planning of project-production processes The object of the study is the project-production activity of project-oriented enterprises in the fields of mechanical engineering, aircraft construction, shipbuilding, instrumentation, and metallurgy. The problem addressed was the informational integration and synchronization of project management processes, implemented by a specific project-oriented enterprise, with the management of its production processes using digital twins. The solution to this problem is aimed at improving the efficiency of project-production management by utilizing digital twins of projects and production in the planning process. The influence of trends on the development of digital technologies was analysed. The need to consider both project and operational activities of enterprises in mechanical engineering, aircraft construction, shipbuilding, instrumentation, and metallurgy as a single project-production activity was identified. It was shown that this approach can be successful when processes and changes in project-production activity can be modelled, forming a rational plan for product release and project execution. The use of digital twins for objects and processes in project-production activities was proposed. The aim and objectives of the study were formulated, focusing on the creation of a concept for planning project-production activities using digital twins for objects and processes in both project and operational activities. The concept defines the structure of the digital environment for project-oriented production, a model of the interpenetration of project and production planning processes, an aggregated critical path method, and simulation modelling for planning project-production activities using digital twins. It was shown that to model project-production activities, models and methods for managing the interaction between the operational and project processes of the company must be applied. It was proposed to use the scientific and practical tools of information interaction theory to manage this interaction. The results of applying the concept in project-oriented companies were demonstrated. The use of tools created based on the proposed concept allowed a reduction in project execution time by 10–15 % and a decrease in production costs by 5–10 % due to effective planning of project-production processes

Sleep-related epilepsies in the course of development in childhood

AbstractEpilepsy, as one of the most prevalent neurological diseases in childhood, has a strong reciprocal relationship with sleep. Sleep-associated epilepsy syndromes in childhood are mostly genetic and can be divided into (a) the group of self-limited focal epilepsies of childhood including self-limited epilepsy with autonomic seizures (SeLEAS) and self-limited epilepsy with centrotemporal spikes (SeLECTS) and (b) (non-self-limited) sleep-related hypermotor epilepsy (SHE). Sleep-accentuated (developmental and) epileptic encephalopathies (DEE/EE-SWAS, Landau–Kleffner syndrome [LKS]) are either genetic (possible transition from SeLEAS or SeLECTS) or structural, and they are characterized by continuous bilateral focal or generalized epileptic activity throughout the night with a clinical manifestation of stagnation or regression, in particular of cognition (verbal agnosia in LKS). Epilepsy syndromes with increased seizure frequency after sleep deprivation or with seizures in the transition to awakening include juvenile generalized epilepsy syndromes such as epilepsy with generalized tonic-clonic seizures alone (GTCA) or juvenile myoclonic epilepsy (JME), but also SeLECTS. Sleep is a very active process: Regeneration, reorganization, and consolidation of memory facilitate development and cognitive functioning. Epilepsy can alter sleep architecture and vice versa, which can appear as a vicious circle in epilepsies that are sleep related. Macrostructural elements of sleep such as sleep efficiency, sleep onset latency, wakefulness after sleep onset, REM and non-REM sleep fraction, as well as microstructural sleep elements such as slow-wave activity, slope of slow waves, cyclic alternating pattern (CAP), and physiological sleep figures, are important biomarkers with which to understand clinical symptoms such as cognitive stagnation and regression, to monitor treatment, but also to determine prognostic factors and will be an important tool for future studies.

The Critical Thing about the Ear's Sensory Hair Cells.

The capabilities of the human ear are remarkable. We can normally detect acoustic stimuli down to a threshold sound-pressure level of 0 dB (decibels) at the entrance to the external ear, which elicits eardrum vibrations in the picometer range. From this threshold up to the onset of pain, 120 dB, our ears can encompass sounds that differ in power by a trillionfold. The comprehension of speech and enjoyment of music result from our ability to distinguish between tones that differ in frequency by only 0.2%. All these capabilities vanish upon damage to the ear's receptors, the mechanoreceptive sensory hair cells. Each cochlea, the auditory organ of the inner ear, contains some 16,000 such cells that are frequency-tuned between ∼20 Hz (cycles per second) and 20,000 Hz. Remarkably enough, hair cells do not simply capture sound energy: they can also exhibit an active process whereby sound signals are amplified, tuned, and scaled. This article describes the active process in detail and offers evidence that its striking features emerge from the operation of hair cells on the brink of an oscillatory instability-one example of the critical phenomena that are widespread in physics.

Predictive brain activity related to auditory information is associated with performance in speech comprehension tasks in noisy environments

IntroductionUnderstanding speech in noisy environments is challenging even for individuals with normal hearing, and it poses a significant challenge for those with hearing impairments or listening difficulties. There are limitations associated with the current methods of evaluating speech comprehension in such environments, especially in individuals with peripheral hearing impairments. According to the predictive coding model, speech comprehension is an active inference process that integrates sensory information through the interaction of bottom-up and top-down processing. Therefore, in this study, we aimed to examine the role of prediction in speech comprehension using an electrophysiological marker of anticipation: stimulus-preceding negativity (SPN).MethodsWe measured SPN amplitude in young adults with normal hearing during a time-estimation task with auditory feedback under both quiet and noisy conditions.ResultsThe results showed that SPN amplitude significantly increased in noisy environments. Moreover, individual differences in SPN amplitude correlated with performance in a speech-in-noise test.DiscussionThe increase in SPN amplitude was interpreted as reflecting the increased requirement for attentional resources for accurate prediction of speech information. These findings suggest that SPN could serve as a noninvasive neural marker for assessing individual differences in top-down processing involved in speech comprehension in noisy environments.

A Study on Employee Job Satisfaction

Employee job satisfaction is a critical factor in organizational success, impacting productivity, retention, and overall morale in the workplace. This study examines the multifaceted components of job satisfaction, including internal factors such as recognition, career advancement, and work-life balance, as well as external elements such as compensation and work environment. Thanks to the approach of mixed methods, such as public opinion polls and interviews with various employees in various industries, this survey determines important factors in work satisfaction and an increase in satisfactory employees and organizational indicators. We emphasize the correlation. The results indicate that the aggressive culture in the workplace and the active processing of employee reviews is an important strategy to increase the satisfaction of work. The study highlights the importance of continually evaluating and adapting workplace policies to meet the changing needs of employees, which ultimately contributes to sustainable organizational growth and employee well-being.

Fe oxide modification of yerba mate waste-derived biochar and activated biochar via three methodologies: Effects of material surface properties on the Fe oxides grown and implications for paracetamol and atenolol sorption

This work presents, for the first time, the development of magnetic composites using activated biochar (BC-Act) derived from yerba mate waste. It includes an analysis of the effect of the activation process on the formation of iron oxides using the most applied methodologies, an aspect that has not been studied before. Three methodologies have been considered for Fe oxides grown: coprecipitation (COP), impregnation-pyrolysis (IP), and alkaline oxidation in the presence of nitrates (AOPN). The materials with magnetic response and good enough BET area have been used to sorb Paracetamol (PCT) and Atenolol (ATE) from aqueous solutions. The activation process has resulted in the formation of mesopores, an increase of surface area due to the destruction/release of impurities, the transformation of whewellite to calcite, and changes in magnetic behavior. These changes seem to affect the formation of Fe oxides. The COP and IP methods allow the development of magnetic composites based on BC-Act, BC-Act-COP and BC-Act-IP, with saturation magnetization of 3.1 Am2/kg and 1.5 Am2/kg, respectively, attributed to magnetite/maghemite formation and a minimal distance for manipulation by a magnetic field of 12.1 mm and 7.9 mm, respectively. These distances must be considered when developing efficient removal systems using magnetic composites. PCT was sorbed faster and more efficiently than ATE, associated with its smaller molecular size. This presents a valuable contribution to environmental sustainability and advancements in water purification, highlighting the dual advantage of converting the widely available waste product, predominantly found in South America, into an effective sorbent with magnetic characteristics, capable of removing pharmaceutical contaminants from aqueous solutions. This is done in the circular economy, avoiding the final deposition of yerba mate waste in landfills, increasing their lifespan, and safeguarding other natural and non-renewable resources, such as clays, whose preservation rather than exploitation improves environmental quality and saves energy.