Real-time Changes Research Articles

Metabolic insights at the finish line: deciphering physiological changes in ultramarathon runners through breath VOC analysis

Exhaustive exercise can induce unique physiological responses in the lungs and other parts of the human body. The volatile organic compounds (VOCs) in exhaled breath are ideal for studying the effects of exhaustive exercise on the lungs due to the proximity of the breath matrix to the respiratory tract. As breath VOCs can originate from the bloodstream, changes in abundance should also indicate broader physiological effects of exhaustive exercise on the body. Currently, there is limited published data on the effects of exhaustive exercise on breath VOCs. Breath has great potential for biomarker analysis as it can be collected non-invasively, and capture real-time metabolic changes to better understand the effects of exhaustive exercise. In this study, we collected breath samples from a small group of elite runners participating in the 2019 Ultra-Trail du Mont Blanc ultra-marathon. The final analysis included matched paired samples collected before and after the race from 24 subjects. All 48 samples were analyzed using the Breath Biopsy Platform with GC-Orbitrap™ via thermal desorption gas chromatography-mass spectrometry. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjusted P-value < .05). We identified a total of 793 VOCs in the breath samples of elite runners. Of these, 63 showed significant differences between pre- and post-race samples after correction for multiple testing (12 decreased, 51 increased). The specific VOCs identified suggest the involvement of fatty acid oxidation, inflammation, and possible altered gut microbiome activity in response to exhaustive exercise. This study demonstrates significant changes in VOC abundance resulting from exhaustive exercise. Further investigation of VOC changes along with other physiological measurements can help improve our understanding of the effect of exhaustive exercise on the body and subsequent differences in VOCs in exhaled breath.

A Potentiometric Dual-Channel Microsensor Reveals that Fluctuation of H2 S is Less pH-Dependent During Spreading Depolarization in the Rat Brain.

Spreading depolarization (SD) is one of the most common neuropathologic phenomena in the nervous system, relating to numerous diseases. However, real-time monitoring the rapid chemical changes during SD to probe the molecular mechanism remains a great challenge. We develop a potentiometric dual-channel microsensor for simultaneous monitoring of H2 S and pH featuring excellent selectivity and spatiotemporal resolution. Using this microsensor we first observe real time changes of H2 S and pH in the rat brain induced by SD. This changes of H2 S are completely suppressed when the rat pre-treats with aminooxyacetic acid (AOAA), a blocker to inhibit the H2 S-producing enzyme, indicating H2 S fluctuation might be related to enzyme-dependent pathway during SD and less pH-dependent. This study provides a new perspective for studying the function of H2 S and the molecular basis of SD-associated diseases.

A Potentiometric Dual‐Channel Microsensor Reveals that Fluctuation of H2S is Less pH‐Dependent During Spreading Depolarization in the Rat Brain

AbstractSpreading depolarization (SD) is one of the most common neuropathologic phenomena in the nervous system, relating to numerous diseases. However, real‐time monitoring the rapid chemical changes during SD to probe the molecular mechanism remains a great challenge. We develop a potentiometric dual‐channel microsensor for simultaneous monitoring of H2S and pH featuring excellent selectivity and spatiotemporal resolution. Using this microsensor we first observe real time changes of H2S and pH in the rat brain induced by SD. This changes of H2S are completely suppressed when the rat pre‐treats with aminooxyacetic acid (AOAA), a blocker to inhibit the H2S‐producing enzyme, indicating H2S fluctuation might be related to enzyme‐dependent pathway during SD and less pH‐dependent. This study provides a new perspective for studying the function of H2S and the molecular basis of SD‐associated diseases.

Escherichia coli resistance mechanism AcrAB-TolC efflux pump interactions with commonly used antibiotics: a molecular dynamics study

While antibiotic resistance poses a threat from both Gram-positive bacteria (GPB) and Gram-negative bacteria (GNB), GNB pose a more imminent public health hazard globally. GNB are a threat to growing antibiotic resistance because of the complex makeup of the membrane. The AcrAB-TolC efflux pump is a known resistance mechanism of Escherichia coli (E. coli) cells. This study utilized molecular dynamics modeling to visualize some of the changes occurring at a molecular level when airborne bacteria are exposed to stress and antibiotics. This study was conducted to build upon previous experimental research showing that there is an increase in antibiotic resistance and efflux pump activity when exposed to aerosolization. AcrB and AcrAB-TolC proteins were simulated under standard and increased pressure to compare the effect of aerosolization on the binding to the three different antibiotics (puromycin (PUY), ampicillin (AMP) and sulfamethoxazole-trimethoprim (SXT)) to the AcrB binding site. Analysis such as root-mean-square deviation of atomic positions and root-mean-square fluctuation, the opening of TolC, and the significant molecular mechanics with generalized Born and surface area solvation (MM-GBSA) scores associated with specific ligands were recorded. Resistance in experimental data indicated a relationship between the docking scores and some ligand–protein interactions. Results showed that there was more flexibility in the proteins within simulations conducted under standard pressure for the AcrB protein and the full tripartite complex AcrAB-TolC, showing that increased pressure causes more rigidity. MM-GBSA scores, used to calculate the free energy of ligand–protein binding, did not show a significant change, but interestingly, the strongest MM-GBSA scores were for ligands that moved to another binding pocket and did not result in resistance or opening of the efflux pump. However, the ligand moved from the binding site and did not cause the opening of TolC to increase significantly, whereas PUY and AMP were bound to the binding site for the duration of all simulations. AMP ligands under increased pressure showed the largest change in opening of the TolC efflux pump and aligns with experimental data showing E. coli cells had the most resistance to AMP after aerosolization. These results, in addition to other real-time changes such as OM proteins and mutations of targets within the cell, could be used to delineate and mitigate antibiotic resistance mechanisms.

Transition to intelligent fleet management systems in open pit mines: A critical review on application of reinforcement-learning-based systems

The mathematical methods developed so far for addressing truck dispatching problems in fleet management systems (FMSs) of open-pit mines fail to capture the autonomy and dynamicity demanded by Mining 4.0, having led to the popularity of reinforcement learning (RL) methods capable of capturing real-time operational changes. Nonetheless, this nascent field feels the absence of a comprehensive study to elicit the shortfalls of previous studies in favour of more mature future works. To fill the gap, the present study attempts to critically review previously published articles in RL-based mine FMSs through both developing a five-feature-class scale embedded with 29 widely used dispatching features and an insightful review of basics and trends in RL. Results show that 60% of those features were neglected in previous works and that the underlying algorithms have many potentials for improvement. This study also laid out future research directions, pertinent challenges and possible solutions.

Ultrasound and magnetic resonance imaging–based investigation of the role of perfusion and oxygen availability in menstrual pain

BackgroundThe mechanisms responsible for menstrual pain are poorly understood. However, dynamic noninvasive pelvic imaging of menstrual pain sufferers could aid in identifying therapeutic targets and in testing novel treatments. ObjectiveTo study the mechanisms responsible for menstrual pain, we analyzed ultrasonographic and complementary functional Magnetic Resonance Imaging (fMRI) parameters in dysmenorrhea sufferers and pain-free controls under multiple conditions. Study DesignWe performed fMRI on participants with and without dysmenorrhea during menses and non-menses. To clarify whether regional changes in oxygen availability and perfusion occur, fMRI R2* measurements were made of the endometrium and myometrium. R2* measurements are calculated nuclear magnetic resonance relaxation rates sensitive to the paramagnetic properties of oxygenated and deoxygenated hemoglobin. We also compared parameters before and after an analgesic dose of naproxen sodium. In addition, we performed similar measurements with Doppler ultrasonography to identify whether changes in uterine arterial velocity occurred during menstrual cramping in real time. Mixed model statistics were performed to account for within-subject effects across conditions. Corrections for multiple comparisons were made with a false discovery rate adjustment. ResultsDuring menstruation, a notable increase in R2* values, indicative of tissue ischemia, was observed in both the myometrium (beta ± standard error of the mean: 15.74 ± 2.29 s-1, p = 0.001, q = 0.002) and the endometrium (26.37 ± 9.33 s-1, p = 0.005, q = 0.008) among participants experiencing dysmenorrhea. A similar increase was noted in the myometrium (28.89 ± 2.85 s-1, p = 0.001, q = 0.002) and endometrium (75.50 ± 2.57 s-1, p = 0.001, q = 0.003) among pain-free controls. Post-hoc analyses revealed that R2* values during menstruation were significantly higher than those in participants with dysmenorrhea (Myometrium: p = 0.008, Endometrium: p = 0.043). Whereas naproxen sodium increased endometrial R2* in participants with dysmenorrhea (48.29 ± 15.78 s-1,p = 0.005, q = 0.008), it decreased myometrial R2*in pain-free controls. Doppler findings were consistent with fMRI (-8.62 ± 3.25 s-1, p = 0.008, q = 0.011). The pulsatility index (-0.42 ± 0.14, p = 0.004, q = 0.004) and resistance index (-0.042 ± 0.012, p = 0.001, q = 0.001) decreased during menses compared with non-menses, and effects were significantly reversed by naproxen sodium. Naproxen sodium had the opposite effect in pain-free controls. There were no significant real-time changes in the pulsatility index, resistance index, peak systolic velocity, or minimum diastolic velocity during episodes of symptomatic menstrual cramping. ConclusionsfMRI and Doppler metrics suggest participants with dysmenorrhea have better perfusion and oxygen availability than pain-free controls. Naproxen sodium's therapeutic mechanism is associated with relative reductions in uterine perfusion and oxygen availability. An opposite pharmacological effect was observed in pain-free controls. During menstrual cramping, there is insufficient evidence of episodic impaired uterine perfusion. Thus, prostaglandins may have protective vasoconstrictive effects in pain-free controls and opposite effects among participants with dysmenorrhea.

Photosynthesis promotion mechanisms of artificial humic acid depend on plant types: A hydroponic study on C3 and C4 plants

It is feasible to improve plant photosynthesis to address the global climate goals of carbon neutrality. The application of artificial humic acid (AHA) is a promising approach to promote plant photosynthesis, however, the associated mechanisms for C3 and C4 plants are still unclear. In this study, the real-time chlorophyll synthesis and microscopic physiological changes in plant leave cells with the application of AHA were first revealed using the real-time chlorophyll fluorescence parameters and Non-invasive Micro-test Technique. The transcriptomics suggested that the AHA application up-regulated the genes in photosynthesis, especially related to chlorophyll synthesis and light energy capture, in maize and the genes in photosynthetic vitality and carbohydrate metabolic process in lettuce. Structural equation model suggested that the photodegradable substances and growth hormones in AHA directly contributes to photosynthesis of C4 plants (0.37). AHA indirectly promotes the photosynthesis in the C4 plants by upregulating functional genes (e.g., Mg-CHLI and Chlorophyllase) involved in light capture and transformation (0.96). In contrast, AHA mainly indirectly promotes C3 plants photosynthesis by increasing chlorophyll synthesis, and the Rubisco activity and the ZmRbcS expression in the dark reaction of lettuce (0.55). In addition, Mg2+ transfer and flux in C3 plant leaves was significantly improved by AHA, indirectly contributes to plant photosynthesis (0.24). Finally, the AHA increased the net photosynthetic rate of maize by 46.50 % and that of lettuce by 88.00 %. Application of the nutrients- and hormone-rich AHA improves plant growth and photosynthesis even better than traditional Hoagland solution. The revelation of the different photosynthetic promotion mechanisms on C3 and C4 plant in this work guides the synthesis and efficient application of AHA in green agriculture and will propose the development of AHA technology to against climate change resulting from CO2 emissions in near future.

Prediction of transient NOX emission from a non-road diesel engine using a model combining Bayesian search and Population-based training

Due to the rising environmental concerns, particularly air quality, the emission regulations for non-road mobile machinery are becoming increasingly strict. Real-time emission prediction from diesel engines is significant for emission control and regional pollution estimation. This study aims to develop a machine learning model and optimize its hyperparameters by using a hyperparameter optimization method to NOX emission. Firstly, we collected NOX emission data from test under the non-road transient test cycle (NRTC) and built a significant dataset to choose a best model. Then, the model was trained by dataset and the hyperparameters were automatically optimized by combining Bayesian and Population based training. The accuracy of the optimized was indicated by an R2 value of 0.9784 with the 8 input features. The relative error in the cycle level was 1.3%. Lastly, the quality of NOX emissions during the cycle and the effect of each parameter on NOX emissions were analyzed. The results show that the model is able to predict the real-time concentration changes of NOX more accurately. It can provide a reference for the research and development of emission control technology for non-road mobile machinery.

Dynamic Real-Time Analysis of Network Attacks Based on Dynamic Risk Probability Algorithm

With the rapid development of Internet technology and its application, the existence of network vulnerabilities is very common. Attackers may use the defects of software, hardware, or system security policy in the network system to access or destroy the system without authorization. How to nip in the bud and carry out a safety risk assessment and early warning is an urgent problem to be solved. Based on the overall assessment of the risk factors in the whole network, the more dangerous nodes are found and priority measures are taken. The method proposed in this paper can reflect and predict the actions of attackers, repair, and adjust the previously predicted probability. It is compared with the method that evaluates the uncertainty in the network solely by calculating the static probability. The proposed new ideas and methods better reflect the real-time changes in the actual environment of the Internet, thereby better responding to the actual situation. This method can be well applied to threat detection, threat analysis, and risk assessment of monitoring system networks, enabling monitoring network managers to evaluate and protect the security of real-time power grids. It is of great significance to effectively defend against network attacks, ensure system security, and study the resistance of control systems under network attacks.

Real-Time Measurement of Antiglaucoma Drugs in Porcine Eyes Using Boron-Doped Diamond Microelectrodes.

The primary treatment for glaucoma, the most common cause of intermediate vision impairment, involves administering ocular hypotensive drugs in the form of topical eye drops. Observing real-time changes in the drugs that pass through the cornea and reach the anterior chamber of the eye is crucial for improving and developing safe, reliable, and effective medical treatments. Traditional methods for measuring temporal changes in drug concentrations in the aqueous humor employ separation analyzers such as LC-MS/MS. However, this technique requires multiple measurements on the eyes of various test subjects to track changes over time with a high temporal resolution. To address this issue, we have developed a measurement method that employs boron-doped diamond (BDD) microelectrodes to monitor real-time drug concentrations in the anterior chamber of the eye. First, we confirmed the electrochemical reactivity of 13 antiglaucoma drugs in a phosphate buffer solution with a pH of 7.4. Next, we optimized the method for continuous measurement of timolol maleate (TIM), a sympathetic beta-receptor antagonist, and generated calibration curves for each BDD microelectrode using aqueous humor collected from enucleated porcine eyes. We successfully demonstrated the continuous ex vivo monitoring of TIM concentrations in the anterior chambers of these enucleated porcine eyes. The results indicate that changes in intracameral TIM concentrations can be monitored through electrochemical measurements using BDD microelectrodes. This technique holds promise for future advancements in optimizing glaucoma treatment and drug administration strategies.

A non-invasive continuous and real-time volumetric monitoring in spontaneous breathing subjects based on bioimpedance—ExSpiron®Xi: a validation study in healthy volunteers

Tidal volume (TV) monitoring breath-by-breath is not available at bedside in non-intubated patients. However, TV monitoring may be useful to evaluate the work of breathing. A non-invasive device based on bioimpedance provides continuous and real-time volumetric tidal estimation during spontaneous breathing. We performed a prospective study in healthy volunteers aimed at evaluating the accuracy, the precision and the trending ability of measurements of ExSpiron®Xi as compared with the gold standard (i.e. spirometry). Further, we explored whether the differences between the 2 devices would be improved by the calibration of ExSpiron®Xi with a pre-determined tidal volume. Analysis accounted for the repeated nature of measurements within each subject. We enrolled 13 healthy volunteers, including 5 men and 8 women. Tidal volume, TV/ideal body weight (IBW) and respiratory rate (RR) measured with spirometer (TVSpirometer) and with ExSpiron®Xi (TVExSpiron) showed a robust correlation, while minute ventilation (MV) showed a weak correlation, in both non/calibrated and calibrated steps. The analysis of the agreement showed that non-calibrated TVExSpiron underestimated TVspirometer, while in the calibrated steps, TVExSpiron overestimated TVspirometer. The calibration procedure did not reduce the average absolute difference (error) between TVSpirometer and TVExSpiron. This happened similarly for TV/IBW and MV, while RR showed high accuracy and precision. The trending ability was excellent for TV, TV/IBW and RR. The concordance rate (CR) was >95% in both calibrated and non-calibrated measurements. The trending ability of minute ventilation was limited. Absolute error for both calibrated and not calibrated values of TV, TV/IBW and MV accounting for repeated measurements was variably associated with BMI, height and smoking status. Conclusions: Non-invasive TV, TV/IBW and RR estimation by ExSpiron®Xi was strongly correlated with tidal ventilation according to the gold standard spirometer technique. This data was not confirmed for MV. The calibration of the device did not improve its performance. Although the accuracy of ExSpiron®Xi was mild and the precision was limited for TV, TV/IBW and MV, the trending ability of the device was strong specifically for TV, TV/IBW and RR. This makes ExSpiron®Xi a non-invasive monitoring system that may detect real-time tidal volume ventilation changes and then suggest the need to better optimize the patient ventilatory support.

Visualization and monitoring dynamic water levels of steam generators based on deep learning

In a nuclear power system, the steam generator (SG) is the most important part of the second loop, and accurate monitoring of the internal water level of the SG has always been a challenge. In this study, a two-loop steam generator is mounted on a six-degrees-of-freedom (6-DoF) platform to fully imitate the marine condition of the SG, and the internal water level is visualized by opening a window on the SG. The heating power, period and angle of the 6-DoF platform are considered. By calibrating the relationship between vertical pixels and actual height of the water level, the recognition of water level position in the image is mapped to the actual water level height. 700 water level images were obtained for constructing the dataset. An enhanced deep learning recognition network (YOLO-v7-SE) is proposed to focus on the water level of SG, which is added a channel attention mechanism to the backbone network for enhancing the extraction of target features. Compared with the original YOLO-v7 network, the enhanced YOLO-v7-SE network enhanced the detection accuracy of water level recognition by 12.5% and the mean average accuracy (mAP) by 13.3%. In addition, as the heating power increases, the water level without feeding water drops faster, and that with feeding water keeps constant. The swing periods and angles have complex effects on the water level. In contrast, the traditional magnetic flap level gauge could not obtain the variation of the water level. This shows that the enhanced YOLO-v7-SE network can accuracy recognize the real-time water level change, and achieve fast dynamic response to the water level change, which is a reliable and powerful guarantee for the safety of the SG.

A New Open Channel Flow Correction Method Based on Different Boundary Combinations in Hydraulic Modeling

In open-channel water transfer projects, hydrodynamic modeling has been one of the tools commonly used by the scheduling staff to perceive the changes in the water condition of the channel. The reliability of the flow monitoring data has not been guaranteed due to restrictions in the flow monitoring method. Flow monitoring errors directly reduce the accuracy of hydrodynamic modeling calculations, making it impossible to accurately grasp the real-time changes in the internal water conditions of the channel. In this paper, by utilizing the characteristics of the hydrodynamic model itself, which can be solved discretely with multiple boundary conditions, the method of correcting the boundary anomaly data is proposed by changing the combination of hydrodynamic boundaries. This solves the problem of the inaccuracy of hydrodynamic model calculations due to errors in the monitoring data and improves the accuracy of the hydrodynamic model calculations. In the actual operation process, it is found that the method proposed in this paper solves the problem of hydrodynamic model misalignment caused by flow observation errors by correcting the flow during the flow adjustment period, which greatly improves the calculation accuracy of the hydrodynamic model. Secondly, for a stable flow period, the method proposed in this paper can quickly capture and correct the local monitoring of abnormal data and improve the overall hydrodynamic simulation accuracy.

Evolution Characteristics of Void in the Caving Zone Using Fiber Optic Sensing.

Addressing the issue of low filling efficiency in gangue slurry backfilling due to unclear evolution characteristics of voids in the overlying collapsed rock mass during mining, this study utilizes fiber optic sensing technology to monitor real-time strain changes within the rock mass. It proposes a void zoning method based on fiber optic sensing for mining the overlying rock and, in combination with physical model experiments, systematically investigates the dimensions, distribution, and deformation characteristics of rock mass voids. By analyzing fiber optic sensing data, the correlation between the rate of void expansion and the stress state of the rock mass is revealed. The research results demonstrate that as mining progresses, the internal voids of the rock mass gradually expand, exhibiting complex spatial distribution patterns. During the mining process, the expansion of voids within the overlying collapsed rock mass is closely related to the stress state of the rock mass. The rate of void expansion is influenced by changes in stress, making stress regulation a key factor in preventing void expansion and rock mass instability. The application of fiber optic sensing technology allows for more accurate monitoring of changes in rock mass voids, enabling precise zoning of voids in the overlying collapsed rock mass during mining. This zoning method has been validated against traditional theoretical calculations and experimental results. This research expands our understanding of the evolution characteristics of voids in overlying collapsed rock mass and provides valuable reference for backfilling engineering practices and backfilling parameter optimization.

Adaptation of the brainwriting premortem technique to inform the co-creation of COVID-19 testing strategies in underserved communities in South San Diego

IntroductionMeaningful engagement of partners in co-creating and refining health-related programs can increase the initial uptake, sustained implementation, broad reach, and effectiveness of these programs. This is especially important for underserved communities where resources are limited and need to be prioritized. Brainwriting premortem is a novel qualitative approach to partner engagement that combines the strengths of individual idea generation with the concept of premortem exercise that addresses failure points prior to the implementation of new programs.MethodsAn adapted form of brainwriting premortem was used to inform iterative refinements to a COVID-19 testing program at a Federally Qualified Health Center (FQHC) in San Diego. Patients and providers from the FQHC participated in interviews at two time points (early- and mid-implementation of the program). Interview data were transcribed, translated, and analyzed using a rapid qualitative approach. Key themes and sub-themes were identified and used to inform refinements to the program.ResultsA total of 11 patients (7 Spanish- and 4 English-speaking) and 8 providers participated in the brainwriting premortem interviews. Key themes related to possible reasons for COVID-19 testing program failure: advertising/sharing information; access to testing; handling of test results; staff and patient safety; patient beliefs and views regarding the SARS-CoV-2 virus; and COVID-19 testing options offered. Proposed solutions were offered for the key failures except for patient beliefs and views regarding the SARS-CoV-2 virus. Additional solutions offered were related to education, physical operations, and recruitment strategies. Real-time changes to the program flow and components were made in response to 7 suggestions from patients and 11 from providers. Changes related to the process of returning results were the most common, and included sending results via email with distinct workflows based on the test result.ConclusionThe implementation of the adapted brainwriting premortem technique allowed us to incorporate the perspective of key partners in the delivery and iterative refinement of the COVID-19 testing program. This was an effective tool in the context of an FQHC and can be a promising and approach to incorporate iterative input from patients and providers to ensure successful program implementation. Future studies, particularly those requiring rapid response to public health emergencies, should consider the use of this technique.

Understanding Asia’s revolutionary fertility change through Gavin W. Jones’ work

ABSTRACT Gavin Jones’ professional career, from the 1960s to the 2020s, coincided with one of the most important periods of demographic change—the Asian fertility revolution. This article examines how his prolific research offers a unique window into understanding the fertility changes in Asia during the late-twentieth and early-twenty-first century. Across his vast scholarship, Jones documented the fertility change in real time while they were underway, through careful measurement and insightful interpretations of their causes and consequences. As the spread and pace of fertility transitions varied across countries, ethnic communities, geographical regions, and educational groups, he assembled data to measure and analyse patterns and trends. He drew upon multiple theories to explain unexpected patterns. Finally, he documented the emergence of Asia’s ultra-low fertility in recent decades. Jones’ mastery of statistical demography combined with his intimate connection with datasets and vast knowledge about the region, produced a truly comprehensive scholarship on Asia’s fertility transitions.

Monitoring Real-Time Temperature Dynamics of a Short RNA Hairpin Using Förster Resonance Energy Transfer and Circular Dichroism.

RNA molecules play crucial roles in gene expression regulation and cellular signaling, and these functions are governed by the formation of RNA secondary and tertiary structures. These structures are highly dynamic and subject to rapid changes in response to environmental cues, temperature in particular. Thermosensitive RNA secondary structures have been harnessed by multiple organisms to survey their temperature environment and to adjust gene expression accordingly. It is thus highly desirable to observe RNA structural changes in real time over a range of temperatures. Multiple approaches have been developed to study structural dynamics, but many of these require extensive processing of the RNA, large amounts of RNA input, and/or cannot be applied under physiological conditions. Here, we describe the use of a dually fluorescently labeled RNA oligonucleotide (containing a predicted hairpin structure) to monitor subtle RNA structural dynamics in vitro by Förster resonance energy transfer (FRET) and circular dichroism (CD) spectroscopy. These approaches can be employed under physiologically relevant conditions over a range of temperatures and with RNA concentrations as low as 200nM; they enable us to observe RNA structural dynamics in real time and to correlate these dynamics with changes in biological processes such as translation.

An Exploration of the Application of Augmented Reality Technology in Improving Interactivity in Physical Education Teaching and Training

Abstract This paper elaborates on the technology related to augmented reality interaction, introduces in depth the augmented reality device HoloLens, including its composition and the principle of generating indoor three-dimensional data, and designs a teaching interaction model of augmented reality fusion learning environment. The Kinect somatosensory device is introduced into the teaching interaction model, and virtual interactions between virtual objects and real objects are accomplished by judging the human body’s position and posture changes in real time. Group experiments were conducted to examine the learning effect of augmented reality-based technology with or without interactive behavior in terms of interest in sports learning. According to the findings, the augmented reality physical education interactive teaching experiment resulted in a 23-point increase in the average value of physical education knowledge mastery level test scores. Further one-way ANOVA was conducted and the p-value of the scores of group B of the experiment with interaction before and after teaching was 0.001, which is less than 0.05. Augmented reality sports interactive teaching can significantly enhance students’ interest in sports learning, attention to sports, and so on. Therefore, the augmented reality sports interactive teaching technique mentioned in this paper serves as an effective way to improve students’ performance and interest in sports education.

Recent progress in metabolomics for analyzing common infertility conditions that affect ovarian function.

Numerous efforts have been undertaken to identify biomarkers associated with embryo and oocyte quality to improve the success rate of invitro fertilization. Metabolomics has gained traction for its ability to detect dynamic biological changes in real time and provide comprehensive metabolite profiles. This review synthesizes the most recent findings on metabolomic analysis of follicular fluid (FF) in clinical conditions leading to infertility, with a focus on the dynamics of energy metabolism and oocyte quality, and discusses future research directions. A literature search was conducted without time constraints. The metabolites present in FF originate from five primary pathways: glycolysis, oxidative phosphorylation, lipid metabolism and β-oxidation, nucleic acid synthesis, and ketogenesis. Metabolomic profiling can broadly categorize infertile women into two groups: those with infertility due to aging and endometriosis, and those with infertility associated with polycystic ovarian syndrome and obesity. In the former group, glycolysis and lipid metabolism are upregulated to compensate for mitochondrial dysfunction, whereas the latter group exhibits the opposite trend. Assessing the levels of glucose, pyruvate, lactate, and plasmalogens in FF may be valuable for evaluating oocyte quality. Metabolomic analysis, particularly focusing on energy metabolism in FF, holds promise for predicting female reproductive outcomes.

Treatment of Spontaneous Tumors With Algorithmically Controlled Electroporation.

To study the safety and efficacy of algorithmically controlled electroporation (ACE) against spontaneous equine melanoma. A custom temperature sensing coaxial electrode was paired with a high voltage pulse generation system with integrated temperature feedback controls. Computational modeling and ex vivo studies were conducted to evaluate the system's ability to achieve and maintain target temperatures. Twenty-five equine melanoma tumors were treated with a 2000 V protocol consisting of a 2-5-2 waveform, 45 °C temperature set point, and integrated energized times of 0.005 s, 0.01 s, or 0.02 s (2500x, 5000x, and 10000x 2 μs pulses, respectively). Patients returned 20-50 days post treatment to determine the efficacy of the treatment. ACE temperature control algorithms successfully achieved and maintained target temperatures in a diverse population of spontaneous tumors with significant variation in tissue impedance. All treatments were completed successfully without and without adverse events. Complete response rates greater than 93% were achieved in all treatment groups. ACE is a safe and effective treatment for spontaneous equine melanoma. The temperature control algorithm enabled rapid delivery of electroporation treatments without prior knowledge of tissue electrical or thermal properties and could adjust to real time changes in tissue properties. Real time temperature control in electroporation procedures enables treatments near critical structures where thermal damage is contraindicated. Unlike standard approaches, ACE protocols do not require extensive pretreatment planning or knowledge of tissue properties to determine an optimal energy delivery rate and they can account for changes in tissue state (e.g., perfusion) in real time to simultaneously minimize treatment time and potential for thermal damage.