Browse Articles

Effects of chewing on postural learning: An experimental pre-post intervention study

In 16 healthy volunteers (age 42–69 years, 8 females) we investigated chewing effects on postural learning. Initially, the Centre of Pressure (CoP) position in bipedal stance was recorded (1 minute) in 4 conditions: Hard support (HS)-Open Eyes (OE), HS-Closed Eyes (CE), Soft Support (SS)-OE, SS-CE. Following 2 minutes of Chewing (C, n = 8 subjects, 4 females) or rhythmic Hand Grip (HG, n = 8 subjects, 4 females), 10 unipedal stance test (1 minute) were performed for 30 minutes in both groups in HS-OE, with a progressive decrease in CoP Velocity and Path Length. Since the 95% Area of body sway decreased only in the HG group, the Length in Function of Surface (LFS, indicative of balance energy expenditure), increased in the HG and remained constant in the C group. Soon after and 5 hours post-training, bipedal stance tests were performed for 8 minutes, in the same order as before. In both groups, the changes in unipedal stance parameters were found persistent 5 hours post-training. In SS-OE condition of bipedal stance, CoP Velocity was reduced and 95% Area increased by postural training, in the HG and C group, respectively. These modifications were significantly correlated to the corresponding changes in unipedal stance and led to a LSF decrease in the C group. In conclusion, the CoP Velocity during unipedal training was not affected by the previous motor activities. Chewing allowed for a larger compliance concerning the extent of CoP oscillation. Postural training in unipedal stance seem to favour the development of modifications in bipedal stance, according to the conditioning activity. Chewing before a postural training promotes a postural strategy characterized by a constant and a lower energy cost in unipedal and bipedal stance, respectively. Further experiments are necessary to verify whether such a change may promote a more secure balance in trained people.

Public perception and changing attitudes toward antidepressants over a decade in social media: Lessons learned from online discussion using artificial intelligence

Background Antidepressants play a crucial role in treating mental health disorders such as depression and anxiety. Understanding of patients’ perspective on antidepressants is essential for improving treatment outcomes; however, year-to-year change in the public’s perception of antidepressants remains unclear. We aimed to analyze changes in public sentiments and predominant perceptions regarding antidepressants using artificial intelligence pipeline. Methods This study analyzed online discussions related to antidepressants on Reddit from January 1, 2009, to December 31, 2022. Antidepressant-associated communities were explored to collect a list of discussions relevant to antidepressant therapy. Discussion topics on antidepressants were identified using BERTopic, and the sentiments were analyzed using a RoBERTa model. Trends were assessed using the Mann–Kendall test to evaluate shifts in sentiments over time. Results We analyzed 429,510 antidepressant-related discourse over 14 years and found a predominance in negative sentiments. Key discussion topics include the benefits and side effects of antidepressants, experiences with drug switching, and specific concerns regarding bupropion therapy. In trend analyses, negative sentiments decreased, while neutral sentiments increased over time. This aligns with a decline in the annual proportion of topics associated with side effects within each cluster. Conclusions Negative perceptions toward antidepressants are prevalent on social media, mainly focusing on efficacy and side effects. However, a decade-long analysis shows a decline in negative sentiments, with an increase in neutral sentiments with a downturn in yearly proportion of side-effected related topics within each cluster. These trends and information may help improve strategies to address barriers to antidepressant use and adherence.

Correction: What is the lifetime cost of alcohol consumption? An estimation of economic burden in Thailand

Retraction: The non-linear relationship between globalization, financial development and energy consumption: Evidence from BRICS economies

Micronutrient dynamics and deficiency risk across pregnancy and postpartum in a Slovak cohort

Objective To assess the dynamics in blood concentrations of vitamins (A, B6, B12, D, E,), trace elements such as selenium, magnesium, zinc, and iron (transferrin), and metabolite homocysteine during pregnancy and postpartum. Design Cross-sectional, national cohort study conducted between January and June 2024. Setting Slovakia. Population Pregnant and postpartum women. Methods From venous blood and capillary dry blood spot micronutrients were analysed using standard biochemical and biophysical methods. Main outcome measures Group differences in blood micronutrient levels across pregnancy and postpartum. Results Our findings reveal significant differences in maternal micronutrient levels across pregnancy and postpartum. While some nutrients, including vitamin D and folate, remained relatively stable, others such as vitamin A, B12, iron and zinc were observed at lower levels, and vitamin E at higher levels during pregnancy. Vitamin E levels in the 3rd trimester frequently exceeded reference values for the general adult population, whereas zinc levels were significantly lower postpartum. We observed high prevalence of vitamin B12 and iron deficiencies, as indicated by transferrin saturation, particularly in the 3rd trimester. Vitamin D deficiency was prevalent throughout pregnancy and postpartum. Finally, our analysis demonstrated that dried blood spot (DBS) technology provides comparable results to venous blood analysis for measuring vitamin A, D and homocysteine levels.

The optimization of electrochemical hydride generation technology for treating antimony-containing wastewater

Antimony (Sb) is extensively utilized in industrial activities, but most of its compounds exhibit human toxicity and are classified as priority-controlled pollutants. Unlike traditional electrochemical methods that remove metallic pollutants via coagulation or precipitation, electrochemical hydride generation technology converts antimony (Sb) in wastewater into stibine gas (SbH3) for efficient removal. Furthermore, the generated SbH₃ can be decomposed thermally to partially recover metallic antimony. In synthetic wastewater treatment (Sb = 5 mg/L), the proton exchange membrane (Nafion117) electrolysis device achieved an antimony removal efficiency of 72.8 ± 2.2%, outperforming traditional cation-exchange membranes. This enhancement is attributed to the membrane’s proton-selective transport and high H conductivity. Increasing the temperature enhanced the generation and release of SbH3, with the higher removal efficiency of 87.3 ± 2.6% achieved at approximately 30 °C. However, temperatures exceeding 30 °C could lead to the partial decomposition of SbH3 back into the solution, thereby affecting removal efficiency. Ultrasonic stirring in the cathode chamber significantly enhanced Sb removal from high-concentration solutions (5 mg/L), while magnetic stirring was more suitable for lower-concentration solutions. Orthogonal experiments revealed that due to the competitive relationship between hydrogen generation and SbH3 generation, as well as the gas-blocking effect, current intensity and electrode area both had a significant impact on Sb removal. Under appropriate current intensity and electrode area conditions (0.5 A, 20 cm²), a high removal rate of 78.5 ± 4.6% can be achieved. Consequently, employing a Nafion membrane coupled with ultrasonic agitation under optimized conditions (30°C, 25 mA/cm²) effectively accelerates antimony removal kinetics and enhances elimination efficiency. However, the substantial reduction in current efficiency and elevated energy consumption induced by competitive hydrogen evolution represent critical challenges requiring urgent resolution. This treatment approach provides a technical reference‌ for shifting from mere contaminant removal to resource recovery. The integration of removal and recovery processes holds substantial potential‌ for implementing circular economy models in mining and metallurgical industries.

Synergistic reduction of graphene oxide using vitamin C and urea: Enhanced efficiency and material properties

Synergistic reduction of graphene oxide (GO) using different reducing agents represents an effective approach for reduced graphene oxide (rGO) synthesis. In this study, the rGO (rGO-Vc+Urea) was prepared by combining vitamin C (Vc) and urea as co-reducing agents with the modified Hummer’s method. Compared to samples reduced solely with Vc or urea, the co-reducing agents significantly reduced the required reaction time (to 2 hours) and temperature (to 120°C), while yielding material with superior electrical resistivity (1.2 Ω·cm). The structure of the samples was characterized using XRD, FT-IR, Raman spectroscopy, BET surface area analysis, and SEM. Results indicate that the sample prepared from co-reducing agents possesses a typical graphene structure and incorporates C-N bonds. Furthermore, rGO-Vc+Urea exhibits a higher degree of structural order, as evidenced by a lower Raman (Iᴅ/IG = 0.75), compared to rGO-Vc (Iᴅ/IG = 0.91) and rGO-Urea (Iᴅ/IG = 1.49), along with a higher specific surface area (88.60 m2/g). The reduction mechanism of the co-reducing agents was investigated. It was revealed that the alkaline environment generated by urea enhances Vc’s ability to reduce oxygen-containing functional groups in GO, specifically hydroxyl, epoxy, carbonyl, and carboxyl groups, and promotes the elimination of CO2 released during the reaction. This strategy of employing synergistic multiple reducing agents offers new perspectives for the preparation of rGO.

Choreographing rhizosphere effect with agricultural practices for agroecology?

For sustainable agriculture, soil-plant interactions (i.e., the rhizosphere effect) is prominent focus, since they determine plant health and nutrition. However, system-level agricultural management practices interfere with the rhizosphere effect. In this study, we characterized the rhizosphere effect of winter wheat (Triticum aestivum var. LG Absalon) on farm fields along two levels of crop diversity induced by rotation (high or low) combined with two tillage intensities (conventional or reduced). The rhizosphere effect was determined from differences in enzyme activities involved in carbon (β-glucosidase), nitrogen (arylamidase) and phosphorus (acid phosphatase) cycles measured in the rhizosphere and bulk soil. We observed positive rhizosphere effects for all enzymes, but they were significantly altered by soil tillage. High temporal diversification and reduced tillage increased the intensity of the rhizosphere effect for all enzymes studied, suggesting the relevance of agroecological management of arable land to promote nutrient cycling. In contrast, benefits of crop diversification on the rhizosphere effect decreased drastically under conventional tillage. Accordingly, the rhizosphere effect should be carefully synchronize with agricultural practices under agroecological transition.

A robust and dynamic malware detection and classification model using behavioral-based analysis and BERT technique

Malware classification is a challenging task due to the constantly evolving nature of malicious software. Traditional signature-based methods and static analysis often fail to detect sophisticated threats, making behavior-based analysis crucial. This study proposes a malware detection model that analyzes the behavior of executable files (.exe) to classify them as malware. The model submits the file to VirusTotal, where it runs in a secure environment to monitor actions such as file modifications, registry changes, or network connections. To enhance detection accuracy, the BERT model is applied to extract key features from these behavior logs. After 100 training epochs, the model achieved 92.25% accuracy and an F1-score of 91.22%, demonstrating strong overall performance. Class-wise evaluation was also conducted, treating each malware family as a distinct class to assess specific detection accuracy. Furthermore, a correlation matrix was analyzed to explore inter-class relationships and identify overlapping behaviors. Experimental results show that SVM achieved the highest F1-Scores for Adware (0.98) and BackDoor (0.91), while Random Forest showed comparable performance. Naïve Bayes, however, performed poorly for FakeAlert (F1-Score: 0.64). These findings confirm the effectiveness of the proposed behavior-based approach using BERT features, with SVM and Random Forest proving to be the most reliable classifiers.

A delicate calculation method for reservoir initial water saturation based on a novel approach to J-function construction

Accurately determining initial water saturation is essential for assessing reservoir resources and optimizing development, yet conventional J-function methods face challenges such as variability in curve fitting due to sample heterogeneity, less-than-optimal grouping schemes, and limited core sample availability. This study addressed these issues by analyzing 55 J-function curves from the N reservoir in Iraq’s Zagros Basin, identifying permeability as the primary factor influencing curve shape. A novel classification method was developed, categorizing curves into power function and exponential function types based on permeability thresholds. Improved J-function models were established by correlating undetermined coefficients with the reservoir quality index (RQI), enabling continuous, tailored calculations, eliminating the need for sample grouping. Case studies demonstrated that the improved J-function method provides more accurate water saturation (Sw) estimates in Reservoir N (porosity 0.1–0.27) compared to the Archie equation and conventional J-function: (1) In high-quality intervals (e.g., 3682–3683 m), it yields Sw = 0.06–0.11, closely matching core-derived irreducible water saturation (0.1), while Archie overestimates Sw (0.23–0.74); (2) In poor-quality zones (3676.5–3681 m), it agrees with Archie’s Sw but corrects the conventional J-function’s underestimation by accounting for subtle property variations. These advancements provided a robust framework integrating permeability-based J-function classification, RQI-correlated coefficient models, and continuous saturation calculations, eliminating the need for sample grouping.

Electron flow matching for generative reaction mechanism prediction

Examining self-employment policies for persons with disabilities in South Africa: Perspectives from policy actors

Background Despite robust global and national efforts to promote inclusive development, a significant gap persists in countries such as South Africa’s self-employment policies for persons with disabilities. The existing legislative framework, although well-intentioned, lacks clear and comprehensive guidance on self-employment as a viable placement option for persons with disabilities. Consequently, this ambiguity hinders effective policy implementation, limiting economic empowerment and social inclusion. This research aimed to explore the existence of self-employment-specific policies for persons with disabilities and policy actors’ involved in South Africa. Methods The participants (n = 47) had an average of 10 years of experience in self-employment for persons with disabilities, holding qualifications ranging from no formal education to master’s degrees. This qualitative study ensured transparent and systematic reporting using the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. Purposive and snowball sampling were utilised to recruit participants. Data were collected using a piloted question guide and analysed using the NVIVO software. Data was analysed thematically. Ethics clearance, relevant gatekeepers’ permission and informed written consent from participants were obtained. Results Two themes emerged, namely, theme one: The status quo on self-employment-specific policies for persons with disabilities. Participants reported on the absence of explicit policies on self-employment for persons with disabilities, the lack of effectiveness in inclusive South African legal frameworks and their lack of impact on promoting self-employment opportunities. Theme two: policy actors’ involvement in self-employment-specific policies for persons with disabilities. Participants reported on the roles and responsibilities of policy actors and strategies to promote self-employment opportunities for persons with disabilities through policy reforms. Conclusions The research revealed a complex policy landscape where the absence of self-employment-specific policies for persons with disabilities coexists with potentially leverageable inclusive frameworks. These coexist with potential policy actors who have the potential to facilitate implementation. Leveraging existing policies through effective implementation and targeted policy reforms would ensure the full participation of persons with disabilities in self-employment. Key policy actors should familiarise themselves with the existing legal framework and emphasise enforcement and consequence management to ensure policies are implemented effectively. Furthermore, a coordinated approach is necessary, involving: a single or integrated system or database to streamline policy implementation and monitoring; a targeted approach that prioritises persons with disabilities in self-employment; and policies that allow for target setting, accurate measurement of targets, and effective monitoring and evaluation. Thus, a policy brief outlining the key findings should be considered, drafted, and submitted to the relevant government department (e.g., Department of Employment and Labour) for further action.

Correction: Immunoinformatic evaluation for the development of a potent multi-epitope vaccine against bacterial vaginosis caused by Gardnerella vaginalis

Transgenic mice overexpressing Pitx2 in the atria develop tachycardia-bradycardia syndrome

Sinoatrial node (SAN) dysfunction often accompanies supraventricular tachyarrhythmias such as atrial fibrillation (AF), which is referred to as tachycardia-bradycardia syndrome (TBS). Although there have been many studies on electrical remodeling in TBS, the regulatory mechanisms that cause electrical remodeling in the SAN and atrial muscles by chronic bradycardia or tachycardia have not yet been fully investigated. Here we hypothesized Pitx2c, a transcription factor that played a central role in the late aspects of left-right asymmetric morphogenesis, regulated an interrelationship between the SAN and the atrial muscles and was involved in TBS-like pathology. To test this hypothesis, we generated transgenic mice overexpressing Pitx2c specifically in the atria (OE mice). Although Pitx2c is normally expressed only in the left atria (LA), the expression levels of Pitx2c protein in the right atria (RA) were significantly increased to similar levels of those in the LA of non-transgenic control mice (WT). We found the heart rate of OE mice was significantly variable although the average heart rate was similar between WT and OE mice. Electrophysiological examination showed OE mice exhibited prolonged SAN recovery time and higher AF inducibility. Histological analysis revealed SAN-specific ion channel HCN4-positive cells were hardly detected in the SAN of OE mice, along with ectopic expression in the RA. Furthermore, transcription factors associated with SAN formation were down-regulated in the RA of OE mice. We conclude that SAN dysfunction by Pitx2 dysregulation predisposed OE mice to a TBS-like phenotype, and Pitx2c is a key regulator that defines SAN function in the atria.

LWLCM: A novel lightweight stream cipher using logistic chaos function and multiplexer for IoT communications

The Internet of Things (IoT) includes vehicles, homes, and integrated sensors and many interconnected physical devices that gather and share data to interact with their environment. Data moving across multiple levels is vulnerable to various security threats, including leaks and unauthorized access. IoT faces significant challenges in balancing strict security with optimal performance metrics such as energy efficiency, throughput, and memory. We present a novel lightweight stream cipher designed to secure IoT communication and address these challenges. The proposed architecture features four main components: a logistic round module that produces 32-bit chaotic outputs; two 80-bit shift registers, LFSR and NLFSR, for key expansion; and multiplexer units to enhance confusion and diffusion. This model improves the randomness and robustness of the keystream, strengthening the cipher against cryptanalytic attacks. An ablation research is performed by methodically eliminating the chaotic map, NLFSR, and multiplexer components to assess their individual effects on encryption/decryption duration, throughput, entropy, and avalanche analysis. Experimental results demonstrate that each component significantly improves the cipher’s overall performance and security, hence confirming the architecture’s design and also demonstrate that the proposed cipher exceeds the performance of current algorithms, including Grain-128 and RSA-1024, in terms of encryption/decryption time, throughput, and energy efficiency, while maintaining comparable statistical randomness to AES and Trivium. This method achieves an average Shannon entropy of 7.9996, and successfully passing all 15 NIST statistical randomness tests. A subsequent study analyzing the avalanche effect and correlation coefficients reinforces the strength of the encryption. The proposed encryption method, designed for resource-constrained environments, provides efficient and robust cryptographic security to protect IoT data effectively.

Correction: Synovial gene expression after hemarthrosis differs between FVIII-deficient mice treated with recombinant FVIII or FVIII-Fc fusion protein

Retraction: Intersecting paths: Corporate and green innovation in Chinese firms—A panel cointegration analysis

Effects of s-ketamine and midazolam on respiratory variability: A randomized controlled pilot trial

S-ketamine and midazolam are frequently used to provide sedation while maintaining spontaneous respiration. However, the effects of these agents on respiratory variability, which reflects the adaptability of the respiratory system, have not been thoroughly explored. We evaluated these effects in a randomized controlled pilot trial. This study was conducted as part of a randomized controlled trial originally designed to assess the effects of s-ketamine conditioning on pain sensitivity in patients with fibromyalgia syndrome. Participants were randomly assigned to receive an infusion of either s-ketamine (0.3 mg kg-1 h-1), midazolam (0.05 mg kg-1 h-1), or saline in a blinded fashion. Mean respiratory rate, variability of respiratory rate (VRR), and variability of tidal volume (VTV) were measured continuously and non-invasively with a bio-impedance method. Changes during drug infusion were compared in a linear mixed model to assess the effects of s-ketamine and midazolam compared to saline. Data were analyzed for 57 experiments in 28 participants. Their median baseline variabilities of respiratory rate and tidal volume were 0.19 (IQR: 0.16–0.25) and 0.23 (0.19–0.34), respectively. While mean respiratory rate was not affected, midazolam resulted in a significant decrease in both VRR (ß = −0.071, 95% CI: −0.120 to −0.021) and VTV (ß = −0.117, 95% CI: −0.170 to −0.062). In contrast, s-ketamine appeared to produce a smaller decrease in VTV (ß = −0.062, 95% CI: −0.118 to −0.003) with VRR remaining unaffected (ß = −0.036, 95% CI: −0.092 to 0.019). In conclusion, our study demonstrates that midazolam reduces respiratory variability, potentially impairing the adaptability of the respiratory system. In contrast, s-ketamine largely preserved respiratory variability, suggesting it may be a safer alternative for sedation in patients with impaired spontaneous breathing. Further studies are needed to assess the clinical implications of these observations in patients undergoing sedation.

Enhanced electrophysiological recordings in acute brain slices, spheroids, and organoids using 3D high-density multielectrode arrays

Recent advances in three-dimensional (3D) biological brain models in vitro and ex vivo are creating new opportunities to understand the complexity of neural networks but pose the technological challenge of obtaining high-throughput recordings of electrical activity from multiple sites in 3D at high spatiotemporal resolution. This cannot be achieved using planar multi-electrode arrays (MEAs), which contact just one side of the neural structure. Moreover, the specimen adhesion to planar MEAs limits fluid perfusion along with tissue viability and drug application. Here, the efficiency of the tissue-sensor interface provided by advanced 3D high-density (HD)-MEA technology was evaluated in acute brain slices, spheroids, and organoids obtained from different brain regions. The 3D HD-MEA microneedles reached the inner layers of samples without damaging network integrity and the microchannel network between microneedles improved tissue vitality and chemical compound diffusion. In acute cortico-hippocampal and cerebellar slices, signal recording and stimulation efficiency proved higher with the 3D HD-MEA than with a planar MEA improving the characterization of network activity and functional connectivity. The 3D HD-MEA also resolved the challenge of recording from brain spheroids as well as cortical and spinal organoids. Our results show that 3D HD-MEA technology represents a valuable tool to address the complex spatiotemporal organization of activity in brain microcircuits, making it possible to investigate 3D biological models.

Multilayered SDN security with MAC authentication and GAN-based intrusion detection

Computer networks are highly vulnerable to cybersecurity intrusions. Likewise, software-defined networks (SDN), which enable 5G users to broadcast sensitive data, have become a primary target for vulnerability. To protect the network security against attacks, various security protocols, including authorization, the authentication process, and intrusion detection techniques, are essential. However, there are several intrusion detection strategies, but the most prevalent methods show low accuracy and high false positives. To overcome these problems, this research work presents a novel four-Q curve authentication system based on Media Access Control (MAC) addresses for a multilayered SDN intrusion detection system utilizing deep learning techniques to identify and prevent attacks. The Four-Q curve authentication system leverages elliptic curve cryptography, a high-performance algorithm that improves authentication security and computational efficiency. Initially, Four-Q curve authentication is performed, followed by univariate ensemble feature selection to select optimal switches. Then, the data collected through the switches are classified as normal, assault, and suspect packets based on the Dual Discriminator Conditional Generative Adversarial Network (DDcGAN) approach. Further, the optimization of DDcGAN is accomplished using the Sheep Flock Optimization Algorithm (SFOA), whereas suspicious packets are categorized using the Growing Self-Organizing Map (GSOM). The DDcGAN-based intrusion detection system outperforms the state-of-the-art approaches in terms of accuracy, precision, F1 score, sensitivity, false-positive rate, power consumption, and network throughput. It achieved an accuracy of 98.29%, an F1 score of 0.975, and a precision of 95.8%. The system’s true positive rate attained 99.04% at 50% malicious nodes, while the false alarm rate was as low as 2.05% under the same conditions. Moreover, the system exhibits 4.5% energy savings when compared to existing approaches.